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Genome editing is an exciting field that allows for the precise modification of an organism's DNA. One of the most advanced tools in this area is CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein 9), which creates a DSB (Double-strand break) at a specific location in the genome. This break can then be repaired by the cell using one of two pathways – NHEJ (nonhomologous end joining) or HDR (homology-directed repair) HDR leads to more precise repair and is used to create KI (Knock-In) modifications by introducing a homologous piece of DNA with the desired changes. However, HDR is a rare event that competes with the error prone NHEJ pathway, limiting its efficiency. HDR mainly occurs in the G2 and S phases of the cell cycle, making it a challenge to control and target. To improve KI efficiency, researchers have used strategies such as inhibiting NHEJ or activating HDR. This study focuses on identifying direct and indirect activators of HDR through a library assay screening. We established a robust method for screening compounds in HEK293 cells that relies on a plasmid-based delivery Cas9, gRNA (guide RNA), and synthetic ssDNA (single strand DNA). Out of 3,000 compounds screened, 1% showed a higher signal than the positive control, and approximately 10% presented a higher signal than untreated cells. The top 5 compounds were further validated in dose response. Our system opens new avenues for improving the efficiency of KI modifications.

A long-standing goal of computer-based entertainment is the creation of a story where a user is in control of portions of the storyline. These non-linear stories give a user an opportunity to adapt the story to his or her interests, schedule and needs. The Internet has made non-linear video a reality. Different approaches have been taken to create and play non-linear video stories. They suffer from lack of simplicity, smoothness, and television-like experience in story creation and presentation. Flexible Storylines provide a way to easily create and present non-linear video stories. The creation of these stories is done using a time-line based editor that mimics the way video stories are composed by film makers. The viewing experience of flexible stories is very similar to viewing a normal video with an introduction of the choice to see more or less of the current topic. This provides a highly variable experience with a simple, smooth and non-intrusive form of user interaction. We also provide a mechanism that lets a story flow smoothly despite the introduction of user interaction.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第21827号 / 農博第2340号 / 新制||農||1067(附属図書館) / 学位論文||H31||N5199(農学部図書室) / 京都大学大学院農学研究科応用生物科学専攻 / (主査)教授 佐藤 健司, 准教授 豊原 治彦, 准教授 田川 正朋 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

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This study was designed to examine whether a person's prepositioned cognitive style influenced learning achievement in a visually-oriented task for an online learning environment in higher education. Field dependence-independence was used to identify individuals' cognitive styles. A true experimental study was conducted in the fall 2005 term at the University of Central Florida. This researcher followed Dwyer and Moore's research (1991, 2002) and divided learners into three groups (field dependent [FD], field neutral [FN], and the field independent [FI] students). Eighty-three preservice teachers participated in this study; the data from 52 of the FD and the FI participants were analyzed to answer research questions. The findings in this study supported those in the literature review; students from both FD and FI cognitive styles performed equally well in online learning environments. In addition, for providing introductory-level instruction on visually-oriented tasks in an online learning environment, instructions which emphasized an FD approach benefited both FI and FD students in their knowledge-based learning achievement. In this approach, extra cues and sequence of content might have been the reasons that students had higher scores on their knowledge-based learning achievement and satisfaction levels. The findings of this study also indicated that students could demonstrate higher performance-based learning achievement if they had more experiences on the subject matter and higher knowledge-based learning achievement if they felt the instructions were easy to follow and the workload of the module was manageable. Based on the findings and conclusions, the recommendations are: (1) A larger sample size is needed to generalize the findings of the study; (2) In this study, student-to-student and teacher-to-student interactions might affect students' learning achievement. Future studies should consider those interactions as factors and examine their effect on students' learning achievement.

In As You Like It 5.4.107-08 we receive Rosalind returning as herself—a woman—no longer in the guise of Ganymede, the "boy" page. Her first lines upon returning are repetitive: "To you I give myself, for I am yours [To Duke Senior] / To you I give myself, for I am yours [To Orlando]." However, comparing Folio versions of these lines produces a provocative variant. In the third and fourth folios, these lines are no longer a repetitious patriarchal pledging, but a tender dialogic exchange—much like vows—between Rosalind and Orlando. While none of our modern Shakespeare editions make a note of this variant emendation, this article traces the editorial history and mystery surrounding As You Like It 5.4.107-08 from seventeenth-century editors to our modern ones—with an emphasis on the shift in Shakespeare editing during the eighteenth century—to suggest the variant emendation warrants consideration for text and performance. Furthermore, the article examines the plausibility of the third and fourth folio's emendation in congruence with Early Modern conceptions of companionate marriage, parental consent, and marriage rites.

In As You Like It 5.4.107-08 we receive Rosalind returning as herself”a woman”no longer in the guise of Ganymede, the boy page. Her first lines upon returning are repetitive: To you I give myself, for I am yours [To Duke Senior] / To you I give myself, for I am yours [To Orlando]. However, comparing Folio versions of these lines produces a provocative variant. In the third and fourth folios, these lines are no longer a repetitious patriarchal pledging, but a tender dialogic exchange "much like vows" between Rosalind and Orlando. While none of our modern Shakespeare editions make a note of this variant emendation, this article traces the editorial history and mystery surrounding As You Like It 5.4.107-08 from seventeenth-century editors to our modern ones with an emphasis on the shift in Shakespeare editing during the eighteenth century to suggest the variant emendation warrants consideration for text and performance. Furthermore, the article examines the plausibility of the third and fourth folios emendation in congruence with Early Modern conceptions of companionate marriage, parental consent, and marriage rites.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24878号 / 医博第5012号 / 新制||医||1068(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 萩原 正敏, 教授 湊谷 謙司, 教授 江藤 浩之 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Computer graphics has become an integral part of various industries such as entertainment (i.e.,films and content creation), fashion (i.e.,virtual try-on), and video games. Computer graphics has evolved tremendously over the past years. It has shown remarkable image generation improvement from low-quality, pixelated images with limited details to highly realistic images with fine details that can often be mistaken for real images. However, the traditional pipeline of rendering an image in computer graphics is complex and time- consuming. The whole process of creating the geometry, material, and textures requires not only time but also significant expertise. In this work, we aim to replace this complex traditional computer graphics pipeline with a simple machine learning model. This machine learning model can synthesize realistic images without requiring expertise or significant time and effort. Specifically, we address the problem of controllable image synthesis. We propose several approaches that allow the user to synthesize realistic content and manipulate images to achieve their desired goals with ease and flexibility. / Doctor of Philosophy / Computer graphics has become an integral part of various industries such as entertainment (i.e.,films and content creation), fashion (i.e.,virtual try-on), and video games. Computer graphics has evolved tremendously over the past years. It has shown remarkable image generation improvement from low-quality, pixelated images with limited details to highly realistic images with fine details that can often be mistaken for real images. However, the traditional process of generating an image in computer graphics is complex and time- consuming. You need to set up a camera and light, and create objects with all sorts of details. This requires not only time but also significant expertise. In this work, we aim to replace this complex traditional computer graphics pipeline with a simple machine learning model. This machine learning model can generate realistic images without requiring expertise or significant time and effort. Specifically, we address the problem of controllable image synthesis. We propose several approaches that allow the user to synthesize realistic content and manipulate images to achieve their desired goals with ease and flexibility.