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This presentation will illustrate the ways that the teacher practices within the Cycle of Inquiry system (COI) align with the practices of scientists recommended by the National Research Council and guide emergent inquiry with children that aligns with the Next Generation Science Standards (NGSS). The COI and NGSS are organized around constructivist principles for teaching, learning, and research.

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The cloud is evolving due to additional demands introduced by new technological advancements and the wide movement toward digitalization. Therefore, next-generation data centers (DCs) and clouds are expected (and need) to become cheaper, more efficient, and capable of offering more predictable services. Aligned with this, we examine the concept of software-defined “hardware” infrastructures (SDHI) based on hardware resource disaggregation as one possible way of realizing next-generation DCs. We start with an overview of the functional architecture of a cloud based on SDHI. Following this, we discuss a series of use-cases and deployment scenarios enabled by SDHI and explore the role of each functional block of SDHI’s architecture, i.e., cloud infrastructure, cloud platforms, cloud execution environments, and applications. Next, we propose a framework to evaluate the impact of SDHI on techno-economic efficiency of DCs, specifically focusing on application profiling, hardware dimensioning, and total cost of ownership (TCO). Our study shows that combining resource disaggregation and software-defined capabilities makes DCs less expensive and easier to expand; hence they can rapidly follow the exponential demand growth. Additionally, we elaborate on technologies behind SDHI, its challenges, and its potential future directions. Finally, to identify a suitable memory management scheme for SDHI and show its advantages, we focus on the management of Last Level Cache (LLC) in currently available Intel processors. Aligned with this, we investigate how better management of LLC can provide higher performance, more predictable response time, and improved isolation between threads. More specifically, we take advantage of LLC’s non-uniform cache architecture (NUCA) in which the LLC is divided into “slices,” where access by the core to which it closer is faster than access to other slices. Based upon this, we introduce a new memory management scheme, called slice-aware memory management, which carefully maps the allocated memory to LLC slices based on their access time latency rather than the de facto scheme that maps them uniformly. Many applications can benefit from our memory management scheme with relatively small changes. As an example, we show the potential benefits that Key-Value Store (KVS) applications gain by utilizing our memory management scheme. Moreover, we discuss how this scheme could be used to provide explicit CPU slicing – which is one of the expectations of SDHI  and hardware resource disaggregation. / <p>QC 20190415</p>

Previous studies have shown that genes can be switched on or off by age, environmental factors, diseases, and lifestyles. The open or compact structures of chromatin is a crucial factor that affects gene expression. Epigenetics refers to hereditary mechanisms that change gene expression and regulations without changing DNA sequences. Epigenetic modifications, such as DNA methylation, histone modification, and non-coding RNA interaction, play critical roles in cell differentiation and disease processes. The conventional approach requires the use of a few million or more cells as starting material. However, such quantity is not available when samples from patients and small lab animals are examined. Microfluidic technology offers advantages to utilize low-input starting material and for high-throughput. In this thesis, I developed novel microfluidic technologies to study epigenomic regulations, including 1) profiling epigenomic changes associated with LPS-induced murine monocytes for immunotherapy, 2) examining cell-type-specific epigenomic changes associated with BRCA1 mutation in breast tissues for breast cancer treatment, and 3) developing a novel microfluidic oscillatory hybridized ChIRP-seq assay to profile genome-wide lncRNA binding for numerous human diseases. We used 20,000 and 50,000 primary cells to study histone modifications in inflammation and breast cancer of BRCA1 mutation, respectively. In the project of whole-genome lncRNA bindings, our microfluidic ChIRP-seq assay, for the first time, allowed us to probe native lncRNA bindings in mouse tissue samples successfully. The technology is a promising approach for scientists to study lncRNA bindings in primary patients. Our works pave the way for low-input and high-throughput epigenomic profiling for precision medicine development. / Doctor of Philosophy / Traditionally, physicians treat patients with a one-size-fits-all approach, in which disease prevention and treatment are designed for the average person. The one-size-fits-all approach fits many patients, but does not work on some. Precision medicine is launched to improve the low efficiency and diminish side effects, and all of these drawbacks are happening in the traditional approaches. The genomic, transcriptomic, and epigenomic data from patients is a valuable resource for developing precision medicine. Conventional approaches in profiling functional epigenomic regulation use tens to hundreds of millions cells per assay, that is why applications in clinical samples are restricted for several decades. Due to the small volume manipulated in microfluidic devices, microfluidic technology exhibits high efficiency in easy operation, reducing the required number of cells, and improving the sensitivity of assays. In order to examine functional epigenomic regulations, we developed novel microfluidic technologies for applications with the small number of cells. We used 20,000 cells from mice to study the epigenomic changes in monocytes. We also used 50,000 cells from patients and mice to study epigenomic changes associated with BRCA1 mutation in different cell types. We developed a novel microfluidic technology for studying lncRNA bindings. We used 100,000-500,000 cells from cell lines and primary tissues to test several lncRNAs. Traditional approaches require 20-100 million cells per assay, and these cells are infected by virus for over-producing specific lncRNA. However, our technology just needs 100,000 cells (non-over-producing state) to study lncRNA bindings. To the best of our knowledge, this is the first allowed us to study native lncRNA bindings in mouse samples successfully. Our efforts in developing microfluidic technologies and studying epigenomic regulations pave the way for precision medicine development.