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value="desc">Date Descending</option></select></div><div class="o-input__droplist1 c-sort__page-input"><label for="c-sort2">Show:</label><select name="rows" id="c-sort2" form="facetForm"><option selected="" value="10">10</option><option value="20">20</option><option value="30">30</option><option value="40">40</option><option value="50">50</option><option value="100">100</option></select></div></div><input type="hidden" name="start" form="facetForm" value="0"/><nav class="c-pagination--next"><ul><li><a href="" aria-label="you are on result set 1" class="c-pagination__item--current">1</a></li><li><a href="" aria-label="go to result set 2" class="c-pagination__item">2</a></li><li><a href="" aria-label="go to result set 3" class="c-pagination__item">3</a></li><li><a href="" aria-label="go to result set 4" class="c-pagination__item">4</a></li><li><a href="" aria-label="go to result set 16" class="c-pagination__item">16</a></li><li class="c-pagination__next"><a href="" aria-label="go to Next result set">Next</a></li></ul></nav></div><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/0dm6p6ks"><div class="c-clientmarkup">The Role of Word Order in Syntactic Change: Sentence-final Prominency in Korean Negation</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AKim%2C%20Hyun-Oak%20Alan">Kim, Hyun-Oak Alan</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/bling_proceedings/3/3">Proceedings of the Annual Meeting of the Berkeley Linguistics Society, Volume 3</a> (1977)</div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/0dm6p6ks"><img src="/cms-assets/ba4bf358f5d6e17fa84f21cad752941df1bd0081daf39e5692b4b9ca066ee2b1" alt="Cover page: The Role of Word Order in Syntactic Change: Sentence-final Prominency in Korean Negation"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/7d51q40n"><div class="c-clientmarkup">Qualitative Reasoning about the Geometry of Fluid Flow</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AKim%2C%20Hyun-Kyung">Kim, Hyun-Kyung</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/cognitivesciencesociety/12/0">Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 12</a> (1990)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">Understanding the interaction between dynamics and geometry is crucial to capturing commonsense physics. This paper presents a qualitative analysis of the direction of fluid flow. This analysis is dependent on qualitative descriptions of the surface geometry of rigid bodies in contact with the fluid and a pressure change in fluid. The key problem in designing an intelligent system to reason about fluid motion is how to partition the fluid at an appropriate level of representation. The basic idea of our approach is to incrementally generate the qualitatively different parts of fluid. We do this by dynamically analyzing the intereiction of geometry and pressure disturbance. Using this technique, we can derive all possible fluid flows.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/7d51q40n"><img src="/cms-assets/8454fe00f4563fcfe151cc6cf9bf1dc3bd42a3cc40b7a473d658c28e8cd262b1" alt="Cover page: Qualitative Reasoning about the Geometry of Fluid Flow"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-thesis">Thesis</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/3nr7t6rr"><div class="c-clientmarkup">Engineering of Synthetic Peptide Substrates for the Expansion and Differentiation of Human Pluripotent Stem Cell-Derived Neural Progenitor Cells</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AKim%2C%20Hyun-Je">Kim, Hyun-Je</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucsd_etd">UC San Diego Electronic Theses and Dissertations</a> (2014)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">Neurodegenerative diseases, characterized by traumatic or progressive loss of neurons in the brain and spinal, present significant medical and economic challenges to society with fast growing patient populations and yet unavailability of permanent cure-giving therapies. Current drug-based treatments are highly limited in a sense that they merely alleviate symptoms, delay the onset or slow down the progression of the disease at best--even so, with low efficacy and wide variability in therapeutic outcome at the cost of many side effects. With prospect to induce permanent recovery in affected neural functions at molecular level, stem cell therapies based on neural progenitor cell (NPC) transplantation have been extensively performed in animal models of neurodegeneration. However, laminin (LN) substrates commonly used to culture these NPCs may contain animal- derived pathogens and are relatively expensive to produce. In this project, we developed optimal combinations of synthetic peptides that mimick functional sequences of extracellular matrix proteins, on which human embryonic stem cell (hESC) derived NPCs were expanded for 10 passages and differentiated into neurons after 10th passage followed by molecular profiling. Cells cultured on the optimized peptide substrates proliferated at a rate similar to those on the LN substrate, also expressing multipotent NPC markers such as NESTIN, SOX1, and SOX2 at a comparable degree to LN-based culture as verified by gene expression analysis, immunofluorescence staining and flow cytometry. Cells differentiated on the synthetic peptides expressed neuronal markers, MAP2 and B3T, at a similar level to those on LN. Therefore, our synthetic peptide combinations support the long-term expansion and neuronal differentiation of NPCs, providing cheaper and xenogenic contaminant-free alternatives for the LN substrate, and holding promise as a feasible substrate for the large-scale production of NPCs and neurons for cell- based therapies</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/3nr7t6rr"><img src="/cms-assets/3eac0750854bf98d74a760c584e61a44e96f4596a3662a692ea058987ccd4894" alt="Cover page: Engineering of Synthetic Peptide Substrates for the Expansion and Differentiation of Human Pluripotent Stem Cell-Derived Neural Progenitor Cells"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/5v6337c5"><div class="c-clientmarkup">Emergent Hidden Grammar: Stochastic Patterning in Korean Accentuation of Novel Words</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AKim%2C%20Hyun-Ju">Kim, Hyun-Ju</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/bling_proceedings/36/36">Proceedings of the Annual Meeting of the Berkeley Linguistics Society, Volume 36</a> (2016)</div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/5v6337c5"><img src="/cms-assets/13394dddbbd9a1a1df9da427cf7fd984702ba53a3fc40aa9e457507ebd814eab" alt="Cover page: Emergent Hidden Grammar: Stochastic Patterning in Korean Accentuation of Novel Words"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/74w11545"><div class="c-clientmarkup">Augmenting Qualitative Simulation with Global Filtering</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AKim%2C%20Hyun-Kyung">Kim, Hyun-Kyung</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/cognitivesciencesociety/14/0">Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 14</a> (1992)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">Capturing correct changes both locally and globally is crucial to predicting the behavior of physical systems. However, due to the nature of qualitative simulation techniques, they cannot avoid losing some information which is useful for finding precise global behavior. This paper describes how global constraints are represented and m a nipulated in current simulation systems, using a model of an internal combustion engine. The basic idea of our approach is to automatically generate additional information for maintaining global constraints during simulation so that simulation techniques can filter global behaviors with the sufficient information. This is done by automatically introducing variables and controlling their values to guide correct transitions between the behaviors. W e express this idea within the framework of Qualitative Process (QP) theory. This technique has been implemented and integrated into an existing qualitative simulation program QPE.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/74w11545"><img src="/cms-assets/5df53ba93f3dde07bbe2607339582079a555d7619051368f8ff6ddbdd74b47b4" alt="Cover page: Augmenting Qualitative Simulation with Global Filtering"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-thesis">Thesis</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/7f40m7jn"><div class="c-clientmarkup">Improving Hardware Multithreading in General Purpose Graphics Processing Units</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AKim%2C%20Hyun%20Jin">Kim, Hyun Jin</a> </li><li class="c-authorlist__begin"><span class="c-authorlist__heading">Advisor(s):</span> <a href="/search/?q=author%3AReinman%2C%20Glenn">Reinman, Glenn</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucla_etd">UCLA Electronic Theses and Dissertations</a> (2017)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup"><p>General-purpose graphics processing unit (GPGPU) is one of the most popular many-core accelerators</p><p>that deliver a massive computing power in parallel applications. GPGPUs mainly</p><p>rely on the hardware multithreading to hide a short pipeline stall and a long memory latency.</p><p>Thus, the performance of GPGPU can be signicantly aected by how GPGPU's</p><p>hardware multithreading is applied. However, nding the optimal hardware multithreading</p><p>is a complex problem since there are many aspects to be considered. This work studies the</p><p>mechanisms for improving the eectiveness of hardware multithreading. First, it studies</p><p>the various scheduling policies and proposes an adaptive scheduling policy that chooses the</p><p>best scheduling policy at runtime. In addition, it proposes simple but eective warp throttling</p><p>mechanism that can increase the cache locality. Furthermore, it proposes a hardware</p><p>prefetching mechanism to extend the memory latency hiding degree of hardware multithreading.</p><p>Finally, it shows how a limited scalability of the conventional cache miss handling architecture</p><p>constrains the degree of hardware multithreading and proposes the highly scalable</p><p>cache miss handling architecture.</p></div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/7f40m7jn"><img src="/cms-assets/bff3cdb1472f31930677265c13cbdf56dc6dc63ccf3818151f8c1204c86b63fd" alt="Cover page: Improving Hardware Multithreading in General Purpose Graphics Processing Units"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/93m3x1v2"><div class="c-clientmarkup">Nuclear Factor Erythroid-2 Like 1 (NFE2L1): Structure, function and regulation</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AKim%2C%20Hyun%20Min">Kim, Hyun Min</a>; </li><li><a href="/search/?q=author%3AHan%2C%20Jeong%20Woo">Han, Jeong Woo</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3AChan%2C%20Jefferson%20Y">Chan, Jefferson Y</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/uci_postprints">UC Irvine Previously Published Works</a> (2016)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">Nrf1 (also referred to as NFE2L1) is a member of the CNC-bZIP family of transcription factors that are characterized by a highly conserved CNC-domain, and a basic-leucine zipper domain required for dimerization and DNA binding. Nrf1 is ubiquitously expressed across tissue and cell types as various isoforms, and is induced by stress signals from a broad spectrum of stimuli. Evidence indicates that Nrf1 plays an important role in regulating a range of cellular functions including oxidative stress response, differentiation, inflammatory response, metabolism, and maintaining proteostasis. Thus, Nrf1 has been implicated in the pathogenesis of various disease processes including cancer development, and degenerative and metabolic disorders. This review summarizes our current understanding of Nrf1 and the molecular mechanism underlying its regulation and action in different cellular functions.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/93m3x1v2"><img src="/cms-assets/3363bd2d1851cf3aa7a256a03814d3bd536218a7e6c2a52f6ced80a27561b812" alt="Cover page: Nuclear Factor Erythroid-2 Like 1 (NFE2L1): Structure, function and regulation"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/7gr9853s"><div class="c-clientmarkup">PECAN Predicts Patterns of Cancer Cell Cytostatic Activity of Natural Products Using Deep Learning.</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AKim%2C%20Hyun">Kim, Hyun</a>; </li><li><a href="/search/?q=author%3AGerwick%2C%20William">Gerwick, William</a>; </li><li><a href="/search/?q=author%3ACottrell%2C%20Garrison">Cottrell, Garrison</a>; </li><li><a href="/search/?q=author%3AGahl%2C%20Martha">Gahl, Martha</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3AGlukhov%2C%20Evgenia">Glukhov, Evgenia</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucsd_postprints">UC San Diego Previously Published Works</a> (2024)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">Many machine learning techniques are used as drug discovery tools with the intent to speed characterization by determining relationships between compound structure and biological function. However, particularly in anticancer drug discovery, these models often make only binary decisions about the biological activity for a narrow scope of drug targets. We present a feed-forward neural network, PECAN (Prediction Engine for the Cytostatic Activity of Natural product-like compounds), that simultaneously classifies the potential antiproliferative activity of compounds against 59 cancer cell lines. It predicts the activity to be one of six categories, indicating not only if activity is present but the degree of activity. Using an independent subset of NCI data as a test set, we show that PECAN can reach 60.1% accuracy in a six-way classification and present further evidence that it classifies based on useful structural features of compounds using a within-one measure that reaches 93.0% accuracy.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/7gr9853s"><img src="/cms-assets/df5bed3980edb86ce056efbbacf6a637f8e3701db289d80c5935963faba90163" alt="Cover page: PECAN Predicts Patterns of Cancer Cell Cytostatic Activity of Natural Products Using Deep Learning."/></a><a href="https://creativecommons.org/licenses/by/4.0/" class="c-scholworks__license"><img class="c-lazyimage" data-src="/images/cc-by-small.svg" alt="Creative Commons 'BY' version 4.0 license"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/52x192ng"><div class="c-clientmarkup">Triglyceride induces DNA damage leading to monocyte death by activating caspase-2 and caspase-8.</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AJung%2C%20Byung">Jung, Byung</a>; </li><li><a href="/search/?q=author%3AKim%2C%20Hyun-Kyung">Kim, Hyun-Kyung</a>; </li><li><a href="/search/?q=author%3AKim%2C%20Sung">Kim, Sung</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3AKim%2C%20Yoon">Kim, Yoon</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucb_postprints">UC Berkeley Previously Published Works</a> (2023)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">Monocytes are peripheral leukocytes that function in innate immunity. Excessive triglyceride (TG) accumulation causes monocyte death and thus can compromise innate immunity. However, the mechanisms by which TG mediates monocyte death remain unclear to date. Thus, this study aimed to elucidate the mechanisms by which TG induces monocyte death. Results showed that TG induced monocyte death by activating caspase-3/7 and promoting poly (ADP-ribose) polymerase (PARP) cleavage. In addition, TG induced DNA damage and activated the ataxia telangiectasia mutated (ATM)/checkpoint kinase 2 and ATM-and Rad3-related (ATR)/checkpoint kinase 1 pathways, leading to the cell death. Furthermore, TG-induced DNA damage and monocyte death were mediated by caspase-2 and -8, and caspase-8 acted as an upstream molecule of caspase-2. Taken together, these results suggest that TG-induced monocyte death is mediated via the caspase-8/caspase-2/DNA damage/executioner caspase/PARP pathways. [BMB Reports 2023; 56(3): 166-171].</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/52x192ng"><img src="/cms-assets/1b2701b7b2d06b3d23b8853a570bb43131f576825625a62bcb6c0faf58b1eca0" alt="Cover page: Triglyceride induces DNA damage leading to monocyte death by activating caspase-2 and caspase-8."/></a><a href="https://creativecommons.org/licenses/by-nc/4.0/" class="c-scholworks__license"><img class="c-lazyimage" data-src="/images/cc-by-nc-small.svg" alt="Creative Commons 'BY-NC' version 4.0 license"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/6rn7p5zz"><div class="c-clientmarkup">DNA methylation patterns associated with breast cancer prognosis that are specific to tumor subtype and menopausal status</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AKim%2C%20Hyun">Kim, Hyun</a>; </li><li><a href="/search/?q=author%3ABinder%2C%20Alexandra%20M">Binder, Alexandra M</a>; </li><li><a href="/search/?q=author%3AZhou%2C%20Hua">Zhou, Hua</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3AJung%2C%20Su%20Yon">Jung, Su Yon</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucla_postprints">UCLA Previously Published Works</a> (2023)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">Tumor subtype and menopausal status are strong predictors of breast cancer (BC) prognosis. We aimed to find and validate subtype- or menopausal-status-specific changes in tumor DNA methylation (DNAm) associated with all-cause mortality or BC progression. Associations between site-specific tumor DNAm and BC prognosis were estimated among The Cancer Genome Atlas participants (<i>n</i> = 692) with Illumina Infinium HumanMethylation450 BeadChip array data. All-cause mortality and BC progression were modeled using Cox proportional hazards models stratified by tumor subtypes, adjusting for age, race, stage, menopausal status, tumor purity, and cell type proportion. Effect measure modification by subtype and menopausal status were evaluated by incorporating a product term with DNAm. Site-specific inference was used to identify subtype- or menopausal-status-specific differentially methylated regions (DMRs) and functional pathways. The validation of the results was carried out on an independent dataset (GSE72308; <i>n</i> = 180). We identified a total of fifteen unique CpG probes that were significantly associated ( P ≤ 1 × 10 - 7 with survival outcomes in subtype- or menopausal-status-specific manner. Seven probes were associated with overall survival (OS) or progression-free interval (PFI) for women with luminal A subtype, and four probes were associated with PFI for women with luminal B subtype. Five probes were associated with PFI for post-menopausal women. A majority of significant probes showed a lower risk of OS or BC progression with higher DNAm. We identified subtype- or menopausal-status-specific DMRs and functional pathways of which top associated pathways differed across subtypes or menopausal status. None of significant probes from site-specific analyses met genome-wide significant level in validation analyses while directions and magnitudes of coefficients showed consistent pattern. We have identified subtype- or menopausal-status-specific DNAm biomarkers, DMRs and functional pathways associated with all-cause mortality or BC progression, albeit with limited validation. Future studies with larger independent cohort of non-post-menopausal women with non-luminal A subtypes are warranted for identifying subtype- and menopausal-status-specific DNAm biomarkers for BC prognosis.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/6rn7p5zz"><img src="/cms-assets/22e04321378b2c09551feff84ddae780661e4244ac58e5c5bddcd317c671d95c" alt="Cover page: DNA methylation patterns associated with breast cancer prognosis that are specific to tumor subtype and menopausal status"/></a><a href="https://creativecommons.org/licenses/by/4.0/" class="c-scholworks__license"><img class="c-lazyimage" data-src="/images/cc-by-small.svg" alt="Creative Commons 'BY' version 4.0 license"/></a></div></section><nav class="c-pagination--next"><ul><li><a href="" aria-label="you are on result set 1" class="c-pagination__item--current">1</a></li><li><a href="" aria-label="go to result set 2" class="c-pagination__item">2</a></li><li><a href="" aria-label="go to result set 3" class="c-pagination__item">3</a></li><li><a href="" aria-label="go to result set 4" class="c-pagination__item">4</a></li><li><a href="" aria-label="go to result set 16" class="c-pagination__item">16</a></li><li class="c-pagination__next"><a href="" aria-label="go to Next result set">Next</a></li></ul></nav></section></main></form></div><div><div class="c-toplink"><a href="javascript:window.scrollTo(0, 0)">Top</a></div><footer class="c-footer"><nav class="c-footer__nav"><ul><li><a href="/">Home</a></li><li><a href="/aboutEschol">About eScholarship</a></li><li><a href="/campuses">Campus Sites</a></li><li><a href="/ucoapolicies">UC Open Access Policy</a></li><li><a href="/publishing">eScholarship Publishing</a></li><li><a href="https://www.cdlib.org/about/accessibility.html">Accessibility</a></li><li><a href="/privacypolicy">Privacy Statement</a></li><li><a href="/policies">Site Policies</a></li><li><a href="/terms">Terms of Use</a></li><li><a href="/login"><strong>Admin Login</strong></a></li><li><a href="https://help.escholarship.org"><strong>Help</strong></a></li></ul></nav><div class="c-footer__logo"><a href="/"><img class="c-lazyimage" data-src="/images/logo_footer-eschol.svg" alt="eScholarship, University of California"/></a></div><div class="c-footer__copyright">Powered by the<br/><a href="http://www.cdlib.org">California Digital Library</a><br/>Copyright © 2017<br/>The Regents of the University of California</div></footer></div></div></div></div> <script src="/js/vendors~app-bundle-2aefc956e545366a5d4e.js"></script> <script src="/js/app-bundle-4477d7630fb8c6f70662.js"></script> </body> </html>