{ "109308-21821373": { "datetime_modified":"20240418T091654", "datetime_start":"20240429T110000", "datetime_end":"20240429T121500", "has_end_time":1, "date_start":"2024-04-29", "date_end":"2024-04-29", "time_start":"11:00:00", "time_end":"12:15:00", "time_zone":"America\/Detroit", "event_title":"Gomberg Lecture - DNA where you expect RNA: from fluorescent aptamers to stress granules", "occurrence_title":"", "combined_title":"Gomberg Lecture - DNA where you expect RNA: from fluorescent aptamers to stress granules: Adrian Ferre-D'Amare (NIH\/NHLBI)", "event_subtitle":"Adrian Ferre-D'Amare (NIH\/NHLBI)", "event_type":"Workshop \/ Seminar", "event_type_id":"21", "description":"It is generally taken for granted that DNA is a biochemically \"inert\", structurally sedate repository of genetic information, while RNA carries out sophisticated activities such as selective small-molecule binding and catalysis. In this lecture, I will discuss two instances where we have encountered DNA performing unexpected roles. In the first, our structural determination of the fluorogenic DNA aptamer \"Lettuce\" revealed a novel 4-way junctional fold that provides a binding site for its cognate fluorophore. While numerous RNA molecules capable of inducing the fluorescence (often in excess of 5000-fold) of otherwise non-fluorescent dyes have been isolated through in vitro evolution, the 3D structures of these \"turn-on\" aptamers rely on extensive interactions of the ribose 2'-OH. Lettuce adopts its complex fold using completely different strategies, therefore hinting that new principles of nucleic acid structure may emerge from the analysis of functional DNAs. In the second, we have discovered that DNA in the cytoplasm is essential for the eukaryotic stress response. From yeast to human, when cells encounter stressors (heat, cold, hypoxia, etc.), their initial response is the cessation of protein synthesis, and the condensation of their now ribosome-free mRNAs into large cytoplasmic bodies known as stress granules. We have recently devised new methods to purify stress granule cores from both budding yeast and mammalian (HEK293T) cells, finding that they are biochemically stable, ~200 nm particles. Analysis of their protein and RNA composition largely confirmed the results of previous reports on stress granule composition. Unexpectedly, we discovered that stress granule cores also contain double-stranded circular DNA (extrachromosomal circular DNA, eccDNA), and that their structural integrity in vitro depends on this DNA. Moreover, targeting cytoplasmic eccDNAs through CRISPR technology abrogates the ability of live yeast to form stress granules. Previous studies have found eccDNAs in all eukaryotic cells, but had not hinted at a cytoplasmic role. Our work provides a new functional connection, mediated by DNA, between the nucleus, cytoplasmic membraneless organelles, protein synthesis, and the response of eukaryotic cells to their changing environments.", "occurrence_notes":null, "guid":"109308-21821373@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/109308", "building_id":"1000158", "building_name":"Chemistry Dow Lab", "campus_maps_id":"34", "room":"1640", "location_name":"Chemistry Dow Lab", "has_livestream":0, "cost":"", "tags":["Chemical Biology","Chemistry","Gomberg Lecture","Science"], "website":"https:\/\/irp.nih.gov\/pi\/adrian-ferre-damare", "sponsors":[ { "group_name":"Department of Chemistry", "group_id":"3761", "website":"https:\/\/lsa.umich.edu\/chem" }, { "group_name":"Chemical Biology", "group_id":"4385", "website":"" } ], "image_url":"", "styled_images":{ "event_thumb":"", "event_large":"", "event_large_2x":"", "event_large_lightbox":"", "group_thumb":"", "group_thumb_square":"", "group_large":"", "group_large_lightbox":"", "event_large_crop":"", "event_list":"", "event_list_2x":"", "event_grid":"", "event_grid_2x":"", "event_feature_large":"", "event_feature_thumb":"" }, "occurrence_count":1, "first_occurrence":21821373 } , "119891-21843785": { "datetime_modified":"20240422T121503", "datetime_start":"20240429T110000", "datetime_end":"20240429T170000", "has_end_time":1, "date_start":"2024-04-29", "date_end":"2024-04-29", "time_start":"11:00:00", "time_end":"17:00:00", "time_zone":"America\/Detroit", "event_title":"Impressions: 2024 Stamps Senior Exhibition", "occurrence_title":"", "combined_title":"Impressions: 2024 Stamps Senior Exhibition", "event_subtitle":"", "event_type":"Exhibition", "event_type_id":"7", "description":"Impressions: 2024 Stamps Senior Exhibition features work in a range of media by graduating Stamps BA, BFA, and Interarts Performance students at the University of Michigan Stamps School of Art & Design.\nOpening Reception: Friday, April 19, 2024 from 4 - 8 p.m.Screening of Time-Based Work: Friday, April 19, 2024 from 6 - 8 p.m., Art & Architecture Auditorium (room 2104)\nExhibition Hours: Open daily Monday - Saturday, 11 a.m. - 5 p.m. from April 20 through May 4, 2024. Closed Sundays.", "occurrence_notes":null, "guid":"119891-21843785@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/119891", "building_id":"null", "building_name":"Off Campus Location", "campus_maps_id":"1", "room":"", "location_name":"Art & Architecture Building, 2000 Bonisteel Blvd, Ann Arbor MI 48109-2069", "has_livestream":0, "cost":"", "tags":["Art"], "website":"https:\/\/stamps.umich.edu\/events\/2024-senior-exhibition", "sponsors":[ { "group_name":"Penny W Stamps School of Art & Design", "group_id":"3157", "website":"http:\/\/stamps.umich.edu\/" } ], "image_url":"https:\/\/events.umich.edu\/media\/attachments\/2024\/03\/event_119891_original-1.jpg", "styled_images":{ "event_thumb":"https:\/\/events.umich.edu\/media\/cache\/event_thumb\/media\/attachments\/2024\/03\/event_119891_original-1.jpg", "event_large":"https:\/\/events.umich.edu\/media\/cache\/event_large\/media\/attachments\/2024\/03\/event_119891_original-1.jpg", "event_large_2x":"https:\/\/events.umich.edu\/media\/cache\/event_large_2x\/media\/attachments\/2024\/03\/event_119891_original-1.jpg", "event_large_lightbox":"https:\/\/events.umich.edu\/media\/cache\/event_large_lightbox\/media\/attachments\/2024\/03\/event_119891_original-1.jpg", "group_thumb":"https:\/\/events.umich.edu\/media\/cache\/group_thumb\/media\/attachments\/2024\/03\/event_119891_original-1.jpg", "group_thumb_square":"https:\/\/events.umich.edu\/media\/cache\/group_thumb_square\/media\/attachments\/2024\/03\/event_119891_original-1.jpg", "group_large":"https:\/\/events.umich.edu\/media\/cache\/group_large\/media\/attachments\/2024\/03\/event_119891_original-1.jpg", "group_large_lightbox":"https:\/\/events.umich.edu\/media\/cache\/group_large_lightbox\/media\/attachments\/2024\/03\/event_119891_original-1.jpg", "event_large_crop":"https:\/\/events.umich.edu\/media\/cache\/event_large_crop\/media\/attachments\/2024\/03\/event_119891_original-1.jpg", "event_list":"https:\/\/events.umich.edu\/media\/cache\/event_list\/media\/attachments\/2024\/03\/event_119891_original-1.jpg", "event_list_2x":"https:\/\/events.umich.edu\/media\/cache\/event_list_2x\/media\/attachments\/2024\/03\/event_119891_original-1.jpg", "event_grid":"https:\/\/events.umich.edu\/media\/cache\/event_grid\/media\/attachments\/2024\/03\/event_119891_original-1.jpg", "event_grid_2x":"https:\/\/events.umich.edu\/media\/cache\/event_grid_2x\/media\/attachments\/2024\/03\/event_119891_original-1.jpg", "event_feature_large":"https:\/\/events.umich.edu\/media\/cache\/event_feature_large\/media\/attachments\/2024\/03\/event_119891_original-1.jpg", "event_feature_thumb":"https:\/\/events.umich.edu\/media\/cache\/event_feature_thumb\/media\/attachments\/2024\/03\/event_119891_original-1.jpg" }, "occurrence_count":14, "first_occurrence":21843777 } , "121286-21846314": { "datetime_modified":"20240409T160341", "datetime_start":"20240429T110000", "datetime_end":"20240429T130000", "has_end_time":1, "date_start":"2024-04-29", "date_end":"2024-04-29", "time_start":"11:00:00", "time_end":"13:00:00", "time_zone":"America\/Detroit", "event_title":"Interpretable Latent Variable Models: Identifiability, Estimation, and Inference", "occurrence_title":"", "combined_title":"Interpretable Latent Variable Models: Identifiability, Estimation, and Inference: Jing Ouyang", "event_subtitle":"Jing Ouyang", "event_type":"Lecture \/ Discussion", "event_type_id":"13", "description":"Latent variable models play an increasingly crucial role in modern statistics and machine learning for analyzing large-scale and complex-structured data, with wide-ranging applications across various scientific fields. For instance, in educational assessments, latent variable models capture unobservable traits, such as intelligence, personality, and attitude. In biology and genomics, latent variable models uncover underlying genetic factors, gene expression patterns, or hidden biological mechanisms. By inferring the latent variables, researchers gain a deeper understanding of the mechanisms governing the observed data. Despite wide applications of latent variable models, the large scale and complex structures of data, and the involvement of covariates pose numerous challenges for its identifiability, estimation, and inference. This dissertation focuses on developing identifiability theory, estimation approaches, and inference methodologies for interpretable latent variable models, addressing three important problems:\r\n(I): The first part addresses the identifiability issues of latent class models with covariates. Despite that these models are widely used in various applications, the fundamental identifiability issue of the latent class models with covariates has not been fully addressed. To address this open identifiability issue, we establish conditions to ensure the global identifiability of the model parameters in both strict and generic sense. Moreover, our results extend to polytomous-response Cognitive Diagnosis Models (CDMs) with covariates, which generalizes the existing identifiability results for CDMs.\r\n(II) The second part develops estimation and inference methods for generalized linear framework with latent confounders. Statistical inferences for high-dimensional regression models have been extensively studied for their wide applications ranging from genomics, neuroscience, to economics. However, in practice, there are often potential unmeasured confounders associated with both the response and covariates, which can lead to invalidity of standard debiasing methods. In this part, we focus on a generalized linear regression framework with hidden confounding and propose a debiasing approach to address this high-dimensional problem, by adjusting for the effects induced by the unmeasured confounders. We establish consistency and asymptotic normality for the proposed debiased estimator.\r\n(III) The third part focuses on statistical inference for covariate-adjusted generalized factor models. In addition to understanding the latent factors, the covariate effects on responses controlling for latent factors is also of great scientific interest and has wide applications, such as evaluating the fairness of educational testing, where the covariate effect reflects whether a test question is biased toward certain individual characteristics (e.g. gender and race) taking into account their latent abilities. However, the large sample size, substantial covariate dimension, and great test length pose great challenges to developing efficient methods and drawing valid inferences. Moreover, to accommodate the commonly encountered discrete type of responses, nonlinear factor models are often assumed, bringing in further complexity to the problem. To address these challenges, we consider a covariate-adjusted generalized factor model and develop novel and interpretable conditions to address the identifiability issue. Based on the identifiability conditions, we propose a joint maximum likelihood estimation method and establish estimation consistency and asymptotic normality results for the covariate effects under a practical yet challenging asymptotic regime. Furthermore, we derive estimation and inference results for latent factors and the factor loadings.", "occurrence_notes":null, "guid":"121286-21846314@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/121286", "building_id":"1000167", "building_name":"West Hall", "campus_maps_id":"163", "room":"438", "location_name":"West Hall", "has_livestream":0, "cost":"", "tags":["Dissertation"], "website":"", "sponsors":[ { "group_name":"Department of Statistics Dissertation Defenses", "group_id":"4045", "website":"" } ], "image_url":"", "styled_images":{ "event_thumb":"", "event_large":"", "event_large_2x":"", "event_large_lightbox":"", "group_thumb":"", "group_thumb_square":"", "group_large":"", "group_large_lightbox":"", "event_large_crop":"", "event_list":"", "event_list_2x":"", "event_grid":"", "event_grid_2x":"", "event_feature_large":"", "event_feature_thumb":"" }, "occurrence_count":1, "first_occurrence":21846314 } }