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DTSTART;TZID=America/New_York:20220823T100000
DTEND;TZID=America/New_York:20220823T113000
DTSTAMP:20260405T175634
CREATED:20220809T131939Z
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SUMMARY:MEAM Seminar: "Disease Indicator Prediction in Vascular Flows via Physics-Informed Deep Learning"
DESCRIPTION:Unlike diseases with strong genetic predisposition\, such as cancer\, there exist no accurate personalized diagnostic tools for disorders such as Hypertensive Pregnancy Disorders (HPD) and its mechanism remain understudied. For the purpose of performing diagnostics\, we require disease indicators (biomarkers) that are strongly correlated with the underlying condition\, i.e. absolute vascular pressure for hypertension. Measuring absolute pressure requires an invasive procedure and it is impossible for small vessels and in pregnant subjects\, but an informed estimate is possible via the use of Computational Fluid Dynamics. Computational Fluid Dynamics can also be employed for studying the relation between disease outcomes and biomarkers\, by constructing virtual representations of a human body called Digital Twins and performing simulations for different parameters. Unfortunately the computational cost of CFD is very high thus studying the physiology of large cohorts of patients or making real time predictions is impossible. Therefore\, fast surrogates that generalize across different patients and parameters are required. \nIn this talk\, I will leverage aspects of both computational mechanics and deep learning to develop methods for predicting complex biomarkers that cannot be measured in the clinic\, such as absolute pressure\, for performing patient specific diagnostics. First\, I will discuss the feasibility of estimating vascular parameters for assessing Hypertensive Pregnancy Disorders using a combination of Bayesian inference and a reduced order Navier-Stokes model and show that despite the accuracy of this procedure\, its computational complexity makes it impossible to be employed in clinical practice. Second\, I will discuss leveraging Physics-Informed Neural Networks (PINNs) for designing Deep Learning surrogates to alleviate computational drawbacks of the above pipeline for one patient. Third\, I will discuss machine learning inference in function spaces\, called Operator Learning\, and more specifically the LOCA method and the potential of online biomarker predictions for different patients employing operator learning on manifolds and quantifying the uncertainty of predictions.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-disease-indicator-prediction-in-vascular-flows-via-physics-informed-deep-learning/
LOCATION:Towne 313\, 220 S. 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220823T100000
DTEND;TZID=America/New_York:20220823T140000
DTSTAMP:20260405T175634
CREATED:20220822T145428Z
LAST-MODIFIED:20220822T145428Z
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SUMMARY:CBE Dissertation Defense: "Visualizing Genetics: Quantifying Gene Expression Through Genetic and Epigenetic Modifications"
DESCRIPTION:“Visualizing Genetics: Quantifying Gene Expression Through Genetic and Epigenetic Modifications” \nAbstract: \nPrecise control over gene expression is essential for proper development of an organism. However\, genes are regulated in a variety of ways\, the mechanisms of which are not well understood. This thesis explores gene regulation at two different levels: genetic control and epigenetic control. Genetic control focuses on components of the genome such as enhancers\, promoters\, gene composition\, and gene length. Each of these facets can potentially affect either the timing\, amount\, or location of gene expression. Epigenetic control looks at how different DNA markers\, which do not modify the DNA sequence\, are able to manipulate gene production. Using quantitative live-imaging in Drosophila embryos and immunofluorescence staining in human fibroblasts\, we are able to capture and analyze the impact each of these factors have on gene expression. In genetic control\, we see that enhancers play a dominant role in controlling expression\, specifically through the individual transcription factor binding sites within an enhancer. Based on the binding affinity and type of binding site\, the enhancer can influence when genes are active as well as the amount of mRNA produced. The level of mRNA production is also influenced by the rate of RNA polymerase II (Pol II) elongation. The speed that Pol II moves along a gene body can impact how much mRNA is made within a certain developmental time period. We find that not only do enhancers play a role in controlling Pol II elongation rate\, but the composition of the gene itself also contributes to modulating the rate of elongation. In epigenetic control\, there are many different markers interacting with DNA. Here we identified H3K9me3 as a key epigenetic modification that controls DNA compaction\, and thus can silence a large number of genes. As a result\, specific epigenetic markers can ultimately control gene expression and dramatically impact a cell’s ability to reprogram itself. Slight changes in gene regulatory mechanisms can cause extreme changes in gene expression and ensuring that organisms develop properly is contingent upon having a better understanding of how different factors influence expression.
URL:https://seasevents.nmsdev7.com/event/cbe-dissertation-defense-visualizing-genetics-quantifying-gene-expression-through-genetic-and-epigenetic-modifications/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220823T150000
DTEND;TZID=America/New_York:20220823T170000
DTSTAMP:20260405T175634
CREATED:20220819T162454Z
LAST-MODIFIED:20220819T162454Z
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SUMMARY:BE Doctoral Dissertation Defense: "Molecular Imaging Biomarkers and Regulation Approaches for Next-Generation Cellular Therapies" (Iris Lee)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Mark Sellmeyer are pleased to announce the Doctoral Dissertation Defense of Iris Lee.\n \nTitle: Molecular Imaging Biomarkers and Regulation Approaches for Next-Generation Cellular Therapies\nDate: Tuesday\, August 23rd\, 2022\nTime: 3 PM ET\nLocation: Biomedical Research Building (BRB) 0252\n\nThe public is welcome to attend.
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-molecular-imaging-biomarkers-and-regulation-approaches-for-next-generation-cellular-therapies-iris-lee/
LOCATION:BRB 0252
CATEGORIES:Doctoral,Graduate,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
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DTSTART;TZID=America/New_York:20220825T100000
DTEND;TZID=America/New_York:20220825T113000
DTSTAMP:20260405T175634
CREATED:20220819T162002Z
LAST-MODIFIED:20220819T162002Z
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SUMMARY:MEAM Seminar: "High-performance Electroadhesives for Materials and Robots with Programmable Stiffness"
DESCRIPTION:Materials with electroprogrammable stiffness and adhesion can enhance the performance of robotic systems but achieving large changes in stiffness and adhesive forces in real time is an ongoing challenge. Electroadhesive clutches can rapidly adhere high stiffness elements\, although their low force capacities\, high activation voltages\, and inability to separate and turn off stiffness changes reliably have limited their applications. A major challenge in realizing stronger electroadhesive clutches is that current parallel-plate models poorly predict clutch force capacity and cannot be used to design better devices. Furthermore\, soft material interfaces have not been utilized for stronger electroadhesive clutches due to latent adhesion at the contact interface that prevents programmable release. \nIn this talk\, a fracture mechanics framework to understand the relationship between clutch design\, force capacity and contact area is discussed. This mechanics-based framework predicts clutch performance across multiple geometries and applied voltages. Based on this approach\, a Coulombic electrostatic clutch with 94 times the force capacity per unit electrostatic force of state-of-the-art electroadhesive clutches is realized. These electroadhesive clutches are used to increase the load capacity of a soft\, pneumatic finger by a factor of 62 times compared to a finger without an electroadhesive. Finally\, this mechanics-based design methodology is applied to the design of low-voltage ionoelastomer clutches with soft material interfaces for wearable robotic applications with increased force capacities and programmable release at reduced device sizes.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-high-performance-electroadhesives-for-materials-and-robots-with-programmable-stiffness/
LOCATION:Towne 313\, 220 S. 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Doctoral
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220826T120000
DTEND;TZID=America/New_York:20220826T130000
DTSTAMP:20260405T175634
CREATED:20220628T170908Z
LAST-MODIFIED:20220628T170908Z
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SUMMARY:Quantum Engineering Summer Seminar Series: Riccardo Manenti\, PhD [scalable quantum computing systems] (Rigetti)
DESCRIPTION:About the Series: The Quantum Engineering Summer Seminar Series is hosted by the Quantum Engineering Graduate Association (QEGA) every Friday at 12:00 – 1:00 pm EDT throughout the summer months and will be followed by a separate Fall series. The series invites leading world leading experts across academia\, industry\, and government working on experimental\, theoretical\, and policy aspects of quantum science and engineering. Each seminar will be followed by a 10 minute networking session with the invited speaker. \nSpeaker List: \nJuly 8th\, 2022 – Jon Felbinger\, PhD [private-public partnerships] (Quantum Economic Development Consortium) \nJuly 15th\, 2022 – William Oliver\, PhD [superconducting qubits] (MIT and MIT Lincoln Labs) \nJuly 22nd\, 2022 – Sinead Griffin\, PhD [novel qubit materials design] (Lawrence Berkeley National Laboratory) \nJuly 29th\, 2022 – Hannes Bernien\, PhD [trapped ion systems] (University of Chicago) \nAugust 5th\, 2022 – Prineha Narang\, PhD [computational quantum dynamics] (Harvard University) \nAugust 12th\, 2022 – Peter McMahon\, PhD [quantum annealing] (Cornell University) \nAugust 19th\, 2022 – Nick Bronn\, PhD [NISQ era quantum computing] (IBM) \nAugust 26th\, 2022 – Riccardo Manenti\, PhD [scalable quantum computing systems] (Rigetti) \nMeeting Information: The seminar series will be a hybrid event with talks broadcasted on campus (Wu and Chen Auditorium\, Levine Hall) and available online via Zoom. The Zoom link will be posted weekly for each individual talk. \nJoin QEGA: The Quantum Engineering Graduate Association (QEGA) is a new organization at Penn founded by Nima Leclerc and Noah Johnson\, two Penn ESE PhD students in the Sigillito Group. QEGA’s goal is to increase graduate student and faculty engagement in quantum engineering at Penn through school-wide events such as this seminar series\, quantum-career networking opportunities\, and a quantum engineering outreach program program. If you would like to stay connected with us\, please email Nima at nleclerc@seas.upenn.edu with the subject line ‘qega listserv’ to get added to our mailing list!
URL:https://seasevents.nmsdev7.com/event/quantum-engineering-summer-seminar-series-riccardo-manenti-phd-scalable-quantum-computing-systems-rigetti/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
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