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  1. AU="Chen, Emma"
  2. AU="Delean, Ada"
  3. AU="Gurao, Ankita"
  4. AU="Lang, Zhen"
  5. AU="Mahnaz Mohammadpour"
  6. AU="Britta Grillitsch"
  7. AU=Hoeffner Ellen G
  8. AU="Al Harbi, Shmeylan"
  9. AU=Polevoda Bogdan
  10. AU="Raffaele Galiero"
  11. AU=Hruskova Z
  12. AU="Ayers, J"
  13. AU="Cohen, A D"
  14. AU="Brunetti, Gian Luca"
  15. AU=Andrade Daniel
  16. AU=Hay William W Jr

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  1. Artikel ; Online: The MAIDA initiative: establishing a framework for global medical-imaging data sharing.

    Saenz, Agustina / Chen, Emma / Marklund, Henrik / Rajpurkar, Pranav

    The Lancet. Digital health

    2023  Band 6, Heft 1, Seite(n) e6–e8

    Mesh-Begriff(e) Information Dissemination/methods ; Diagnostic Imaging ; Radiography
    Sprache Englisch
    Erscheinungsdatum 2023-11-15
    Erscheinungsland England
    Dokumenttyp Journal Article
    ISSN 2589-7500
    ISSN (online) 2589-7500
    DOI 10.1016/S2589-7500(23)00222-4
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  2. Artikel ; Online: A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring.

    Chen, Emma / Prakash, Shvetank / Janapa Reddi, Vijay / Kim, David / Rajpurkar, Pranav

    Nature biomedical engineering

    2023  

    Abstract: The complex relationships between continuously monitored health signals and therapeutic regimens can be modelled via machine learning. However, the clinical implementation of the models will require changes to clinical workflows. Here we outline ... ...

    Abstract The complex relationships between continuously monitored health signals and therapeutic regimens can be modelled via machine learning. However, the clinical implementation of the models will require changes to clinical workflows. Here we outline ClinAIOps ('clinical artificial-intelligence operations'), a framework that integrates continuous therapeutic monitoring and the development of artificial intelligence (AI) for clinical care. ClinAIOps leverages three feedback loops to enable the patient to make treatment adjustments using AI outputs, the clinician to oversee patient progress with AI assistance, and the AI developer to receive continuous feedback from both the patient and the clinician. We lay out the central challenges and opportunities in the deployment of ClinAIOps by means of examples of its application in the management of blood pressure, diabetes and Parkinson's disease. By enabling more frequent and accurate measurements of a patient's health and more timely adjustments to their treatment, ClinAIOps may substantially improve patient outcomes.
    Sprache Englisch
    Erscheinungsdatum 2023-11-06
    Erscheinungsland England
    Dokumenttyp Journal Article ; Review
    ISSN 2157-846X
    ISSN (online) 2157-846X
    DOI 10.1038/s41551-023-01115-0
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  3. Artikel ; Online: Lateral hypothalamic glutamatergic inputs to VTA glutamatergic neurons mediate prioritization of innate defensive behavior over feeding.

    Barbano, M Flavia / Zhang, Shiliang / Chen, Emma / Espinoza, Orlando / Mohammad, Uzma / Alvarez-Bagnarol, Yocasta / Liu, Bing / Hahn, Suyun / Morales, Marisela

    Nature communications

    2024  Band 15, Heft 1, Seite(n) 403

    Abstract: The lateral hypothalamus (LH) is involved in feeding behavior and defense responses by interacting with different brain structures, including the Ventral Tegmental Area (VTA). Emerging evidence indicates that LH-glutamatergic neurons infrequently synapse ...

    Abstract The lateral hypothalamus (LH) is involved in feeding behavior and defense responses by interacting with different brain structures, including the Ventral Tegmental Area (VTA). Emerging evidence indicates that LH-glutamatergic neurons infrequently synapse on VTA-dopamine neurons but preferentially establish multiple synapses on VTA-glutamatergic neurons. Here, we demonstrated that LH-glutamatergic inputs to VTA promoted active avoidance, long-term aversion, and escape attempts. By testing feeding in the presence of a predator, we observed that ongoing feeding was decreased, and that this predator-induced decrease in feeding was abolished by photoinhibition of the LH-glutamatergic inputs to VTA. By VTA specific neuronal ablation, we established that predator-induced decreases in feeding were mediated by VTA-glutamatergic neurons but not by dopamine or GABA neurons. Thus, we provided evidence for an unanticipated neuronal circuitry between LH-glutamatergic inputs to VTA-glutamatergic neurons that plays a role in prioritizing escape, and in the switch from feeding to escape in mice.
    Mesh-Begriff(e) Animals ; Mice ; Hypothalamic Area, Lateral ; Ventral Tegmental Area ; GABAergic Neurons ; Dopaminergic Neurons ; Affect
    Sprache Englisch
    Erscheinungsdatum 2024-01-09
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-44633-w
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  4. Artikel ; Online: AI in health and medicine.

    Rajpurkar, Pranav / Chen, Emma / Banerjee, Oishi / Topol, Eric J

    Nature medicine

    2022  Band 28, Heft 1, Seite(n) 31–38

    Abstract: Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical AI. We cover ... ...

    Abstract Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical AI. We cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. We also address several promising avenues for novel medical AI research, including non-image data sources, unconventional problem formulations and human-AI collaboration. Finally, we consider serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, AI's potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide.
    Mesh-Begriff(e) Algorithms ; Artificial Intelligence ; Delivery of Health Care ; Humans ; Medicine ; Prospective Studies
    Sprache Englisch
    Erscheinungsdatum 2022-01-20
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural ; Review
    ZDB-ID 1220066-9
    ISSN 1546-170X ; 1078-8956
    ISSN (online) 1546-170X
    ISSN 1078-8956
    DOI 10.1038/s41591-021-01614-0
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Verfügbar in ZB MED Köln/Königswinter

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  5. Buch ; Online: Multimodal Clinical Benchmark for Emergency Care (MC-BEC)

    Chen, Emma / Kansal, Aman / Chen, Julie / Jin, Boyang Tom / Reisler, Julia Rachel / Kim, David A / Rajpurkar, Pranav

    A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine

    2023  

    Abstract: We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), a comprehensive benchmark for evaluating foundation models in Emergency Medicine using a dataset of 100K+ continuously monitored Emergency Department visits from 2020-2022. MC-BEC ... ...

    Abstract We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), a comprehensive benchmark for evaluating foundation models in Emergency Medicine using a dataset of 100K+ continuously monitored Emergency Department visits from 2020-2022. MC-BEC focuses on clinically relevant prediction tasks at timescales from minutes to days, including predicting patient decompensation, disposition, and emergency department (ED) revisit, and includes a standardized evaluation framework with train-test splits and evaluation metrics. The multimodal dataset includes a wide range of detailed clinical data, including triage information, prior diagnoses and medications, continuously measured vital signs, electrocardiogram and photoplethysmograph waveforms, orders placed and medications administered throughout the visit, free-text reports of imaging studies, and information on ED diagnosis, disposition, and subsequent revisits. We provide performance baselines for each prediction task to enable the evaluation of multimodal, multitask models. We believe that MC-BEC will encourage researchers to develop more effective, generalizable, and accessible foundation models for multimodal clinical data.

    Comment: Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-11-07
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  6. Artikel ; Online: Improving hospital readmission prediction using individualized utility analysis.

    Ko, Michael / Chen, Emma / Agrawal, Ashwin / Rajpurkar, Pranav / Avati, Anand / Ng, Andrew / Basu, Sanjay / Shah, Nigam H

    Journal of biomedical informatics

    2021  Band 119, Seite(n) 103826

    Abstract: Objective: Machine learning (ML) models for allocating readmission-mitigating interventions are typically selected according to their discriminative ability, which may not necessarily translate into utility in allocation of resources. Our objective was ... ...

    Abstract Objective: Machine learning (ML) models for allocating readmission-mitigating interventions are typically selected according to their discriminative ability, which may not necessarily translate into utility in allocation of resources. Our objective was to determine whether ML models for allocating readmission-mitigating interventions have different usefulness based on their overall utility and discriminative ability.
    Materials and methods: We conducted a retrospective utility analysis of ML models using claims data acquired from the Optum Clinformatics Data Mart, including 513,495 commercially-insured inpatients (mean [SD] age 69 [19] years; 294,895 [57%] Female) over the period January 2016 through January 2017 from all 50 states with mean 90 day cost of $11,552. Utility analysis estimates the cost, in dollars, of allocating interventions for lowering readmission risk based on the reduction in the 90-day cost.
    Results: Allocating readmission-mitigating interventions based on a GBDT model trained to predict readmissions achieved an estimated utility gain of $104 per patient, and an AUC of 0.76 (95% CI 0.76, 0.77); allocating interventions based on a model trained to predict cost as a proxy achieved a higher utility of $175.94 per patient, and an AUC of 0.62 (95% CI 0.61, 0.62). A hybrid model combining both intervention strategies is comparable with the best models on either metric. Estimated utility varies by intervention cost and efficacy, with each model performing the best under different intervention settings.
    Conclusion: We demonstrate that machine learning models may be ranked differently based on overall utility and discriminative ability. Machine learning models for allocation of limited health resources should consider directly optimizing for utility.
    Mesh-Begriff(e) Aged ; Female ; Humans ; Machine Learning ; Patient Readmission ; Retrospective Studies
    Sprache Englisch
    Erscheinungsdatum 2021-06-01
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2057141-0
    ISSN 1532-0480 ; 1532-0464
    ISSN (online) 1532-0480
    ISSN 1532-0464
    DOI 10.1016/j.jbi.2021.103826
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  7. Buch ; Online: CheXbreak

    Chen, Emma / Kim, Andy / Krishnan, Rayan / Long, Jin / Ng, Andrew Y. / Rajpurkar, Pranav

    Misclassification Identification for Deep Learning Models Interpreting Chest X-rays

    2021  

    Abstract: A major obstacle to the integration of deep learning models for chest x-ray interpretation into clinical settings is the lack of understanding of their failure modes. In this work, we first investigate whether there are patient subgroups that chest x-ray ...

    Abstract A major obstacle to the integration of deep learning models for chest x-ray interpretation into clinical settings is the lack of understanding of their failure modes. In this work, we first investigate whether there are patient subgroups that chest x-ray models are likely to misclassify. We find that patient age and the radiographic finding of lung lesion or pneumothorax are statistically relevant features for predicting misclassification for some chest x-ray models. Second, we develop misclassification predictors on chest x-ray models using their outputs and clinical features. We find that our best performing misclassification identifier achieves an AUROC close to 0.9 for most diseases. Third, employing our misclassification identifiers, we develop a corrective algorithm to selectively flip model predictions that have high likelihood of misclassification at inference time. We observe F1 improvement on the prediction of Consolidation (0.008 [95\% CI 0.005, 0.010]) and Edema (0.003, [95\% CI 0.001, 0.006]). By carrying out our investigation on ten distinct and high-performing chest x-ray models, we are able to derive insights across model architectures and offer a generalizable framework applicable to other medical imaging tasks.

    Comment: Accepted to ACM Conference on Health, Inference, and Learning (ACM-CHIL) Workshop 2021
    Schlagwörter Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2021-03-17
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    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  8. Buch ; Online: Machine Learning Sensors

    Warden, Pete / Stewart, Matthew / Plancher, Brian / Banbury, Colby / Prakash, Shvetank / Chen, Emma / Asgar, Zain / Katti, Sachin / Reddi, Vijay Janapa

    2022  

    Abstract: Machine learning sensors represent a paradigm shift for the future of embedded machine learning applications. Current instantiations of embedded machine learning (ML) suffer from complex integration, lack of modularity, and privacy and security concerns ... ...

    Abstract Machine learning sensors represent a paradigm shift for the future of embedded machine learning applications. Current instantiations of embedded machine learning (ML) suffer from complex integration, lack of modularity, and privacy and security concerns from data movement. This article proposes a more data-centric paradigm for embedding sensor intelligence on edge devices to combat these challenges. Our vision for "sensor 2.0" entails segregating sensor input data and ML processing from the wider system at the hardware level and providing a thin interface that mimics traditional sensors in functionality. This separation leads to a modular and easy-to-use ML sensor device. We discuss challenges presented by the standard approach of building ML processing into the software stack of the controlling microprocessor on an embedded system and how the modularity of ML sensors alleviates these problems. ML sensors increase privacy and accuracy while making it easier for system builders to integrate ML into their products as a simple component. We provide examples of prospective ML sensors and an illustrative datasheet as a demonstration and hope that this will build a dialogue to progress us towards sensor 2.0.
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Hardware Architecture ; Electrical Engineering and Systems Science - Signal Processing
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2022-06-07
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  9. Artikel ; Online: Complex identities, intersectionality and research approaches in millennial family caregivers in the United States.

    Aaron, Siobhan P / Waters, Austin / Tolentino, Anthony / Rascon, Aliria / Phan, Cuong / Chen, Emma / Travers, Jasmine / Jones, Miranda G / Kent-Marvick, Jacqueline / Thomas Hebdon, Megan

    Journal of advanced nursing

    2022  Band 79, Heft 5, Seite(n) 1724–1734

    Abstract: Aims: A discussion of the personal and social contexts for Millennial family caregivers and the value of including complex identity and intersectionality in Millennial family caregiving research with practical application.: Design: Discussion paper.!# ...

    Abstract Aims: A discussion of the personal and social contexts for Millennial family caregivers and the value of including complex identity and intersectionality in Millennial family caregiving research with practical application.
    Design: Discussion paper.
    Data sources: This discussion paper is based on our own experiences and supported by literature and theory.
    Implications for nursing: Millennial family caregivers have distinct generational, historical and developmental experiences that contribute to the care they provide as well as their own well-being. Complex identity, the integration of multiple identities, and intersectionality, systems and structures that disempower and oppress individuals with multiple identities, need to be addressed in nursing research so intervention tailoring and health equity can be better supported in this population. From research conceptualization and design to data analysis, data must be used intentionally to promote equity and reduce bias. The inclusion of diverse Millennial caregivers throughout all stages of the research process and having a diverse nursing research workforce will support these efforts.
    Conclusion: Millennial family caregivers comprise one-quarter of the family caregiving population in the United States, and they are more diverse than previous family caregiving generational cohorts. Their needs will be more fully supported by nursing scientists with the adoption of methods and techniques that address complex identity and intersectionality.
    Impact: Nursing researchers can use the following research approaches to address complex identity and intersectionality in Millennial caregivers: inclusion of qualitative demographic data collection (participants can self-describe); data disaggregation; data visualization techniques to augment or replace frequencies and descriptive statistics for demographic reporting; use of researcher reflexivity throughout the research process; advanced statistical modelling techniques that can handle complex demographic data and test for interactions and differential effects of health outcomes; and qualitative approaches such as phenomenology that centre the stories and experiences of individuals within the population of interest.
    Mesh-Begriff(e) Humans ; United States ; Caregivers ; Intersectional Framework ; Family ; Social Environment
    Sprache Englisch
    Erscheinungsdatum 2022-10-27
    Erscheinungsland England
    Dokumenttyp Journal Article ; Review
    ZDB-ID 197634-5
    ISSN 1365-2648 ; 0309-2402
    ISSN (online) 1365-2648
    ISSN 0309-2402
    DOI 10.1111/jan.15452
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Verfügbar in ZB MED Köln/Königswinter

    Zs.B 1851: Hefte anzeigen Standort:
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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
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  10. Artikel ; Online: Generation of LexA enhancer-trap lines in Drosophila by an international scholastic network.

    Kim, Ella S / Rajan, Arjun / Chang, Kathleen / Govindarajan, Sanath / Gulick, Clara / English, Eva / Rodriguez, Bianca / Bloomfield, Orion / Nakada, Stella / Beard, Charlotte / O'Connor, Sarah / Mastroianni, Sophia / Downey, Emma / Feigenbaum, Matthew / Tolentino, Caitlin / Pace, Abigail / Khan, Marina / Moon, Soyoun / DiPrima, Jordan /
    Syed, Amber / Lin, Flora / Abukhadra, Yasmina / Bacon, Isabella / Beckerle, John / Cho, Sophia / Donkor, Nana Esi / Garberg, Lucy / Harrington, Ava / Hoang, Mai / Lawani, Nosa / Noori, Ayush / Park, Euwie / Parsons, Ella / Oravitan, Philip / Chen, Matthew / Molina, Cristian / Richmond, Caleb / Reddi, Adith / Huang, Jason / Shugrue, Cooper / Coviello, Rose / Unver, Selma / Indelicarto, Matthew / Islamovic, Emir / McIlroy, Rosemary / Yang, Alana / Hamad, Mahdi / Griffin, Elizabeth / Ahmed, Zara / Alla, Asha / Fitzgerald, Patricia / Choi, Audrey / Das, Tanya / Cheng, Yuchen / Yu, Joshua / Roderiques, Tabor / Lee, Ethan / Liu, Longchao / Harper, Jaekeb / Wang, Jason / Suhr, Chris / Tan, Max / Luque, Jacqueline / Tam, A Russell / Chen, Emma / Triff, Max / Zimmermann, Lyric / Zhang, Eric / Wood, Jackie / Clark, Kaitlin / Kpodonu, Nat / Dey, Antar / Ecker, Alexander / Chuang, Maximilian / López, Ramón Kodi Suzuki / Sun, Harry / Wei, Zijing / Stone, Henry / Chi, Chia Yu Joy / Silvestri, Aiden / Orloff, Petra / Nedumaran, Neha / Zou, Aletheia / Ünver, Leyla / Page, Oscair / Kim, Minseo / Chan, Terence Yan Tao / Tulloch, Akili / Hernandez, Andrea / Pillai, Aruli / Chen, Caitlyn / Chowdhury, Neil / Huang, Lina / Mudide, Anish / Paik, Garrett / Wingate, Alexandra / Quinn, Lily / Conybere, Chris / Baumgardt, Luca Laiza / Buckley, Rollo / Kolberg, Zara / Pattison, Ruth / Shazli, Ashlyn Ahmad / Ganske, Pia / Sfragara, Luca / Strub, Annina / Collier, Barney / Tamana, Hari / Ravindran, Dylan / Howden, James / Stewart, Madeleine / Shimizu, Sakura / Braniff, Julia / Fong, Melanie / Gutman, Lucy / Irvine, Danny / Malholtra, Sahil / Medina, Jillian / Park, John / Yin, Alicia / Abromavage, Harrison / Barrett, Breanna / Chen, Jacqueline / Cho, Rachelle / Dilatush, Mac / Gaw, Gabriel / Gu, Caitlin / Huang, Jupiter / Kilby, Houston / Markel, Ethan / McClure, Katie / Phillips, William / Polaski, Benjamin / Roselli, Amelia / Saint-Cyr, Soleil / Shin, Ellie / Tatum, Kylan / Tumpunyawat, Tai / Wetherill, Lucia / Ptaszynska, Sara / Zeleznik, Maddie / Pesendorfer, Alexander / Nolan, Anna / Tao, Jeffrey / Sammeta, Divya / Nicholson, Laney / Dinh, Giao Vu / Foltz, Merrin / Vo, An / Ross, Maggie / Tokarski, Andrew / Hariharan, Samika / Wang, Elaine / Baziuk, Martha / Tay, Ashley / Wong, Yuk Hung Maximus / Floyd, Jax / Cui, Aileen / Pierre, Kieran / Coppisetti, Nikita / Kutam, Matthew / Khurjekar, Dhruv / Gadzi, Anthony / Gubbay, Ben / Pedretti, Sophia / Belovich, Sofiya / Yeung, Tiffany / Fey, Mercy / Shaffer, Layla / Li, Arthur / Beritela, Giancarlo / Huyghue, Kyle / Foster, Greg / Durso-Finley, Garrett / Thierfelder, Quinn / Kiernan, Holly / Lenkowsky, Andrew / Thomas, Tesia / Cheng, Nicole / Chao, Olivia / L'Etoile-Goga, Pia / King, Alexa / McKinley, Paris / Read, Nicole / Milberg, David / Lin, Leila / Wong, Melinda / Gilman, Io / Brown, Samantha / Chen, Lila / Kosai, Jordyn / Verbinsky, Mark / Belshaw-Hood, Alice / Lee, Honon / Zhou, Cathy / Lobo, Maya / Tse, Asia / Tran, Kyle / Lewis, Kira / Sonawane, Pratmesh / Ngo, Jonathan / Zuzga, Sophia / Chow, Lillian / Huynh, Vianne / Yang, Wenyi / Lim, Samantha / Stites, Brandon / Chang, Shannon / Cruz-Balleza, Raenalyn / Pelta, Michaela / Kujawski, Stella / Yuan, Christopher / Standen-Bloom, Elio / Witt, Oliver / Anders, Karina / Duane, Audrey / Huynh, Nancy / Lester, Benjamin / Fung-Lee, Samantha / Fung, Melanie / Situ, Mandy / Canigiula, Paolo / Dijkgraaf, Matijs / Romero, Wilbert / Baula, Samantha Karmela / Wong, Kimberly / Xu, Ivana / Martinez, Benjamin / Nuygen, Reena / Norris, Lucy / Nijensohn, Noah / Altman, Naomi / Maajid, Elise / Burkhardt, Olivia / Chanda, Jullian / Doscher, Catherine / Gopal, Alex / Good, Aaron / Good, Jonah / Herrera, Nate / Lanting, Lucas / Liem, Sophia / Marks, Anila / McLaughlin, Emma / Lee, Audrey / Mohr, Collin / Patton, Emma / Pyarali, Naima / Oczon, Claire / Richards, Daniel / Good, Nathan / Goss, Spencer / Khan, Adeeb / Madonia, Reagan / Mitchell, Vivian / Sun, Natasha / Vranka, Tarik / Garcia, Diogo / Arroyo, Frida / Morales, Eric / Camey, Steven / Cano, Giovanni / Bernabe, Angelica / Arroyo, Jennifer / Lopez, Yadira / Gonzalez, Emily / Zumba, Bryan / Garcia, Josue / Vargas, Esmeralda / Trinidad, Allen / Candelaria, Noel / Valdez, Vanessa / Campuzano, Faith / Pereznegron, Emily / Medrano, Jenifer / Gutierrez, Jonathan / Gutierrez, Evelyn / Abrego, Ericka Taboada / Gutierrez, Dayanara / Ortiz, Cristian / Barnes, Angelica / Arms, Eleanor / Mitchell, Leo / Balanzá, Ciara / Bradford, Jake / Detroy, Harrison / Ferguson, Devin / Guillermo, Ethel / Manapragada, Anusha / Nanula, Daniella / Serna, Brigitte / Singh, Khushi / Sramaty, Emily / Wells, Brian / Wiggins, Matthew / Dowling, Melissa / Schmadeke, Geraldine / Cafferky, Samantha / Good, Stephanie / Reese, Margaret / Fleig, Miranda / Gannett, Alex / Cain, Cory / Lee, Melody / Oberto, Paul / Rinehart, Jennifer / Pan, Elaine / Mathis, Sallie Anne / Joiner, Jessica / Barr, Leslie / Evans, Cory J / Baena-Lopez, Alberto / Beatty, Andrea / Collette, Jeanette / Smullen, Robert / Suttie, Jeanne / Chisholm, Townley / Rotondo, Cheryl / Lewis, Gareth / Turner, Victoria / Stark, Lloyd / Fox, Elizabeth / Amirapu, Anjana / Park, Sangbin / Lantz, Nicole / Rankin, Anne E / Kim, Seung K / Kockel, Lutz

    G3 (Bethesda, Md.)

    2023  Band 13, Heft 9

    Abstract: Conditional gene regulation in Drosophila through binary expression systems like the LexA-LexAop system provides a superb tool for investigating gene and tissue function. To increase the availability of defined LexA enhancer trap insertions, we present ... ...

    Abstract Conditional gene regulation in Drosophila through binary expression systems like the LexA-LexAop system provides a superb tool for investigating gene and tissue function. To increase the availability of defined LexA enhancer trap insertions, we present molecular, genetic, and tissue expression studies of 301 novel Stan-X LexA enhancer traps derived from mobilization of the index SX4 line. This includes insertions into distinct loci on the X, II, and III chromosomes that were not previously associated with enhancer traps or targeted LexA constructs, an insertion into ptc, and seventeen insertions into natural transposons. A subset of enhancer traps was expressed in CNS neurons known to produce and secrete insulin, an essential regulator of growth, development, and metabolism. Fly lines described here were generated and characterized through studies by students and teachers in an international network of genetics classes at public, independent high schools, and universities serving a diversity of students, including those underrepresented in science. Thus, a unique partnership between secondary schools and university-based programs has produced and characterized novel resources in Drosophila, establishing instructional paradigms devoted to unscripted experimental science.
    Mesh-Begriff(e) Animals ; Drosophila/genetics ; Drosophila/metabolism ; Drosophila Proteins/genetics ; Drosophila Proteins/metabolism ; Gene Expression Regulation ; Enhancer Elements, Genetic
    Chemische Substanzen Drosophila Proteins
    Sprache Englisch
    Erscheinungsdatum 2023-06-07
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 2629978-1
    ISSN 2160-1836 ; 2160-1836
    ISSN (online) 2160-1836
    ISSN 2160-1836
    DOI 10.1093/g3journal/jkad124
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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