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4. RCNN-SliceNet: A Slice and Cluster Approach for Nuclei Centroid Detection in Three-Dimensional Fluorescence Microscopy Images

Author

Wu, Liming, Han, Shuo, Chen, Alain, Salama, Paul, Dunn, Kenneth W., and Delp, Edward J.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Robust and accurate nuclei centroid detection is important for the understanding of biological structures in fluorescence microscopy images. Existing automated nuclei localization methods face three main challenges: (1) Most of object detection methods work only on 2D images and are difficult to extend to 3D volumes; (2) Segmentation-based models can be used on 3D volumes but it is computational expensive for large microscopy volumes and they have difficulty distinguishing different instances of objects; (3) Hand annotated ground truth is limited for 3D microscopy volumes. To address these issues, we present a scalable approach for nuclei centroid detection of 3D microscopy volumes. We describe the RCNN-SliceNet to detect 2D nuclei centroids for each slice of the volume from different directions and 3D agglomerative hierarchical clustering (AHC) is used to estimate the 3D centroids of nuclei in a volume. The model was trained with the synthetic microscopy data generated using Spatially Constrained Cycle-Consistent Adversarial Networks (SpCycleGAN) and tested on different types of real 3D microscopy data. Extensive experimental results demonstrate that our proposed method can accurately count and detect the nuclei centroids in a 3D microscopy volume.

Published
2021

5. Precision Medicine in Nephrology: An Integrative Framework of Multidimensional Data in the Kidney Precision Medicine Project

Author

Lake, Blue, Zhang, Kun, Lecker, Stewart, Morales, Alexander, Bogen, Steve, Amodu, Afolarin A., Beck, Laurence, Henderson, Joel, Ilori, Titlayo, Maikhor, Shana, Onul, Ingrid, Schmidt, Insa, Verma, Ashish, Waikar, Sushrut, Yadati, Pranav, Yu, Guanghao, Colona, Mia R., McMahon, Gearoid, Hacohen, Nir, Greka, Anna, Hoover, Paul J., Marshall, Jamie L., Aulisio, Mark, Bush, William, Chen, Yijiang, Crawford, Dana, Madabhushi, Anant, Viswanathan, Vidya S., Bush, Lakeshia, Cooperman, Leslie, Gadegbeku, Crystal, Herlitz, Leal, Jolly, Stacey, Nguyen, Jane, O’Malley, Charles, O’Toole, John, Palmer, Ellen, Poggio, Emilio, Spates-Harden, Kassandra, Sedor, John, Sendrey, Dianna, Taliercio, Jonathan, Appelbaum, Paul, Balderes, Olivia, Barasch, Jonathan, Berroue, Cecilia, Bomback, Andrew, Canetta, Pietro A., D’Agati, Vivette, Kiryluk, Krzysztof, Kudose, Satoru, Mehl, Karla, Sabatello, Maya, Shang, Ning, de Pinho Gonçalves, Joana, Lardenoije, Roy, Migas, Lukasz, Van de Plas, Raf, Rennke, Helmut, Azeloglu, Evren, Campbell, Kirk, Coca, Steven, He, Cijang, He, John, Iyengar, Srinivas Ravi, Lefferts, Seanee, Nadkarni, Girish, Patel, Marissa, Tokita, Joji, Ward, Stephen, Xiong, Yuguang, Verdoes, Abraham, Sabo, Angela, Barwinska, Daria, Gisch, Debora Lidia, Williams, James, Kelly, Katherine, Dunn, Kenneth, Asghari, Mahla, Eadon, Michael, Ferkowicz, Michael, Dagher, Pierre, Ferreira, Ricardo Melo, Winfree, Seth, Bledsoe, Sharon, Wofford, Stephanie, El-Achkar, Tarek, Sutton, Timothy, Bowen, William, Cheng, Ying-Hua, Slade, Austen, Record, Elizabeth, Cheng, Yinghua, Borner, Katy, Herr, Bruce, Jain, Yashvardhan, Quardokus, Ellen, Atta, Mohamed, Bernard, Lauren, Menez, Steven, Parikh, Chirag, Corona Villalobos, Celia Pamela, Wang, Ashley, Wen, Yumeng, Xu, Alan, Chen, Sarah, Donohoe, Isabel, Johansen, Camille, Rosas, Sylvia, Sun, Jennifer, Ardayfio, Joseph, Bebiak, Jack, Campbell, Taneisha, Fox, Monica, Knight, Richard, Koewler, Robert, Pinkeney, Roy, Saul, John, Shpigel, Anna, Prasad, Pottumarthi, Madhavan, Sethu M., Parikh, Samir, Rovin, Brad, Shapiro, John P., Anderton, Christopher, Lukowski, Jessica, Pasa-Tolic, Ljiljana, Velickovic, Dusan, Oliver, George, Mao, Weiguang, Sealfon, Rachel, Troyanskaya, Olga, Pollack, Ari, Goltsev, Yury, Ginley, Brandon, Anjani, Kavya, Laszik, Zoltan G., Mukatash, Tariq, Nolan, Garry, Beyda, David, Bracamonte, Erika, Brosius, Frank, Campos, Baltazar, Marquez, Nicole, Mendoza, Katherine, Scott, Raymond, Thajudeen, Bijin, Tsosie, Rebecca, Woodhead, Gregory, Saunders, Milda, Alloway, Rita R., Lee, Paul J., Rike, Adele, Shi, Tiffany, Woodle, E. Steve, Bjornstad, Petter, Hsieh, Elena, Kendrick, Jessica, Pyle, Laura, Thurman, Joshua, Vinovskis, Carissa, Wrobel, Julia, Lucarelli, Nicholas, Sarder, Pinaki, Bui, James, Carmona-Powell; Ron Gaba, Eunice, Kelly, Tanika, Lash, James, Meza, Natalie, Redmond, Devona, Renteria, Amada, Ricardo, Ana, Setty, Suman, Srivastava, Anand, Alakwaa, Fadhl, Ascani, Heather, Balis, Ul, Bitzer, Markus, Blanc, Victoria, Bonevich, Nikki, Conser, Ninive, Demeke, Dawit, Dull, Rachel, Eddy, Sean, Frey, Renee, Hartman, John, He, Yongqun Oliver, Hodgin, Jeffrey, Kretzler, Matthias, Lienczewski, Chrysta, Luo, Jinghui, Mariani, Laura, McCown, Phillip, Menon, Rajasree, Nair, Viji, Otto, Edgar, Reamy, Rebecca, Rose, Michael, Schaub, Jennifer, Steck, Becky, Wright, Zachary, Coleman, Alyson, Henderson-Brown; Jerica Berge, Dorisann, Caramori, Maria Luiza, Adeyi, Oyedele, Nachman, Patrick, Safadi, Sami, Flanagan, Siobhan, Ma, Sisi, Klett, Susan, Wolf, Susan, Harindhanavudhi, Tasma, Rao, Via, Bream, Peter, Froment, Anne, Kelley, Sara, Mottl, Amy, Chaudhury; Evan Zeitler, Prabir Roy, Bender, Filitsa, Elder, Michele, Gilliam, Matthew, Hall, Daniel E., Kellum, John A., Murugan, Raghavan, Palevsky, Paul, Rosengart, Matthew, Tan, Roderick, Tublin, Mitchell, Winters, James, Bansal, Shweta, Montellano, Richard, Pamreddy, Annapurna, Sharma, Kumar, Venkatachalam, Manjeri, Ye, Hongping, Zhang, Guanshi, Basit, Mujeeb, Cai, Qi, Hendricks, Allen, Hedayati, Susan, Kermani, Asra, Lee, Simon C., Ma, Shihong, Miller, Richard Tyler, Moe, Orson W., Park, Harold, Patel, Jiten, Pillai, Anil, Sambandam, Kamalanathan, Torrealba, Jose, Toto, Robert D., Vazquez, Miguel, Wang, Nancy, Wen, Natasha, Zhang, Dianbo, Alpers, Charles, Berglund, Ashley, Berry, Brooke, Blank, Kristina, Brown, Keith, Carson, Jonas, Daniel, Stephen, de Boer, Ian H., Dighe, Ashveena L., Dowd, Frederick, Grewenow, Stephanie M., Himmelfarb, Jonathan, Hoofnagle, Andrew, Jefferson, Nichole, Larson, Brandon, Limonte, Christine, McClelland, Robyn, Mooney, Sean, Nam, Yunbi, Park, Christopher, Phuong, Jimmy, Rezaei, Kasra, Roberts, Glenda, Sarkisova, Natalya, Shankland, Stuart, Snyder, Jaime, Stutzke, Christy, Tuttle, Katherine, Wangperawong, Artit, Wilcox, Adam, Williams, Kayleen, Young, Bessie, Allen, Jamie, Caprioli, Richard M., de Caestecker, Mark, Djambazova, Katerina, Dufresne, Martin, Farrow, Melissa, Fogo, Agnes, Sharman, Kavya, Spraggins, Jeffrey, Basta, Jeannine, Conlon, Kristine, Diettman, Sabine M., Gaut, Joseph, Kaushal, Madhurima, Jain, Sanjay, Knoten, Amanda, Minor, Brittany, Nwanne, Gerald, Vijayan, Anitha, Zhang, Bo, Arora, Tanima, Cantley, Lloyd, Victoria Castro, Angela M., Kakade, Vijayakumar, Moeckel, Gilbert, Moledina, Dennis, Shaw, Melissa, Wilson, Francis P., El-Achkar, Tarek M., and Eadon, Michael T.

Published
2024
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6. Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)

Author

Mannam, Varun, Zhang, Yide, Yuan, Xiaotong, Hato, Takashi, Dagher, Pierre C., Nichols, Evan L., Smith, Cody J., Dunn, Kenneth W., and Howard, Scott

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Systems and Control
Abstract

Fluorescence lifetime imaging microscopy (FLIM) systems are limited by their slow processing speed, low signal-to-noise ratio (SNR), and expensive and challenging hardware setups. In this work, we demonstrate applying a denoising convolutional network to improve FLIM SNR. The network will be integrated with an instant FLIM system with fast data acquisition based on analog signal processing, high SNR using high-efficiency pulse-modulation, and cost-effective implementation utilizing off-the-shelf radio-frequency components. Our instant FLIM system simultaneously provides the intensity, lifetime, and phasor plots \textit{in vivo} and \textit{ex vivo}. By integrating image denoising using the trained deep learning model on the FLIM data, provide accurate FLIM phasor measurements are obtained. The enhanced phasor is then passed through the K-means clustering segmentation method, an unbiased and unsupervised machine learning technique to separate different fluorophores accurately. Our experimental \textit{in vivo} mouse kidney results indicate that introducing the deep learning image denoising model before the segmentation effectively removes the noise in the phasor compared to existing methods and provides clearer segments. Hence, the proposed deep learning-based workflow provides fast and accurate automatic segmentation of fluorescence images using instant FLIM. The denoising operation is effective for the segmentation if the FLIM measurements are noisy. The clustering can effectively enhance the detection of biological structures of interest in biomedical imaging applications., Comment: SPIE Proceedings Volume 11648, Multiphoton Microscopy in the Biomedical Sciences XXI; 116481C (2021)

Published
2021
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7. Molecular Signatures of Glomerular Neovascularization in a Patient with Diabetic Kidney Disease

Author

Ferkowicz, Michael J., Verma, Ashish, Barwinska, Daria, Melo Ferreira, Ricardo, Henderson, Joel M., Kirkpatrick, Mary, Silva, Paolo S., Steenkamp, Devin W., Phillips, Carrie L., Waikar, Sushrut S., Sutton, Timothy A., Lake, Blue, Zhang, Kun, Lecker, Stewart, Morales, Alexander, Stillman, Isaac, Bogen, Steve, Amodu, Afolarin A., Beck, Laurence, Henderson, Joel, Ilori, Titlayo, Maikhor, Shana, Onul, Ingrid, Schmidt, Insa, Verma, Ashish, Waikar, Sushrut, Yadati, Pranav, Yu, Guanghao, Colona, Mia R., McMahon, Gearoid, Weins, Astrid, Hacohen, Nir, Greka, Anna, Hoover, Paul J., Marshall, Jamie L., Aulisio, Mark, Bush, William, Chen, Yijiang, Crawford, Dana, Madabhushi, Anant, Viswanathan, Vidya S., Bush, Lakeshia, Cooperman, Leslie, Gadegbeku, Crystal, Herlitz, Leal, Jolly, Stacey, Nguyen, Jane, O’Malley, Charles, O’Toole, John, Palmer, Ellen, Poggio, Emilio, Spates-Harden, Kassandra, Sedor, John, Sendrey, Dianna, Taliercio, Jonathan, Appelbaum, Paul, Balderes, Olivia, Barasch, Jonathan, Berroue, Cecilia, Bomback, Andrew, Canetta, Pietro A., D’Agati, Vivette, Kiryluk, Krzysztof, Kudose, Satoru, Mehl, Karla, Sabatello, Maya, Shang, Ning, Varela, German, de Pinho Gonçalves, Joana, Lardenoije, Roy, Migas, Lukasz, Van de Plas, Raf, Barisoni, Laura, Rennke, Helmut, Azeloglu, Evren, Campbell, Kirk, Coca, Steven, He, Cijang, He, John, Iyengar, Srinivas Ravi, Lefferts, Seanee, Nadkarni, Girish, Patel, Marissa, Tokita, Joji, Ward, Stephen, Xiong, Yuguang, Verdoes, Abraham, Sabo, Angela, Barwinska, Daria, Gisch, Debora Lidia, Williams, James, Kelly, Katherine, Dunn, Kenneth, Asghari, Mahla, Eadon, Michael, Ferkowicz, Michael, Dagher, Pierre, Ferreira, Ricardo Melo, Winfree, Seth, Bledsoe, Sharon, Wofford, Stephanie, El-Achkar, Tarek, Sutton, Timothy, Bowen, William, Cheng, Ying-Hua, Slade, Austen, Record, Elizabeth, Cheng, Yinghua, Borner, Katy, Herr, Bruce, Jain, Yashvardhan, Quardokus, Ellen, Atta, Mohamed, Bernard, Lauren, Menez, Steven, Parikh, Chirag, Corona Villalobos, Celia Pamela, Wang, Ashley, Wen, Yumeng, Xu, Alan, Chen, Sarah, Donohoe, Isabel, Johansen, Camille, Rosas, Sylvia, Sun, Jennifer, Ardayfio, Joseph, Bebiak, Jack, Brown, Keith, Campbell, Taneisha, Fox, Monica, Hayashi, Lynda, Jefferson, Nichole, Richard Knight, Jennifer Jones, Koewler, Robert, Pinkeney, Roy, Saul, John, Shpigel, Anna, Stutzke, Christy, Prasad, Pottumarthi, Madhavan, Sethu M., Parikh, Samir, Rovin, Brad, Shapiro, John P., Anderton, Christopher, Lukowski, Jessica, Pasa-Tolic, Ljiljana, Velickovic, Dusan, Oliver, George, Mao, Weiguang, Sealfon, Rachel, Troyanskaya, Olga, Wong, Aaron, Pollack, Ari, Goltsev, Yury, Ginley, Brandon, Lutnick, Brendon, Anjani, Kavya, Laszik, Zoltan G., Mukatash, Tariq, Nolan, Garry, Beyda, David, Bracamonte, Erika, Brosius, Frank, Campos, Baltazar, Marquez, Nicole, Mendoza, Katherine, Scott, Raymond, Thajudeen, Bijin, Tsosie, Rebecca, Woodhead, Gregory, Saunders, Milda, Alloway, Rita R., Lee, Paul J., Rike, Adele, Shi, Tiffany, Woodle, E. Steve, Bjornstad, Petter, Hsieh, Elena, Kendrick, Jessica, Pyle, Laura, Thurman, Joshua, Vinovskis, Carissa, Wrobel, Julia, Lucarelli, Nicholas, Sarder, Pinaki, Bui, James, Carmona-Powell, Eunice, Gaba, Ron, Kelly, Tanika, Lash, James, Meza, Natalie, Redmond, Devona, Renteria, Amada, Ricardo, Ana, Setty, Suman, Srivastava, Anand, Alakwaa, Fadhl, Ascani, Heather, Balis, Ul, Bitzer, Markus, Blanc, Victoria, Bonevich, Nikki, Conser, Ninive, Demeke, Dawit, Dull, Rachel, Eddy, Sean, Frey, Renee, Hartman, John, He, Yongqun Oliver, Hodgin, Jeffrey, Kretzler, Matthias, Lienczewski, Chrysta, Luo, Jinghui, Mariani, Laura, McCown, Phillip, Menon, Rajasree, Nair, Viji, Otto, Edgar, Reamy, Rebecca, Rose, Michael, Schaub, Jennifer, Steck, Becky, Wright, Zachary, Coleman, Alyson, Henderson-Brown, Dorisann, Berge, Jerica, Caramori, Maria Luiza, Adeyi, Oyedele, Nachman, Patrick, Safadi, Sami, Flanagan, Siobhan, Ma, Sisi, Klett, Susan, Wolf, Susan, Harindhanavudhi, Tasma, Rao, Via, Bream, Peter, Froment, Anne, Kelley, Sara, Mottl, Amy, Roy-Chaudhury, Prabir, Zeitler, Evan, Bender, Filitsa, Elder, Michele, Gilliam, Matthew, Hall, Daniel E., Kellum, John A., Murugan, Raghavan, Palevsky, Paul, Rosengart, Matthew, Tan, Roderick, Tublin, Mitchell, Winters, James, Bansal, Shweta, Montellano, Richard, Pamreddy, Annapurna, Sharma, Kumar, Venkatachalam, Manjeri, Ye, Hongping, Zhang, Guanshi, Basit, Mujeeb, Cai, Qi, Hendricks, Allen, Hedayati, Susan, and Asra

Published
2024
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8. Participant Experience with Protocol Research Kidney Biopsies in the Kidney Precision Medicine Project

Author

Victoria-Castro, Angela M., Corona-Villalobos, Celia P., Xu, Alan Y., Onul, Ingrid, Huynh, Courtney, Chen, Sarah W., Ugwuowo, Ugochukwu, Sarkisova, Natalya, Dighe, Ashveena L., Blank, Kristina N., Blanc, Victoria M., Rose, Michael P., Himmelfarb, Jonathan, de Boer, Ian H., Tuttle, Katherine R., Roberts, Glenda V., Alexandrov, Theodore, Alloway, Rita R., Alpers, Charles E., Amodu, Afolarin A., Anderton, Christopher R., Anjani, Kavya, Appelbaum, Paul, Ardayfio, Joseph, Arora, Tanima, Ascani, Heather, El-Achkar, Tarek M., Aulisio, Mark, Azeloglu, Evren U., Balderes, Olivia, Balis, Ulysses G.J., Bansal, Shweta, Barasch, Jonathan M., Bansal, Shweta, Barkell, Alex, Barwinska, Daria, Basit, Mujeeb, Basta, Jeanine, Bebiak, Jack, Beck, Laurence H., Bender, Filitsa, Berglund, Ashley, Bernard, Lauren, Berrouet, Cecilia, Berry, Brooke, Bjornstad, Petter M., Blanc, Victoria M., Blank, Kristina N., Bledsoe, Sharon, Boada, Patrick, Bogen, Steve, Bomback, Andrew S., Bonevich, Nikole, Borner, Katy, Brown, Keith, Bueckle, Andreas, Burg, Ashley R., Burgess, Adam, Bush, Lakeshia, Bush, William S., Campbell, Catherine E., Campbell, Taneisha, Canetta, Pietro A., Cantley, Lloyd G., Caprioli, Richard M., Carson, Jonas, Chen, Sarah, Chen, Yijiang M., Cheng, Yinghua, Cimino, Jim, Colona, Mia R., Conser, Ninive C., Cooperman, Leslie, Crawford, Dana C., DʼAgati, Vivette D., Dagher, Pierre C., Daniel, Stephen, Daratha, Kenn, de Boer, Ian H., Diettman, Sabine M., Dighe, Ashveena L., Donohoe, Isabel, Dowd, Frederick, Dunn, Kenneth W., Eadon, Michael T., Eddy, Sean, Elder, Michele M., Ferkowicz, Michael J., Frey, Renee, Gadegbeku, Crystal A., Gaut, Joseph P., Gilliam, Matthew, Ginley, Brandon, Gisch, Debora, Goltsev, Yury, Gonzalez-Vicente, Agustin, Greka, Anna, Grewenow, Stephanie M., Hacohen, Nir, Hall, Daniel E., Hansen, Jens, Hayashi, Lynda, He, Cijang, He, Yougqun, Hedayati, S. Susan, Henderson, Joel M., Hendricks, Allen H., Herlitz, Leal, Herr, Bruce W., Himmelfarb, Jonathan, Hodgin, Jeffrey B., Hoofnagle, Andrew N., Hoover, Paul J., Ilori, Titlayo, Iyengar, Ravi, Jain, Sanjay, Jain, Yashvardhan, Janowczyk, Andrew, Jefferson, Nichole, Johansen, Camille, Jolly, Stacey, Kakade, Vijaykumar R., Kellum, John A., Kelly, Katherine J., Kermani, Asra, Kiryluk, Krzysztof, Knight, Richard, Koewler, Robert, Kretzler, Matthias, Kudose, Satoru, Lake, Blue B., Larson, Brandon, Laszik, Zoltan G., Lecker, Stewart H., Lee, Paul J., Lee, Simon C., Lienczewski, Chrysta, Limonte, Christine, Lu, Christopher Y., Lucarelli, Nicholas, Lukowski, Jessica, Luo, Jinghui, Lutnick, Brendon, Ma, Shihong, Madabhushi, Anant, Madhavan, Sethu M., Maikhor, Shana, Mariani, Laura H., Marshall, Jamie L., McClelland, Robyn L., McMahon, Gearoid M., Mehl, Karla, Ferreira, Ricardo Melo, Menez, Steven, Menon, Rajasree, Miller, R. Tyler, Moe, Orson W., Moledina, Dennis, Montellano, Richard, Mooney, Sean D., Morales, Martha Catalina, Mukatash, Tariq, Murugan, Raghavan, Nam, Yunbi, Nguyen, Jane, Nolan, Garry, Oʼtoole, John, Oliver, George (Holt), Onul, Ingrid, Otto, Edgar, Palevsky, Paul M., Palmer, Ellen, Pamreddy, Annapurna, Parikh, Chirag R., Parikh, Samir, Park, Christopher, Park, Harold, Pasa-Tolic, Ljiljana, Patel, Jiten, Patterson, Nathan, Phuong, Jim, Pillai, Anil, Pinkeney, Roy, Poggio, Emilio, Pollack, Ari, Prasad, Pottumarthi, Pyle, Laura, Quardokus, Ellen M., Randhawa, Parmjeet, Rauchman, Michael I., Record, Elizabeth, Rennke, Helmut, Rezaei, Kasra, Rike, Adele, Rivera, Marcelino, Roberts, Glenda V., Rosas, Sylvia E., Rosenberg, Avi, Rosengart, Matthew, Rovin, Brad, Roy, Neil, Sabatello, Maya, Sambandam, Kamalanathan, Sarder, Pinaki, Sarkisova, Natalya, Sarwal, Minnie, Saul, John, Schaub, Jennifer, Schmidt, Insa, Sealfon, Rachel, Sedor, John, Sendrey, Dianna, Shang, Ning, Shankland, Stuart, Shapiro, John P., Sharma, Kumar, Sharman, Kavya, Shaw, Melissa M., Shi, Tiffany, Shpigel, Anna, Sigdel, Tara, Slade, Austen, Snyder, Jamie, Spates-Harden, Kassandra, Spraggins, Jeffrey M., Srivastava, Anand, Steck, Becky, Stillman, Isaac, Stutzke, Christy, Su, Jing, Sun, Jennifer, Sutton, Timothy A., Taliercio, Jonathan, Tan, Roderick, Torrealba, Jose, Toto, Robert D., Troyanskaya, Olga, Tublin, Mitchell, Tuttle, Katherine R., Ugwuowo, Ugochukwu, Valerius, M. Todd, Van de Plas, Raf, Varela, German, Vazquez, Miguel, Velickovic, Dusan, Venkatachalam, Manjeri, Verma, Ashish, Victoria-Castro, Angela M., Vijayan, Anitha, Corona-Villalobos, Celia P., Vinovskis, Carissa, Viswanathan, Vidya S., Vita, Tina, Waikar, Sushrut, Wang, Ashley, Wang, Ruikang, Wang, Nancy, Weins, Astrid, Wen, Natasha, Wen, Yumeng, Wilcox, Adam, Williams, James C., Jr., Kayleen Williams, Williams, Mark, Wilson, Francis P., Winfree, Seth, Winters, James, Wofford, Stephanie, Wong, Aaron, Woodle, E. Steve, Xiong, Yuguang, Xu, Alan, Yadati, Pranav, Ye, Hongping, Yu, Guanghao, Zhang, Dianbo, Zhang, Guanshi, and Zhang, Kun

Published
2024
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9. Language tools

Author

Dunn, Kenneth

Subjects
LANGUAGE AND LANGUAGES - Translating and Interpreting, ARMY - United States, INTELLIGENCE, MILITARY - United States
Published
2003

10. Rationale and design of the Kidney Precision Medicine Project

Author

de Boer, Ian H, Alpers, Charles E, Azeloglu, Evren U, Balis, Ulysses GJ, Barasch, Jonathan M, Barisoni, Laura, Blank, Kristina N, Bomback, Andrew S, Brown, Keith, Dagher, Pierre C, Dighe, Ashveena L, Eadon, Michael T, El-Achkar, Tarek M, Gaut, Joseph P, Hacohen, Nir, He, Yongqun, Hodgin, Jeffrey B, Jain, Sanjay, Kellum, John A, Kiryluk, Krzysztof, Knight, Richard, Laszik, Zoltan G, Lienczewski, Chrysta, Mariani, Laura H, McClelland, Robyn L, Menez, Steven, Moledina, Dennis G, Mooney, Sean D, O’Toole, John F, Palevsky, Paul M, Parikh, Chirag R, Poggio, Emilio D, Rosas, Sylvia E, Rosengart, Matthew R, Sarwal, Minnie M, Schaub, Jennifer A, Sedor, John R, Sharma, Kumar, Steck, Becky, Toto, Robert D, Troyanskaya, Olga G, Tuttle, Katherine R, Vazquez, Miguel A, Waikar, Sushrut S, Williams, Kayleen, Wilson, Francis Perry, Zhang, Kun, Iyengar, Ravi, Kretzler, Matthias, Himmelfarb, Jonathan, Project, Kidney Precision Medicine, Lecker, Stewart, Stillman, Isaac, Waikar, Sushrut, Mcmahon, Gearoid, Weins, Astrid, Short, Samuel, Hoover, Paul, Aulisio, Mark, Cooperman, Leslie, Herlitz, Leal, O’Toole, John, Poggio, Emilio, Sedor, John, Jolly, Stacey, Appelbaum, Paul, Balderes, Olivia, Barasch, Jonathan, Bomback, Andrew, Canetta, Pietro A, d’Agati, Vivette D, Kudose, Satoru, Mehl, Karla, Radhakrishnan, Jai, Weng, Chenhua, Alexandrov, Theodore, Ashkar, Tarek, Barwinska, Daria, Dagher, Pierre, Dunn, Kenneth, Eadon, Michael, Ferkowicz, Michael, Kelly, Katherine, Sutton, Timothy, Winfree, Seth, Parikh, Chirag, Rosenberg, Avi, Villalobos, Pam, Malik, Rubab, Fine, Derek, Atta, Mohammed, Trujillo, Jose Manuel Monroy, Slack, Alison, Rosas, Sylvia, and Williams, Mark

Subjects
Clinical Research, Transplantation, Kidney Disease, Prevention, Renal and urogenital, Good Health and Well Being, Acute Kidney Injury, Adult, Humans, Kidney, Precision Medicine, Prospective Studies, Proteomics, Renal Insufficiency, Chronic, acute kidney injury, chronic kidney disease, diabetes, hypertension, precision medicine, Kidney Precision Medicine Project, Clinical Sciences, Urology & Nephrology
Abstract

Chronic kidney disease (CKD) and acute kidney injury (AKI) are common, heterogeneous, and morbid diseases. Mechanistic characterization of CKD and AKI in patients may facilitate a precision-medicine approach to prevention, diagnosis, and treatment. The Kidney Precision Medicine Project aims to ethically and safely obtain kidney biopsies from participants with CKD or AKI, create a reference kidney atlas, and characterize disease subgroups to stratify patients based on molecular features of disease, clinical characteristics, and associated outcomes. An additional aim is to identify critical cells, pathways, and targets for novel therapies and preventive strategies. This project is a multicenter prospective cohort study of adults with CKD or AKI who undergo a protocol kidney biopsy for research purposes. This investigation focuses on kidney diseases that are most prevalent and therefore substantially burden the public health, including CKD attributed to diabetes or hypertension and AKI attributed to ischemic and toxic injuries. Reference kidney tissues (for example, living-donor kidney biopsies) will also be evaluated. Traditional and digital pathology will be combined with transcriptomic, proteomic, and metabolomic analysis of the kidney tissue as well as deep clinical phenotyping for supervised and unsupervised subgroup analysis and systems biology analysis. Participants will be followed prospectively for 10 years to ascertain clinical outcomes. Cell types, locations, and functions will be characterized in health and disease in an open, searchable, online kidney tissue atlas. All data from the Kidney Precision Medicine Project will be made readily available for broad use by scientists, clinicians, and patients.

Published
2021

11. Factor VIII trafficking to CD4+ T cells shapes its immunogenicity and requires several types of antigen-presenting cells

Author

Kaczmarek, Radoslaw, Piñeros, Annie R., Patterson, Paige E., Bertolini, Thais B., Perrin, George Q., Sherman, Alexandra, Born, Jameson, Arisa, Sreevani, Arvin, Matthew C., Kamocka, Malgorzata M., Martinez, Michelle M., Dunn, Kenneth W., Quinn, Sean M., Morris, Johnathan J., Wilhelm, Amelia R., Kaisho, Tsuneyasu, Munoz-Melero, Maite, Biswas, Moanaro, Kaplan, Mark H., Linnemann, Amelia K., George, Lindsey A., Camire, Rodney M., and Herzog, Roland W.

Published
2023
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12. Center-Extraction-Based Three Dimensional Nuclei Instance Segmentation of Fluorescence Microscopy Images

Author

Ho, David Joon, Han, Shuo, Fu, Chichen, Salama, Paul, Dunn, Kenneth W., and Delp, Edward J.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Fluorescence microscopy is an essential tool for the analysis of 3D subcellular structures in tissue. An important step in the characterization of tissue involves nuclei segmentation. In this paper, a two-stage method for segmentation of nuclei using convolutional neural networks (CNNs) is described. In particular, since creating labeled volumes manually for training purposes is not practical due to the size and complexity of the 3D data sets, the paper describes a method for generating synthetic microscopy volumes based on a spatially constrained cycle-consistent adversarial network. The proposed method is tested on multiple real microscopy data sets and outperforms other commonly used segmentation techniques., Comment: Presented at the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI 2019)

Published
2019
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13. Quantitative Kinetic Models from Intravital Microcopy: A Case Study Using Hepatic Transport

Author

Tavakoli, Meysam, Tsekouras, Konstantinos, Day, Richard, Dunn, Kenneth W., and Presse, Steve

Subjects
Physics - Biological Physics, Quantitative Biology - Quantitative Methods
Abstract

The liver performs critical physiological functions, including metabolizing and removing substances, such as toxins and drugs, from the bloodstream. Hepatotoxicity itself is intimately linked to abnormal hepatic transport and hepatotoxicity remains the primary reason drugs in development fail and approved drugs are withdrawn from the market. For this reason, we propose to analyze, across liver compartments, the transport kinetics of fluorescein-a fluorescent marker used as a proxy for drug molecules-using intravital microscopy data. To resolve the transport kinetics quantitatively from fluorescence data, we account for the effect that different liver compartments (with different chemical properties) have on fluorescein's emission rate. To do so, we develop ordinary differential equation transport models from the data where the kinetics are related to the observable fluorescence levels by "measurement parameters" that vary across different liver compartments. On account of the steep non-linearities in the kinetics and stochasticity inherent to the model, we infer kinetic and measurement parameters by generalizing the method of parameter cascades. For this application, the method of parameter cascades ensures fast and precise parameter estimates from noisy time traces., Comment: 38 pages, 7 figures

Published
2019
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14. Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks

Author

Lee, Soonam, Han, Shuo, Salama, Paul, Dunn, Kenneth W., and Delp, Edward J.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Due to image blurring image deconvolution is often used for studying biological structures in fluorescence microscopy. Fluorescence microscopy image volumes inherently suffer from intensity inhomogeneity, blur, and are corrupted by various types of noise which exacerbate image quality at deeper tissue depth. Therefore, quantitative analysis of fluorescence microscopy in deeper tissue still remains a challenge. This paper presents a three dimensional blind image deconvolution method for fluorescence microscopy using 3-way spatially constrained cycle-consistent adversarial networks. The restored volumes of the proposed deconvolution method and other well-known deconvolution methods, denoising methods, and an inhomogeneity correction method are visually and numerically evaluated. Experimental results indicate that the proposed method can restore and improve the quality of blurred and noisy deep depth microscopy image visually and quantitatively., Comment: IEEE International Symposium on Biomedical Imaging (ISBI) 2019

Published
2019
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15. Integrated Cytometry With Machine Learning Applied to High-Content Imaging of Human Kidney Tissue for In Situ Cell Classification and Neighborhood Analysis

Author

Winfree, Seth, McNutt, Andrew T., Khochare, Suraj, Borgard, Tyler J., Barwinska, Daria, Sabo, Angela R., Ferkowicz, Michael J., Williams, James C., Jr., Lingeman, James E., Gulbronson, Connor J., Kelly, Katherine J., Sutton, Timothy A., Dagher, Pierre C., Eadon, Michael T., Dunn, Kenneth W., and El-Achkar, Tarek M.

Published
2023
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16. Renal Proximal Tubule Cell-specific Megalin Deletion Induces Tubulointerstitial Nephritis in Mice Fed Western Diet

Author

Amioka, Naofumi, primary, Franklin, Michael K, additional, Kukida, Masayoshi, additional, Sawada, Hisashi, additional, Moorleghen, Jessica J, additional, Howatt, Deborah A, additional, Katsumata, Yuriko, additional, Mullick, Adam E, additional, Yanagita, Motoko, additional, Martinez-Irizarry, Michelle, additional, Sandoval, Ruben M, additional, Dunn, Kenneth W, additional, Daugherty, Alan, additional, and Lu, Hong S., additional

Published
2024
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19. Tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction

Author

Lee, Soonam, Fu, Chichen, Salama, Paul, Dunn, Kenneth W., and Delp, Edward J.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Fluorescence microscopy has become a widely used tool for studying various biological structures of in vivo tissue or cells. However, quantitative analysis of these biological structures remains a challenge due to their complexity which is exacerbated by distortions caused by lens aberrations and light scattering. Moreover, manual quantification of such image volumes is an intractable and error-prone process, making the need for automated image analysis methods crucial. This paper describes a segmentation method for tubular structures in fluorescence microscopy images using convolutional neural networks with data augmentation and inhomogeneity correction. The segmentation results of the proposed method are visually and numerically compared with other microscopy segmentation methods. Experimental results indicate that the proposed method has better performance with correctly segmenting and identifying multiple tubular structures compared to other methods., Comment: IS&T International Symposium on Electronic Imaging 2018

Published
2018
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20. Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation

Author

Fu, Chichen, Lee, Soonam, Ho, David Joon, Han, Shuo, Salama, Paul, Dunn, Kenneth W., and Delp, Edward J.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue. Segmentation is a required step to characterize and analyze biological structures in the images and recent 3D segmentation using deep learning has achieved promising results. One issue is that deep learning techniques require a large set of groundtruth data which is impractical to annotate manually for large 3D microscopy volumes. This paper describes a 3D deep learning nuclei segmentation method using synthetic 3D volumes for training. A set of synthetic volumes and the corresponding groundtruth are generated using spatially constrained cycle-consistent adversarial networks. Segmentation results demonstrate that our proposed method is capable of segmenting nuclei successfully for various data sets., Comment: Accepted by CVPR Workshop on Computer Vision for Microscopy Image Analysis (CVMI)

Published
2018
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22. Cadherin-11, Sparc-related modular calcium binding protein-2, and Pigment epithelium-derived factor are promising non-invasive biomarkers of kidney fibrosis

Author

Knight, Richard, Lecker, Stewart H., Stillman, Isaac, Bogen, Steve, Amodu, Afolarin A., Ilori, Titlayo, Maikhor, Shana, Schmidt, Insa M., Beck, Laurence H., Henderson, Joel M., Onul, Ingrid, Verma, Ashish, McMahon, Gearoid M., Valerius, M. Todd, Waikar, Sushrut, Weins, Astrid, Colona, Mia R., Greka, Anna, Hacohen, Nir, Hoover, Paul J., Marshall, Jamie L., Aulisio, Mark, Chen, Yijiang M., Janowczyk, Andrew, Jayapandian, Catherine, Viswanathan, Vidya S., Bush, William S., Crawford, Dana C., Madabhushi, Anant, Bush, Lakeshia, Cooperman, Leslie, Gonzalez-Vicente, Agustin, Herlitz, Leal, Jolly, Stacey, Nguyen, Jane, O’toole, John, Palmer, Ellen, Poggio, Emilio, Sedor, John, Sendrey, Dianna, Spates-Harden, Kassandra, Taliercio, Jonathan, Bjornstad, Petter M., Pyle, Laura, Vinovskis, Carissa, Appelbaum, Paul, Balderes, Olivia, Barasch, Jonathan M., Bomback, Andrew S., Canetta, Pietro A., D’Agati, Vivette D., Kiryluk, Krzysztof, Kudose, Satoru, Mehl, Karla, Shang, Ning, Bansal, Shweta, Alexandrov, Theodore, Rennke, Helmut, El-Achkar, Tarek M., Barwinska, Daria, Bledso, Sharon, Borner, Katy, Bueckle, Andreas, Cheng, Yinghua, Dagher, Pierre C., Dunn, Kenneth W., Eadon, Michael T., Ferkowicz, Michael J., Herr, Bruce W., Kelly, Katherine J., Ferreira, Ricardo Melo, Quardokus, Ellen M., Record, Elizabeth, Rivera, Marcelino, Su, Jing, Sutton, Timothy A., Williams, James C., Jr., Winfree, Seth, Jain, Yashvardhan, Menez, Steven, Parikh, Chirag R., Rosenberg, Avi, Corona-Villalobos, Celia P., Wen, Yumeng, Johansen, Camille, Rosas, Sylvia E., Roy, Neil, Sun, Jennifer, Williams, Mark, Azeloglu, Evren U., Hansen, Jens, He, Cijang, Iyengar, Ravi, Xiong, Yuguang, Prasad, Pottumarthi, Srivastava, Anand, Madhavan, Sethu M., Parikh, Samir, Rovin, Brad, Shapiro, John P., Anderton, Christopher R., Lukowski, Jessica, Pasa-Tolic, Ljiljana, Velickovic, Dusan, Oliver, George (Holt), Ardayfio, Joseph, Bebiak, Jack, Brown, Keith, Campbell, Taneisha, Campbell, Catherine E., Hayashi, Lynda, Jefferson, Nichole, Roberts, Glenda V., Saul, John, Shpigel, Anna, Stutzke, Christy, Koewler, Robert, Pinkeney, Roy, Sealfon, Rachel, Troyanskaya, Olga, Wong, Aaron, Tuttle, Katherine R., Pollack, Ari, Goltsev, Yury, Ginley, Brandon, Lucarelli, Nicholas, Lutnick, Brendon, Sarder, Pinaki, Lake, Blue B., Zhang, Kun, Boada, Patrick, Laszik, Zoltan G., Nolan, Garry, Anjani, Kavya, Sarwal, Minnie, Mukatash, Tariq, Sigdel, Tara, Alloway, Rita R., Burg, Ashley R., Lee, Paul J., Rike, Adele, Shi, Tiffany, Woodle, E. Steve, Ascani, Heather, Balis, Ulysses G.J., Blanc, Victoria M., Conser, Ninive C., Eddy, Sean, Frey, Renee, He, Yougqun, Hodgin, Jeffrey B., Kretzler, Matthias, Lienczewski, Chrysta, Luo, Jinghui, Mariani, Laura H., Menon, Rajasree, Otto, Edgar, Schaub, Jennifer, Steck, Becky, Elder, Michele M., Gilliam, Matthew, Hall, Daniel E., Murugan, Raghavan, Palevsky, Paul M., Randhawa, Parmjeet, Rosengart, Matthew, Tublin, Mitchell, Vita, Tina, Winters, James, Kellum, John A., Alpers, Charles E., Berglund, Ashley, Berry, Brooke, Blank, Kristina N., Carson, Jonas, Daniel, Stephen, De Boer, Ian H., Dighe, Ashveena L., Dowd, Frederick, Grewenow, Stephanie M., Himmelfarb, Jonathan, Hoofnagle, Andrew N., Limonte, Christine, McClelland, Robyn L., Mooney, Sean D., Rezaei, Kasra, Shankland, Stuart, Snyder, Jamie, Wang, Ruikang, Wilcox, Adam, Williams, Kayleen, Park, Christopher, Montellano, Richard, Pamreddy, Annapurna, Sharma, Kumar, Venkatachalam, Manjeri, Ye, Hongping, Zhang, Guanshi, Basit, Mujeeb, Hedayati, S. Susan, Kermani, Asra, Lee, Simon C., Lu, Christopher Y., Miller, R. Tyler, Moe, Orson W., Patel, Jiten, Pillai, Anil, Sambandam, Kamalanathan, Torrealba, Jose, Toto, Robert D., Vazquez, Miguel, Wang, Nancy, Wen, Natasha, Zhang, Dianbo, Park, Harold, Caprioli, Richard M., Patterson, Nathan, Sharman, Kavya, Spraggins, Jeffrey M., Van de Plas, Raf, Basta, Jeanine, Diettman, Sabine M., Gaut, Joseph P., Jain, Sanjay, Rauchman, Michael I., Vijayan, Anitha, Cantley, Lloyd G., Kakade, Vijaykumar R., Moledina, Dennis, Shaw, Melissa M., Ugwuowo, Ugochukwu, Wilson, Francis P., Arora, Tanima, Kestenbaum, Bryan R., Alexopoulos, Leonidas G., Palsson, Ragnar, Liu, Jing, Stillman, Isaac E., Rennke, Helmut G., Vaidya, Vishal S., Wu, Haojia, Humphreys, Benjamin D., and Waikar, Sushrut S.

Published
2021
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35. Epigenetic basis of diabetic vasculopathy

Author

Bhamidipati, Theja, primary, Kumar, Manishekhar, additional, Verma, Sumit S., additional, Mohanty, Sujit K., additional, Kacar, Sedat, additional, Reese, Diamond, additional, Martinez, Michelle M., additional, Kamocka, Malgorzata M., additional, Dunn, Kenneth W., additional, Sen, Chandan K., additional, and Singh, Kanhaiya, additional

Published
2022
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38. Letter to the Editor

Author

Dunn, Kenneth D.

Subjects
Military and naval science
Abstract

In 1995, while one of a handful of Black students at the National War College (NWC) at Fort Lesley J. McNair in Washingto DC, I requested to attend the Million [...]

Published
2022

42. A Participant-Centered Approach to Understanding Risks and Benefits of Participation in Research Informed by the Kidney Precision Medicine Project

Author

Butler, Catherine R., primary, Appelbaum, Paul S., additional, Ascani, Heather, additional, Aulisio, Mark, additional, Campbell, Catherine E., additional, de Boer, Ian H., additional, Dighe, Ashveena L., additional, Hall, Daniel E., additional, Himmelfarb, Jonathan, additional, Knight, Richard, additional, Mehl, Karla, additional, Murugan, Raghavan, additional, Rosas, Sylvia E., additional, Sedor, John R., additional, O’Toole, John F., additional, Tuttle, Katherine R., additional, Waikar, Sushrut S., additional, Freeman, Michael, additional, Alexandrov, Theodore, additional, Alpers, Charles E., additional, Anderton, Christopher R., additional, Ardayfio, Joseph, additional, Arora, Tanima, additional, El-Achkar, Tarek M., additional, Azeloglu, Evren U., additional, Balderes, Olivia, additional, Balis, Ulysses G.J., additional, Bansal, Shweta, additional, Barasch, Jonathan M., additional, Barwinska, Daria, additional, Bebiak, Jack, additional, Blanc, Victoria M., additional, Blank, Kristina N., additional, Bomback, Andrew S., additional, Brown, Keith D., additional, Bush, William S., additional, Campbell, Taneisha, additional, Canetta, Pietro A., additional, Carson, Jonas, additional, Cooperman, Leslie, additional, Crawford, Dana C., additional, D’Agati, Vivette D., additional, Dagher, Pierre C., additional, Daniel, Stephen, additional, Dowd, Frederick, additional, Dunn, Kenneth W., additional, Eadon, Michael T., additional, Eddy, Sean, additional, Elder, Michele M., additional, Ferkowicz, Michael J., additional, Gaut, Joe P., additional, Goltsev, Yury, additional, Gonzalez-Vicente, Agustin, additional, Hacohen, Nir, additional, Hansen, Jens, additional, Hayashi, Lynda, additional, He, Oliver, additional, He, Cijang, additional, Hedayati, S. Susan, additional, Herlitz, Leal, additional, Hodgin, Jeffrey B., additional, Hoofnagle, Andrew N., additional, Hoover, Paul J., additional, Iyengar, Ravi, additional, Jain, Sanjay, additional, Jefferson, Nichole, additional, Jolly, Stacey, additional, Kellum, John A., additional, Kelly, Katherine J., additional, Kermani, Asra, additional, Kiryluk, Krzysztof, additional, Koewler, Robert, additional, Kretzler, Matthias, additional, Lake, Blue B., additional, Laszik, Zoltan G., additional, Lecker, Stewart H., additional, Lee, Simon C., additional, Lienczewski, Chrysta, additional, Lu, Christopher Y., additional, Mariani, Laura H., additional, McClelland, Robyn L., additional, McMahon, Gearoid M., additional, Menez, Steven, additional, Menon, Rajasree, additional, Miller, Tyler, additional, Moe, Orson W., additional, Moledina, Dennis, additional, Mooney, Sean D., additional, Nguyen, Jane, additional, Nolan, Garry, additional, Oliver, George, additional, Otto, Edgar, additional, Palevsky, Paul M., additional, Palmer, Ellen, additional, Pamreddy, Annapurna, additional, Parikh, Chirag R., additional, Parikh, Samir, additional, Park, Christopher, additional, Park, Harold, additional, Pasa-Tolic, Ljiljana, additional, Pinkeney, Roy, additional, Poggio, Emilio, additional, Randhawa, Parmjeet, additional, Rennke, Helmut, additional, Roberts, Glenda V., additional, Rosenberg, Avi, additional, Rosengart, Matthew, additional, Rovin, Brad, additional, Roy, Neil, additional, Sambandam, Kamalanathan, additional, Sarwal, Minnie, additional, Saul, John, additional, Schaub, Jennifer, additional, Sealfon, Rachel, additional, Shang, Ning, additional, Shankland, Stuart, additional, Sharma, Kumar, additional, Shpigel, Anna, additional, Sigdel, Tara, additional, Steck, Becky, additional, Stillman, Isaac, additional, Stutzke, Edith Christine, additional, Sutton, Timothy A., additional, Torrealba, Jose, additional, Toto, Robert D., additional, Troyanskaya, Olga, additional, Tublin, Mitchell, additional, Ugwuowo, Ugochukwu, additional, Vazquez, Miguel, additional, Velickovic, Dusan, additional, Venkatachalam, Manjeri, additional, Vijayan, Anitha, additional, Corona-Villalobos, Celia P., additional, Wang, Nancy, additional, Weins, Astrid, additional, Wilcox, Adam, additional, Williams, Kayleen, additional, Williams, Mark, additional, Wilson, Francis P., additional, Winfree, Seth, additional, Xiong, Yuguang, additional, Zhang, Kun, additional, and Zhang, Guanshi, additional

Published
2022
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45. Integrated single-cell sequencing and histopathological analyses reveal diverse injury and repair responses in a participant with acute kidney injury: a clinical-molecular-pathologic correlation

Author

Menon, Rajasree, primary, Bomback, Andrew S., additional, Lake, Blue B., additional, Stutzke, Christy, additional, Grewenow, Stephanie M., additional, Menez, Steven, additional, D’Agati, Vivette D., additional, Jain, Sanjay, additional, Knight, Richard, additional, Lecker, Stewart H., additional, Stillman, Isaac, additional, Bogen, Steve, additional, Beck, Laurence H., additional, Waikar, Sushrut, additional, McMahon, Gearoid M., additional, Weins, Astrid, additional, Colona, Mia R., additional, Hacohen, Nir, additional, Hoover, Paul J., additional, Aulisio, Mark, additional, Bush, William S., additional, Crawford, Dana C., additional, O'toole, John, additional, Poggio, Emilio, additional, Sedor, John, additional, Cooperman, Leslie, additional, Jolly, Stacey, additional, Herlitz, Leal, additional, Nguyen, Jane, additional, Gonzalez-Vicente, Agustin, additional, Palmer, Ellen, additional, Sendrey, Dianna, additional, Vinovskis, Carissa, additional, Bjornstad, Petter M., additional, Appelbaum, Paul, additional, Barasch, Jonathan M., additional, D'Agati, Vivette D., additional, Kiryluk, Krzysztof, additional, Mehl, Karla, additional, Canetta, Pietro A., additional, Shang, Ning, additional, Balderes, Olivia, additional, Kudose, Satoru, additional, Bansal, Shweta, additional, Alexandrov, Theodore, additional, Rennke, Helmut, additional, El-Achkar, Tarek M., additional, Cheng, Yinghua, additional, Dagher, Pierre C., additional, Eadon, Michael T., additional, Dunn, Kenneth W., additional, Kelly, Katherine J., additional, Sutton, Timothy A., additional, Barwinska, Daria, additional, Ferkowicz, Michael J., additional, Winfree, Seth, additional, Bledsoe, Sharon, additional, Rivera, Marcelino, additional, Williams, James C., additional, Ferreira, Ricardo Melo, additional, Parikh, Chirag R., additional, Corona-Villalobos, Celia P., additional, Rosenberg, Avi, additional, Rosas, Sylvia E., additional, Roy, Neil, additional, Williams, Mark, additional, Azeloglu, Evren U., additional, He, Cijang, additional, Iyengar, Ravi, additional, Hansen, Jens, additional, Xiong, Yuguang, additional, Rovin, Brad, additional, Parikh, Samir, additional, Shapiro, John P., additional, Anderton, Christopher R., additional, Pasa-Tolic, Ljiljana, additional, Velickovic, Dusan, additional, Lukowski, Jessica, additional, Oliver, George, additional, Ardayfio, Joseph, additional, Bebiak, Jack, additional, Brown, Keith, additional, Campbell, Catherine E., additional, Saul, John, additional, Shpigel, Anna, additional, Koewler, Robert, additional, Campbell, Taneisha, additional, Hayashi, Lynda, additional, Jefferson, Nichole, additional, Roberts, Glenda V., additional, Pinkeney, Roy, additional, Troyanskaya, Olga, additional, Sealfon, Rachel, additional, Tuttle, Katherine R., additional, Goltsev, Yury, additional, Zhang, Kun, additional, Laszik, Zoltan G., additional, Nolan, Garry, additional, Boada, Patrick, additional, Sarwal, Minnie, additional, Sigdel, Tara, additional, Lee, Paul J., additional, Alloway, Rita R., additional, Woodle, E. Steve, additional, Ascani, Heather, additional, Balis, Ulysses G.J., additional, Hodgin, Jeffrey B., additional, Kretzler, Matthias, additional, Lienczewski, Chrysta, additional, Mariani, Laura H., additional, Menon, Rajasree, additional, Steck, Becky, additional, He, Yougqun, additional, Otto, Edgar, additional, Schaub, Jennifer, additional, Blanc, Victoria M., additional, Eddy, Sean, additional, Conser, Ninive C., additional, Luo, Jinghui, additional, Palevsky, Paul M., additional, Rosengart, Matthew, additional, Kellum, John A., additional, Hall, Daniel E., additional, Randhawa, Parmjeet, additional, Tublin, Mitchell, additional, Murugan, Raghavan, additional, Elder, Michele M., additional, Winters, James, additional, Alpers, Charles E., additional, Blank, Kristina N., additional, Carson, Jonas, additional, De Boer, Ian H., additional, Dighe, Ashveena L., additional, Himmelfarb, Jonathan, additional, Mooney, Sean D., additional, Shankland, Stuart, additional, Williams, Kayleen, additional, Park, Christopher, additional, Dowd, Frederick, additional, McClelland, Robyn L., additional, Daniel, Stephen, additional, Hoofnagle, Andrew N., additional, Wilcox, Adam, additional, Sharma, Kumar, additional, Venkatachalam, Manjeri, additional, Zhang, Guanshi, additional, Pamreddy, Annapurna, additional, Ye, Hongping, additional, Montellano, Richard, additional, Toto, Robert D., additional, Vazquez, Miguel, additional, Lee, Simon C., additional, Miller, R. Tyler, additional, Moe, Orson W., additional, Torrealba, Jose, additional, Wang, Nancy, additional, Kermani, Asra, additional, Sambandam, Kamalanathan, additional, Park, Harold, additional, Hedayati, S. Susan, additional, Lu, Christopher Y., additional, Vijayan, Anitha, additional, Gaut, Joseph P., additional, Moledina, Dennis, additional, Wilson, Francis P., additional, Ugwuowo, Ugochukwu, additional, and Arora, Tanima, additional

Published
2022
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48. Integrated single-cell sequencing and histopathological analyses reveal diverse injury and repair responses in a participant with acute kidney injury: a clinical-molecular-pathologic correlation

Author

Knight, Richard, Lecker, Stewart H., Stillman, Isaac, Bogen, Steve, Beck, Laurence H., Waikar, Sushrut, McMahon, Gearoid M., Weins, Astrid, Colona, Mia R., Hacohen, Nir, Hoover, Paul J., Aulisio, Mark, Bush, William S., Crawford, Dana C., O'toole, John, Poggio, Emilio, Sedor, John, Cooperman, Leslie, Jolly, Stacey, Herlitz, Leal, Nguyen, Jane, Gonzalez-Vicente, Agustin, Palmer, Ellen, Sendrey, Dianna, Vinovskis, Carissa, Bjornstad, Petter M., Appelbaum, Paul, Barasch, Jonathan M., Bomback, Andrew S., D'Agati, Vivette D., Kiryluk, Krzysztof, Mehl, Karla, Canetta, Pietro A., Shang, Ning, Balderes, Olivia, Kudose, Satoru, Bansal, Shweta, Alexandrov, Theodore, Rennke, Helmut, El-Achkar, Tarek M., Cheng, Yinghua, Dagher, Pierre C., Eadon, Michael T., Dunn, Kenneth W., Kelly, Katherine J., Sutton, Timothy A., Barwinska, Daria, Ferkowicz, Michael J., Winfree, Seth, Bledsoe, Sharon, Rivera, Marcelino, Williams, James C., Jr., Ferreira, Ricardo Melo, Parikh, Chirag R., Corona-Villalobos, Celia P., Menez, Steven, Rosenberg, Avi, Rosas, Sylvia E., Roy, Neil, Williams, Mark, Azeloglu, Evren U., He, Cijang, Iyengar, Ravi, Hansen, Jens, Xiong, Yuguang, Rovin, Brad, Parikh, Samir, Shapiro, John P., Anderton, Christopher R., Pasa-Tolic, Ljiljana, Velickovic, Dusan, Lukowski, Jessica, Oliver, George, Ardayfio, Joseph, Bebiak, Jack, Brown, Keith, Campbell, Catherine E., Saul, John, Shpigel, Anna, Stutzke, Christy, Koewler, Robert, Campbell, Taneisha, Hayashi, Lynda, Jefferson, Nichole, Roberts, Glenda V., Pinkeney, Roy, Troyanskaya, Olga, Sealfon, Rachel, Tuttle, Katherine R., Goltsev, Yury, Zhang, Kun, Lake, Blue B., Laszik, Zoltan G., Nolan, Garry, Boada, Patrick, Sarwal, Minnie, Sigdel, Tara, Lee, Paul J., Alloway, Rita R., Woodle, E. Steve, Ascani, Heather, Balis, Ulysses G.J., Hodgin, Jeffrey B., Kretzler, Matthias, Lienczewski, Chrysta, Mariani, Laura H., Menon, Rajasree, Steck, Becky, He, Yougqun, Otto, Edgar, Schaub, Jennifer, Blanc, Victoria M., Eddy, Sean, Conser, Ninive C., Luo, Jinghui, Palevsky, Paul M., Rosengart, Matthew, Kellum, John A., Hall, Daniel E., Randhawa, Parmjeet, Tublin, Mitchell, Murugan, Raghavan, Elder, Michele M., Winters, James, Alpers, Charles E., Blank, Kristina N., Carson, Jonas, De Boer, Ian H., Dighe, Ashveena L., Himmelfarb, Jonathan, Mooney, Sean D., Shankland, Stuart, Williams, Kayleen, Park, Christopher, Dowd, Frederick, McClelland, Robyn L., Daniel, Stephen, Hoofnagle, Andrew N., Wilcox, Adam, Grewenow, Stephanie M., Sharma, Kumar, Venkatachalam, Manjeri, Zhang, Guanshi, Pamreddy, Annapurna, Ye, Hongping, Montellano, Richard, Toto, Robert D., Vazquez, Miguel, Lee, Simon C., Miller, R. Tyler, Moe, Orson W., Torrealba, Jose, Wang, Nancy, Kermani, Asra, Sambandam, Kamalanathan, Park, Harold, Hedayati, S. Susan, Lu, Christopher Y., Jain, Sanjay, Vijayan, Anitha, Gaut, Joseph P., Moledina, Dennis, Wilson, Francis P., Ugwuowo, Ugochukwu, Arora, Tanima, and D’Agati, Vivette D.

Published
2022
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50. Thirty-Five Years of Research on Perceptual Strengths: Essential Strategies to Promote Learning

Author

Dunn, Rita and Dunn, Kenneth

Abstract

This article discusses the evolution of teaching approaches in concert with the findings of over three decades of researches on student perceptual strengths. Confusing reports of successes and only limited successes for students with varied perceptual strengths suggest that combined auditory, visual, tactual, and/or kinesthetic instructional resources--a multi-sensory approach--is not necessarily beneficial for all students. Rather, students required to master new and difficult academic material should initially be exposed to it through their primary perceptual strength. If statistically higher achievement-test scores are to be realized, the resources most complementary to each individual should begin the concentration process. It may seem obvious, but thirty-five years of research suggest that teaching strategies and resources should complement individuals' perceptual strengths when introducing new and difficult academic content. When schools with underachieving minority, poor students in various sections of this nation introduced tactual and kinesthetic instruction, they evidenced statistically higher standardized achievement test scores in reading and mathematics within one year.

Published
2005

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