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3,297 results on '"Harrison, P. J."'

1. Benchmarking with Supernovae: A Performance Study of the FLASH Code

Author

Martin, Joshua, Feldman, Catherine, Siegmann, Eva, Curtis, Tony, Carlson, David, Coskun, Firat, Wood, Daniel, Gonzalez, Raul, Harrison, Robert J., and Calder, Alan C.

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Astrophysical simulations are computation, memory, and thus energy intensive, thereby requiring new hardware advances for progress. Stony Brook University recently expanded its computing cluster "SeaWulf" with an addition of 94 new nodes featuring Intel Sapphire Rapids Xeon Max series CPUs. We present a performance and power efficiency study of this hardware performed with FLASH: a multi-scale, multi-physics, adaptive mesh-based software instrument. We extend this study to compare performance to that of Stony Brook's Ookami testbed which features ARM-based A64FX-700 processors, and SeaWulf's AMD EPYC Milan and Intel Skylake nodes. Our application is a stellar explosion known as a thermonuclear (Type Ia) supernova and for this 3D problem, FLASH includes operators for hydrodynamics, gravity, and nuclear burning, in addition to routines for the material equation of state. We perform a strong-scaling study with a 220 GB problem size to explore both single- and multi-node performance. Our study explores the performance of different MPI mappings and the distribution of processors across nodes. From these tests, we determined the optimal configuration to balance runtime and energy consumption for our application., Comment: Accepted to PEARC '24 (Practice and Experience in Advanced Research Computing)

Published
2024
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3. Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

Author

Yu, Yuetong, Cui, Hao‐Qi, Haas, Shalaila S, New, Faye, Sanford, Nicole, Yu, Kevin, Zhan, Denghuang, Yang, Guoyuan, Gao, Jia‐Hong, Wei, Dongtao, Qiu, Jiang, Banaj, Nerisa, Boomsma, Dorret I, Breier, Alan, Brodaty, Henry, Buckner, Randy L, Buitelaar, Jan K, Cannon, Dara M, Caseras, Xavier, Clark, Vincent P, Conrod, Patricia J, Crivello, Fabrice, Crone, Eveline A, Dannlowski, Udo, Davey, Christopher G, de Haan, Lieuwe, de Zubicaray, Greig I, Di Giorgio, Annabella, Fisch, Lukas, Fisher, Simon E, Franke, Barbara, Glahn, David C, Grotegerd, Dominik, Gruber, Oliver, Gur, Raquel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hatton, Sean, Hickie, Ian B, Pol, Hilleke E Hulshoff, Jamieson, Alec J, Jernigan, Terry L, Jiang, Jiyang, Kalnin, Andrew J, Kang, Sim, Kochan, Nicole A, Kraus, Anna, Lagopoulos, Jim, Lazaro, Luisa, McDonald, Brenna C, McDonald, Colm, McMahon, Katie L, Mwangi, Benson, Piras, Fabrizio, Rodriguez‐Cruces, Raul, Royer, Jessica, Sachdev, Perminder S, Satterthwaite, Theodore D, Saykin, Andrew J, Schumann, Gunter, Sevaggi, Pierluigi, Smoller, Jordan W, Soares, Jair C, Spalletta, Gianfranco, Tamnes, Christian K, Trollor, Julian N, Ent, Dennis Van't, Vecchio, Daniela, Walter, Henrik, Wang, Yang, Weber, Bernd, Wen, Wei, Wierenga, Lara M, Williams, Steven CR, Wu, Mon‐Ju, Zunta‐Soares, Giovana B, Bernhardt, Boris, Thompson, Paul, Frangou, Sophia, Ge, Ruiyang, and Group, ENIGMA‐Lifespan Working

Subjects
Biological Psychology, Psychology, Neurosciences, Clinical Research, Aging, Neurological, Mental health, Humans, Adolescent, Female, Aged, Adult, Child, Young Adult, Male, Brain, Aged, 80 and over, Child, Preschool, Middle Aged, Magnetic Resonance Imaging, Neuroimaging, Sample Size, benchmarking, brain aging, brainAGE, ENIGMA‐Lifespan Working Group, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Published
2024

5. A mega-analysis of functional connectivity and network abnormalities in youth depression

Author

Tse, Nga Yan, Ratheesh, Aswin, Tian, Ye Ella, Connolly, Colm G., Davey, Christopher G., Ganesan, Saampras, Gotlib, Ian H., Harrison, Ben J., Han, Laura K. M., Ho, Tiffany C., Jamieson, Alec J., Kirshenbaum, Jaclyn S., Liu, Yong, Ma, Xiaohong, Ojha, Amar, Qiu, Jiang, Sacchet, Matthew D., Schmaal, Lianne, Simmons, Alan N., Suckling, John, Wei, Dongtao, Yang, Xiao, Yang, Tony T., Cash, Robin F. H., and Zalesky, Andrew

Published
2024
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8. Benchmarking Pathology Feature Extractors for Whole Slide Image Classification

Author

Wölflein, Georg, Ferber, Dyke, Meneghetti, Asier R., Nahhas, Omar S. M. El, Truhn, Daniel, Carrero, Zunamys I., Harrison, David J., Arandjelović, Ognjen, and Kather, Jakob Nikolas

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Weakly supervised whole slide image classification is a key task in computational pathology, which involves predicting a slide-level label from a set of image patches constituting the slide. Constructing models to solve this task involves multiple design choices, often made without robust empirical or conclusive theoretical justification. To address this, we conduct a comprehensive benchmarking of feature extractors to answer three critical questions: 1) Is stain normalisation still a necessary preprocessing step? 2) Which feature extractors are best for downstream slide-level classification? 3) How does magnification affect downstream performance? Our study constitutes the most comprehensive evaluation of publicly available pathology feature extractors to date, involving more than 10,000 training runs across 14 feature extractors, 9 tasks, 5 datasets, 3 downstream architectures, 2 levels of magnification, and various preprocessing setups. Our findings challenge existing assumptions: 1) We observe empirically, and by analysing the latent space, that skipping stain normalisation and image augmentations does not degrade performance, while significantly reducing memory and computational demands. 2) We develop a novel evaluation metric to compare relative downstream performance, and show that the choice of feature extractor is the most consequential factor for downstream performance. 3) We find that lower-magnification slides are sufficient for accurate slide-level classification. Contrary to previous patch-level benchmarking studies, our approach emphasises clinical relevance by focusing on slide-level biomarker prediction tasks in a weakly supervised setting with external validation cohorts. Our findings stand to streamline digital pathology workflows by minimising preprocessing needs and informing the selection of feature extractors., Comment: For the conference version see: arXiv:2311.11772v4. For the longer journal version with additional experiments see arXiv:2311.11772v5

Published
2023

9. Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model

Author

Goya-Maldonado, Roberto, Erwin-Grabner, Tracy, Zeng, Ling-Li, Ching, Christopher R. K., Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Brosch, Katharina, Bülow, Robin, Colle, Romain, Connolly, Colm G., Corruble, Emmanuelle, Couvy-Duchesne, Baptiste, Cullen, Kathryn, Dannlowski, Udo, Davey, Christopher G., Dols, Annemiek, Ernsting, Jan, Evans, Jennifer W., Fisch, Lukas, Fuentes-Claramonte, Paola, Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke A., Grotegerd, Dominik, Hahn, Tim, Hamilton, J. Paul, Han, Laura K. M., Harrison, Ben J., Ho, Tiffany C., Jahanshad, Neda, Jamieson, Alec J., Karuk, Andriana, Kircher, Tilo, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Leenings, Ramona, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Meinert, Susanne, Melloni, Elisa, Mueller, Bryon A., Mwangi, Benson, Nenadić, Igor, Ojha, Amar, Okamoto, Yasumasa, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Radua, Joaquim, Rodríguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Schrantee, Anouk, Sim, Kang, Soares, Jair C., Solanes, Aleix, Stein, Dan J., Stein, Frederike, Stolicyn, Aleks, Thomopoulos, Sophia I., Toenders, Yara J., Uyar-Demir, Aslihan, Vieta, Eduard, Vives-Gilabert, Yolanda, Völzke, Henry, Walter, Martin, Whalley, Heather C., Whittle, Sarah, Winter, Nils, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, and Thompson, Paul M.

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. Here, we used globally representative data from the ENIGMA-MDD working group containing 7,012 participants from 30 sites (N=2,772 MDD and N=4,240 HC), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of such features and classifiers is unfeasible. Perhaps more sophisticated integration of multimodal information may lead to a higher performance in this diagnostic task., Comment: arXiv admin note: text overlap with arXiv:2206.08122

Published
2023

10. Ookami: An A64FX Computing Resource

Author

Calder, A. C., Siegmann, E., Feldman, C., Chheda, S., Smolarski, D. C., Swesty, F. D., Curtis, A., Dey, J., Carlson, D., Michalowicz, B., and Harrison, R. J.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

We present a look at Ookami, a project providing community access to a testbed supercomputer with the ARM-based A64FX processors developed by a collaboration between RIKEN and Fujitsu and deployed in the Japanese supercomputer Fugaku. We describe the project, provide details about the user base and education/training program, and present highlights from performance studies of two astrophysical simulation codes., Comment: 9 pages, 3 figures, submitted to the Proceedings of 15th International Conference on Numerical Modeling of Space Plasma Flows

Published
2023

12. Compassionate access to virus-specific T cells for adoptive immunotherapy over 15 years

Author

Neller, Michelle A., Ambalathingal, George R., Hamad, Nada, Sasadeusz, Joe, Pearson, Rebecca, Holmes-Liew, Chien-Li, Singhal, Deepak, Tunbridge, Matthew, Ng, Wei Yang, Sharplin, Kirsty, Moore, Andrew, Deambrosis, David, Soosay-Raj, Trisha, McNaughton, Peter, Whyte, Morag, Fraser, Chris, Grigg, Andrew, Kliman, David, Bajel, Ashish, Cummins, Katherine, Dowling, Mark, Yeoh, Zhi Han, Harrison, Simon J., Khot, Amit, Tan, Sarah, Roos, Izanne, Koo, Ray Mun, Dohrmann, Sara, Ritchie, David, Wainstein, Brynn, McCleary, Karen, Nelson, Adam, Gardiner, Bradley, Inam, Shafqat, Badoux, Xavier, Ma, Kris, Toro, Claudia, Hanna, Diane, Hughes, David, Conyers, Rachel, Cole, Theresa, Wang, Shiqi Stacie, Chee, Lynette, Fleming, Jacqueline, Irish, Ashley, Purtill, Duncan, Cooney, Julian, Shaw, Peter, Tey, Siok-Keen, Hunt, Stewart, Subramonia Pillai, Elango, John, George, Ng, Michelle, Ramachandran, Shanti, Hopkins, Peter, Chambers, Daniel, Campbell, Scott, Francis, Ross, Isbel, Nicole, Marlton, Paula, Reddiex, Hilary, Matthews, Katherine K., Voogt, Meggie, Panikkar, Archana, Beagley, Leone, Rehan, Sweera, Best, Shannon, Raju, Jyothy, Le Texier, Laetitia, Crooks, Pauline, Solomon, Matthew, Lekieffre, Lea, Srihari, Sriganesh, Smith, Corey, and Khanna, Rajiv

Published
2024
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16. Sustained remission following finite duration bispecific antibody therapy in patients with relapsed/refractory myeloma

Author

Chakraborty, Rajshekhar, Cheruvalath, Heloise, Patwari, Anannya, Szabo, Aniko, Schinke, Carolina, Dhakal, Binod, Lentzsch, Suzanne, D’Souza, Anita, Mohyuddin, Ghulam Rehman, Julian, Kelley, Midha, Shonali, Costello, Patrick, Kaiser, Martin, Hing, Melissa Ng Liet, Harrison, Simon J., Cliff, Edward R. Scheffer, and Mohan, Meera

Published
2024
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17. A chimeric vaccine derived from Australian genotype IV Japanese encephalitis virus protects mice from lethal challenge

Author

Harrison, Jessica J., Nguyen, Wilson, Morgan, Mahali S., Tang, Bing, Habarugira, Gervais, de Malmanche, Henry, Freney, Morgan E., Modhiran, Naphak, Watterson, Daniel, Cox, Abigail L., Yan, Kexin, Yuen, Nicholas K. Y., Bowman, Dylan H., Kirkland, Peter D., Bielefeldt-Ohmann, Helle, Suhrbier, Andreas, Hall, Roy A., Rawle, Daniel J., and Hobson-Peters, Jody

Published
2024
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18. Acute lymphoblastic leukaemia

Author

Pagliaro, Luca, Chen, Sai-Juan, Herranz, Daniel, Mecucci, Cristina, Harrison, Christine J., Mullighan, Charles G., Zhang, Ming, Chen, Zhu, Boissel, Nicolas, Winter, Stuart S., and Roti, Giovanni

Published
2024
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22. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Aghajani, Moji, Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Bülow, Robin, Ching, Christopher R. K., Connolly, Colm G., Cullen, Kathryn, Davey, Christopher G., Dima, Danai, Dols, Annemiek, Evans, Jennifer W., Fu, Cynthia H. Y., Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke, Hamilton, J Paul, Harrison, Ben J., Ho, Tiffany C., Mwangi, Benson, Jaworska, Natalia, Jahanshad, Neda, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Melloni, Elisa, Mueller, Bryon A., Ojha, Amar, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Reneman, Liesbeth, Sacchet, Matthew D., Sämann, Philipp G., Schrantee, Anouk, Sim, Kang, Soares, Jair C., Stein, Dan J., Thomopoulos, Sophia I., Uyar-Demir, Aslihan, van der Wee, Nic J. A., van der Werff, Steven J. A., Völzke, Henry, Whittle, Sarah, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Published
2024
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23. Challenges for Animal Health and Production in the Tropics and Mediterranean for the next 55 years

Author

Sacarrão-Birrento, Laura, Harrison, Leslie J. S., Pienaar, Ronel, Toka, Felix N., Torres-Acosta, Juan F. J., Vilela, Vinícius Longo Ribeiro, Hernández-Castellano, Lorenzo E., Arriaga-Jordán, Carlos Manuel, Soltan, Yosra Ahmed, Ungerfeld, Rodolfo, Özkan, Sezen, van Harten, Sofia, Ferlizza, Enea, Rossiter, Paul, Patra, Amlan Kumar, Gunal, Aysel Caglan, Bianchi, Carolina Paula, Starič, Jože, Lach, Gilliard, and de Almeida, André M.

Published
2024
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24. A Further Study of Linux Kernel Hugepages on A64FX with FLASH, an Astrophysical Simulation Code

Author

Feldman, Catherine, Chheda, Smeet, Calder, Alan C., Siegmann, Eva, Dey, John, Curtis, Tony, and Harrison, Robert J.

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Performance, C.1.4, I.6.0, J.2
Abstract

We present an expanded study of the performance of FLASH when using Linux Kernel Hugepages on Ookami, an HPE Apollo 80 A64FX platform. FLASH is a multi-scale, multi-physics simulation code written principally in modern Fortran and makes use of the PARAMESH library to manage a block-structured adaptive mesh. Our initial study used only the Fujitsu compiler to utilize standard hugepages (hp), but further investigation allowed us to utilize hp for multiple compilers by linking to the Fujitsu library libmpg and transparent hugepages (thp) by enabling it at the node level. By comparing the results of hardware counters and in-code timers, we found that hp and thp do not significantly impact the runtime performance of FLASH. Interestingly, there is a significant reduction in the TLB misses, differences in cache and memory access counters, and strange behavior is observed when using thp., Comment: 10 pages, 2 figures, 7 tables. Proceedings for Practice and Experience in Advanced Research Computing (PEARC '23), July 23--27, 2023, Portland, OR, USA

Published
2023
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25. Silicosis Among Immigrant Engineered Stone (Quartz) Countertop Fabrication Workers in California

Author

Fazio, Jane C, Gandhi, Sheiphali A, Flattery, Jennifer, Heinzerling, Amy, Kamangar, Nader, Afif, Nawal, Cummings, Kristin J, and Harrison, Robert J

Subjects
Biomedical and Clinical Sciences, Human Resources and Industrial Relations, Commerce, Management, Tourism and Services
Abstract

Key Points: Question: What are the characteristics of patients in California with silicosis from occupational exposure to dust from engineered stone (quartz), a popular material that is high in silica content and that is used to fabricate countertops? Findings: In this case series of 52 patients, the median age was 45 years at diagnosis, and nearly all were Latino immigrant men. Diagnosis was delayed in 58%, with 38% presenting with advanced disease (progressive massive fibrosis), and 19% died. Meaning: In California, silicosis associated with occupational exposure to dust from engineered stone primarily occurred among young Latino immigrant men; many patients presented with severe disease, and some cases were fatal.

Published
2024

28. Phylogenomics and the rise of the angiosperms

Author

Zuntini, Alexandre R., Carruthers, Tom, Maurin, Olivier, Bailey, Paul C., Leempoel, Kevin, Brewer, Grace E., Epitawalage, Niroshini, Françoso, Elaine, Gallego-Paramo, Berta, McGinnie, Catherine, Negrão, Raquel, Roy, Shyamali R., Simpson, Lalita, Toledo Romero, Eduardo, Barber, Vanessa M. A., Botigué, Laura, Clarkson, James J., Cowan, Robyn S., Dodsworth, Steven, Johnson, Matthew G., Kim, Jan T., Pokorny, Lisa, Wickett, Norman J., Antar, Guilherme M., DeBolt, Lucinda, Gutierrez, Karime, Hendriks, Kasper P., Hoewener, Alina, Hu, Ai-Qun, Joyce, Elizabeth M., Kikuchi, Izai A. B. S., Larridon, Isabel, Larson, Drew A., de Lírio, Elton John, Liu, Jing-Xia, Malakasi, Panagiota, Przelomska, Natalia A. S., Shah, Toral, Viruel, Juan, Allnutt, Theodore R., Ameka, Gabriel K., Andrew, Rose L., Appelhans, Marc S., Arista, Montserrat, Ariza, María Jesús, Arroyo, Juan, Arthan, Watchara, Bachelier, Julien B., Bailey, C. Donovan, Barnes, Helen F., Barrett, Matthew D., Barrett, Russell L., Bayer, Randall J., Bayly, Michael J., Biffin, Ed, Biggs, Nicky, Birch, Joanne L., Bogarín, Diego, Borosova, Renata, Bowles, Alexander M. C., Boyce, Peter C., Bramley, Gemma L. C., Briggs, Marie, Broadhurst, Linda, Brown, Gillian K., Bruhl, Jeremy J., Bruneau, Anne, Buerki, Sven, Burns, Edie, Byrne, Margaret, Cable, Stuart, Calladine, Ainsley, Callmander, Martin W., Cano, Ángela, Cantrill, David J., Cardinal-McTeague, Warren M., Carlsen, Mónica M., Carruthers, Abigail J. A., de Castro Mateo, Alejandra, Chase, Mark W., Chatrou, Lars W., Cheek, Martin, Chen, Shilin, Christenhusz, Maarten J. M., Christin, Pascal-Antoine, Clements, Mark A., Coffey, Skye C., Conran, John G., Cornejo, Xavier, Couvreur, Thomas L. P., Cowie, Ian D., Csiba, Laszlo, Darbyshire, Iain, Davidse, Gerrit, Davies, Nina M. J., Davis, Aaron P., van Dijk, Kor-jent, Downie, Stephen R., Duretto, Marco F., Duvall, Melvin R., Edwards, Sara L., Eggli, Urs, Erkens, Roy H. J., Escudero, Marcial, de la Estrella, Manuel, Fabriani, Federico, Fay, Michael F., Ferreira, Paola de L., Ficinski, Sarah Z., Fowler, Rachael M., Frisby, Sue, Fu, Lin, Fulcher, Tim, Galbany-Casals, Mercè, Gardner, Elliot M., German, Dmitry A., Giaretta, Augusto, Gibernau, Marc, Gillespie, Lynn J., González, Cynthia C., Goyder, David J., Graham, Sean W., Grall, Aurélie, Green, Laura, Gunn, Bee F., Gutiérrez, Diego G., Hackel, Jan, Haevermans, Thomas, Haigh, Anna, Hall, Jocelyn C., Hall, Tony, Harrison, Melissa J., Hatt, Sebastian A., Hidalgo, Oriane, Hodkinson, Trevor R., Holmes, Gareth D., Hopkins, Helen C. F., Jackson, Christopher J., James, Shelley A., Jobson, Richard W., Kadereit, Gudrun, Kahandawala, Imalka M., Kainulainen, Kent, Kato, Masahiro, Kellogg, Elizabeth A., King, Graham J., Klejevskaja, Beata, Klitgaard, Bente B., Klopper, Ronell R., Knapp, Sandra, Koch, Marcus A., Leebens-Mack, James H., Lens, Frederic, Leon, Christine J., Léveillé-Bourret, Étienne, Lewis, Gwilym P., Li, De-Zhu, Li, Lan, Liede-Schumann, Sigrid, Livshultz, Tatyana, Lorence, David, Lu, Meng, Lu-Irving, Patricia, Luber, Jaquelini, Lucas, Eve J., Luján, Manuel, Lum, Mabel, Macfarlane, Terry D., Magdalena, Carlos, Mansano, Vidal F., Masters, Lizo E., Mayo, Simon J., McColl, Kristina, McDonnell, Angela J., McDougall, Andrew E., McLay, Todd G. B., McPherson, Hannah, Meneses, Rosa I., Merckx, Vincent S. F. T., Michelangeli, Fabián A., Mitchell, John D., Monro, Alexandre K., Moore, Michael J., Mueller, Taryn L., Mummenhoff, Klaus, Munzinger, Jérôme, Muriel, Priscilla, Murphy, Daniel J., Nargar, Katharina, Nauheimer, Lars, Nge, Francis J., Nyffeler, Reto, Orejuela, Andrés, Ortiz, Edgardo M., Palazzesi, Luis, Peixoto, Ariane Luna, Pell, Susan K., Pellicer, Jaume, Penneys, Darin S., Perez-Escobar, Oscar A., Persson, Claes, Pignal, Marc, Pillon, Yohan, Pirani, José R., Plunkett, Gregory M., Powell, Robyn F., Prance, Ghillean T., Puglisi, Carmen, Qin, Ming, Rabeler, Richard K., Rees, Paul E. J., Renner, Matthew, Roalson, Eric H., Rodda, Michele, Rogers, Zachary S., Rokni, Saba, Rutishauser, Rolf, de Salas, Miguel F., Schaefer, Hanno, Schley, Rowan J., Schmidt-Lebuhn, Alexander, Shapcott, Alison, Al-Shehbaz, Ihsan, Shepherd, Kelly A., Simmons, Mark P., Simões, André O., Simões, Ana Rita G., Siros, Michelle, Smidt, Eric C., Smith, James F., Snow, Neil, Soltis, Douglas E., Soltis, Pamela S., Soreng, Robert J., Sothers, Cynthia A., Starr, Julian R., Stevens, Peter F., Straub, Shannon C. K., Struwe, Lena, Taylor, Jennifer M., Telford, Ian R. H., Thornhill, Andrew H., Tooth, Ifeanna, Trias-Blasi, Anna, Udovicic, Frank, Utteridge, Timothy M. A., Del Valle, Jose C., Verboom, G. Anthony, Vonow, Helen P., Vorontsova, Maria S., de Vos, Jurriaan M., Al-Wattar, Noor, Waycott, Michelle, Welker, Cassiano A. D., White, Adam J., Wieringa, Jan J., Williamson, Luis T., Wilson, Trevor C., Wong, Sin Yeng, Woods, Lisa A., Woods, Roseina, Worboys, Stuart, Xanthos, Martin, Yang, Ya, Zhang, Yu-Xiao, Zhou, Meng-Yuan, Zmarzty, Sue, Zuloaga, Fernando O., Antonelli, Alexandre, Bellot, Sidonie, Crayn, Darren M., Grace, Olwen M., Kersey, Paul J., Leitch, Ilia J., Sauquet, Hervé, Smith, Stephen A., Eiserhardt, Wolf L., Forest, Félix, and Baker, William J.

Published
2024
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29. FOXO1 enhances CAR T cell stemness, metabolic fitness and efficacy

Author

Chan, Jack D., Scheffler, Christina M., Munoz, Isabelle, Sek, Kevin, Lee, Joel N., Huang, Yu-Kuan, Yap, Kah Min, Saw, Nicole Y. L., Li, Jasmine, Chen, Amanda X. Y., Chan, Cheok Weng, Derrick, Emily B., Todd, Kirsten L., Tong, Junming, Dunbar, Phoebe A., Li, Jiawen, Hoang, Thang X., de Menezes, Maria N., Petley, Emma V., Kim, Joelle S., Nguyen, Dat, Leung, Patrick S. K., So, Joan, Deguit, Christian, Zhu, Joe, House, Imran G., Kats, Lev M., Scott, Andrew M., Solomon, Benjamin J., Harrison, Simon J., Oliaro, Jane, Parish, Ian A., Quinn, Kylie M., Neeson, Paul J., Slaney, Clare Y., Lai, Junyun, Beavis, Paul A., and Darcy, Phillip K.

Published
2024
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32. Deep Multiple Instance Learning with Distance-Aware Self-Attention

Author

Wölflein, Georg, Magister, Lucie Charlotte, Liò, Pietro, Harrison, David J., and Arandjelović, Ognjen

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Traditional supervised learning tasks require a label for every instance in the training set, but in many real-world applications, labels are only available for collections (bags) of instances. This problem setting, known as multiple instance learning (MIL), is particularly relevant in the medical domain, where high-resolution images are split into smaller patches, but labels apply to the image as a whole. Recent MIL models are able to capture correspondences between patches by employing self-attention, allowing them to weigh each patch differently based on all other patches in the bag. However, these approaches still do not consider the relative spatial relationships between patches within the larger image, which is especially important in computational pathology. To this end, we introduce a novel MIL model with distance-aware self-attention (DAS-MIL), which explicitly takes into account relative spatial information when modelling the interactions between patches. Unlike existing relative position representations for self-attention which are discrete, our approach introduces continuous distance-dependent terms into the computation of the attention weights, and is the first to apply relative position representations in the context of MIL. We evaluate our model on a custom MNIST-based MIL dataset that requires the consideration of relative spatial information, as well as on CAMELYON16, a publicly available cancer metastasis detection dataset, where we achieve a test AUROC score of 0.91. On both datasets, our model outperforms existing MIL approaches that employ absolute positional encodings, as well as existing relative position representation schemes applied to MIL. Our code is available at https://anonymous.4open.science/r/das-mil.

Published
2023

33. Microenvironmental stiffness induces metabolic reprogramming in glioblastoma

Author

Sohrabi, Alireza, Lefebvre, Austin EYT, Harrison, Mollie J, Condro, Michael C, Sanazzaro, Talia M, Safarians, Gevick, Solomon, Itay, Bastola, Soniya, Kordbacheh, Shadi, Toh, Nadia, Kornblum, Harley I, Digman, Michelle A, and Seidlits, Stephanie K

Subjects
Biochemistry and Cell Biology, Biological Sciences, Neurosciences, Rare Diseases, Bioengineering, Cancer, Brain Disorders, Brain Cancer, Humans, Animals, Mice, Glioblastoma, Cell Line, Tumor, Brain, Brain Neoplasms, Tumor Microenvironment, CP: Cancer, CP: Metabolism, extracellular matrix, tissue mechanics, Medical Physiology, Biological sciences
Abstract

The mechanical properties of solid tumors influence tumor cell phenotype and the ability to invade surrounding tissues. Using bioengineered scaffolds to provide a matrix microenvironment for patient-derived glioblastoma (GBM) spheroids, this study demonstrates that a soft, brain-like matrix induces GBM cells to shift to a glycolysis-weighted metabolic state, which supports invasive behavior. We first show that orthotopic murine GBM tumors are stiffer than peritumoral brain tissues, but tumor stiffness is heterogeneous where tumor edges are softer than the tumor core. We then developed 3D scaffolds with μ-compressive moduli resembling either stiffer tumor core or softer peritumoral brain tissue. We demonstrate that the softer matrix microenvironment induces a shift in GBM cell metabolism toward glycolysis, which manifests in lower proliferation rate and increased migration activities. Finally, we show that these mechanical cues are transduced from the matrix via CD44 and integrin receptors to induce metabolic and phenotypic changes in cancer cells.

Published
2023

35. Silicosis Among Immigrant Engineered Stone (Quartz) Countertop Fabrication Workers in California

Author

Fazio, Jane C, Gandhi, Sheiphali A, Flattery, Jennifer, Heinzerling, Amy, Kamangar, Nader, Afif, Nawal, Cummings, Kristin J, and Harrison, Robert J

Subjects
Rare Diseases, Transplantation, Prevention, Lung, Infection, Good Health and Well Being, Clinical Sciences, Opthalmology and Optometry, Public Health and Health Services
Abstract

ImportanceSilicosis associated with inhalation of respirable crystalline silica among engineered stone countertop fabrication workers is an emerging health concern.ObjectiveTo describe clinical, socioeconomic, and occupational characteristics of patients diagnosed with silicosis associated with engineered stone in California.Design, setting, and participantsThis case series included reported cases of silicosis associated with fabrication of engineered stone countertops, as identified by statewide surveillance by the California Department of Public Health (2019-2022). Data analysis was performed from October 2022 to March 2023.ExposuresPatient interviews and medical record abstractions were used to assess occupational exposure to respirable crystalline silica, including duration of work tenure and preventive measures undertaken.Main outcomes and measuresDemographics, clinical characteristics, health care utilization, and clinical outcomes were obtained, including vital status, hypoxia, and lung transplant.ResultsThis case series identified 52 male patients meeting inclusion criteria; median (IQR) age was 45 (40-49) years, and 51 were Latino immigrants. Ten (19%) were uninsured, and 20 (39%) had restricted-scope Medi-Cal; 25 (48%) presented initially to an emergency department. A delay in diagnosis occurred in 30 (58%) patients, most commonly due to alternative initial diagnoses of bacterial pneumonia (9 [30%]) or tuberculosis (8 [27%]). At diagnosis, 20 (38%) patients had advanced disease (progressive massive fibrosis) with severely or very severely reduced forced expiratory volume in 1 second in 8 (18%) and 5 (11%), respectively. Of the cases, 10 (19%) were fatal; median (IQR) age at death was 46 (38-51) years, and 6 patients (12%) were alive with chronic resting hypoxia. Eleven were referred for lung transplant: 3 underwent transplant with 1 fatality; 7 were declined transplant, with 6 fatalities; and 1 died prior to listing. Median (IQR) work tenure was 15 (10-20) years; 23 (45%) reported use of water suppression for dust mitigation, and 25 (48%) continued to fabricate stone after being diagnosed with silicosis.Conclusions and relevanceIn this case series performed in California, silicosis associated with occupational exposure to dust from engineered stone primarily occurred among young Latino immigrant men. Many patients presented with severe disease, and some cases were fatal.

Published
2023

36. Glioblastoma Spheroid Invasion through Soft, Brain‐Like Matrices Depends on Hyaluronic Acid–CD44 Interactions

Author

Safarians, Gevick, Sohrabi, Alireza, Solomon, Itay, Xiao, Weikun, Bastola, Soniya, Rajput, Bushra W, Epperson, Mary, Rosenzweig, Isabella, Tamura, Kelly, Singer, Breahna, Huang, Joyce, Harrison, Mollie J, Sanazzaro, Talia, Condro, Michael C, Kornblum, Harley I, and Seidlits, Stephanie K

Subjects
Engineering, Biomedical Engineering, Rare Diseases, Cancer, Neurosciences, Brain Cancer, Brain Disorders, Humans, Glioblastoma, Hyaluronic Acid, Cell Line, Tumor, Brain, Cell Movement, Hyaluronan Receptors, CD44, ezrin, glioblastoma, hyaluronic acids, hydrogels, Medicinal and Biomolecular Chemistry, Medical Biotechnology, Medical biotechnology, Biomedical engineering
Abstract

Increased secretion of hyaluronic acid (HA), a glycosaminoglycan abundant in the brain extracellular matrix (ECM), correlates with worse clinical outcomes for glioblastoma (GBM) patients. GBM cells aggressively invade the brain parenchyma while encountering spatiotemporal changes in their local ECM, including HA concentration. To investigate how varying HA concentrations affect GBM invasion, patient-derived GBM cells are cultured within a soft, 3D matrix in which HA concentration is precisely varied and cell migration observed. Data demonstrate that HA concentration can determine the invasive activity of patient-derived GBM cells in a biphasic and highly sensitive manner, where the absolute concentration of HA at which cell migration peaked is specific to each patient-derived line. Furthermore, evidence that this response relies on phosphorylated ezrin, which interacts with the intracellular domain of HA-engaged CD44 to effectively link the actin cytoskeleton to the local ECM is provided. Overall, this study highlights CD44-HA binding as a major mediator of GBM cell migration that acts independently of integrins and focal adhesion complexes and suggests that targeting HA-CD44-ezrin interactions represents a promising therapeutic strategy to prevent tumor cell invasion in the brain.

Published
2023

37. Information theory and machine learning illuminate large‐scale metabolomic responses of Brachypodium distachyon to environmental change

Author

Mahood, Elizabeth H, Bennett, Alexandra A, Komatsu, Karyn, Kruse, Lars H, Lau, Vincent, Ishka, Maryam Rahmati, Jiang, Yulin, Bravo, Armando, Louie, Katherine, Bowen, Benjamin P, Harrison, Maria J, Provart, Nicholas J, Vatamaniuk, Olena K, and Moghe, Gaurav D

Subjects
Plant Biology, Biochemistry and Cell Biology, Biological Sciences, Machine Learning and Artificial Intelligence, Brachypodium, Chromatography, Liquid, Information Theory, Copper, Tandem Mass Spectrometry, Metabolomics, Metabolome, computational biology, metabolomics, mass spectrometry, abiotic stress, mycorrhizal symbiosis, Plant Biology & Botany, Biochemistry and cell biology, Plant biology
Abstract

Plant responses to environmental change are mediated via changes in cellular metabolomes. However,

Published
2023

41. HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial Networks

Author

Wölflein, Georg, Um, In Hwa, Harrison, David J, and Arandjelović, Ognjen

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

The presence and density of specific types of immune cells are important to understand a patient's immune response to cancer. However, immunofluorescence staining required to identify T cell subtypes is expensive, time-consuming, and rarely performed in clinical settings. We present a framework to virtually stain Hoechst images (which are cheap and widespread) with both CD3 and CD8 to identify T cell subtypes in clear cell renal cell carcinoma using generative adversarial networks. Our proposed method jointly learns both staining tasks, incentivising the network to incorporate mutually beneficial information from each task. We devise a novel metric to quantify the virtual staining quality, and use it to evaluate our method., Comment: Accepted at IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023

Published
2022

42. Nasal and Plasma Severe Acute Respiratory Syndrome Coronavirus 2 RNA Levels Are Associated With Timing of Symptom Resolution in the ACTIV-2 Trial of Nonhospitalized Adults With Coronavirus Disease 2019

Author

Li, Yijia, Harrison, Linda J, Chew, Kara W, Currier, Judy S, Wohl, David A, Daar, Eric S, Evering, Teresa H, Wu, Ryan, Giganti, Mark, Ritz, Justin, Javan, Arzhang Cyrus, Coombs, Robert W, Moser, Carlee, Hughes, Michael D, Eron, Joseph J, Smith, Davey M, and Li, Jonathan Z

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Trials and Supportive Activities, Clinical Research, Adult, Humans, COVID-19, RNA, SARS-CoV-2, Viral Load, symptom duration, Biological Sciences, Medical and Health Sciences, Microbiology, Clinical sciences
Abstract

Acute Coronavirus Disease 2019 symptoms limit daily activities, but little is known about its association with severe acute respiratory syndrome coronavirus 2 viral burden. In this exploratory analysis of placebo recipients in the ACTIV-2/A5401 platform trial, we showed that high anterior nasal RNA levels and detectable plasma RNA were associated with delayed symptom improvement. Clinical Trials Registration. https://clinicaltrials.gov/ct2/show/NCT04518410.

Published
2023

45. Barriers to Increasing Teacher Diversity: The Need to Move beyond Aspirational Legislation

Author

Smith, Joanna, Porter, Lorna, Harrison, Brandon J., and Wohlstetter, Priscilla

Abstract

Research has found that teachers of color contribute to better academic, behavioral, and socioemotional outcomes for diverse students. Despite these benefits, the diversity of the teacher workforce in the United States has not kept pace with increases in student diversity. States have adopted aspirational legislation aimed at increasing teaching diversity, but have fallen short of diversity targets. This qualitative study examines barriers to increasing teacher diversity by presenting the efforts to do so at 28 diverse-by-design charter schools across five locales. Despite a range of approaches, these schools struggled to meet teacher diversity targets, suggesting a need for states to adopt alternate policy instruments rather than relying on legislative mandates.

Published
2023
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46. On Using Linux Kernel Huge Pages with FLASH, an Astrophysical Simulation Code

Author

Calder, Alan C., Feldman, Catherine, Siegmann, Eva, Dey, John, Curtis, Anthony, Chheda, Smeet, and Harrison, Robert J.

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We present efforts at improving the performance of FLASH, a multi-scale, multi-physics simulation code principally for astrophysical applications, by using huge pages on Ookami, an HPE Apollo 80 A64FX platform. FLASH is written principally in modern Fortran and makes use of the PARAMESH library to manage a block-structured adaptive mesh. We explored options for enabling the use of huge pages with several compilers, but we were only able to successfully use huge pages when compiling with the Fujitsu compiler. The use of huge pages substantially reduced the number of translation lookaside buffer misses, but overall performance gains were marginal., Comment: 6 pages, 1 figure, accepted to Embracing Arm for HPC, An IEEE Cluster 2022 Workshop

Published
2022

47. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Gonul, Ali Saffet, Amod, Alyssa R., Ojha, Amar, Aleman, Andre, Dols, Annemiek, Scharntee, Anouk, Uyar-Demir, Aslihan, Harrison, Ben J, Irungu, Benson M., Besteher, Bianca, Klimes-Dougan, Bonnie, Penninx, Brenda W. J. H., Mueller, Bryon A., Zarate, Carlos, Davey, Christopher G., Ching, Christopher R. K., Connolly, Colm G., Fu, Cynthia H. Y., Stein, Dan J., Dima, Danai, Linden, David E. J., Mehler, David M. A., Pomarol-Clotet, Edith, Pozzi, Elena, Melloni, Elisa, Benedetti, Francesco, MacMaster, Frank P., Grabe, Hans J., Völzke, Henry, Gotlib, Ian H., Soares, Jair C., Evans, Jennifer W., Sim, Kang, Wittfeld, Katharina, Cullen, Kathryn, Reneman, Liesbeth, Oudega, Mardien L., Wright, Margaret J., Portella, Maria J., Sacchet, Matthew D., Li, Meng, Aghajani, Moji, Wu, Mon-Ju, Jaworska, Natalia, Jahanshad, Neda, van der Wee, Nic J. A., Groenewold, Nynke, Hamilton, Paul J., Saemann, Philipp, Bülow, Robin, Poletti, Sara, Whittle, Sarah, Thomopoulos, Sophia I., van, Steven J. A., Werff, der, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Ho, Tiffany C., Yang, Tony T., Basgoze, Zeynep, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (n=5,356) to provide a generalizable ML classification benchmark of major depressive disorder (MDD). Using brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD vs healthy controls (HC) with around 62% balanced accuracy, but when harmonizing the data using ComBat balanced accuracy dropped to approximately 52%. Similar results were observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may achieve more encouraging prospects., Comment: main document 37 pages; supplementary material 24 pages

Published
2022

48. Virtual Ontogeny of Cortical Growth Preceding Mental Illness

Author

Patel, Yash, Shin, Jean, Abé, Christoph, Agartz, Ingrid, Alloza, Clara, Alnæs, Dag, Ambrogi, Sonia, Antonucci, Linda A, Arango, Celso, Arolt, Volker, Auzias, Guillaume, Ayesa-Arriola, Rosa, Banaj, Nerisa, Banaschewski, Tobias, Bandeira, Cibele, Başgöze, Zeynep, Cupertino, Renata Basso, Bau, Claiton HD, Bauer, Jochen, Baumeister, Sarah, Bernardoni, Fabio, Bertolino, Alessandro, Bonnin, Caterina Del Mar, Brandeis, Daniel, Brem, Silvia, Bruggemann, Jason, Bülow, Robin, Bustillo, Juan R, Calderoni, Sara, Calvo, Rosa, Canales-Rodríguez, Erick J, Cannon, Dara M, Carmona, Susanna, Carr, Vaughan J, Catts, Stanley V, Chenji, Sneha, Chew, Qian Hui, Coghill, David, Connolly, Colm G, Conzelmann, Annette, Craven, Alexander R, Crespo-Facorro, Benedicto, Cullen, Kathryn, Dahl, Andreas, Dannlowski, Udo, Davey, Christopher G, Deruelle, Christine, Díaz-Caneja, Covadonga M, Dohm, Katharina, Ehrlich, Stefan, Epstein, Jeffery, Erwin-Grabner, Tracy, Eyler, Lisa T, Fedor, Jennifer, Fitzgerald, Jacqueline, Foran, William, Ford, Judith M, Fortea, Lydia, Fuentes-Claramonte, Paola, Fullerton, Janice, Furlong, Lisa, Gallagher, Louise, Gao, Bingchen, Gao, Si, Goikolea, Jose M, Gotlib, Ian, Goya-Maldonado, Roberto, Grabe, Hans J, Green, Melissa, Grevet, Eugenio H, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Haavik, Jan, Hahn, Tim, Harrison, Ben J, Heindel, Walter, Henskens, Frans, Heslenfeld, Dirk J, Hilland, Eva, Hoekstra, Pieter J, Hohmann, Sarah, Holz, Nathalie, Howells, Fleur M, Ipser, Jonathan C, Jahanshad, Neda, Jakobi, Babette, Jansen, Andreas, Janssen, Joost, Jonassen, Rune, Kaiser, Anna, Kaleda, Vasiliy, Karantonis, James, King, Joseph A, Kircher, Tilo, Kochunov, Peter, Koopowitz, Sheri-Michelle, Landén, Mikael, Landrø, Nils Inge, and Lawrie, Stephen

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Serious Mental Illness, Behavioral and Social Science, Genetics, Women's Health, Neurosciences, Schizophrenia, Brain Disorders, Preterm, Low Birth Weight and Health of the Newborn, Mental Health, Perinatal Period - Conditions Originating in Perinatal Period, Pregnancy, Pediatric, Mental Illness, Prevention, 2.1 Biological and endogenous factors, 2.3 Psychological, social and economic factors, Reproductive health and childbirth, Mental health, Neurological, Good Health and Well Being, Attention Deficit Disorder with Hyperactivity, Autism Spectrum Disorder, Bipolar Disorder, Cerebral Cortex, Child, Depressive Disorder, Major, Female, Humans, Infant, Newborn, Magnetic Resonance Imaging, Premature Birth, Cortical growth, Cortical surface area, Mental illness, Neurodevelopment, Neurogenesis, Psychiatric disorders, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological sciences, Biomedical and clinical sciences
Abstract

BackgroundMorphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life.MethodsInterregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed.ResultsAcross the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth.ConclusionsOur findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from typical brain development during pregnancy.

Published
2022

49. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms

Author

Alaggio, Rita, Amador, Catalina, Anagnostopoulos, Ioannis, Attygalle, Ayoma D, Araujo, Iguaracyra Barreto de Oliveira, Berti, Emilio, Bhagat, Govind, Borges, Anita Maria, Boyer, Daniel, Calaminici, Mariarita, Chadburn, Amy, Chan, John KC, Cheuk, Wah, Chng, Wee-Joo, Choi, John K, Chuang, Shih-Sung, Coupland, Sarah E, Czader, Magdalena, Dave, Sandeep S, de Jong, Daphne, Du, Ming-Qing, Elenitoba-Johnson, Kojo S, Ferry, Judith, Geyer, Julia, Gratzinger, Dita, Guitart, Joan, Gujral, Sumeet, Harris, Marian, Harrison, Christine J, Hartmann, Sylvia, Hochhaus, Andreas, Jansen, Patty M, Karube, Kennosuke, Kempf, Werner, Khoury, Joseph, Kimura, Hiroshi, Klapper, Wolfram, Kovach, Alexandra E, Kumar, Shaji, Lazar, Alexander J, Lazzi, Stefano, Leoncini, Lorenzo, Leung, Nelson, Leventaki, Vasiliki, Li, Xiao-Qiu, Lim, Megan S, Liu, Wei-Ping, Louissaint, Abner, Marcogliese, Andrea, Medeiros, L Jeffrey, Michal, Michael, Miranda, Roberto N, Mitteldorf, Christina, Montes-Moreno, Santiago, Morice, William, Nardi, Valentina, Naresh, Kikkeri N, Natkunam, Yasodha, Ng, Siok-Bian, Oschlies, Ilske, Ott, German, Parrens, Marie, Pulitzer, Melissa, Rajkumar, S Vincent, Rawstron, Andrew C, Rech, Karen, Rosenwald, Andreas, Said, Jonathan, Sarkozy, Clémentine, Sayed, Shahin, Saygin, Caner, Schuh, Anna, Sewell, William, Siebert, Reiner, Sohani, Aliyah R, Tooze, Reuben, Traverse-Glehen, Alexandra, Vega, Francisco, Vergier, Beatrice, Wechalekar, Ashutosh D, Wood, Brent, Xerri, Luc, and Xiao, Wenbin

Subjects
Cancer, Hematologic Neoplasms, Humans, Lymphoma, World Health Organization, Clinical Sciences, Oncology and Carcinogenesis, Immunology
Abstract

We herein present an overview of the upcoming 5th edition of the World Health Organization Classification of Haematolymphoid Tumours focussing on lymphoid neoplasms. Myeloid and histiocytic neoplasms will be presented in a separate accompanying article. Besides listing the entities of the classification, we highlight and explain changes from the revised 4th edition. These include reorganization of entities by a hierarchical system as is adopted throughout the 5th edition of the WHO classification of tumours of all organ systems, modification of nomenclature for some entities, revision of diagnostic criteria or subtypes, deletion of certain entities, and introduction of new entities, as well as inclusion of tumour-like lesions, mesenchymal lesions specific to lymph node and spleen, and germline predisposition syndromes associated with the lymphoid neoplasms.

Published
2022

50. Children's Drawings of Student-Teacher Relationship Quality: Examining Change over the First Year of School

Author

Murray, Elizabeth, Harrison, Linda J., and Trapolini, Tania

Abstract

The relationships students form with their teachers are critical to children's early school adjustment and later school success. While previous research has relied on teachers' single-point ratings of student-teacher relationship (STR) quality, this study included data provided by children and teachers at two time-points in the kindergarten year. Children's drawings of themselves and their teacher were coded using Fury, Carlson, and Sroufe's (1997) attachment-based scoring of Relational Negativity. Children's feelings about their teacher were rated on the School Feelings Questionnaire (Bowes et al., 2009). Teachers completed the Student Teacher Relationship Scale (Pianta, 2001) and Teacher Rating Scale of School Adjustment (Birch & Ladd, 1997). Results showed that Relational Negativity at the start of school aligned with teachers' STR ratings, but by the end of the year, children's and teachers' ratings had diverged. Children were twice as likely as teachers to express increased negativity. Possible explanations for different perceptions of STR are proposed.

Published
2023
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