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9,962 results on '"Ionita, A."'

1. Self-Calibrated Epipolar Reconstruction for Assessment of Aneurysms in the Internal Carotid Artery Using In-Silico Biplane Angiograms

Author

Williams, Kyle A., Nagesh, S. V. Setlur, Bednarek, Daniel R., Rudin, Stephen, and Ionita, Ciprian N.

Subjects
Physics - Medical Physics
Abstract

The treatment of intracranial aneurysms (IA) relies on angiography guidance using biplane views. However, accurate flow estimation and device sizing for treatment are often compromised by vessel overlap and foreshortening, which can obscure critical details. This study introduces an epipolar reconstruction approach to enhance 3D rendering of the internal carotid artery (ICA) and aneurysm dome using routinely acquired biplane angiographic data. Our method aims to improve procedural guidance by overcoming the limitations of traditional two-dimensional imaging techniques. This study employed three 3D geometries of ICA aneurysms to simulate virtual angiograms. These angiograms were generated using a computational fluid dynamics (CFD) solver, followed by the simulation of biplane angiography using a cone-beam geometry. Self-calibration was accomplished by matching contrast media position as a function of time between biplane views. Feature-matching was used to triangulate and reconstruct vascular structures in 3D. The projection data was utilized to refine the 3D estimation, including elimination of erroneous structures and ellipse-fitting. The accuracy of the reconstructions was evaluated using the Dice-Sorensen coefficient, comparing the 3D reconstructions to the original models. The proposed epipolar reconstruction method generalized well across the three tested aneurysm models, with respective Dice-Sorensen coefficients of 0.745, 0.759, and 0.654. Errors were primarily due to partial vessel overlap. The average reconstruction time for all three volumes was approximately 10 seconds. The proposed epipolar reconstruction method enhanced 3D visualization, addressing challenges such as projection-induced vessel foreshortening. This method provides a solution to the complexity of IA visualization, with the potential to provide more accurate analysis and device sizing for treatment., Comment: 15 pages, 10 figures, under review by Medical Physics

Published
2025

2. Automated Registration of 3D Neurovascular Territory Atlas to 2D DSA for Targeted Quantitative Angiography Analysis

Author

Dimopoulos, George, Parra, Sabrina De Los Angeles Reverol, Mondal, Parmita, Udin, Michael, Williams, Kyle, Naghdi, Parisa, Rahmatpour, Ahmad, Nagesh, Swetadri Vasan Setlur, Bhurwani, Mohammad Mahdi Shiraz, Davies, Jason, and Ionita, Ciprian N.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Physics - Medical Physics
Abstract

Subarachnoid hemorrhage (SAH), typically due to intracranial aneurysms, demands precise imaging for effective treatment. Digital Subtraction Angiography (DSA), despite being the gold standard, broadly visualizes cerebral blood flow, potentially masking key details in areas. This study introduces an approach integrating a 3D vascular atlas with 2D DSA images to allow targeted quantitative analysis in these crucial regions, thus enhancing diagnostic accuracy during interventions. Initially, DSA data was examined to ascertain the injection site. Following this, the appropriate viewing angle was determined to align accurately with the 3D vascular atlas. Utilizing this atlas, regions corresponding to the areas indicated as perfused were selected. Concurrently, a mask representing the perfused areas was created from the DSA sequence. This mask facilitated the initial coarse alignment of the projected 3D atlas to the DSA perfused territory deformable registration techniques, ensuring a precise overlay with the DSAs perfused territories. The performance of each overlay was measured using the Structural Similarity Index Measure (SSIM). The coregistration process revealed that deformable registrations was essential to achieve precise overlays of the 3D atlas projections with the 2D DSA perfused areas. This approach enabled the extraction of targeted quantitative angiography parameters, essential for detailed vascular assessment in subarachnoid hemorrhage cases. The integration of 3D atlas registration with 2D DSA projections facilitates a more precise and targeted diagnostic process for SAH during critical interventions. This image processing strategy enhances the visualization of affected arterial territories, potentially improving the accuracy of diagnostics and supporting better informed clinical decisions at the time of intervention, Comment: 5 pages, 2 figures, to be published in SPIE Medical Imaging 2025 conference proceeds

Published
2024

3. Exploring Methods for Integrating and Augmenting Multimodal Data to Improve Prognostic Accuracy in Imbalanced Datasets for Intraoperative Aneurysm Occlusion

Author

Naghdi, Parisa, Bhurwani, Mohammad Mahdi Shiraz, Rahmatpour, Ahmad, Mondal, Parmita, Udin, Michael, Williams, Kyle A, Nagesh, Swetadri Vasan Setlur, and Ionita, Ciprian N

Subjects
Physics - Medical Physics
Abstract

This study evaluates a multimodal machine learning framework for predicting treatment outcomes in intracranial aneurysms (IAs). Combining angiographic parametric imaging (API), patient biomarkers, and disease morphology, the framework aims to enhance prognostic accuracy. Data from 340 patients were analyzed, with separate deep neural networks processing quantitative and categorical data. These networks' pre decision layers were concatenated and inputted into a final predictive network. Various data augmentation strategies, including Synthetic Minority Oversampling Technique for Nominal and Continuous data (SMOTE NC), addressed dataset imbalances. Performance metrics, evaluated through Monte Carlo cross validation, showed significant improvements with augmentation, particularly in intermediate fusion models. This study validates the framework's efficacy in accurately predicting IA treatment outcomes, demonstrating that data augmentation techniques can substantially enhance model performance., Comment: 6 pages

Published
2024

4. Injection Bias Reduction Techniques in Quantitative Angiography Using Patient-Specific Phantoms of Intracranial Aneurysm

Author

Mondal, Parmita, Williams, Kyle A, Naghdi, Parisa, Rahmatpour, Ahmad, Bhurwani, Mohammad Mahdi Shiraz, Nagesh, Swetadri Vasan Setlur, and Ionita, Ciprian N

Subjects
Physics - Medical Physics
Abstract

In intracranial aneurysm (IA) treatment, digital subtraction angiography (DSA) monitors device-induced hemodynamic changes. Quantitative angiography (QA) provides more precise assessments but is limited by hand-injection variability. This study evaluates correction methods using in vitro phantoms that mimic diverse aneurysm morphologies and locations, addressing the 2D and temporal limitations of DSA. We used a patient-specific phantom to replicate three distinct IA morphologies at various Circle of Willis points: the middle cerebral artery (MCA), anterior communicating artery (ACA), and the internal carotid artery (ICA), each varying in size and shape. The diameters of the IA at MCA, ACA and ICA are 10.1, 10 and 7 millimeters, respectively. QA parameters for both non-stenosed and stenosed conditions were measured with 5ml and 10ml boluses over various injection durations to generate time density curves (TDCs). To address the variability in injection, several singular value decomposition (SVD) variants, standard SVD (sSVD) with Tikhonov regularization, block-circulant SVD (bSVD), and oscillation index SVD (oSVD) were applied. These methods enabled the extraction of IA impulse response function (IRF), peak height (PHIRF), area under the curve (AUCIRF), and mean transit time (MTT). We evaluated the robustness of bias-reducing methods by observing the invariance of these parameters with respect to the injection conditions, and the location and size of the aneurysm. The application of SVD variants, sSVD, bSVD, and oSVD, significantly reduced QA parameter variability due to injection techniques., Comment: arXiv admin note: text overlap with arXiv:2411.03655

Published
2024

5. Leveraging Convolutional Neural Networks for 3D Quantitative Angiography Reconstructions from Sparse Cone Beam CT Projections Utilizing CFD Data

Author

Rahmatpour, Ahmad, Shields, Allison, Mondal, Parmita, Naghdi, Parisa, Udin, Michael, Williams, Kyle A, Bhurwani, Mohammad Mahdi Shiraz, Nagesh, Swetadri Vasan Setlur, and Ionita, Ciprian N

Subjects
Physics - Medical Physics
Abstract

This study leverages convolutional neural networks to enhance the temporal resolution of 3D angiography in intracranial aneurysms focusing on the reconstruction of volumetric contrast data from sparse and limited projections. Three patient-specific IA geometries were segmented and converted into stereolithography files to facilitate computational fluid dynamics simulations. These simulations first modeled blood flow under steady conditions with varying inlet velocities: 0.25 m/s, 0.35 m/s, and 0.45 m/s. Subsequently, 3D angiograms were simulated by labeling inlet particles to represent contrast bolus injections over durations of 0.5s, 1.0s, 1.5s, and 2.0s. The angiographic simulations were then used within a simulated cone beam C arm CT system to generate in-silico rotational DSAs, capturing projections every 10 ms over a 220-degree arc at 27 frames per second. From these simulations, both fully sampled (108 projections) and truncated projection datasets were generated the latter using a maximum of 49 projections. High fidelity volumetric images were reconstructed using a Parker weighted Feldkamp Davis Kress algorithm. A modified U Net CNN was subsequently trained on these datasets to reconstruct 3D angiographic volumes from the truncated projections. The network incorporated multiple convolutional layers with ReLU activations and Max pooling, complemented by upsampling and concatenation to preserve spatial detail. Model performance was evaluated using mean squared error (MSE). Evaluating our U net model across the test set yielded a MSE of 0.0001, indicating good agreement with ground truth reconstructions and demonstrating acceptable capabilities in capturing relevant transient angiographic features. This study confirms the feasibility of using CNNs for reconstructing 3D angiographic images from truncated projections.

Published
2024

6. Analysis of Quantitative Angiography using Projection Foreshortening Correction and Injection Bias Removal

Author

Mondal, Parmita, Shields, Allison, Bhurwani, Mohammad Mahdi Shiraz, Williams, Kyle A, Nagesh, Swetadri Vasan Setlur, Siddiqui, Adnan H, and Ionita, Ciprian N

Subjects
Physics - Medical Physics
Abstract

This study aims to mitigate these biases and enhance QA analysis by applying a path-length correction (PLC) correction, followed by singular value decomposition (SVD)-based deconvolution, to angiograms obtained through both in-silico and in-vitro methods. We utilized DSA data from in-silico and in-vitro patient-specific intracranial aneurysm models. To remove projection bias, PLC for various views were developed by co-registering the pre-existing 3D vascular geometry mask with the DSA projections, followed by ray tracing to determine paths across 3D vessel structures. These maps were used to normalize the logarithmic angiographic images, correcting for projection-induced foreshortening across different angles. Subsequently, we focused on eliminating injection bias by analyzing the corrected angiograms under varied projection views, injection rates, and flow conditions. Regions of interest at the aneurysm dome and inlet were placed to extract Time Density Curves for the lesion and the arterial input function, respectively. Using three standard SVD methodologies, we extracted the aneurysm Impulse Response function (IRF) and its associated parameters Peak Height (PHIRF), Area Under the Curve (AUCIRF), and Mean Transit Time (MTT). Our methodology employing PLC and SVD-based deconvolution ensures reliable quantitative angiographic measurements across varying conditions, supporting consistent assessments of disease severity and treatment efficacy. This approach significantly enhances intrapatient and intraprocedural reliability in neurovascular diagnostics., Comment: 11 pages, 6figures

Published
2024

7. In-Silico Analysis of Curve Fitting in Angiographic Parametric Imaging in Intracranial Aneurysms

Author

Mondal, Parmita, Shields, Allison, Bhurwani, Mohammad Mahdi Shiraz, Williams, Kyle A, and Ionita, Ciprian N

Subjects
Physics - Medical Physics
Abstract

In Angiographic Parametric Imaging (API), accurate estimation of parameters from Time Density Curves (TDC) is crucial. However, these estimations are often marred by errors arising from factors such as patient motion, procedural preferences, image noise, and injection variability. While fitting methods like gamma-variate fitting offer a solution to recover incomplete or corrupted TDC data, they might also introduce unforeseen biases. This study investigates the trade-offs and benefits of employing gamma-variate fitting on virtual angiograms to enhance the precision of API biomarkers. Utilizing Computational Fluid Dynamics (CFD) in patient specific 3D geometries, we generated a series of high-definition virtual angiograms at distinct inlet velocities: 0.25m/s, 0.35m/s, and 0.45m/s. These velocities were investigated across injection durations ranging from 0.5s to 2.0s. From these angiograms, TDCs for aneurysms and their corresponding inlets were constructed. To emulate typical clinical challenges, we introduced noise, simulated patient motion, and generated temporally incomplete data sets. These modified TDCs underwent gamma-variate fitting. We quantified both the original and fitted TDC curves using standard angiography metrics such as Cross-Correlation (Cor), Time to Peak (TTP), Mean Transit Time (MTT), Peak Height (PH), Area Under the Curve (AUC), and Maximum Gradient (Max-Gr) for a comprehensive comparison. TDCs enhanced by gamma-variate fitting exhibited a robust correlation with vascular flow dynamics. Our results affirm that gamma-variate fitting can adeptly restore TDCs from fragmentary sequences, elevating the precision of derived API parameters.

Published
2024
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8. Intra-operative Optimal Flow Diverter Selection for Intracranial aneurysm treatment

Author

Mondal, Parmita, Bhurwani, Mohammed Mahdi Shiraz, Williams, Kyle A, and Ionita, Ciprian N

Subjects
Physics - Medical Physics
Abstract

During intracranial aneurysm (IA) treatment with Diverters (FDs), the device/parent artery diameters ratio may influence the ability of the device to induce aneurysm healing response. Oversized FDs are safer to deploy but may not induce enough hemodynamic resistance to ensure aneurysm occlusion. Methods based on Computational Fluid Dynamics (CFD) could allow optimal device selection but are time-consuming and inadequate for intra-operative guidance. To address this limitation, we propose to investigate a method for optimal FD selection using Angiographic Parametric Imaging (API) and machine learning (ML). We selected 128 pre-treatment angiographic sequences of IAs which demonstrated full occlusion at six months follow-up. For each IA, we extracted five API parameters from the aneurysm dome and normalized them to the feeding artery corresponding parameters. We dichotomized the dataset based on the FD/ proximal artery diameter ratio as undersized, if the ratio<1 or if multiple FDs were used and oversized otherwise. Single API parameter and ML analysis were used to determine whether API parameters could be used to determine the need for FD under-sizing (i.e., increased flow resistance). Classification accuracy was assessed using area under the receiver operator characteristic (AUROC). In total we identified 51 and 77 cases for the undersized and oversized cohorts respectively. Single API parameter analysis yielded an inadequate AUROC ~0.5 while machine learning using all five API parameters yielded and AUROC of 0.72.

Published
2024
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9. Effect of Singular Value Decomposition Algorithms on Removing Injection Variability in 2D Quantitative Angiography of Intracranial Aneurysms

Author

Mondal, Parmita, Nagesh, Swetadri Vasan Setlur, Sommers-Thaler, Sam, Shields, Allison, Bhurwani, Mohammad Mahdi Shiraz, Williams, Kyle A, Baig, Ammad, Snyder, Kenneth, Siddiqui, Adnan H, Levy, Elad, and Ionita, Ciprian N

Subjects
Physics - Medical Physics
Abstract

Intraoperative 2D quantitative angiography (QA) for intracranial aneurysms (IAs) has accuracy challenges due to the variability of hand injections. Despite the success of singular value decomposition (SVD) algorithms in reducing biases in computed tomography perfusion (CTP), their application in 2D QA has not been extensively explored. This study seeks to bridge this gap by investigating the potential of SVD-based deconvolution methods in 2D QA, particularly in addressing the variability of injection durations. The study included three internal carotid aneurysm (ICA) cases. Virtual angiograms were generated using Computational Fluid Dynamics (CFD) for three physiologically relevant inlet velocities to simulate contrast media injection durations. Time-density curves (TDCs) were produced for both the inlet and aneurysm dome. Various SVD variants, including standard SVD (sSVD) with and without classical Tikhonov regularization, block-circulant SVD (bSVD), and oscillation index SVD (oSVD), were applied to virtual angiograms. The method was applied on virtual angiograms to recover the aneurysmal dome impulse response function (IRF) and extract flow related parameters such as Peak Height PHIRF, Area Under the Curve AUCIRF, and Mean transit time MTT. Furthermore, we found that SVD can effectively reduce QA parameter variability across various injection durations, enhancing the potential of QA analysis parameters in neurovascular disease diagnosis and treatment. Implementing SVD-based deconvolution techniques in QA analysis can enhance the precision and reliability of neurovascular diagnostics by effectively reducing the impact of injection duration on hemodynamic parameters.

Published
2024

10. Bone marrow niches orchestrate stem-cell hierarchy and immune tolerance

Author

Furuhashi, Kazuhiro, Kakiuchi, Miwako, Ueda, Ryosuke, Oda, Hiroko, Ummarino, Simone, Ebralidze, Alexander K., Bassal, Mahmoud A., Meng, Chen, Sato, Tatsuyuki, Lyu, Jing, Han, Min-guk, Maruyama, Shoichi, Watanabe, Yu, Sawa, Yuriko, Kato, Daisuke, Wake, Hiroaki, Reizis, Boris, Frangos, John A., Owens, David M., Tenen, Daniel G., Ghiran, Ionita C., Robson, Simon C., and Fujisaki, Joji

Published
2025
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15. Gemini: A Family of Highly Capable Multimodal Models

Author

Gemini Team, Anil, Rohan, Borgeaud, Sebastian, Alayrac, Jean-Baptiste, Yu, Jiahui, Soricut, Radu, Schalkwyk, Johan, Dai, Andrew M., Hauth, Anja, Millican, Katie, Silver, David, Johnson, Melvin, Antonoglou, Ioannis, Schrittwieser, Julian, Glaese, Amelia, Chen, Jilin, Pitler, Emily, Lillicrap, Timothy, Lazaridou, Angeliki, Firat, Orhan, Molloy, James, Isard, Michael, Barham, Paul R., Hennigan, Tom, Lee, Benjamin, Viola, Fabio, Reynolds, Malcolm, Xu, Yuanzhong, Doherty, Ryan, Collins, Eli, Meyer, Clemens, Rutherford, Eliza, Moreira, Erica, Ayoub, Kareem, Goel, Megha, Krawczyk, Jack, Du, Cosmo, Chi, Ed, Cheng, Heng-Tze, Ni, Eric, Shah, Purvi, Kane, Patrick, Chan, Betty, Faruqui, Manaal, Severyn, Aliaksei, Lin, Hanzhao, Li, YaGuang, Cheng, Yong, Ittycheriah, Abe, Mahdieh, Mahdis, Chen, Mia, Sun, Pei, Tran, Dustin, Bagri, Sumit, Lakshminarayanan, Balaji, Liu, Jeremiah, Orban, Andras, Güra, Fabian, Zhou, Hao, Song, Xinying, Boffy, Aurelien, Ganapathy, Harish, Zheng, Steven, Choe, HyunJeong, Weisz, Ágoston, Zhu, Tao, Lu, Yifeng, Gopal, Siddharth, Kahn, Jarrod, Kula, Maciej, Pitman, Jeff, Shah, Rushin, Taropa, Emanuel, Merey, Majd Al, Baeuml, Martin, Chen, Zhifeng, Shafey, Laurent El, Zhang, Yujing, Sercinoglu, Olcan, Tucker, George, Piqueras, Enrique, Krikun, Maxim, Barr, Iain, Savinov, Nikolay, Danihelka, Ivo, Roelofs, Becca, White, Anaïs, Andreassen, Anders, von Glehn, Tamara, Yagati, Lakshman, Kazemi, Mehran, Gonzalez, Lucas, Khalman, Misha, Sygnowski, Jakub, Frechette, Alexandre, Smith, Charlotte, Culp, Laura, Proleev, Lev, Luan, Yi, Chen, Xi, Lottes, James, Schucher, Nathan, Lebron, Federico, Rrustemi, Alban, Clay, Natalie, Crone, Phil, Kocisky, Tomas, Zhao, Jeffrey, Perz, Bartek, Yu, Dian, Howard, Heidi, Bloniarz, Adam, Rae, Jack W., Lu, Han, Sifre, Laurent, Maggioni, Marcello, Alcober, Fred, Garrette, Dan, Barnes, Megan, Thakoor, Shantanu, Austin, Jacob, Barth-Maron, Gabriel, Wong, William, Joshi, Rishabh, Chaabouni, Rahma, Fatiha, Deeni, Ahuja, Arun, Tomar, Gaurav Singh, Senter, Evan, Chadwick, Martin, Kornakov, Ilya, Attaluri, Nithya, Iturrate, Iñaki, Liu, Ruibo, Li, Yunxuan, Cogan, Sarah, Chen, Jeremy, Jia, Chao, Gu, Chenjie, Zhang, Qiao, Grimstad, Jordan, Hartman, Ale Jakse, Garcia, Xavier, Pillai, Thanumalayan Sankaranarayana, Devlin, Jacob, Laskin, Michael, Casas, Diego de Las, Valter, Dasha, Tao, Connie, Blanco, Lorenzo, Badia, Adrià Puigdomènech, Reitter, David, Chen, Mianna, Brennan, Jenny, Rivera, Clara, Brin, Sergey, Iqbal, Shariq, Surita, Gabriela, Labanowski, Jane, Rao, Abhi, Winkler, Stephanie, Parisotto, Emilio, Gu, Yiming, Olszewska, Kate, Addanki, Ravi, Miech, Antoine, Louis, Annie, Teplyashin, Denis, Brown, Geoff, Catt, Elliot, Balaguer, Jan, Xiang, Jackie, Wang, Pidong, Ashwood, Zoe, Briukhov, Anton, Webson, Albert, Ganapathy, Sanjay, Sanghavi, Smit, Kannan, Ajay, Chang, Ming-Wei, Stjerngren, Axel, Djolonga, Josip, Sun, Yuting, Bapna, Ankur, Aitchison, Matthew, Pejman, Pedram, Michalewski, Henryk, Yu, Tianhe, Wang, Cindy, Love, Juliette, Ahn, Junwhan, Bloxwich, Dawn, Han, Kehang, Humphreys, Peter, Sellam, Thibault, Bradbury, James, Godbole, Varun, Samangooei, Sina, Damoc, Bogdan, Kaskasoli, Alex, Arnold, Sébastien M. R., Vasudevan, Vijay, Agrawal, Shubham, Riesa, Jason, Lepikhin, Dmitry, Tanburn, Richard, Srinivasan, Srivatsan, Lim, Hyeontaek, Hodkinson, Sarah, Shyam, Pranav, Ferret, Johan, Hand, Steven, Garg, Ankush, Paine, Tom Le, Li, Jian, Li, Yujia, Giang, Minh, Neitz, Alexander, Abbas, Zaheer, York, Sarah, Reid, Machel, Cole, Elizabeth, Chowdhery, Aakanksha, Das, Dipanjan, Rogozińska, Dominika, Nikolaev, Vitaliy, Sprechmann, Pablo, Nado, Zachary, Zilka, Lukas, Prost, Flavien, He, Luheng, Monteiro, Marianne, Mishra, Gaurav, Welty, Chris, Newlan, Josh, Jia, Dawei, Allamanis, Miltiadis, Hu, Clara Huiyi, de Liedekerke, Raoul, Gilmer, Justin, Saroufim, Carl, Rijhwani, Shruti, Hou, Shaobo, Shrivastava, Disha, Baddepudi, Anirudh, Goldin, Alex, Ozturel, Adnan, Cassirer, Albin, Xu, Yunhan, Sohn, Daniel, Sachan, Devendra, Amplayo, Reinald Kim, Swanson, Craig, Petrova, Dessie, Narayan, Shashi, Guez, Arthur, Brahma, Siddhartha, Landon, Jessica, Patel, Miteyan, Zhao, Ruizhe, Villela, Kevin, Wang, Luyu, Jia, Wenhao, Rahtz, Matthew, Giménez, Mai, Yeung, Legg, Keeling, James, Georgiev, Petko, Mincu, Diana, Wu, Boxi, Haykal, Salem, Saputro, Rachel, Vodrahalli, Kiran, Qin, James, Cankara, Zeynep, Sharma, Abhanshu, Fernando, Nick, Hawkins, Will, Neyshabur, Behnam, Kim, Solomon, Hutter, Adrian, Agrawal, Priyanka, Castro-Ros, Alex, Driessche, George van den, Wang, Tao, Yang, Fan, Chang, Shuo-yiin, Komarek, Paul, McIlroy, Ross, Lučić, Mario, Zhang, Guodong, Farhan, Wael, Sharman, Michael, Natsev, Paul, Michel, Paul, Bansal, Yamini, Qiao, Siyuan, Cao, Kris, Shakeri, Siamak, Butterfield, Christina, Chung, Justin, Rubenstein, Paul Kishan, Agrawal, Shivani, Mensch, Arthur, Soparkar, Kedar, Lenc, Karel, Chung, Timothy, Pope, Aedan, Maggiore, Loren, Kay, Jackie, Jhakra, Priya, Wang, Shibo, Maynez, Joshua, Phuong, Mary, Tobin, Taylor, Tacchetti, Andrea, Trebacz, Maja, Robinson, Kevin, Katariya, Yash, Riedel, Sebastian, Bailey, Paige, Xiao, Kefan, Ghelani, Nimesh, Aroyo, Lora, Slone, Ambrose, Houlsby, Neil, Xiong, Xuehan, Yang, Zhen, Gribovskaya, Elena, Adler, Jonas, Wirth, Mateo, Lee, Lisa, Li, Music, Kagohara, Thais, Pavagadhi, Jay, Bridgers, Sophie, Bortsova, Anna, Ghemawat, Sanjay, Ahmed, Zafarali, Liu, Tianqi, Powell, Richard, Bolina, Vijay, Iinuma, Mariko, Zablotskaia, Polina, Besley, James, Chung, Da-Woon, Dozat, Timothy, Comanescu, Ramona, Si, Xiance, Greer, Jeremy, Su, Guolong, Polacek, Martin, Kaufman, Raphaël Lopez, Tokumine, Simon, Hu, Hexiang, Buchatskaya, Elena, Miao, Yingjie, Elhawaty, Mohamed, Siddhant, Aditya, Tomasev, Nenad, Xing, Jinwei, Greer, Christina, Miller, Helen, Ashraf, Shereen, Roy, Aurko, Zhang, Zizhao, Ma, Ada, Filos, Angelos, Besta, Milos, Blevins, Rory, Klimenko, Ted, Yeh, Chih-Kuan, Changpinyo, Soravit, Mu, Jiaqi, Chang, Oscar, Pajarskas, Mantas, Muir, Carrie, Cohen, Vered, Lan, Charline Le, Haridasan, Krishna, Marathe, Amit, Hansen, Steven, Douglas, Sholto, Samuel, Rajkumar, Wang, Mingqiu, Austin, Sophia, Lan, Chang, Jiang, Jiepu, Chiu, Justin, Lorenzo, Jaime Alonso, Sjösund, Lars Lowe, Cevey, Sébastien, Gleicher, Zach, Avrahami, Thi, Boral, Anudhyan, Srinivasan, Hansa, Selo, Vittorio, May, Rhys, Aisopos, Konstantinos, Hussenot, Léonard, Soares, Livio Baldini, Baumli, Kate, Chang, Michael B., Recasens, Adrià, Caine, Ben, Pritzel, Alexander, Pavetic, Filip, Pardo, Fabio, Gergely, Anita, Frye, Justin, Ramasesh, Vinay, Horgan, Dan, Badola, Kartikeya, Kassner, Nora, Roy, Subhrajit, Dyer, Ethan, Campos, Víctor Campos, Tomala, Alex, Tang, Yunhao, Badawy, Dalia El, White, Elspeth, Mustafa, Basil, Lang, Oran, Jindal, Abhishek, Vikram, Sharad, Gong, Zhitao, Caelles, Sergi, Hemsley, Ross, Thornton, Gregory, Feng, Fangxiaoyu, Stokowiec, Wojciech, Zheng, Ce, Thacker, Phoebe, Ünlü, Çağlar, Zhang, Zhishuai, Saleh, Mohammad, Svensson, James, Bileschi, Max, Patil, Piyush, Anand, Ankesh, Ring, Roman, Tsihlas, Katerina, Vezer, Arpi, Selvi, Marco, Shevlane, Toby, Rodriguez, Mikel, Kwiatkowski, Tom, Daruki, Samira, Rong, Keran, Dafoe, Allan, FitzGerald, Nicholas, Gu-Lemberg, Keren, Khan, Mina, Hendricks, Lisa Anne, Pellat, Marie, Feinberg, Vladimir, Cobon-Kerr, James, Sainath, Tara, Rauh, Maribeth, Hashemi, Sayed Hadi, Ives, Richard, Hasson, Yana, Noland, Eric, Cao, Yuan, Byrd, Nathan, Hou, Le, Wang, Qingze, Sottiaux, Thibault, Paganini, Michela, Lespiau, Jean-Baptiste, Moufarek, Alexandre, Hassan, Samer, Shivakumar, Kaushik, van Amersfoort, Joost, Mandhane, Amol, Joshi, Pratik, Goyal, Anirudh, Tung, Matthew, Brock, Andrew, Sheahan, Hannah, Misra, Vedant, Li, Cheng, Rakićević, Nemanja, Dehghani, Mostafa, Liu, Fangyu, Mittal, Sid, Oh, Junhyuk, Noury, Seb, Sezener, Eren, Huot, Fantine, Lamm, Matthew, De Cao, Nicola, Chen, Charlie, Mudgal, Sidharth, Stella, Romina, Brooks, Kevin, Vasudevan, Gautam, Liu, Chenxi, Chain, Mainak, Melinkeri, Nivedita, Cohen, Aaron, Wang, Venus, Seymore, Kristie, Zubkov, Sergey, Goel, Rahul, Yue, Summer, Krishnakumaran, Sai, Albert, Brian, Hurley, Nate, Sano, Motoki, Mohananey, Anhad, Joughin, Jonah, Filonov, Egor, Kępa, Tomasz, Eldawy, Yomna, Lim, Jiawern, Rishi, Rahul, Badiezadegan, Shirin, Bos, Taylor, Chang, Jerry, Jain, Sanil, Padmanabhan, Sri Gayatri Sundara, Puttagunta, Subha, Krishna, Kalpesh, Baker, Leslie, Kalb, Norbert, Bedapudi, Vamsi, Kurzrok, Adam, Lei, Shuntong, Yu, Anthony, Litvin, Oren, Zhou, Xiang, Wu, Zhichun, Sobell, Sam, Siciliano, Andrea, Papir, Alan, Neale, Robby, Bragagnolo, Jonas, Toor, Tej, Chen, Tina, Anklin, Valentin, Wang, Feiran, Feng, Richie, Gholami, Milad, Ling, Kevin, Liu, Lijuan, Walter, Jules, Moghaddam, Hamid, Kishore, Arun, Adamek, Jakub, Mercado, Tyler, Mallinson, Jonathan, Wandekar, Siddhinita, Cagle, Stephen, Ofek, Eran, Garrido, Guillermo, Lombriser, Clemens, Mukha, Maksim, Sun, Botu, Mohammad, Hafeezul Rahman, Matak, Josip, Qian, Yadi, Peswani, Vikas, Janus, Pawel, Yuan, Quan, Schelin, Leif, David, Oana, Garg, Ankur, He, Yifan, Duzhyi, Oleksii, Älgmyr, Anton, Lottaz, Timothée, Li, Qi, Yadav, Vikas, Xu, Luyao, Chinien, Alex, Shivanna, Rakesh, Chuklin, Aleksandr, Li, Josie, Spadine, Carrie, Wolfe, Travis, Mohamed, Kareem, Das, Subhabrata, Dai, Zihang, He, Kyle, von Dincklage, Daniel, Upadhyay, Shyam, Maurya, Akanksha, Chi, Luyan, Krause, Sebastian, Salama, Khalid, Rabinovitch, Pam G, M, Pavan Kumar Reddy, Selvan, Aarush, Dektiarev, Mikhail, Ghiasi, Golnaz, Guven, Erdem, Gupta, Himanshu, Liu, Boyi, Sharma, Deepak, Shtacher, Idan Heimlich, Paul, Shachi, Akerlund, Oscar, Aubet, François-Xavier, Huang, Terry, Zhu, Chen, Zhu, Eric, Teixeira, Elico, Fritze, Matthew, Bertolini, Francesco, Marinescu, Liana-Eleonora, Bölle, Martin, Paulus, Dominik, Gupta, Khyatti, Latkar, Tejasi, Chang, Max, Sanders, Jason, Wilson, Roopa, Wu, Xuewei, Tan, Yi-Xuan, Thiet, Lam Nguyen, Doshi, Tulsee, Lall, Sid, Mishra, Swaroop, Chen, Wanming, Luong, Thang, Benjamin, Seth, Lee, Jasmine, Andrejczuk, Ewa, Rabiej, Dominik, Ranjan, Vipul, Styrc, Krzysztof, Yin, Pengcheng, Simon, Jon, Harriott, Malcolm Rose, Bansal, Mudit, Robsky, Alexei, Bacon, Geoff, Greene, David, Mirylenka, Daniil, Zhou, Chen, Sarvana, Obaid, Goyal, Abhimanyu, Andermatt, Samuel, Siegler, Patrick, Horn, Ben, Israel, Assaf, Pongetti, Francesco, Chen, Chih-Wei "Louis", Selvatici, Marco, Silva, Pedro, Wang, Kathie, Tolins, Jackson, Guu, Kelvin, Yogev, Roey, Cai, Xiaochen, Agostini, Alessandro, Shah, Maulik, Nguyen, Hung, Donnaile, Noah Ó, Pereira, Sébastien, Friso, Linda, Stambler, Adam, Kuang, Chenkai, Romanikhin, Yan, Geller, Mark, Yan, ZJ, Jang, Kane, Lee, Cheng-Chun, Fica, Wojciech, Malmi, Eric, Tan, Qijun, Banica, Dan, Balle, Daniel, Pham, Ryan, Huang, Yanping, Avram, Diana, Shi, Hongzhi, Singh, Jasjot, Hidey, Chris, Ahuja, Niharika, Saxena, Pranab, Dooley, Dan, Potharaju, Srividya Pranavi, O'Neill, Eileen, Gokulchandran, Anand, Foley, Ryan, Zhao, Kai, Dusenberry, Mike, Liu, Yuan, Mehta, Pulkit, Kotikalapudi, Ragha, Safranek-Shrader, Chalence, Goodman, Andrew, Kessinger, Joshua, Globen, Eran, Kolhar, Prateek, Gorgolewski, Chris, Ibrahim, Ali, Song, Yang, Eichenbaum, Ali, Brovelli, Thomas, Potluri, Sahitya, Lahoti, Preethi, Baetu, Cip, Ghorbani, Ali, Chen, Charles, Crawford, Andy, Pal, Shalini, Sridhar, Mukund, Gurita, Petru, Mujika, Asier, Petrovski, Igor, Cedoz, Pierre-Louis, Li, Chenmei, Chen, Shiyuan, Santo, Niccolò Dal, Goyal, Siddharth, Punjabi, Jitesh, Kappaganthu, Karthik, Kwak, Chester, LV, Pallavi, Velury, Sarmishta, Choudhury, Himadri, Hall, Jamie, Shah, Premal, Figueira, Ricardo, Thomas, Matt, Lu, Minjie, Zhou, Ting, Kumar, Chintu, Jurdi, Thomas, Chikkerur, Sharat, Ma, Yenai, Yu, Adams, Kwak, Soo, Ähdel, Victor, Rajayogam, Sujeevan, Choma, Travis, Liu, Fei, Barua, Aditya, Ji, Colin, Park, Ji Ho, Hellendoorn, Vincent, Bailey, Alex, Bilal, Taylan, Zhou, Huanjie, Khatir, Mehrdad, Sutton, Charles, Rzadkowski, Wojciech, Macintosh, Fiona, Shagin, Konstantin, Medina, Paul, Liang, Chen, Zhou, Jinjing, Shah, Pararth, Bi, Yingying, Dankovics, Attila, Banga, Shipra, Lehmann, Sabine, Bredesen, Marissa, Lin, Zifan, Hoffmann, John Eric, Lai, Jonathan, Chung, Raynald, Yang, Kai, Balani, Nihal, Bražinskas, Arthur, Sozanschi, Andrei, Hayes, Matthew, Alcalde, Héctor Fernández, Makarov, Peter, Chen, Will, Stella, Antonio, Snijders, Liselotte, Mandl, Michael, Kärrman, Ante, Nowak, Paweł, Wu, Xinyi, Dyck, Alex, Vaidyanathan, Krishnan, R, Raghavender, Mallet, Jessica, Rudominer, Mitch, Johnston, Eric, Mittal, Sushil, Udathu, Akhil, Christensen, Janara, Verma, Vishal, Irving, Zach, Santucci, Andreas, Elsayed, Gamaleldin, Davoodi, Elnaz, Georgiev, Marin, Tenney, Ian, Hua, Nan, Cideron, Geoffrey, Leurent, Edouard, Alnahlawi, Mahmoud, Georgescu, Ionut, Wei, Nan, Zheng, Ivy, Scandinaro, Dylan, Jiang, Heinrich, Snoek, Jasper, Sundararajan, Mukund, Wang, Xuezhi, Ontiveros, Zack, Karo, Itay, Cole, Jeremy, Rajashekhar, Vinu, Tumeh, Lara, Ben-David, Eyal, Jain, Rishub, Uesato, Jonathan, Datta, Romina, Bunyan, Oskar, Wu, Shimu, Zhang, John, Stanczyk, Piotr, Zhang, Ye, Steiner, David, Naskar, Subhajit, Azzam, Michael, Johnson, Matthew, Paszke, Adam, Chiu, Chung-Cheng, Elias, Jaume Sanchez, Mohiuddin, Afroz, Muhammad, Faizan, Miao, Jin, Lee, Andrew, Vieillard, Nino, Park, Jane, Zhang, Jiageng, Stanway, Jeff, Garmon, Drew, Karmarkar, Abhijit, Dong, Zhe, Lee, Jong, Kumar, Aviral, Zhou, Luowei, Evens, Jonathan, Isaac, William, Irving, Geoffrey, Loper, Edward, Fink, Michael, Arkatkar, Isha, Chen, Nanxin, Shafran, Izhak, Petrychenko, Ivan, Chen, Zhe, Jia, Johnson, Levskaya, Anselm, Zhu, Zhenkai, Grabowski, Peter, Mao, Yu, Magni, Alberto, Yao, Kaisheng, Snaider, Javier, Casagrande, Norman, Palmer, Evan, Suganthan, Paul, Castaño, Alfonso, Giannoumis, Irene, Kim, Wooyeol, Rybiński, Mikołaj, Sreevatsa, Ashwin, Prendki, Jennifer, Soergel, David, Goedeckemeyer, Adrian, Gierke, Willi, Jafari, Mohsen, Gaba, Meenu, Wiesner, Jeremy, Wright, Diana Gage, Wei, Yawen, Vashisht, Harsha, Kulizhskaya, Yana, Hoover, Jay, Le, Maigo, Li, Lu, Iwuanyanwu, Chimezie, Liu, Lu, Ramirez, Kevin, Khorlin, Andrey, Cui, Albert, LIN, Tian, Wu, Marcus, Aguilar, Ricardo, Pallo, Keith, Chakladar, Abhishek, Perng, Ginger, Abellan, Elena Allica, Zhang, Mingyang, Dasgupta, Ishita, Kushman, Nate, Penchev, Ivo, Repina, Alena, Wu, Xihui, van der Weide, Tom, Ponnapalli, Priya, Kaplan, Caroline, Simsa, Jiri, Li, Shuangfeng, Dousse, Olivier, Piper, Jeff, Ie, Nathan, Pasumarthi, Rama, Lintz, Nathan, Vijayakumar, Anitha, Andor, Daniel, Valenzuela, Pedro, Lui, Minnie, Paduraru, Cosmin, Peng, Daiyi, Lee, Katherine, Zhang, Shuyuan, Greene, Somer, Nguyen, Duc Dung, Kurylowicz, Paula, Hardin, Cassidy, Dixon, Lucas, Janzer, Lili, Choo, Kiam, Feng, Ziqiang, Zhang, Biao, Singhal, Achintya, Du, Dayou, McKinnon, Dan, Antropova, Natasha, Bolukbasi, Tolga, Keller, Orgad, Reid, David, Finchelstein, Daniel, Raad, Maria Abi, Crocker, Remi, Hawkins, Peter, Dadashi, Robert, Gaffney, Colin, Franko, Ken, Bulanova, Anna, Leblond, Rémi, Chung, Shirley, Askham, Harry, Cobo, Luis C., Xu, Kelvin, Fischer, Felix, Xu, Jun, Sorokin, Christina, Alberti, Chris, Lin, Chu-Cheng, Evans, Colin, Dimitriev, Alek, Forbes, Hannah, Banarse, Dylan, Tung, Zora, Omernick, Mark, Bishop, Colton, Sterneck, Rachel, Jain, Rohan, Xia, Jiawei, Amid, Ehsan, Piccinno, Francesco, Wang, Xingyu, Banzal, Praseem, Mankowitz, Daniel J., Polozov, Alex, Krakovna, Victoria, Brown, Sasha, Bateni, MohammadHossein, Duan, Dennis, Firoiu, Vlad, Thotakuri, Meghana, Natan, Tom, Geist, Matthieu, Girgin, Ser tan, Li, Hui, Ye, Jiayu, Roval, Ofir, Tojo, Reiko, Kwong, Michael, Lee-Thorp, James, Yew, Christopher, Sinopalnikov, Danila, Ramos, Sabela, Mellor, John, Sharma, Abhishek, Wu, Kathy, Miller, David, Sonnerat, Nicolas, Vnukov, Denis, Greig, Rory, Beattie, Jennifer, Caveness, Emily, Bai, Libin, Eisenschlos, Julian, Korchemniy, Alex, Tsai, Tomy, Jasarevic, Mimi, Kong, Weize, Dao, Phuong, Zheng, Zeyu, Liu, Frederick, Zhu, Rui, Teh, Tian Huey, Sanmiya, Jason, Gladchenko, Evgeny, Trdin, Nejc, Toyama, Daniel, Rosen, Evan, Tavakkol, Sasan, Xue, Linting, Elkind, Chen, Woodman, Oliver, Carpenter, John, Papamakarios, George, Kemp, Rupert, Kafle, Sushant, Grunina, Tanya, Sinha, Rishika, Talbert, Alice, Wu, Diane, Owusu-Afriyie, Denese, Thornton, Chloe, Pont-Tuset, Jordi, Narayana, Pradyumna, Li, Jing, Fatehi, Saaber, Wieting, John, Ajmeri, Omar, Uria, Benigno, Ko, Yeongil, Knight, Laura, Héliou, Amélie, Niu, Ning, Gu, Shane, Pang, Chenxi, Li, Yeqing, Levine, Nir, Stolovich, Ariel, Santamaria-Fernandez, Rebeca, Goenka, Sonam, Yustalim, Wenny, Strudel, Robin, Elqursh, Ali, Deck, Charlie, Lee, Hyo, Li, Zonglin, Levin, Kyle, Hoffmann, Raphael, Holtmann-Rice, Dan, Bachem, Olivier, Arora, Sho, Koh, Christy, Yeganeh, Soheil Hassas, Põder, Siim, Tariq, Mukarram, Sun, Yanhua, Ionita, Lucian, Seyedhosseini, Mojtaba, Tafti, Pouya, Liu, Zhiyu, Gulati, Anmol, Liu, Jasmine, Ye, Xinyu, Chrzaszcz, Bart, Wang, Lily, Sethi, Nikhil, Li, Tianrun, Brown, Ben, Singh, Shreya, Fan, Wei, Parisi, Aaron, Stanton, Joe, Koverkathu, Vinod, Choquette-Choo, Christopher A., Li, Yunjie, Lu, TJ, Shroff, Prakash, Varadarajan, Mani, Bahargam, Sanaz, Willoughby, Rob, Gaddy, David, Desjardins, Guillaume, Cornero, Marco, Robenek, Brona, Mittal, Bhavishya, Albrecht, Ben, Shenoy, Ashish, Moiseev, Fedor, Jacobsson, Henrik, Ghaffarkhah, Alireza, Rivière, Morgane, Walton, Alanna, Crepy, Clément, Parrish, Alicia, Zhou, Zongwei, Farabet, Clement, Radebaugh, Carey, Srinivasan, Praveen, van der Salm, Claudia, Fidjeland, Andreas, Scellato, Salvatore, Latorre-Chimoto, Eri, Klimczak-Plucińska, Hanna, Bridson, David, de Cesare, Dario, Hudson, Tom, Mendolicchio, Piermaria, Walker, Lexi, Morris, Alex, Mauger, Matthew, Guseynov, Alexey, Reid, Alison, Odoom, Seth, Loher, Lucia, Cotruta, Victor, Yenugula, Madhavi, Grewe, Dominik, Petrushkina, Anastasia, Duerig, Tom, Sanchez, Antonio, Yadlowsky, Steve, Shen, Amy, Globerson, Amir, Webb, Lynette, Dua, Sahil, Li, Dong, Bhupatiraju, Surya, Hurt, Dan, Qureshi, Haroon, Agarwal, Ananth, Shani, Tomer, Eyal, Matan, Khare, Anuj, Belle, Shreyas Rammohan, Wang, Lei, Tekur, Chetan, Kale, Mihir Sanjay, Wei, Jinliang, Sang, Ruoxin, Saeta, Brennan, Liechty, Tyler, Sun, Yi, Zhao, Yao, Lee, Stephan, Nayak, Pandu, Fritz, Doug, Vuyyuru, Manish Reddy, Aslanides, John, Vyas, Nidhi, Wicke, Martin, Ma, Xiao, Eltyshev, Evgenii, Martin, Nina, Cate, Hardie, Manyika, James, Amiri, Keyvan, Kim, Yelin, Xiong, Xi, Kang, Kai, Luisier, Florian, Tripuraneni, Nilesh, Madras, David, Guo, Mandy, Waters, Austin, Wang, Oliver, Ainslie, Joshua, Baldridge, Jason, Zhang, Han, Pruthi, Garima, Bauer, Jakob, Yang, Feng, Mansour, Riham, Gelman, Jason, Xu, Yang, Polovets, George, Liu, Ji, Cai, Honglong, Chen, Warren, Sheng, XiangHai, Xue, Emily, Ozair, Sherjil, Angermueller, Christof, Li, Xiaowei, Sinha, Anoop, Wang, Weiren, Wiesinger, Julia, Koukoumidis, Emmanouil, Tian, Yuan, Iyer, Anand, Gurumurthy, Madhu, Goldenson, Mark, Shah, Parashar, Blake, MK, Yu, Hongkun, Urbanowicz, Anthony, Palomaki, Jennimaria, Fernando, Chrisantha, Durden, Ken, Mehta, Harsh, Momchev, Nikola, Rahimtoroghi, Elahe, Georgaki, Maria, Raul, Amit, Ruder, Sebastian, Redshaw, Morgan, Lee, Jinhyuk, Zhou, Denny, Jalan, Komal, Li, Dinghua, Hechtman, Blake, Schuh, Parker, Nasr, Milad, Milan, Kieran, Mikulik, Vladimir, Franco, Juliana, Green, Tim, Nguyen, Nam, Kelley, Joe, Mahendru, Aroma, Hu, Andrea, Howland, Joshua, Vargas, Ben, Hui, Jeffrey, Bansal, Kshitij, Rao, Vikram, Ghiya, Rakesh, Wang, Emma, Ye, Ke, Sarr, Jean Michel, Preston, Melanie Moranski, Elish, Madeleine, Li, Steve, Kaku, Aakash, Gupta, Jigar, Pasupat, Ice, Juan, Da-Cheng, Someswar, Milan, M., Tejvi, Chen, Xinyun, Amini, Aida, Fabrikant, Alex, Chu, Eric, Dong, Xuanyi, Muthal, Amruta, Buthpitiya, Senaka, Jauhari, Sarthak, Khandelwal, Urvashi, Hitron, Ayal, Ren, Jie, Rinaldi, Larissa, Drath, Shahar, Dabush, Avigail, Jiang, Nan-Jiang, Godhia, Harshal, Sachs, Uli, Chen, Anthony, Fan, Yicheng, Taitelbaum, Hagai, Noga, Hila, Dai, Zhuyun, Wang, James, Hamer, Jenny, Ferng, Chun-Sung, Elkind, Chenel, Atias, Aviel, Lee, Paulina, Listík, Vít, Carlen, Mathias, van de Kerkhof, Jan, Pikus, Marcin, Zaher, Krunoslav, Müller, Paul, Zykova, Sasha, Stefanec, Richard, Gatsko, Vitaly, Hirnschall, Christoph, Sethi, Ashwin, Xu, Xingyu Federico, Ahuja, Chetan, Tsai, Beth, Stefanoiu, Anca, Feng, Bo, Dhandhania, Keshav, Katyal, Manish, Gupta, Akshay, Parulekar, Atharva, Pitta, Divya, Zhao, Jing, Bhatia, Vivaan, Bhavnani, Yashodha, Alhadlaq, Omar, Li, Xiaolin, Danenberg, Peter, Tu, Dennis, Pine, Alex, Filippova, Vera, Ghosh, Abhipso, Limonchik, Ben, Urala, Bhargava, Lanka, Chaitanya Krishna, Clive, Derik, Li, Edward, Wu, Hao, Hongtongsak, Kevin, Li, Ianna, Thakkar, Kalind, Omarov, Kuanysh, Majmundar, Kushal, Alverson, Michael, Kucharski, Michael, Patel, Mohak, Jain, Mudit, Zabelin, Maksim, Pelagatti, Paolo, Kohli, Rohan, Kumar, Saurabh, Kim, Joseph, Sankar, Swetha, Shah, Vineet, Ramachandruni, Lakshmi, Zeng, Xiangkai, Bariach, Ben, Weidinger, Laura, Vu, Tu, Andreev, Alek, He, Antoine, Hui, Kevin, Kashem, Sheleem, Subramanya, Amar, Hsiao, Sissie, Hassabis, Demis, Kavukcuoglu, Koray, Sadovsky, Adam, Le, Quoc, Strohman, Trevor, Wu, Yonghui, Petrov, Slav, Dean, Jeffrey, and Vinyals, Oriol

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

Published
2023

21. A long-term drought reconstruction based on oxygen isotope tree ring data for central and eastern parts of Europe (Romania)

Author

V. Nagavciuc, G. Helle, M. Rădoane, C.-C. Roibu, M.-G. Cotos, and M. Ionita

Subjects
Ecology, QH540-549.5, Life, QH501-531, Geology, QE1-996.5
Abstract

This study investigates the relationship between oxygen isotope ratios (δ18O) in oak tree ring cellulose and past drought variability in Letea Forest, Romania. A δ18O site chronology spanning 1803–2020 was compiled from seven individual time series. δ18O values exhibited a significant negative correlation with moisture-related variables (cloud cover, relative humidity, and precipitation) and a positive correlation with temperature and sunshine duration. This confirms that δ18O from tree rings can be a good proxy for moisture availability. The strongest correlation was found between δ18O and the August Standardized Precipitation Evapotranspiration Index for an accumulation period of 9 months (SPEI9) for central and eastern Europe. This highlights SPEI9 as a superior indicator of drought compared to individual parameters like temperature or precipitation. Using a linear regression model, we reconstructed August SPEI9 variability for the past 200 years. The reconstruction captured interannual and decadal variations, with distinct wet and dry periods. Analysis of large-scale atmospheric circulation patterns revealed a link between high δ18O values (indicating dry conditions) and a high-pressure system over the North Atlantic. Conversely, low δ18O values (indicating wet conditions) corresponded to negative pressure anomalies over Europe. Moreover, extreme values of δ18O are also associated with the prevalence of a hemispheric teleconnection pattern, namely wave number 4. This δ18O chronology and the corresponding August SPEI9 reconstruction offer valuable tools for understanding past climate variability and its relationship with large-scale atmospheric and oceanic circulation patterns.

Published
2025
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22. Examining the Eastern European extreme summer temperatures of 2023 from a long-term perspective: the role of natural variability vs. anthropogenic factors

Author

M. Ionita, P. Vaideanu, B. Antonescu, C. Roibu, Q. Ma, and V. Nagavciuc

Subjects
Environmental technology. Sanitary engineering, TD1-1066, Geography. Anthropology. Recreation, Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

Amidst unprecedented rising global temperatures, this study investigates the historical context of heat wave (HW) events in Eastern Europe. The record-breaking 2023 summer, featuring a HW lasting for 19 d in the southeastern part of Romania, extending up to Ukraine, necessitates a deeper understanding of past extreme events. Utilizing statistical methods on long-term station data spanning from 1885 to 2023, we aim to detect and analyze historical HWs, particularly focusing on events predating 1960. This extended time frame allows for a more comprehensive assessment of noteworthy extremes compared to recent decades. We used both a percentile-based threshold and a fixed absolute temperature threshold to identify HW events. Our analysis identifies two critical periods with increased HW frequency and intensity: 1920–1965 and 1980–2023, respectively, highlighting the most extreme events in August 1946, August 1952, July 2012, June 2019, and August 2023. Furthermore, reanalysis data show that historical HWs, similar to the 2023 event, were associated with large-scale European heat extremes linked to high-pressure systems, and they were accompanied by extreme drought, thus leading to compound extreme events. We find that while a clear trend emerges towards more frequent HWs from the 1980s onward, the analysis also uncovers substantial HW activity on daily timescales throughout the 1885–1960 period. Moreover, we highlight the intertwined impacts of climate change and multidecadal internal variability on HW patterns, with evidence suggesting that both contribute to the increasing frequency and intensity of these extreme events. Attribution analysis reveals that the extreme summer temperatures observed in 2023 would not have been possible in the absence of anthropogenic climate change. Regardless of future warming levels, such temperatures will occur every year by the end of the century. Our research highlights the value of extending the historical record for a more nuanced understanding of HW behavior and suggests that extreme heat events, comparable to those experienced in recent decades, have occurred throughout the analyzed period.

Published
2024
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23. Corrigendum to 'Atmospheric pressure plasma synthesis of adaptable coatings based on castor oil urethane dimethacrylate and their properties' [Applied Surface Science Advances volume 25 (January 2025) 100680]

Author

Eusebiu-Rosini Ionita, Maria-Daniela Ionita, Antoniu Moldovan, Cristina Surdu-Bob, Violeta Melinte, Andreea L. Chibac-Scutaru, and Andrada Lazea-Stoyanova

Subjects
Materials of engineering and construction. Mechanics of materials, TA401-492, Industrial electrochemistry, TP250-261
Published
2025
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24. Atmospheric pressure plasma synthesis of adaptable coatings based on castor oil urethane dimethacrylate and their properties

Author

Eusebiu-Rosini Ionita, Maria-Daniela Ionita, Antoniu Moldovan, Cristina Surdu-Bob, Violeta Melinte, Andreea L. Chibac-Scutaru, and Andrada Lazea-Stoyanova

Subjects
atmospheric pressure plasma, plasma synthesis, castor oil polymethacrylate films, microscale ripples surface, photoinitiator free polymerization, Materials of engineering and construction. Mechanics of materials, TA401-492, Industrial electrochemistry, TP250-261
Abstract

Urethane-dimethacrylate polymers are generated using light energy, in the presence of photoinitiators, and are the choice materials for many composite materials, biomaterials, optical materials or coatings. In this study, we present an alternative, new, synthesis method for castor oil polymethacrylate (CO-PMA) films by exposure of a liquid precursor (namely castor oil urethane dimethacrylate (CO-UDMA)) to a non-thermal atmospheric pressure cylindrical plasma source. This is a photoinitiator free process, an open-air atmosphere technique and uses a single plasma polymerization step. We have obtained polymeric layers in two modes, in stationary or dynamic plasma exposure mode. The experimental results show that our plasma method is suitable to generate compact polymeric layers, with no trapped unpolymerized precursor, showing a bumpy or microscale ripples surface appearance. However, the surface and cross-section analyses indicate that the dynamic layer has a nanometric flat surface, a more pronounced hydrophobic character and is thicker. In short, our plasma-based method for generation of CO-PMA layers, in dynamic mode, does not require toxic photoinitiators, is simple, can be applied to temperature-sensitive materials and is scalable to large areas.

Published
2025
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26. Strongly interacting 2D electron systems: Evidence for enhanced 1D edge-channel coupling

Author

Marty, C., Reichl, C., Parolo, S., Grandt-Ionita, I., Scharnetzky, J., Dietsche, W., and Wegscheider, W.

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Strongly Correlated Electrons
Abstract

We observe nearly vanishing Hall resistances for integer filling factors in a counterflow (CF) experiment on a density balanced 2D bilayer system. Filling factor dependent equilibration lengths demonstrate enhanced 1D coupling via edge-channels. Due to the narrow barrier the edge-modes of the two 2DEGs are in close proximity allowing for 1D excitonic correlations. Electron drag measurements confirm the observed quantum state selective coupling between the layers., Comment: Added references

Published
2023

27. Heuristics Optimization of Boolean Circuits with application in Attribute Based Encryption

Author

Ionita, Alexandru, Banu, Denis-Andrei, and Oleniuc, Iulian

Subjects
Computer Science - Computational Complexity
Abstract

We propose a method of optimizing monotone Boolean circuits by re-writing them in a simpler, equivalent form. We use in total six heuristics: Hill Climbing, Simulated Annealing, and variations of them, which operate on the representation of the circuit as a logical formula. Our main motivation is to improve performance in Attribute-Based Encryption (ABE) schemes for Boolean circuits. Therefore, we show how our heuristics improve ABE systems for Boolean circuits. Also, we run tests to evaluate the performance of our heuristics, both as a standalone optimization for Boolean circuits and also inside ABE systems.

Published
2023

28. Holomorphic Factorization of Vector Bundle Automorphisms

Author

Ionita, George and Kutzschebauch, Frank

Subjects
Mathematics - Complex Variables, Mathematics - K-Theory and Homology, 32Q56, 19B14
Abstract

We prove that any null-homotopic special holomorphic vector bundle automorphisms of a rank 2 vector bundle E over a Stein space X can be written as a finite product of unipotent holomorphic vector bundle automorphism as well as a finite product of exponentials.

Published
2023

29. Long term health outcomes in people with diabetes 12 months after hospitalisation with COVID-19 in the UK: a prospective cohort studyResearch in context

Author

Safoora Gharibzadeh, Ash Routen, Cameron Razieh, Francesco Zaccardi, Claire Lawson, Clare Gillies, Simon Heller, Melanie Davies, Helen Atkins, Stephen C. Bain, Nazir L. Lone, Krisnah Poinasamy, Tunde Peto, Elizabeth Robertson, Bob Young, Desmond Johnston, Jennifer Quint, Jonathan Valabhji, Khalida Ismail, Michael Marks, Alex Horsley, Annemarie Docherty, Ewen Harrison, James Chalmers, Ling-Pei Ho, Betty Raman, Chris Brightling, Omer Elneima, Rachel Evans, Neil Greening, Victoria C. Harris, Linzy Houchen-Wolloff, Marco Sereno, Aarti Shikotra, Amisha Singapuri, Louise Wain, Claudia Langenberg, John Dennis, John Petrie, Naveed Sattar, Olivia Leavy, Mattew Richardson, Ruth M. Saunders, Anne McArdle, Hamish McASuley, Tom Yates, Kamlesh Khunti, C.E. Brightling, R.A. Evans, L.V. Wain, J.D. Chalmers, V.C. Harris, L.P. Ho, A. Horsley, M. Marks, K. Poinasamy, B. Raman, A. Shikotra, A. Singapuri, R. Dowling, C. Edwardson, O. Elneima, S. Finney, N.J. Greening, B. Hargadon, L. Houchen--Wolloff, O.C. Leavy, H.J.C. McAuley, C. Overton, T. Plekhanova, R.M. Saunders, M. Sereno, C. Taylor, S. Terry, C. Tong, B. Zhao, D. Lomas, E. Sapey, C. Berry, C.E. Bolton, N. Brunskill, E.R. Chilvers, R. Djukanovic, Y. Ellis, D. Forton, N. French, J. George, N.A. Hanley, N. Hart, L. McGarvey, N. Maskell, H. McShane, M. Parkes, D. Peckham, P. Pfeffer, A. Sayer, A. Sheikh, A.A.R. Thompson, N. Williams, W. Greenhalf, M.G. Semple, M. Ashworth, H.E. Hardwick, L. Lavelle-Langham, W. Reynolds, V. Shaw, B. Venson, A.B. Docherty, E.M. Harrison, J.K. Baillie, L. Daines, R. Free, S. Kerr, N.I. Lone, D. Lozano-Rojas, K. Ntotsis, R. Pius, J. Quint, M. Richardson, M. Thorpe, M. Halling-Brown, F. Gleeson, J. Jacob, S. Neubauer, S. Siddiqui, J.M. Wild, S. Aslani, G. Baxter, M. Beggs, C. Bloomfield, M.P. Cassar, A. Chiribiri, E. Cox, D.J. Cuthbertson, V.M. Ferreira, L. Finnigan, S. Francis, P. Jezzard, G.J. Kemp, H. Lamlum, E. Lukaschuk, C. Manisty, G.P. McCann, C. McCracken, K. McGlynn, R. Menke, C.A. Miller, A.J. Moss, T.E. Nichols, C. Nikolaidou, C. O'Brien, G. Ogbole, B. Rangelov, D.P. O'Regan, A. Pakzad, S. Piechnik, S. Plein, I. Propescu, A.A. Samat, L. Saunders, Z.B. Sanders, R. Steeds, T. Treibel, E.M. Tunnicliffe, M. Webster, J. Willoughby, J. Weir McCall, C. Xie, M. Xu, H. Baxendale, M. Brown, B. Gooptu, R.G. Jenkins, D. Jones, I. Koychev, C. Langenberg, A. Lawrie, P.L. Molyneaux, J. Pearl, M. Ralser, N. Sattar, J.T. Scott, T. Shaw, D. Thomas, D. Wilkinson, L.G. Heaney, A. De Soyza, D. Adeloye, J.S. Brown, J. Busby, C. Echevarria, J. Hurst, P. Novotny, C. Nicolaou, I. Rudan, M. Shankar-Hari, S. Walker, B. Zheng, J.R. Geddes, M. Hotopf, K. Abel, R. Ahmed, L. Allan, C. Armour, D. Baguley, D. Baldwin, C. Ballard, K. Bhui, G. Breen, K. Breeze, M. Broome, T. Brugha, E. Bullmore, D. Burn, F. Callard, J. Cavanagh, T. Chalder, D. Clark, A. David, B. Deakin, H. Dobson, B. Elliott, J. Evans, R. Francis, E. Guthrie, P. Harrison, M. Henderson, A. Hosseini, N. Huneke, M. Husain, T. Jackson, I. Jones, T. Kabir, P. Kitterick, A. Korszun, J. Kwan, A. Lingford-Hughes, P. Mansoori, H. McAllister-Williams, K. McIvor, B. Michael, L. Milligan, R. Morriss, E. Mukaetova-Ladinska, K. Munro, A. Nevado-Holgado, T. Nicholson, S. Paddick, C. Pariante, J. Pimm, K. Saunders, M. Sharpe, G. Simons, J.P. Taylor, R. Upthegrove, S. Wessely, S. Amoils, C. Antoniades, A. Banerjee, A. Bularga, P. Chowienczyk, J.P. Greenwood, A.D. Hughes, K. Khunti, C. Lawson, N.L. Mills, A.N. Sattar, C.L. Sudlow, M. Toshner, P.J.M. Openshaw, D. Altmann, R. Batterham, N. Bishop, P.C. Calder, C.M. Efstathiou, J.L. Heeney, T. Hussell, P. Klenerman, F. Liew, J.M. Lord, P. Moss, S.L. Rowland-Jones, W. Schwaeble, R.S. Thwaites, L. Turtle, S. Walmsley, D. Wraith, M.J. Rowland, A. Rostron, B. Connolly, D.F. McAuley, D. Parekh, J. Simpson, C. Summers, J. Porter, R.J. Allen, R. Aul, S. Barratt, P. Beirne, J. Blaikley, R.C. Chambers, N. Chaudhuri, C. Coleman, E. Denneny, L. Fabbri, P.M. George, M. Gibbons, B. Guillen Guio, I. Hall, E. Hufton, I. Jarrold, G. Jenkins, S. Johnson, M.G. Jones, S. Jones, F. Khan, P. Mehta, J. Mitchell, J.E. Pearl, K. Piper Hanley, P. Rivera-Ortega, L.C. Saunders, D. Smith, M. Spears, L.G. Spencer, S. Stanel, I. Stewart, D. Thickett, R. Thwaites, S. Walsh, D.G. Wootton, L. Wright, S. Heller, M.J. Davies, H. Atkins, S. Bain, J. Dennis, K. Ismail, D. Johnston, P. Kar, P. McArdle, A. McGovern, T. Peto, J. Petrie, E. Robertson, K. Shah, J. Valabhji, B. Young, L.S. Howard, Mark Toshner, J. Newman, L. Price, A. Reddy, J. Rossdale, C. Sudlow, M. Wilkins, S.J. Singh, W.D.-C. Man, N. Armstrong, E. Baldry, M. Baldwin, N. Basu, M. Beadsworth, L. Bishop, A. Briggs, M. Buch, G. Carson, H. Chinoy, C. Dawson, E. Daynes, S. Defres, L. Gardiner, P. Greenhaff, S. Greenwood, M. Harvie, L. HOuchen-Wolloff, S. MacDonald, A. McArdle, A. McMahon, M. McNarry, G. Mills, C. Nolan, K. O'Donnell, Pimm, J. Sargent, L. Sigfrid, M. Steiner, D. Stensel, A.L. Tan, I. Vogiatzis, J. Whitney, D. Wilson, M. Witham, T. Yates, C. Laing, K. Bramham, P. Chowdhury, A. Frankel, L. Lightstone, S. McAdoo, K. McCafferty, M. Ostermann, N. Selby, C. Sharpe, M. Willicombe, L. Houchen-Wolloff, J. Bunker, R. Gill, C. Hastie, R. Nathu, N. Rogers, N. Smith, A. Shaw, L. Armstrong, B. Hairsine, H. Henson, C. Kurasz, L. Shenton, S. Fairbairn, A. Dell, N. Hawkings, J. Haworth, M. Hoare, A. Lucey, V. Lewis, G. Mallison, H. Nassa, C. Pennington, A. Price, C. Price, A. Storrie, G. Willis, S. Young, K. Chong-James, C. David, W.Y. James, A. Martineau, O. Zongo, A. Sanderson, V. Brown, T. Craig, S. Drain, B. King, N. Magee, D. McAulay, E. Major, J. McGinness, R. Stone, A. Haggar, A. Bolger, F. Davies, J. Lewis, A. Lloyd, R. Manley, E. McIvor, D. Menzies, K. Roberts, W. Saxon, D. Southern, C. Subbe, V. Whitehead, H. El-Taweel, J. Dawson, L. Robinson, D. Saralaya, L. Brear, K. Regan, K. Storton, J. Fuld, A. Bermperi, I. Cruz, K. Dempsey, A. Elmer, H. Jones, S. Jose, S. Marciniak, C. Ribeiro, J. Taylor, L. Watson, J. Worsley, R. Sabit, L. Broad, A. Buttress, T. Evans, M. Haynes, L. Jones, L. Knibbs, A. McQueen, C. Oliver, K. Paradowski, J. Williams, E. Harris, C. Sampson, C. Lynch, E. Davies, C. Evenden, A. Hancock, K. Hancock, M. Rees, L. Roche, N. Stroud, T. Thomas-Woods, M. Babores, J. Bradley-Potts, M. Holland, N. Keenan, S. Shashaa, H. Wassall, E. Beranova, H. Weston, T. Cosier, L. Austin, J. Deery, T. Hazelton, H. Ramos, R. Solly, S. Turney, L. Pearce, W. McCormick, S. Pugmire, W. Stoker, A. Wilson, L.A. Aguilar Jimenez, G. Arbane, S. Betts, K. Bisnauthsing, A. Dewar, G. Kaltsakas, H. Kerslake, M.M. Magtoto, P. Marino, L.M. Martinez, T.S. Solano, E. Wynn, W. Storrar, M. Alvarez Corral, A. Arias, E. Bevan, D. Griffin, J. Martin, J. Owen, S. Payne, A. Prabhu, A. Reed, C. Wrey Brown, T. Burdett, J. Featherstone, A. Layton, C. Mills, L. Stephenson, N. Easom, P. Atkin, K. Brindle, M.G. Crooks, K. Drury, R. Flockton, L. Holdsworth, A. Richards, D.L. Sykes, S. Thackray-Nocera, C. Wright, K.E. Lewis, A. Mohamed, G. Ross, S. Coetzee, K. Davies, R. Hughes, R. Loosley, L. O'Brien, Z. Omar, H. McGuinness, E. Perkins, J. Phipps, A. Taylor, H. Tench, R. Wolf-Roberts, O. Kon, D.C. Thomas, S. Anifowose, L. Burden, E. Calvelo, B. Card, C. Carr, D. Copeland, P. Cullinan, P. Daly, L. Evison, T. Fayzan, H. Gordon, S. Haq, C. King, K. March, M. Mariveles, L. McLeavey, N. Mohamed, S. Moriera, U. Munawar, J. Nunag, U. Nwanguma, L. Orriss-Dib, A. Ross, M. Roy, E. Russell, K. Samuel, J. Schronce, N. Simpson, L. Tarusan, C. Wood, N. Yasmin, R. Reddy, A.-M. Guerdette, M. Hewitt, K. Warwick, S. White, A.M. Shah, C.J. Jolley, O. Adeyemi, R. Adrego, H. Assefa-Kebede, J. Breeze, S. Byrne, P. Dulawan, A. Hayday, A. Hoare, A. Knighton, M. Malim, S. Patale, I. Peralta, N. Powell, A. Ramos, K. Shevket, F. Speranza, A. Te, A. Ashworth, J. Clarke, C. Coupland, M. Dalton, E. Wade, C. Favager, J. Greenwood, J. Glossop, L. Hall, T. Hardy, A. Humphries, J. Murira, J. Rangeley, G. Saalmink, B. Whittam, N. Window, J. Woods, G. Coakley, L. Allerton, A. Berridge, J. Brown, S. Cooper, A. Cross, S.L. Dobson, J. Earley, K. Hainey, J. Hawkes, V. Highett, S. Kaprowska, A.L. Key, S. Koprowska, N. Lewis-Burke, G. Madzamba, F. Malein, S. Marsh, C. Mears, L. Melling, M.J. Noonan, L. Poll, J. Pratt, E. Richardson, A. Rowe, K.A. Tripp, B. Vinson, L.O. Wajero, S.A. Williams-Howard, J. Wyles, S.N. Diwanji, P. Papineni, S. Gurram, S. Quaid, G.F. Tiongson, E. Watson, B. Al-Sheklly, C. Avram, P. Barran, J. Blaikely, N. Choudhury, D. Faluyi, T. Felton, T. Gorsuch, Z. Kausar, N. Odell, R. Osbourne, K. Radhakrishnan, S. Stockdale, D. Trivedi, A. Ayoub, G. Burns, G. Davies, H. Fisher, C. Francis, A. Greenhalgh, P. Hogarth, J. Hughes, K. Jiwa, G. Jones, G. MacGowan, D. Price, H. Tedd, S. Thomas, S. West, S. Wright, A. Young, M.J. McMahon, P. Neill, D. Anderson, H. Bayes, D. Grieve, I.B. McInnes, A. Brown, A. Dougherty, K. Fallon, L. Gilmour, K. Mangion, A. Morrow, K. Scott, R. Sykes, R. Touyz, E.K. Sage, F. Barrett, A. Donaldson, M. Patel, D. Bell, R. Hamil, K. Leitch, L. Macliver, J. Quigley, A. Smith, B. Welsh, G. Choudhury, S. Clohisey, A. Deans, J. Furniss, S. Kelly, D.E. Newby, D. Connell, A. Elliott, C. Deas, S. Mohammed, J. Rowland, A.R. Solstice, D. Sutherland, C.J. Tee, D. Arnold, S. Barrett, H. Adamali, A. Dipper, S. Dunn, A. Morley, L. Morrison, L. Stadon, S. Waterson, H. Welch, B. Jayaraman, T. Light, P. Almeida, J. Bonnington, M. Chrystal, C. Dupont, A. Gupta, L. Howard, W. Jang, S. Linford, L. Matthews, R. Needham, A. Nikolaidis, S. Prosper, K. Shaw, A.K. Thomas, N.M. Rahman, M. Ainsworth, A. Alamoudi, A. Bates, A. Bloss, A. Burns, P. Carter, M. Cassar, K.M. Channon, J. Chen, F. Conneh, T. Dong, R.I. Evans, E. Fraser, X. Fu, M. Havinden-Williams, N. Kanellakis, P. Kurupati, X. Li, C. Megson, K. Motohashi, D. Nicoll, G. Ogg, E. Pacpaco, M. Pavlides, Y. Peng, N. Petousi, J. Propescu, N. Rahman, N. Talbot, E. Tunnicliffe, B. Patel, R.E. Barker, D. Cristiano, N. Dormand, M. Gummadi, S. Kon, K. Liyanage, C.M. Nolan, S. Patel, O. Polgar, P. Shah, J.A. Walsh, H. Jarvis, S. Mandal, S. Ahmad, S. Brill, L. Lim, D. Matila, O. Olaosebikan, C. Singh, L. Garner, C. Johnson, J. Mackie, A. Michael, J. Pack, K. Paques, H. Parfrey, J. Parmar, N. Diar Bakerly, P. Dark, D. Evans, E. Hardy, A. Harvey, D. Holgate, S. Knight, N. Mairs, N. Majeed, L. McMorrow, J. Oxton, J. Pendlebury, C. Summersgill, R. Ugwuoke, S. Whittaker, W. Matimba-Mupaya, S. Strong-Sheldrake, J. Bagshaw, M. Begum, K. Birchall, R. Butcher, H. Carborn, F. Chan, K. Chapman, Y. Cheng, L. Chetham, C. Clark, Z. Coburn, J. Cole, M. Dixon, A. Fairman, J. Finnigan, H. Foot, D. Foote, A. Ford, R. Gregory, K. Harrington, L. Haslam, L. Hesselden, J. Hockridge, A. Holbourn, B. Holroyd-Hind, L. Holt, A. Howell, E. Hurditch, F. Ilyas, C. Jarman, E. Lee, J.-H. Lee, R. Lenagh, A. Lye, I. Macharia, M. Marshall, A. Mbuyisa, J. McNeill, S. Megson, J. Meiring, L. Milner, S. Misra, H. Newell, T. Newman, C. Norman, L. Nwafor, D. Pattenadk, M. Plowright, P. Ravencroft, C. Roddis, J. Rodger, P. Saunders, J. Sidebottom, J. Smith, L. Smith, N. Steele, G. Stephens, R. Stimpson, B. Thamu, N. Tinker, K. Turner, H. Turton, P. Wade, J. Watson, I. Wilson, A. Zawia, M. Ali, A. Dunleavy, N. Msimanga, M. Mencias, T. Samakomva, S. Siddique, J. Teixeira, V. Tavoukjian, J. Hutchinson, L. Allsop, K. Bennett, P. Buckley, M. Flynn, M. Gill, C. Goodwin, M. Greatorex, H. Gregory, C. Heeley, L. Holloway, M. Holmes, J. Kirk, W. Lovegrove, T.A. Sewell, S. Shelton, D. Sissons, K. Slack, S. Smith, D. Sowter, S. Turner, V. Whitworth, I. Wynter, L. Warburton, S. Painter, J. Tomlinson, C. Vickers, T. Wainwright, D. Redwood, J. Tilley, S. Palmer, G.A. Davies, L. Connor, A. Cook, T. Rees, F. Thaivalappil, C. Thomas, A. Butt, M. Coulding, S. Kilroy, J. McCormick, J. McIntosh, H. Savill, V. Turner, J. Vere, E. Fraile, J. Ugoji, S.S. Kon, H. Lota, G. Landers, M. Nasseri, S. Portukhay, A. Hormis, A. Daniels, J. Ingham, L. Zeidan, M. Chablani, L. Osborne, N. Ahwireng, B. Bang, D. Basire, A. Checkley, R. Evans, M. Heightman, T. Hillman, S. Janes, R. Jastrub, M. Lipman, S. Logan, M. Merida Morillas, H. Plant, J.C. Porter, K. Roy, E. Wall, B. Williams, N. Ahmad Haider, C. Atkin, R. Baggott, M. Bates, A. Botkai, A. Casey, B. Cooper, J. Dasgin, K. Draxlbauer, N. Gautam, J. Hazeldine, T. Hiwot, S. Holden, K. Isaacs, V. Kamwa, D. Lewis, S. Madathil, C. McGhee, K. Mcgee, A. Neal, A. Newton Cox, J. Nyaboko, Z. Peterkin, H. Qureshi, L. Ratcliffe, J. Short, T. Soulsby, J. Stockley, Z. Suleiman, T. Thompson, M. Ventura, S. Walder, C. Welch, S. Yasmin, K.P. Yip, P. Beckett, C. Dickens, U. Nanda, M. Aljaroof, H. Arnold, H. Aung, M. Bakali, M. Bakau, M. Bingham, M. Bourne, C. Bourne, P. Cairns, L. Carr, A. Charalambou, C. Christie, S. Diver, S. Edwards, H. Evans, J. Finch, S. Glover, N. Goodman, B. Gootpu, K. Hadley, P. Haldar, W. Ibrahim, L. Ingram, A. Lea, D. Lee, P. McCourt, T. Mcnally, A. Moss, W. Monteiro, M. Pareek, S. Parker, A. Rowland, A. Prickett, I.N. Qureshi, R. Russell, N. Samani, M. Sharma, J. Skeemer, M. Soares, E. Stringer, T. Thornton, M. Tobin, E. Turner, T.J.C. Ward, F. Woodhead, J. Wormleighton, A. Yousuf, C. Childs, S. Fletcher, M. Harvey, E. Marouzet, B. Marshall, R. Samuel, T. Sass, T. Wallis, H. Wheeler, R. Dharmagunawardena, E. Bright, P. Crisp, M. Stern, A. Wight, L. Bailey, A. Reddington, A. Ashish, J. Cooper, E. Robinson, A. Broadley, K. Howard, L. Barman, C. Brookes, K. Elliott, L. Griffiths, Z. Guy, D. Ionita, H. Redfearn, C. Sarginson, A. Turnbull, K. Holmes, and K. Lewis

Subjects
Diabetes, Covid-19, Long Covid, Medicine (General), R5-920
Abstract

Summary: Background: People with diabetes are at increased risk of hospitalisation, morbidity, and mortality following SARS-CoV-2 infection. Long-term outcomes for people with diabetes previously hospitalised with COVID-19 are, however, unknown. This study aimed to determine the longer-term physical and mental health effects of COVID-19 in people with and without diabetes. Methods: The PHOSP-COVID study is a multicentre, long-term follow-up study of adults discharged from hospital between 1 February 2020 and 31 March 2021 in the UK following COVID-19, involving detailed assessment at 5 and 12 months after discharge. The association between diabetes status and outcomes were explored using multivariable linear and logistic regressions. Findings: People with diabetes who survived hospital admission with COVID-19 display worse physical outcomes compared to those without diabetes at 5- and 12-month follow-up. People with diabetes displayed higher fatigue (only at 5 months), frailty, lower physical performance, and health-related quality of life and poorer cognitive function. Differences in outcomes between diabetes status groups were largely consistent from 5 to 12-months. In regression models, differences at 5 and 12 months were attenuated after adjustment for BMI and presence of other long-term conditions. Interpretation: People with diabetes reported worse physical outcomes up to 12 months after hospital discharge with COVID-19 compared to those without diabetes. These data support the need to reduce inequalities in long-term physical and mental health effects of SARS-CoV-2 infection in people with diabetes. Funding: UK Research and Innovation and National Institute for Health Research. The study was approved by the Leeds West Research Ethics Committee (20/YH/0225) and is registered on the ISRCTN Registry (ISRCTN10980107).

Published
2025
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30. Enhanced proteomic profiling of human plasma‐derived extracellular vesicles through charge‐based fractionation to advance biomarker discovery potential

Author

Xianyi Su, Getúlio Pereira de Oliveira Júnior, Anne‐Lise Marie, Michal Gregus, Amanda Figueroa‐Navedo, Ionita C. Ghiran, and Alexander R. Ivanov

Subjects
EV sub‐populations, extracellular vesicles, ion exchange chromatography, prostate cancer diagnostics, proteomics, Cytology, QH573-671
Abstract

Abstract The study introduces a charge‐based fractionation method for fractionating plasma‐derived extracellular vesicles (EVs) into sub‐populations aimed at the improved purification from free plasma proteins to enhance the diagnostic potential of EV sub‐populations for specific pathophysiological states. Here, we present a novel approach for EV fractionation that leverages EVs’ inherent surface charges, differentiating them from other plasma components and, thus, reducing the sample complexity and increasing the purity of EVs. The developed method was optimized and thoroughly evaluated using proteomic analysis, transmission electron microscopy, nanoparticle tracking, and western blotting of isolated EVs from healthy donors. Subsequently, we pilot‐tested the developed technique for its applicability to real‐world specimens using a small set of clinical prostate cancer samples and matched controls. The presented technique demonstrates the effective isolation and fractionation of EV sub‐populations based on their surface charge, which may potentially help enhance EV‐based diagnostics, biomarker discovery, and basic biology research. The method is designed to be straightforward, scalable, easy‐to‐use, and it does not require specialized skills or equipment.

Published
2024
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32. exRNA-eCLIP intersection analysis reveals a map of extracellular RNA binding proteins and associated RNAs across major human biofluids and carriers

Author

LaPlante, Emily L, Stürchler, Alessandra, Fullem, Robert, Chen, David, Starner, Anne C, Esquivel, Emmanuel, Alsop, Eric, Jackson, Andrew R, Ghiran, Ionita, Pereira, Getulio, Rozowsky, Joel, Chang, Justin, Gerstein, Mark B, Alexander, Roger P, Roth, Matthew E, Franklin, Jeffrey L, Coffey, Robert J, Raffai, Robert L, Mansuy, Isabelle M, Stavrakis, Stavros, deMello, Andrew J, Laurent, Louise C, Wang, Yi-Ting, Tsai, Chia-Feng, Liu, Tao, Jones, Jennifer, Van Keuren-Jensen, Kendall, Van Nostrand, Eric, Mateescu, Bogdan, and Milosavljevic, Aleksandar

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, Biotechnology, Underpinning research, 1.1 Normal biological development and functioning, Generic health relevance, NIH ERCC, RNA binding proteins, RNA footprint correlation, cell-free RNAs, cell-free biomarkers, eCLIP, exRNA carriers, human biofluids, liquid biopsies, public resource
Abstract

Although the role of RNA binding proteins (RBPs) in extracellular RNA (exRNA) biology is well established, their exRNA cargo and distribution across biofluids are largely unknown. To address this gap, we extend the exRNA Atlas resource by mapping exRNAs carried by extracellular RBPs (exRBPs). This map was developed through an integrative analysis of ENCODE enhanced crosslinking and immunoprecipitation (eCLIP) data (150 RBPs) and human exRNA profiles (6,930 samples). Computational analysis and experimental validation identified exRBPs in plasma, serum, saliva, urine, cerebrospinal fluid, and cell-culture-conditioned medium. exRBPs carry exRNA transcripts from small non-coding RNA biotypes, including microRNA (miRNA), piRNA, tRNA, small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), Y RNA, and lncRNA, as well as protein-coding mRNA fragments. Computational deconvolution of exRBP RNA cargo reveals associations of exRBPs with extracellular vesicles, lipoproteins, and ribonucleoproteins across human biofluids. Overall, we mapped the distribution of exRBPs across human biofluids, presenting a resource for the community.

Published
2023

41. A unified quantile framework for nonlinear heterogeneous transcriptome-wide associations

Author

Wang, Tianying, Ionita-Laza, Iuliana, and Wei, Ying

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Transcriptome-wide association studies (TWAS) are powerful tools for identifying gene-level associations by integrating genome-wide association studies and gene expression data. However, most TWAS methods focus on linear associations between genes and traits, ignoring the complex nonlinear relationships that may be present in biological systems. To address this limitation, we propose a novel framework, QTWAS, which integrates a quantile-based gene expression model into the TWAS model, allowing for the discovery of nonlinear and heterogeneous gene-trait associations. Via comprehensive simulations and applications to both continuous and binary traits, we demonstrate that the proposed model is more powerful than conventional TWAS in identifying gene-trait associations.

Published
2022

42. Current challenges and future directions for engineering extracellular vesicles for heart, lung, blood and sleep diseases

Author

Li, Guoping, Chen, Tianji, Dahlman, James, Eniola‐Adefeso, Lola, Ghiran, Ionita C, Kurre, Peter, Lam, Wilbur A, Lang, Jennifer K, Marbán, Eduardo, Martín, Pilar, Momma, Stefan, Moos, Malcolm, Nelson, Deborah J, Raffai, Robert L, Ren, Xi, Sluijter, Joost PG, Stott, Shannon L, Vunjak‐Novakovic, Gordana, Walker, Nykia D, Wang, Zhenjia, Witwer, Kenneth W, Yang, Phillip C, Lundberg, Martha S, Ochocinska, Margaret J, Wong, Renee, Zhou, Guofei, Chan, Stephen Y, Das, Saumya, and Sundd, Prithu

Subjects
Biochemistry and Cell Biology, Biological Sciences, Prevention, Sleep Research, Good Health and Well Being, United States, Extracellular Vesicles, Cell Communication, Nucleic Acids, Lung, Sleep, Heart, lung, blood and sleep (HLBS) diseases, extracellular vesicles, therapeutics and diagnostics, Heart, lung, blood and sleep (HLBS) diseases, Biochemistry and cell biology
Abstract

Extracellular vesicles (EVs) carry diverse bioactive components including nucleic acids, proteins, lipids and metabolites that play versatile roles in intercellular and interorgan communication. The capability to modulate their stability, tissue-specific targeting and cargo render EVs as promising nanotherapeutics for treating heart, lung, blood and sleep (HLBS) diseases. However, current limitations in large-scale manufacturing of therapeutic-grade EVs, and knowledge gaps in EV biogenesis and heterogeneity pose significant challenges in their clinical application as diagnostics or therapeutics for HLBS diseases. To address these challenges, a strategic workshop with multidisciplinary experts in EV biology and U.S. Food and Drug Administration (USFDA) officials was convened by the National Heart, Lung and Blood Institute. The presentations and discussions were focused on summarizing the current state of science and technology for engineering therapeutic EVs for HLBS diseases, identifying critical knowledge gaps and regulatory challenges and suggesting potential solutions to promulgate translation of therapeutic EVs to the clinic. Benchmarks to meet the critical quality attributes set by the USFDA for other cell-based therapeutics were discussed. Development of novel strategies and approaches for scaling-up EV production and the quality control/quality analysis (QC/QA) of EV-based therapeutics were recognized as the necessary milestones for future investigations.

Published
2023

43. Radiological Society of North America (RSNA) 3D Printing Special Interest Group (SIG) clinical situations for which 3D printing is considered an appropriate representation or extension of data contained in a medical imaging examination: breast conditions

Author

Arribas, Elsa M, Kelil, Tatiana, Santiago, Lumarie, Ali, Arafat, Chadalavada, Seetharam C, Chepelev, Leonid, Ghodadra, Anish, Ionita, Ciprian N, Lee, Joonhyuk, Ravi, Prashanth, Ryan, Justin R, Sheikh, Adnan M, Rybicki, Frank J, and Ballard, David H

Subjects
Engineering, Biomedical Engineering, Breast Cancer, Cancer, Good Health and Well Being, RSNA Special Interest 3D Printing Breast Conditions Voting Group, Biomedical engineering
Abstract

The use of medical 3D printing has expanded dramatically for breast diseases. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (SIG) provides updated appropriateness criteria for breast 3D printing in various clinical scenarios. Evidence-based appropriateness criteria are provided for the following clinical scenarios: benign breast lesions and high-risk breast lesions, breast cancer, breast reconstruction, and breast radiation (treatment planning and radiation delivery).

Published
2023

44. Predicting the Global Impact of Authors from the Learning Analytics Community--A Case Study Grounded in CNA

Author

Ionita, Remus Florentin, Dascalu, Mihai, Corlatescu, Dragos-Georgian, and McNamara, Danielle S

Abstract

Exploring new or emerging research domains or subdomains can become overwhelming due to the magnitude of available resources and the high speed at which articles are published. As such, a tool that curates the information and underlines central entities, both authors and articles from a given research context, is highly desirable. Starting from the articles of the International Conference of Learning Analytics & Knowledge (LAK) in its first decade, this paper proposes a novel method grounded in Cohesion Network Analysis (CNA) to analyze subcommunities of authors based on the semantic similarities between authors and papers, and estimate their global impact. Paper abstracts are represented as embeddings using a fine-tuned SciBERT language model, alongside a custom trained LSA model. The extrapolation between the local LAK community to a worldwide importance was also underlined by the comparison between the rankings obtained from our method and statistics from ResearchGate. The accuracies for binary classifications in terms of high/low impact predictions were around 70% for authors, and around 80% for articles. Our method can guide researchers by providing valuable information on the interactions between the members of a knowledge community and by highlighting central local authors who may potentially have a high global impact.

Published
2021
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48. Climate change and occupational health and safety. Risk of injuries, productivity loss and the co-benefits perspective

Author

Ariani, Filippo, Binazzi, Alessandra, Bogi, Andrea, Bonafede, Michela, Buccelli, Raimondo, Crisci, Alfonso, de’Donato, Francesca, De Francesco, Fabio, Del Ferraro, Simona, Galzerano, Pina, Gariazzo, Claudio, Ghelfi, Rino, Giliberti, Claudia, Gozzini, Bernardo, Grasso, Valentina, Grifoni, Daniele, Guerri, Giulia, Giuli, Emanuela, Ionita, Giulia, Levi, Miriam, Marinaccio, Alessandro, Martini, Agnese, Messeri, Alessandro, Michelozzi, Paola, Molinaro, Vincenzo, Monti, Stefano, Morabito, Marco, Moschetto, Antonio, Pasi, Francesco, Picciolo, Francesco, Pietrafesa, Emma, Raggi, Meri, Secci, Stefano, Stacchini, Nicola, Sorrentino, Edvige, Talini, Donatella, Targetti, Stefano, Viaggi, Daniele, Marinaccio, A., Gariazzo, C., Taiano, L., Bonafede, M., Martini, D., D'Amario, S., de’Donato, F., and Morabito, M.

Published
2025
Full Text
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