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1,525 results on '"Hodson, A. J."'

1. Quantifying fault tolerant simulation of strongly correlated systems using the Fermi-Hubbard model

Author

Agrawal, Anjali A., Job, Joshua, Wilson, Tyler L., Saadatmand, S. N., Hodson, Mark J., Mutus, Josh Y., Caesura, Athena, Johnson, Peter D., Elenewski, Justin E., Morrell, Kaitlyn J., and Kemper, Alexander F.

Subjects
Quantum Physics, Condensed Matter - Strongly Correlated Electrons
Abstract

Understanding the physics of strongly correlated materials is one of the grand challenge problems for physics today. A large class of scientifically interesting materials, from high-$T_c$ superconductors to spin liquids, involve medium to strong correlations, and building a holistic understanding of these materials is critical. Doing so is hindered by the competition between the kinetic energy and Coulomb repulsion, which renders both analytic and numerical methods unsatisfactory for describing interacting materials. Fault-tolerant quantum computers have been proposed as a path forward to overcome these difficulties, but this potential capability has not yet been fully assessed. Here, using the multi-orbital Fermi-Hubbard model as a representative model and a source of scalable problem specifications, we estimate the resource costs needed to use fault-tolerant quantum computers for obtaining experimentally relevant quantities such as correlation function estimation. We find that advances in quantum algorithms and hardware will be needed in order to reduce quantum resources and feasibly address utility-scale problem instances.

Published
2024

2. Fault-tolerant resource estimation using graph-state compilation on a modular superconducting architecture

Author

Saadatmand, S. N., Wilson, Tyler L., Field, Mark, Vijayan, Madhav Krishnan, Le, Thinh P., Ruh, Jannis, Maan, Arshpreet Singh, Moflic, Ioana, Caesura, Athena, Paler, Alexandru, Hodson, Mark J., Devitt, Simon J., and Mutus, Josh Y.

Subjects
Quantum Physics
Abstract

The development of fault-tolerant quantum computers (FTQCs) is gaining increased attention within the quantum computing community. Like their digital counterparts, FTQCs, equipped with error correction and large qubit numbers, promise to solve some of humanity's grand challenges. Estimates of the resource requirements for future FTQC systems are essential to making design choices and prioritizing R&D efforts to develop critical technologies. Here, we present a resource estimation framework and software tool that estimates the physical resources required to execute specific quantum algorithms, compiled into their graph-state form, and laid out onto a modular superconducting hardware architecture. This tool can predict the size, power consumption, and execution time of these algorithms at as they approach utility-scale according to explicit assumptions about the system's physical layout, thermal load, and modular connectivity. We use this tool to study the total resources on a proposed modular architecture and the impact of tradeoffs between and inter-module connectivity, latency and resource requirements.

Published
2024

5. Benchmarking Quantum Optimization for the Maximum-Cut Problem on a Superconducting Quantum Computer

Author

Dupont, Maxime, Sundar, Bhuvanesh, Evert, Bram, Neira, David E. Bernal, Peng, Zedong, Jeffrey, Stephen, and Hodson, Mark J.

Subjects
Quantum Physics
Abstract

Achieving high-quality solutions faster than classical solvers on computationally hard problems is a challenge for quantum optimization to deliver utility. Using a superconducting quantum computer, we experimentally investigate the performance of a hybrid quantum-classical algorithm inspired by semidefinite programming approaches for solving the maximum-cut problem on 3-regular graphs up to several thousand variables. We leverage the structure of the input problems to address sizes beyond what current quantum machines can naively handle. We attain an average approximation ratio of 99% over a random ensemble of thousands of problem instances. We benchmark the quantum solver against similarly high-performing classical heuristics, including the Gurobi optimizer, simulated annealing, and the Burer-Monteiro algorithm. A run-time analysis shows that the quantum solver on large-scale problems is competitive against Gurobi but short of others on a projected 100-qubit quantum computer. We explore multiple leads to close the gap and discuss prospects for a practical quantum speedup., Comment: 35 pages, 26 figures

Published
2024
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6. A neuronal circuit driven by GLP-1 in the olfactory bulb regulates insulin secretion

Author

Montaner, Mireia, Denom, Jessica, Simon, Vincent, Jiang, Wanqing, Holt, Marie K., Brierley, Daniel I., Rouch, Claude, Foppen, Ewout, Kassis, Nadim, Jarriault, David, Khan, Dawood, Eygret, Louise, Mifsud, Francois, Hodson, David J., Broichhagen, Johannes, Van Oudenhove, Lukas, Fioramonti, Xavier, Gault, Victor, Cota, Daniela, Reimann, Frank, Gribble, Fiona M., Migrenne-Li, Stephanie, Trapp, Stefan, Gurden, Hirac, and Magnan, Christophe

Published
2024
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7. Quantum-Enhanced Greedy Combinatorial Optimization Solver

Author

Dupont, Maxime, Evert, Bram, Hodson, Mark J., Sundar, Bhuvanesh, Jeffrey, Stephen, Yamaguchi, Yuki, Feng, Dennis, Maciejewski, Filip B., Hadfield, Stuart, Alam, M. Sohaib, Wang, Zhihui, Grabbe, Shon, Lott, P. Aaron, Rieffel, Eleanor G., Venturelli, Davide, and Reagor, Matthew J.

Subjects
Quantum Physics
Abstract

Combinatorial optimization is a broadly attractive area for potential quantum advantage, but no quantum algorithm has yet made the leap. Noise in quantum hardware remains a challenge, and more sophisticated quantum-classical algorithms are required to bolster their performance. Here, we introduce an iterative quantum heuristic optimization algorithm to solve combinatorial optimization problems. The quantum algorithm reduces to a classical greedy algorithm in the presence of strong noise. We implement the quantum algorithm on a programmable superconducting quantum system using up to 72 qubits for solving paradigmatic Sherrington-Kirkpatrick Ising spin glass problems. We find the quantum algorithm systematically outperforms its classical greedy counterpart, signaling a quantum enhancement. Moreover, we observe an absolute performance comparable with a state-of-the-art semidefinite programming method. Classical simulations of the algorithm illustrate that a key challenge to reaching quantum advantage remains improving the quantum device characteristics., Comment: 9 pages, 5 figures (+ 12 pages, 11 figures)

Published
2023
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9. An entanglement perspective on the quantum approximate optimization algorithm

Author

Dupont, Maxime, Didier, Nicolas, Hodson, Mark J., Moore, Joel E., and Reagor, Matthew J.

Subjects
Quantum Physics
Abstract

Many quantum algorithms seek to output a specific bitstring solving the problem of interest--or a few if the solution is degenerate. It is the case for the quantum approximate optimization algorithm (QAOA) in the limit of large circuit depth, which aims to solve quadratic unconstrained binary optimization problems. Hence, the expected final state for these algorithms is either a product state or a low-entangled superposition involving a few bitstrings. What happens in between the initial $N$-qubit product state $\vert 0\rangle^{\otimes N}$ and the final one regarding entanglement? Here, we consider the QAOA algorithm for solving the paradigmatic Max-Cut problem on different types of graphs. We study the entanglement growth and spread resulting from randomized and optimized QAOA circuits and find that there is a volume-law entanglement barrier between the initial and final states. We also investigate the entanglement spectrum in connection with random matrix theory. In addition, we compare the entanglement production with a quantum annealing protocol aiming to solve the same Max-Cut problems. Finally, we discuss the implications of our results for the simulation of QAOA circuits with tensor network-based methods relying on low-entanglement for efficiency, such as matrix product states., Comment: 12 pages, 7 figures

Published
2022
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10. Calibrating the Classical Hardness of the Quantum Approximate Optimization Algorithm

Author

Dupont, Maxime, Didier, Nicolas, Hodson, Mark J., Moore, Joel E., and Reagor, Matthew J.

Subjects
Quantum Physics
Abstract

Trading fidelity for scale enables approximate classical simulators such as matrix product states (MPS) to run quantum circuits beyond exact methods. A control parameter, the so-called bond dimension $\chi$ for MPS, governs the allocated computational resources and the output fidelity. Here, we characterize the fidelity for the quantum approximate optimization algorithm by the expectation value of the cost function it seeks to minimize and find that it follows a scaling law $F\bigl(\ln\chi\bigr/N\bigr)$ with $N$ the number of qubits. With $\ln\chi$ amounting to the entanglement that an MPS can encode, we show that the relevant variable for investigating the fidelity is the entanglement per qubit. Importantly, our results calibrate the classical computational power required to achieve the desired fidelity and benchmark the performance of quantum hardware in a realistic setup. For instance, we quantify the hardness of performing better classically than a noisy superconducting quantum processor by readily matching its output to the scaling function. Moreover, we relate the global fidelity to that of individual operations and establish its relationship with $\chi$ and $N$. We sharpen the requirements for noisy quantum computers to outperform classical techniques at running a quantum optimization algorithm in speed, size, and fidelity., Comment: 13 pages, 11 figures

Published
2022
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11. The influence of spatial configuration in collective transitions: the importance of being sorted

Author

Galvis, Daniel, Hodson, David J., and Wedgwood, Kyle C. A.

Subjects
Quantitative Biology - Quantitative Methods, 37N25
Abstract

We studied the effects of spatial configuration on collective dynamics in a nearest-neighbour and diffusively coupled lattice of heterogeneous nodes. The networks contained nodes from two populations, which differed in their intrinsic excitability. Initially, these populations were uniformly and randomly distributed throughout the lattice. We then developed an iterative algorithm for perturbing the arrangement of the network such that nodes from the same population were increasingly likely to be adjacent to one another. We found that the global input strength, or network drive, necessary to transition the network from a state of quiescence to a state of synchronised and oscillatory activity was decreased as network sortedness was increased. Moreover, for weak coupling, we found that regimes of partial synchronisation exist (i.e., 2:1 resonance in the activity of the two populations), which were dependent both on network drive (sometimes in a non-monotonic fashion) and network sortedness., Comment: 13 pages, 3 figures with further 27 pages and 17 figures in Supplementary Materials (included in document)

Published
2022

12. Realization of arbitrary doubly-controlled quantum phase gates

Author

Hill, Alexander D., Hodson, Mark J., Didier, Nicolas, and Reagor, Matthew J.

Subjects
Quantum Physics
Abstract

Developing quantum computers for real-world applications requires understanding theoretical sources of quantum advantage and applying those insights to design more powerful machines. Toward that end, we introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems. By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis (CCPHASE$(\theta)$). We estimate the process fidelity for this scheme via Cycle Benchmarking of $\mathcal{F}=87.1\pm0.8\%$, higher than reference two-qubit gate decompositions. CCPHASE$(\theta)$ is anticipated to have broad experimental implications, and we report a blueprint demonstration for solving a class of binary constraint satisfaction problems whose construction is consistent with a path to quantum advantage., Comment: 12 pages, 10 figures

Published
2021

17. Response to Bruton’s tyrosine kinase inhibitors in aggressive lymphomas linked to chronic selective autophagy

Author

Phelan, James D., Scheich, Sebastian, Choi, Jaewoo, Wright, George W., Häupl, Björn, Young, Ryan M., Rieke, Sara A., Pape, Martine, Ji, Yanlong, Urlaub, Henning, Bolomsky, Arnold, Doebele, Carmen, Zindel, Alena, Wotapek, Tanja, Kasbekar, Monica, Collinge, Brett, Huang, Da Wei, Coulibaly, Zana A., Morris, Vivian M., Zhuang, Xiaoxuan, Enssle, Julius C., Yu, Xin, Xu, Weihong, Yang, Yandan, Zhao, Hong, Wang, Zhuo, Tran, Andy D., Shoemaker, Christopher J., Shevchenko, Galina, Hodson, Daniel J., Shaffer, Arthur L., III, Staudt, Louis M., and Oellerich, Thomas

Published
2024
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18. Silent hypoxaemia in COVID-19 patients.

Author

Simonson, Tatum S, Baker, Tracy L, Banzett, Robert B, Bishop, Tammie, Dempsey, Jerome A, Feldman, Jack L, Guyenet, Patrice G, Hodson, Emma J, Mitchell, Gordon S, Moya, Esteban A, Nokes, Brandon T, Orr, Jeremy E, Owens, Robert L, Poulin, Marc, Rawling, Jean M, Schmickl, Christopher N, Watters, Jyoti J, Younes, Magdy, and Malhotra, Atul

Subjects
Humans, Dyspnea, Hypoxia, COVID-19, SARS-CoV-2, dyspnoea, happy hypoxia, silent hypoxaemia, Biological Sciences, Medical and Health Sciences, Physiology
Abstract

The clinical presentation of COVID-19 due to infection with SARS-CoV-2 is highly variable with the majority of patients having mild symptoms while others develop severe respiratory failure. The reason for this variability is unclear but is in critical need of investigation. Some COVID-19 patients have been labelled with 'happy hypoxia', in which patient complaints of dyspnoea and observable signs of respiratory distress are reported to be absent. Based on ongoing debate, we highlight key respiratory and neurological components that could underlie variation in the presentation of silent hypoxaemia and define priorities for subsequent investigation.

Published
2021

20. Cytokine receptor IL27RA is an NF-κB–responsive gene involved in CD38 upregulation in multiple myeloma

Author

Brownlie, Rebecca J., Kennedy, Ruth, Wilson, Erica B., Milanovic, Maja, Taylor, Claire F., Wang, Dapeng, Davies, John R., Owston, Heather, Adams, Emma J., Stephenson, Sophie, Caeser, Rebecca, Gewurz, Benjamin E., Giannoudis, Peter V., Scuoppo, Claudio, McGonagle, Dennis, Hodson, Daniel J., Tooze, Reuben M., Doody, Gina M., Cook, Gordon, Westhead, David R., and Klein, Ulf

Published
2023
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22. Architecture of androgen receptor pathways amplifying glucagon-like peptide-1 insulinotropic action in male pancreatic β cells

Author

Xu, Weiwei, Qadir, M.M. Fahd, Nasteska, Daniela, Mota de Sa, Paula, Gorvin, Caroline M., Blandino-Rosano, Manuel, Evans, Charles R., Ho, Thuong, Potapenko, Evgeniy, Veluthakal, Rajakrishnan, Ashford, Fiona B., Bitsi, Stavroula, Fan, Jia, Bhondeley, Manika, Song, Kejing, Sure, Venkata N., Sakamuri, Siva S.V.P., Schiffer, Lina, Beatty, Wandy, Wyatt, Rachael, Frigo, Daniel E., Liu, Xiaowen, Katakam, Prasad V., Arlt, Wiebke, Buck, Jochen, Levin, Lonny R., Hu, Tony, Kolls, Jay, Burant, Charles F., Tomas, Alejandra, Merrins, Matthew J., Thurmond, Debbie C., Bernal-Mizrachi, Ernesto, Hodson, David J., and Mauvais-Jarvis, Franck

Published
2023
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23. Longitudinal expression profiling identifies a poor risk subset of patients with ABC-type diffuse large B-cell lymphoma

Author

Bewicke-Copley, Findlay, Korfi, Koorosh, Araf, Shamzah, Hodkinson, Brendan, Kumar, Emil, Cummin, Thomas, Ashton-Key, Margaret, Barrans, Sharon, van Hoppe, Suzan, Burton, Cathy, Elshiekh, Mohamed, Rule, Simon, Crosbie, Nicola, Clear, Andrew, Calaminici, Maria, Runge, Hendrik, Hills, Robert K., Scott, David W., Rimsza, Lisa M., Menon, Geetha, Sha, Chulin, Davies, John R., Nagano, Ai, Davies, Andrew, Painter, Daniel, Smith, Alexandra, Gribben, John, Naresh, Kikkeri N., Westhead, David R., Okosun, Jessica, Steele, Andrew, Hodson, Daniel J., Balasubramanian, Sriram, Johnson, Peter, Wang, Jun, and Fitzgibbon, Jude

Published
2023
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25. Myeloid cell interferon secretion restricts Zika flavivirus infection of developing and malignant human neural progenitor cells

Author

Bulstrode, Harry, Girdler, Gemma C., Gracia, Tannia, Aivazidis, Alexander, Moutsopoulos, Ilias, Young, Adam M.H., Hancock, John, He, Xiaoling, Ridley, Katherine, Xu, Zhaoyang, Stockley, John H., Finlay, John, Hallou, Clement, Fajardo, Teodoro, Fountain, Daniel M., van Dongen, Stijn, Joannides, Alexis, Morris, Robert, Mair, Richard, Watts, Colin, Santarius, Thomas, Price, Stephen J., Hutchinson, Peter J.A., Hodson, Emma J., Pollard, Steven M., Mohorianu, Irina, Barker, Roger A., Sweeney, Trevor R., Bayraktar, Omer, Gergely, Fanni, and Rowitch, David H.

Published
2022
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27. Diverse mutational landscapes in human lymphocytes

Author

Machado, Heather E., Mitchell, Emily, Øbro, Nina F., Kübler, Kirsten, Davies, Megan, Leongamornlert, Daniel, Cull, Alyssa, Maura, Francesco, Sanders, Mathijs A., Cagan, Alex T. J., McDonald, Craig, Belmonte, Miriam, Shepherd, Mairi S., Vieira Braga, Felipe A., Osborne, Robert J., Mahbubani, Krishnaa, Martincorena, Iñigo, Laurenti, Elisa, Green, Anthony R., Getz, Gad, Polak, Paz, Saeb-Parsy, Kourosh, Hodson, Daniel J., Kent, David G., and Campbell, Peter J.

Published
2022
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30. A Deep Learning Approach for the Classification of Fibroglandular Breast Density in Histology Images of Human Breast Tissue.

Author

Heydarlou, Hanieh, Hodson, Leigh J., Dorraki, Mohsen, Hickey, Theresa E., Tilley, Wayne D., Smith, Eric, Ingman, Wendy V., and Farajpour, Ali

Abstract

Simple Summary: Mammographic breast density is an important risk factor for breast cancer. Women with dense breasts have a high abundance of fibroglandular breast tissue, which can be seen on a mammogram and is associated with a greater risk of developing breast cancer. To progress research into the biological mechanisms that link mammographic density to breast cancer risk, fibroglandular density can be used as a surrogate measure. Fibroglandular density can be evaluated using thin formalin-fixed paraffin-embedded breast tissue sections stained with hematoxylin and eosin. To date, the classification of fibroglandular breast density is not automated and relies on visual assessment by researchers. Hence, this study explored the use of deep learning models to automate the classification of fibroglandular breast density. Background: To progress research into the biological mechanisms that link mammographic breast density to breast cancer risk, fibroglandular breast density can be used as a surrogate measure. This study aimed to develop a computational tool to classify fibroglandular breast density in hematoxylin and eosin (H&E)-stained breast tissue sections using deep learning approaches that would assist future mammographic density research. Methods: Four different architectural configurations of transferred MobileNet-v2 convolutional neural networks (CNNs) and four different models of vision transformers were developed and trained on a database of H&E-stained normal human breast tissue sections (965 tissue blocks from 93 patients) that had been manually classified into one of five fibroglandular density classes, with class 1 being very low fibroglandular density and class 5 being very high fibroglandular density. Results: The MobileNet-Arc 1 and ViT model 1 achieved the highest overall F1 scores of 0.93 and 0.94, respectively. Both models exhibited the lowest false positive rate and highest true positive rate in class 5, while the most challenging classification was class 3, where images from classes 2 and 4 were mistakenly classified as class 3. The area under the curves (AUCs) for all classes were higher than 0.98. Conclusions: Both the ViT and MobileNet models showed promising performance in the accurate classification of H&E-stained tissue sections across all five fibroglandular density classes, providing a rapid and easy-to-use computational tool for breast density analysis. [ABSTRACT FROM AUTHOR]

Published
2025
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31. Evolving molecular classification of aggressive B‐cell lymphoma.

Author

Alig, Stefan K, Chapuy, Björn, Ennishi, Daisuke, Dunleavy, Kieron, and Hodson, Daniel J

Subjects
NOSOLOGY, DIFFUSE large B-cell lymphomas, TUMOR microenvironment, MEDICAL research, LYMPHOMAS
Abstract

This review aims to provide an overview of the latest developments in the classification and molecular understanding of aggressive B‐cell lymphomas, specifically focusing on diffuse large B‐cell lymphoma (DLBCL) and high‐grade B‐cell lymphoma (HGBL). Advances in molecular techniques have led to novel ways to classify these lymphomas based on clinical, histological, transcriptional, and genetic properties. While these methods have predominantly focused on the malignant compartment, recent studies emphasize the value of profiling the tumour microenvironment for a more comprehensive disease classification. Additionally, the integration of liquid biopsies represents a promising advancement, offering less invasive and dynamic insights into tumour characteristics and treatment response. Although molecular profiles are not yet routinely used to guide therapy, emerging data highlight their potential to predict responses to novel treatments. It is our belief that integrating molecular profiling and liquid biopsies into clinical practice and research now will pave the way for more personalized and effective therapies in the future. [ABSTRACT FROM AUTHOR]

Published
2025
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32. Proline synthesis through PYCR1 is required to support cancer cell proliferation and survival in oxygen-limiting conditions

Author

Westbrook, Rebecca L., Bridges, Esther, Roberts, Jennie, Escribano-Gonzalez, Cristina, Eales, Katherine L., Vettore, Lisa A., Walker, Paul D., Vera-Siguenza, Elias, Rana, Himani, Cuozzo, Federica, Eskla, Kattri-Liis, Vellama, Hans, Shaaban, Abeer, Nixon, Colin, Luuk, Hendrik, Lavery, Gareth G., Hodson, David J., Harris, Adrian L., and Tennant, Daniel A.

Published
2022
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33. SHMT2 inhibition disrupts the TCF3 transcriptional survival program in Burkitt lymphoma

Author

Wilke, Anne C., Doebele, Carmen, Zindel, Alena, Lee, Kwang Seok, Rieke, Sara A., Ceribelli, Michele, Comoglio, Federico, Phelan, James D., Wang, James Q., Pikman, Yana, Jahn, Dominique, Häupl, Björn, Schneider, Constanze, Scheich, Sebastian, Tosto, Frances A., Bohnenberger, Hanibal, Stauder, Philipp, Schnütgen, Frank, Slabicki, Mikolaj, Coulibaly, Zana A., Wolf, Sebastian, Bojarczuk, Kamil, Chapuy, Björn, Brandts, Christian H., Stroebel, Philipp, Lewis, Caroline A., Engelke, Michael, Xu, Xincheng, Kim, Hahn, Dang, Thanh Hung, Schmitz, Roland, Hodson, Daniel J., Stegmaier, Kimberly, Urlaub, Henning, Serve, Hubert, Schmitt, Clemens A., Kreuz, Fernando, Knittel, Gero, Rabinowitz, Joshua D., Reinhardt, Hans Christian, Vander Heiden, Matthew G., Thomas, Craig, Staudt, Louis M., Zenz, Thorsten, and Oellerich, Thomas

Published
2022
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34. Viral transduction of primary human lymphoma B cells reveals mechanisms of NOTCH-mediated immune escape

Author

Mangolini, Maurizio, Maiques-Diaz, Alba, Charalampopoulou, Stella, Gerhard-Hartmann, Elena, Bloehdorn, Johannes, Moore, Andrew, Giachetti, Giorgia, Lu, Junyan, Roamio Franklin, Valar Nila, Chilamakuri, Chandra Sekkar Reddy, Moutsopoulos, Ilias, Rosenwald, Andreas, Stilgenbauer, Stephan, Zenz, Thorsten, Mohorianu, Irina, D’Santos, Clive, Deaglio, Silvia, Hodson, Daniel J., Martin-Subero, Jose I., and Ringshausen, Ingo

Published
2022
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39. Sequential inverse dysregulation of the RNA helicases DDX3X and DDX3Y facilitates MYC-driven lymphomagenesis

Author

Gong, Chun, Krupka, Joanna A., Gao, Jie, Grigoropoulos, Nicholas F., Giotopoulos, George, Asby, Ryan, Screen, Michael, Usheva, Zelvera, Cucco, Francesco, Barrans, Sharon, Painter, Daniel, Zaini, Nurmahirah Binte Mohammed, Haupl, Björn, Bornelöv, Susanne, Ruiz De Los Mozos, Igor, Meng, Wei, Zhou, Peixun, Blain, Alex E., Forde, Sorcha, Matthews, Jamie, Khim Tan, Michelle Guet, Burke, G.A. Amos, Sze, Siu Kwan, Beer, Philip, Burton, Cathy, Campbell, Peter, Rand, Vikki, Turner, Suzanne D., Ule, Jernej, Roman, Eve, Tooze, Reuben, Oellerich, Thomas, Huntly, Brian J., Turner, Martin, Du, Ming-Qing, Samarajiwa, Shamith A., and Hodson, Daniel J.

Published
2021
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47. The type 2 diabetes gene product STARD10 is a phosphoinositide-binding protein that controls insulin secretory granule biogenesis

Author

Carrat, Gaelle R., Haythorne, Elizabeth, Tomas, Alejandra, Haataja, Leena, Müller, Andreas, Arvan, Peter, Piunti, Alexandra, Cheng, Kaiying, Huang, Mutian, Pullen, Timothy J., Georgiadou, Eleni, Stylianides, Theodoros, Amirruddin, Nur Shabrina, Salem, Victoria, Distaso, Walter, Cakebread, Andrew, Heesom, Kate J., Lewis, Philip A., Hodson, David J., Briant, Linford J., Fung, Annie C.H., Sessions, Richard B., Alpy, Fabien, Kong, Alice P.S., Benke, Peter I., Torta, Federico, Teo, Adrian Kee Keong, Leclerc, Isabelle, Solimena, Michele, Wigley, Dale B., and Rutter, Guy A.

Published
2020
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48. PI3Kδ inhibition reshapes follicular lymphoma–immune microenvironment cross talk and unleashes the activity of venetoclax

Author

Serrat, Neus, Guerrero-Hernández, Martina, Matas-Céspedes, Alba, Yahiaoui, Anella, Valero, Juan G., Nadeu, Ferran, Clot, Guillem, Di Re, Miriam, Corbera-Bellalta, Marc, Magnano, Laura, Rivas-Delgado, Alfredo, Enjuanes, Anna, Beà, Silvia, Cid, Maria C., Campo, Elías, Montero, Joan, Hodson, Daniel J., López-Guillermo, Armando, Colomer, Dolors, Tannheimer, Stacey, and Pérez-Galán, Patricia

Published
2020
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49. Vitamin-D-Binding Protein Contributes to the Maintenance of α Cell Function and Glucagon Secretion

Author

Viloria, Katrina, Nasteska, Daniela, Briant, Linford J.B., Heising, Silke, Larner, Dean P., Fine, Nicholas H.F., Ashford, Fiona B., da Silva Xavier, Gabriela, Ramos, Maria Jiménez, Hasib, Annie, Cuozzo, Federica, Manning Fox, Jocelyn E., MacDonald, Patrick E., Akerman, Ildem, Lavery, Gareth G., Flaxman, Christine, Morgan, Noel G., Richardson, Sarah J., Hewison, Martin, and Hodson, David J.

Published
2020
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