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1. MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI

Author

Tschand, Arya, Rajan, Arun Tejusve Raghunath, Idgunji, Sachin, Ghosh, Anirban, Holleman, Jeremy, Kiraly, Csaba, Ambalkar, Pawan, Borkar, Ritika, Chukka, Ramesh, Cockrell, Trevor, Curtis, Oliver, Fursin, Grigori, Hodak, Miro, Kassa, Hiwot, Lokhmotov, Anton, Miskovic, Dejan, Pan, Yuechao, Manmathan, Manu Prasad, Raymond, Liz, John, Tom St., Suresh, Arjun, Taubitz, Rowan, Zhan, Sean, Wasson, Scott, Kanter, David, and Reddi, Vijay Janapa

Subjects
Computer Science - Hardware Architecture, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning
Abstract

Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization, but presents novel challenges due to the variety of hardware platforms, workload characteristics, and system-level interactions. This paper introduces MLPerf Power, a comprehensive benchmarking methodology with capabilities to evaluate the energy efficiency of ML systems at power levels ranging from microwatts to megawatts. Developed by a consortium of industry professionals from more than 20 organizations, MLPerf Power establishes rules and best practices to ensure comparability across diverse architectures. We use representative workloads from the MLPerf benchmark suite to collect 1,841 reproducible measurements from 60 systems across the entire range of ML deployment scales. Our analysis reveals trade-offs between performance, complexity, and energy efficiency across this wide range of systems, providing actionable insights for designing optimized ML solutions from the smallest edge devices to the largest cloud infrastructures. This work emphasizes the importance of energy efficiency as a key metric in the evaluation and comparison of the ML system, laying the foundation for future research in this critical area. We discuss the implications for developing sustainable AI solutions and standardizing energy efficiency benchmarking for ML systems., Comment: 16 pages, 11 figures, 1 table

Published
2024

2. Experience of the first adult-focussed undiagnosed disease program in Australia (AHA-UDP): solving rare and puzzling genetic disorders is ageless

Author

Wallis, Mathew, Bodek, Simon D., Munro, Jacob, Rafehi, Haloom, Bennett, Mark F., Ye, Zimeng, Schneider, Amy, Gardiner, Fiona, Valente, Giulia, Murdoch, Emma, Uebergang, Eloise, Hunter, Jacquie, Stutterd, Chloe, Huq, Aamira, Salmon, Lucinda, Scheffer, Ingrid, Eratne, Dhamidhu, Meyn, Stephen, Fong, Chun Y., John, Tom, Mullen, Saul, White, Susan M., Brown, Natasha J., McGillivray, George, Chen, Jesse, Richmond, Chris, Hughes, Andrew, Krzesinski, Emma, Fennell, Andrew, Chambers, Brian, Santoreneos, Renee, Le Fevre, Anna, Hildebrand, Michael S., Bahlo, Melanie, Christodoulou, John, Delatycki, Martin, and Berkovic, Samuel F.

Published
2024
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3. Longitudinal Analyses of Circulating Tumor DNA for the Detection of EGFR Mutation-Positive Advanced NSCLC Progression During Treatment: Data From FLAURA and AURA3

Author

Gray, Jhanelle E., Markovets, Aleksandra, Reungwetwattana, Thanyanan, Majem, Margarita, Nogami, Naoyuki, Peled, Nir, Lee, Jong-Seok, Cho, Byoung Chul, Chewaskulyong, Busayamas, John, Tom, Han, Ji-Youn, Sebastian, Martin, Todd, Alexander, Rukazenkov, Yuri, Barrett, Carl, Chmielecki, Juliann, Lee, Siow Ming, Ramalingam, Suresh S., and Hartmaier, Ryan

Published
2024
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4. MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems

Author

Farrell, Steven, Emani, Murali, Balma, Jacob, Drescher, Lukas, Drozd, Aleksandr, Fink, Andreas, Fox, Geoffrey, Kanter, David, Kurth, Thorsten, Mattson, Peter, Mu, Dawei, Ruhela, Amit, Sato, Kento, Shirahata, Koichi, Tabaru, Tsuguchika, Tsaris, Aristeidis, Balewski, Jan, Cumming, Ben, Danjo, Takumi, Domke, Jens, Fukai, Takaaki, Fukumoto, Naoto, Fukushi, Tatsuya, Gerofi, Balazs, Honda, Takumi, Imamura, Toshiyuki, Kasagi, Akihiko, Kawakami, Kentaro, Kudo, Shuhei, Kuroda, Akiyoshi, Martinasso, Maxime, Matsuoka, Satoshi, Mendonça, Henrique, Minami, Kazuki, Ram, Prabhat, Sawada, Takashi, Shankar, Mallikarjun, John, Tom St., Tabuchi, Akihiro, Vishwanath, Venkatram, Wahib, Mohamed, Yamazaki, Masafumi, and Yin, Junqi

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing systems are pushing the frontiers of performance with a rich diversity of hardware resources and massive scale-out capabilities. There is a critical need to understand fair and effective benchmarking of machine learning applications that are representative of real-world scientific use cases. MLPerf is a community-driven standard to benchmark machine learning workloads, focusing on end-to-end performance metrics. In this paper, we introduce MLPerf HPC, a benchmark suite of large-scale scientific machine learning training applications driven by the MLCommons Association. We present the results from the first submission round, including a diverse set of some of the world's largest HPC systems. We develop a systematic framework for their joint analysis and compare them in terms of data staging, algorithmic convergence, and compute performance. As a result, we gain a quantitative understanding of optimizations on different subsystems such as staging and on-node loading of data, compute-unit utilization, and communication scheduling, enabling overall $>10 \times$ (end-to-end) performance improvements through system scaling. Notably, our analysis shows a scale-dependent interplay between the dataset size, a system's memory hierarchy, and training convergence that underlines the importance of near-compute storage. To overcome the data-parallel scalability challenge at large batch sizes, we discuss specific learning techniques and hybrid data-and-model parallelism that are effective on large systems. We conclude by characterizing each benchmark with respect to low-level memory, I/O, and network behavior to parameterize extended roofline performance models in future rounds.

Published
2021

6. MLPerf Mobile Inference Benchmark

Author

Reddi, Vijay Janapa, Kanter, David, Mattson, Peter, Duke, Jared, Nguyen, Thai, Chukka, Ramesh, Shiring, Ken, Tan, Koan-Sin, Charlebois, Mark, Chou, William, El-Khamy, Mostafa, Hong, Jungwook, John, Tom St., Trinh, Cindy, Buch, Michael, Mazumder, Mark, Markovic, Relia, Atta, Thomas, Cakir, Fatih, Charkhabi, Masoud, Chen, Xiaodong, Chiang, Cheng-Ming, Dexter, Dave, Heo, Terry, Schmuelling, Gunther, Shabani, Maryam, and Zika, Dylan

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

This paper presents the first industry-standard open-source machine learning (ML) benchmark to allow perfor mance and accuracy evaluation of mobile devices with different AI chips and software stacks. The benchmark draws from the expertise of leading mobile-SoC vendors, ML-framework providers, and model producers. It comprises a suite of models that operate with standard data sets, quality metrics and run rules. We describe the design and implementation of this domain-specific ML benchmark. The current benchmark version comes as a mobile app for different computer vision and natural language processing tasks. The benchmark also supports non-smartphone devices, such as laptops and mobile PCs. Benchmark results from the first two rounds reveal the overwhelming complexity of the underlying mobile ML system stack, emphasizing the need for transparency in mobile ML performance analysis. The results also show that the strides being made all through the ML stack improve performance. Within six months, offline throughput improved by 3x, while latency reduced by as much as 12x. ML is an evolving field with changing use cases, models, data sets and quality targets. MLPerf Mobile will evolve and serve as an open-source community framework to guide research and innovation for mobile AI.

Published
2020

7. Transcriptional perturbation of protein arginine methyltransferase-5 exhibits MTAP-selective oncosuppression.

Author

Busacca, Sara, Zhang, Qi, Sharkey, Annabel, Dawson, Alan G, Moore, David A, Waller, David A, Nakas, Apostolos, Jones, Carolyn, Cain, Kelvin, Luo, Jin-Li, Salcedo, Adriana, Salaroglio, Iris Chiara, Riganti, Chiara, Le Quesne, John, John, Tom, Boutros, Paul C, Zhang, Shu-Dong, and Fennell, Dean A

Subjects
Genetics, Lung, 5.1 Pharmaceuticals, 2.1 Biological and endogenous factors
Abstract

We hypothesized that small molecule transcriptional perturbation could be harnessed to target a cellular dependency involving protein arginine methyltransferase 5 (PRMT5) in the context of methylthioadenosine phosphorylase (MTAP) deletion, seen frequently in malignant pleural mesothelioma (MPM). Here we show, that MTAP deletion is negatively prognostic in MPM. In vitro, the off-patent antibiotic Quinacrine efficiently suppressed PRMT5 transcription, causing chromatin remodelling with reduced global histone H4 symmetrical demethylation. Quinacrine phenocopied PRMT5 RNA interference and small molecule PRMT5 inhibition, reducing clonogenicity in an MTAP-dependent manner. This activity required a functional PRMT5 methyltransferase as MTAP negative cells were rescued by exogenous wild type PRMT5, but not a PRMT5E444Q methyltransferase-dead mutant. We identified c-jun as an essential PRMT5 transcription factor and a probable target for Quinacrine. Our results therefore suggest that small molecule-based transcriptional perturbation of PRMT5 can leverage a mutation-selective vulnerability, that is therapeutically tractable, and has relevance to 9p21 deleted cancers including MPM.

Published
2021

9. MLPerf Inference Benchmark

Author

Reddi, Vijay Janapa, Cheng, Christine, Kanter, David, Mattson, Peter, Schmuelling, Guenther, Wu, Carole-Jean, Anderson, Brian, Breughe, Maximilien, Charlebois, Mark, Chou, William, Chukka, Ramesh, Coleman, Cody, Davis, Sam, Deng, Pan, Diamos, Greg, Duke, Jared, Fick, Dave, Gardner, J. Scott, Hubara, Itay, Idgunji, Sachin, Jablin, Thomas B., Jiao, Jeff, John, Tom St., Kanwar, Pankaj, Lee, David, Liao, Jeffery, Lokhmotov, Anton, Massa, Francisco, Meng, Peng, Micikevicius, Paulius, Osborne, Colin, Pekhimenko, Gennady, Rajan, Arun Tejusve Raghunath, Sequeira, Dilip, Sirasao, Ashish, Sun, Fei, Tang, Hanlin, Thomson, Michael, Wei, Frank, Wu, Ephrem, Xu, Lingjie, Yamada, Koichi, Yu, Bing, Yuan, George, Zhong, Aaron, Zhang, Peizhao, and Zhou, Yuchen

Subjects
Computer Science - Machine Learning, Computer Science - Performance, Statistics - Machine Learning
Abstract

Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organizations are building ML inference chips, and the systems that incorporate existing models span at least three orders of magnitude in power consumption and five orders of magnitude in performance; they range from embedded devices to data-center solutions. Fueling the hardware are a dozen or more software frameworks and libraries. The myriad combinations of ML hardware and ML software make assessing ML-system performance in an architecture-neutral, representative, and reproducible manner challenging. There is a clear need for industry-wide standard ML benchmarking and evaluation criteria. MLPerf Inference answers that call. In this paper, we present our benchmarking method for evaluating ML inference systems. Driven by more than 30 organizations as well as more than 200 ML engineers and practitioners, MLPerf prescribes a set of rules and best practices to ensure comparability across systems with wildly differing architectures. The first call for submissions garnered more than 600 reproducible inference-performance measurements from 14 organizations, representing over 30 systems that showcase a wide range of capabilities. The submissions attest to the benchmark's flexibility and adaptability., Comment: ISCA 2020

Published
2019

10. MLPerf Training Benchmark

Author

Mattson, Peter, Cheng, Christine, Coleman, Cody, Diamos, Greg, Micikevicius, Paulius, Patterson, David, Tang, Hanlin, Wei, Gu-Yeon, Bailis, Peter, Bittorf, Victor, Brooks, David, Chen, Dehao, Dutta, Debojyoti, Gupta, Udit, Hazelwood, Kim, Hock, Andrew, Huang, Xinyuan, Ike, Atsushi, Jia, Bill, Kang, Daniel, Kanter, David, Kumar, Naveen, Liao, Jeffery, Ma, Guokai, Narayanan, Deepak, Oguntebi, Tayo, Pekhimenko, Gennady, Pentecost, Lillian, Reddi, Vijay Janapa, Robie, Taylor, John, Tom St., Tabaru, Tsuguchika, Wu, Carole-Jean, Xu, Lingjie, Yamazaki, Masafumi, Young, Cliff, and Zaharia, Matei

Subjects
Computer Science - Machine Learning, Computer Science - Performance, Statistics - Machine Learning
Abstract

Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But ML training presents three unique benchmarking challenges absent from other domains: optimizations that improve training throughput can increase the time to solution, training is stochastic and time to solution exhibits high variance, and software and hardware systems are so diverse that fair benchmarking with the same binary, code, and even hyperparameters is difficult. We therefore present MLPerf, an ML benchmark that overcomes these challenges. Our analysis quantitatively evaluates MLPerf's efficacy at driving performance and scalability improvements across two rounds of results from multiple vendors., Comment: MLSys 2020

Published
2019

13. Biomarkers for immunotherapy resistance in non-small cell lung cancer.

Author

Rother, Catriona, John, Tom, and Wong, Annie

Subjects
PROGRAMMED death-ligand 1, IMMUNE checkpoint inhibitors, NON-small-cell lung carcinoma, TUMOR markers, PATIENT experience
Abstract

Immunotherapy has revolutionised the treatment landscape of non-small cell lung cancer (NSCLC), significantly improving survival outcomes and offering renewed hope to patients with advanced disease. However, the majority of patients experience limited long-term benefits from immune checkpoint inhibition (ICI) due to the development of primary or acquired immunotherapy resistance. Accurate predictive biomarkers for immunotherapy resistance are essential for individualising treatment strategies, improving survival outcomes, and minimising potential treatment-related harm. This review discusses the mechanisms underlying resistance to immunotherapy, addressing both cancer cell-intrinsic and cancer cell-extrinsic resistance processes. We summarise the current utility and limitations of two clinically established biomarkers: programmed death ligand 1 (PD-L1) expression and tumour mutational burden (TMB). Following this, we present a comprehensive review of emerging immunotherapy biomarkers in NSCLC, including tumour neoantigens, epigenetic signatures, markers of the tumour microenvironment (TME), genomic alterations, host–microbiome composition, and circulating biomarkers. The potential clinical applications of these biomarkers, along with novel approaches to their biomarker identification and targeting, are discussed. Additionally, we explore current strategies to overcome immunotherapy resistance and propose incorporating predictive biomarkers into an adaptive clinical trial design, where specific immune signatures guide subsequent treatment selection. [ABSTRACT FROM AUTHOR]

Published
2025
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14. Subcutaneous amivantamab vs intravenous amivantamab, both in combination with lazertinib, in refractory EGFR-mutated, advanced non-small cell lung cancer (NSCLC): Primary results, including overall survival (OS), from the global, phase 3, randomized controlled PALOMA-3 trial.

Author

Leighl, Natasha B., primary, Akamatsu, Hiroaki, additional, Lim, Sun Min, additional, Cheng, Ying, additional, Minchom, Anna Rachel, additional, Marmarelis, Melina Elpi, additional, Sanborn, Rachel E., additional, Yang, James Chih-Hsin, additional, Liu, Baogang, additional, John, Tom, additional, Massuti, Bartomeu, additional, Spira, Alexander I., additional, Xie, John, additional, Ghosh, Debropriya, additional, Alhadab, Ali, additional, Verheijen, Remy B, additional, Gamil, Mohamed, additional, Bauml, Joshua Michael, additional, Baig, Mahadi, additional, and Passaro, Antonio, additional

Published
2024
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17. Longitudinal Analyses of Circulating Tumor DNA for the Detection of EGFRMutation-Positive Advanced NSCLC Progression During Treatment: Data From FLAURA and AURA3

Author

Gray, Jhanelle E., Markovets, Aleksandra, Reungwetwattana, Thanyanan, Majem, Margarita, Nogami, Naoyuki, Peled, Nir, Lee, Jong-Seok, Cho, Byoung Chul, Chewaskulyong, Busayamas, John, Tom, Han, Ji-Youn, Sebastian, Martin, Todd, Alexander, Rukazenkov, Yuri, Barrett, Carl, Chmielecki, Juliann, Lee, Siow Ming, Ramalingam, Suresh S., and Hartmaier, Ryan

Abstract

EGFR tyrosine kinase inhibitor (EGFR-TKI)-sensitizing and -resistance mutations may be detected in plasma through circulating tumor DNA (ctDNA). Circulating tumor DNA level changes reflect alterations in tumor burden and could be a dynamic indicator of treatment effect. This analysis aimed to determine whether longitudinal EGFR-mutation ctDNA testing could detect progressive disease (PD) before radiologic detection.

Published
2024
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22. Comparing Runtime Systems with Exascale Ambitions Using the Parallel Research Kernels

Author

Van der Wijngaart, Rob F., Kayi, Abdullah, Hammond, Jeff R., Jost, Gabriele, St. John, Tom, Sridharan, Srinivas, Mattson, Timothy G., Abercrombie, John, Nelson, Jacob, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Kunkel, Julian M., editor, Balaji, Pavan, editor, and Dongarra, Jack, editor

Published
2016
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24. Rehabilitation outcomes for people with lung cancer (UNITE): protocol for the development of a core outcome set

Author

Edbrooke, Lara, Granger, Catherine L, Francis, Jill J, John, Tom, Kaadan, Nasreen, Halloran, Emma, Connolly, Bronwen, and Denehy, Linda

Subjects
Lung Neoplasms, Treatment Outcome, Delphi Technique, SDG 3 - Good Health and Well-being, Research Design, Lung Cancer, Quality of Life, Humans, Pulmonary Rehabilitation, Exercise, Systematic Reviews as Topic
Abstract

Introduction: With treatment-related improvements in survival, rehabilitation is essential to improve function and health-related quality of life and manage the high symptom burden associated with lung cancer. Despite this, significant heterogeneity exists in the outcomes and instruments used to evaluate lung cancer rehabilitation programme impact. This study aims to develop a core set of clinically relevant lung cancer rehabilitation outcomes for use in clinical practice.Methods and analysis: An international Delphi consensus study involving consumer, healthcare professional and researcher stakeholders to determine which outcomes to include and how to measure these. Stage 1 (preliminary): mixed methods to develop the potential list of outcomes (1) overview of systematic reviews of lung cancer exercise interventions and (2) focus groups and individual interviews with people with lung cancer. Stage 2: outcomes were grouped according to the International Classification of Functioning, Disability and Health domains. Stage 3: to determine priority outcomes for core outcome set (COS) inclusion participants will rate each outcome’s importance (one-nine-point Likert scale) over two-three survey rounds. Stage 4: following review by the steering committee, a consensus meeting will be held if agreement on the COS has not been reached.Stage 5: recommendations will be made regarding a single instrument for measuring each COS outcome by reviewing existing resources where consensus has already been reached. Where resources do not exist the quality and feasibility of potential measurement instruments will be appraised, and the Delphi consensus survey and meeting process outlined in stages 3–4 will be repeated.

Published
2023
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26. Therapeutic misunderstandings in modern research.

Author

Heynemann, Sarah, Lipworth, Wendy, McLachlan, Sue‐Anne, Philip, Jennifer, John, Tom, and Kerridge, Ian

Subjects
OCCUPATIONAL roles, HUMAN research subjects, CLINICAL trials, PATIENT selection, RESEARCH methodology, RESEARCH ethics, INFORMED consent (Medical law), COMMUNICATION, TUMORS, BIOETHICS
Abstract

Clinical trials play a crucial role in generating evidence about healthcare interventions and improving outcomes for current and future patients. For individual trial participants, however, there are inevitably trade‐offs involved in clinical trial participation, given that trials have traditionally been designed to benefit future patient populations rather than to offer personalised care. Failure to understand the distinction between research and clinical care and the likelihood of benefit from participation in clinical trials has been termed the 'therapeutic misconception'. The evolution of the clinical trials landscape, including greater integration of clinical trials into healthcare and development of novel trial methodologies, may reinforce the significance of the therapeutic misconception and other forms of misunderstanding while at the same time (paradoxically) challenging its salience. Using cancer clinical trials as an exemplar, we describe how methodological changes in early‐ and late‐phase clinical trial designs, as well as changes in the design and delivery of healthcare, impact upon the therapeutic misconception. We suggest that this provides an impetus to re‐examine the ethics of clinical research, particularly in relation to trial access, participant selection, communication and consent, and role delineation. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Condition-specific or generic preference-based measures in oncology? A comparison of the EORTC-8D and the EQ-5D-3L

Author

Lorgelly, Paula K., Doble, Brett, Rowen, Donna, Brazier, John, Thomas, David M, Fox, Stephen B, Thorne, Heather, Parisot, John P, Doig, Ken, Fellowes, Andrew, Dobrovic, Alexander, James, Paul A, Lipton, Lara, Ashley, David, Hayes, Theresa, McMurrick, Paul, Richardson, Gary, Lorgelly, Paula, Lucas, Mark, McNeil, John J, John, Tom, and Cancer 2015 investigators

Published
2017
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32. Assessing Tumor-Infiltrating Lymphocytes in Solid Tumors: A Practical Review for Pathologists and Proposal for a Standardized Method from the International Immuno-Oncology Biomarkers Working Group: Part 2: TILs in Melanoma, Gastrointestinal Tract Carcinomas, Non–Small Cell Lung Carcinoma and Mesothelioma, Endometrial and Ovarian Carcinomas, Squamous Cell Carcinoma of the Head and Neck, Genitourinary Carcinomas, and Primary Brain Tumors

Author

Hendry, Shona, Salgado, Roberto, Gevaert, Thomas, Russell, Prudence A., John, Tom, Thapa, Bibhusal, Christie, Michael, van de Vijver, Koen, Estrada, M.V., Gonzalez-Ericsson, Paula I., Sanders, Melinda, Solomon, Benjamin, Solinas, Cinzia, Van den Eynden, Gert G.G.M., Allory, Yves, Preusser, Matthias, Hainfellner, Johannes, Pruneri, Giancarlo, Vingiani, Andrea, Demaria, Sandra, Symmans, Fraser, Nuciforo, Paolo, Comerma, Laura, Thompson, E.A., Lakhani, Sunil, Kim, Seong-Rim, Schnitt, Stuart, Colpaert, Cecile, Sotiriou, Christos, Scherer, Stefan J., Ignatiadis, Michail, Badve, Sunil, Pierce, Robert H., Viale, Giuseppe, Sirtaine, Nicolas, Penault-Llorca, Frederique, Sugie, Tomohagu, Fineberg, Susan, Paik, Soonmyung, Srinivasan, Ashok, Richardson, Andrea, Wang, Yihong, Chmielik, Ewa, Brock, Jane, Johnson, Douglas B., Balko, Justin, Wienert, Stephan, Bossuyt, Veerle, Michiels, Stefan, Ternes, Nils, Burchardi, Nicole, Luen, Stephen J., Savas, Peter, Klauschen, Frederick, Watson, Peter H., Nelson, Brad H., Criscitiello, Carmen, O’Toole, Sandra, Larsimont, Denis, de Wind, Roland, Curigliano, Giuseppe, André, Fabrice, Lacroix-Triki, Magali, van de Vijver, Mark, Rojo, Federico, Floris, Giuseppe, Bedri, Shahinaz, Sparano, Joseph, Rimm, David, Nielsen, Torsten, Kos, Zuzana, Hewitt, Stephen, Singh, Baljit, Farshid, Gelareh, Loibl, Sibylle, Allison, Kimberly H., Tung, Nadine, Adams, Sylvia, Willard-Gallo, Karen, Horlings, Hugo M., Gandhi, Leena, Moreira, Andre, Hirsch, Fred, Dieci, Maria V., Urbanowicz, Maria, Brcic, Iva, Korski, Konstanty, Gaire, Fabien, Koeppen, Hartmut, Lo, Amy, Giltnane, Jennifer, Rebelatto, Marlon C., Steele, Keith E., Zha, Jiping, Emancipator, Kenneth, Juco, Jonathan W., Denkert, Carsten, Reis-Filho, Jorge, Loi, Sherene, and Fox, Stephen B.

Published
2017
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33. Assessing Tumor-infiltrating Lymphocytes in Solid Tumors: A Practical Review for Pathologists and Proposal for a Standardized Method From the International Immunooncology Biomarkers Working Group: Part 1: Assessing the Host Immune Response, TILs in Invasive Breast Carcinoma and Ductal Carcinoma In Situ, Metastatic Tumor Deposits and Areas for Further Research

Author

Hendry, Shona, Salgado, Roberto, Gevaert, Thomas, Russell, Prudence A., John, Tom, Thapa, Bibhusal, Christie, Michael, van de Vijver, Koen, Estrada, M.V., Gonzalez-Ericsson, Paula I., Sanders, Melinda, Solomon, Benjamin, Solinas, Cinzia, Van den Eynden, Gert G.G.M., Allory, Yves, Preusser, Matthias, Hainfellner, Johannes, Pruneri, Giancarlo, Vingiani, Andrea, Demaria, Sandra, Symmans, Fraser, Nuciforo, Paolo, Comerma, Laura, Thompson, E.A., Lakhani, Sunil, Kim, Seong-Rim, Schnitt, Stuart, Colpaert, Cecile, Sotiriou, Christos, Scherer, Stefan J., Ignatiadis, Michail, Badve, Sunil, Pierce, Robert H., Viale, Giuseppe, Sirtaine, Nicolas, Penault-Llorca, Frederique, Sugie, Tomohagu, Fineberg, Susan, Paik, Soonmyung, Srinivasan, Ashok, Richardson, Andrea, Wang, Yihong, Chmielik, Ewa, Brock, Jane, Johnson, Douglas B., Balko, Justin, Wienert, Stephan, Bossuyt, Veerle, Michiels, Stefan, Ternes, Nils, Burchardi, Nicole, Luen, Stephen J., Savas, Peter, Klauschen, Frederick, Watson, Peter H., Nelson, Brad H., Criscitiello, Carmen, O’Toole, Sandra, Larsimont, Denis, de Wind, Roland, Curigliano, Giuseppe, André, Fabrice, Lacroix-Triki, Magali, van de Vijver, Mark, Rojo, Federico, Floris, Giuseppe, Bedri, Shahinaz, Sparano, Joseph, Rimm, David, Nielsen, Torsten, Kos, Zuzana, Hewitt, Stephen, Singh, Baljit, Farshid, Gelareh, Loibl, Sibylle, Allison, Kimberly H., Tung, Nadine, Adams, Sylvia, Willard-Gallo, Karen, Horlings, Hugo M., Gandhi, Leena, Moreira, Andre, Hirsch, Fred, Dieci, Maria V., Urbanowicz, Maria, Brcic, Iva, Korski, Konstanty, Gaire, Fabien, Koeppen, Hartmut, Lo, Amy, Giltnane, Jennifer, Rebelatto, Marlon C., Steele, Keith E., Zha, Jiping, Emancipator, Kenneth, Juco, Jonathan W., Denkert, Carsten, Reis-Filho, Jorge, Loi, Sherene, and Fox, Stephen B.

Published
2017
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35. First-line (1L) nivolumab (NIVO) + ipilimumab (IPI) + 2 cycles of chemotherapy (chemo) versus chemo alone (4 cycles) in patients (pts) with metastatic non–small cell lung cancer (NSCLC): 3-year update from CheckMate 9LA.

Author

Paz-Ares, Luis G., primary, Ciuleanu, Tudor-Eliade, additional, Cobo-Dols, Manuel, additional, Bennouna, Jaafar, additional, Cheng, Ying, additional, Mizutani, Hideaki, additional, Lingua, Alejo, additional, Reyes, Felipe, additional, Reinmuth, Niels, additional, Janoski De Menezes, Juliana, additional, Jassem, Jacek, additional, Protsenko, Svetlana, additional, Feeney, Kynan, additional, De La Mora Jimenez, Emmanuel, additional, Lu, Shun, additional, John, Tom, additional, Carbone, David Paul, additional, Zhang, Xiaoqing, additional, Hu, Nan, additional, and Reck, Martin, additional

Published
2022
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37. Egg-Derived Anti-SARS-CoV-2 Immunoglobulin Y (IgY) With Broad Variant Activity as Intranasal Prophylaxis Against COVID-19

Author

Frumkin, Lyn R., primary, Lucas, Michaela, additional, Scribner, Curtis L., additional, Ortega-Heinly, Nastassja, additional, Rogers, Jayden, additional, Yin, Gang, additional, Hallam, Trevor J., additional, Yam, Alice, additional, Bedard, Kristin, additional, Begley, Rebecca, additional, Cohen, Courtney A., additional, Badger, Catherine V., additional, Abbasi, Shawn A., additional, Dye, John M., additional, McMillan, Brian, additional, Wallach, Michael, additional, Bricker, Traci L., additional, Joshi, Astha, additional, Boon, Adrianus C. M., additional, Pokhrel, Suman, additional, Kraemer, Benjamin R., additional, Lee, Lucia, additional, Kargotich, Stephen, additional, Agochiya, Mahima, additional, John, Tom St., additional, and Mochly-Rosen, Daria, additional

Published
2022
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39. Family carer needs in advanced disease: systematic review of reviews

Author

David John-Tom Marco, Kristina Thomas, Serra Ivynian, Helen Wilding, Deborah Parker, Jennifer Tieman, and Peter Hudson

Subjects
Review Literature as Topic, Medical–Surgical Nursing, Caregivers, Oncology (nursing), Health Personnel, Hospice and Palliative Care Nursing, Palliative Care, Humans, Medicine (miscellaneous), General Medicine, humanities, health care economics and organizations, 1110 Nursing, 1117 Public Health and Health Services
Abstract

BackgroundFamily carers are vital in the management and delivery of home-based palliative care. Decision-makers need to know what the most commonly expressed unmet needs of family carers are to target available support services.AimTo identify the most commonly expressed needs of family carers of people with an advanced disease, assess the quality of current evidence, and set an agenda for future research and clinical practice.DesignA systematic review of reviews, prospectively registered on PROSPERO. Study quality was assessed using the Joanna Briggs Institute critical appraisal checklist for systematic reviews and research syntheses.Data sourcesMEDLINE, Embase, Emcare, PsycINFO, CINAHL, Informit and Cochrane Library were searched for reviews about the needs of carers looking after patients with advanced disease from 2010 to 2020.ResultsFindings from 21 reviews identified emotional support, disease-specific knowledge, carer role responsibilities, self-care and general practical support as the most commonly expressed needs expressed by family carers. Additionally, access to professional services, formal education opportunities and communication with health professionals were identified as caregivers’ preferred ways of having these needs met. Extraction of carer-specific needs was challenging at times as results were often combined with patient results in reviews.ConclusionPractical difficulties exist in effectively resourcing services to meet the needs of family carers. Information regarding the most commonly expressed needs shared by caregivers and their preferred delivery source can provide an opportunity to focus available support services to achieve the highest possible impact for carers of patients with advanced disease.PROSPERO registration numberCRD42018088678.

Published
2021

47. Monopovice : Réalité virtuelle et illusions fatales : Quand le jeu devient dangereux

Author

John Tom Narfen and John Tom Narfen

Abstract

Pauvre Phil. À plus de cinquante ans, il court encore après les jeunes filles! Quand Chloé, la nouvelle employée, lui adresse un sourire, c'est toute sa journée qui s'illumine. Ses collègues s'amusent de la situation, car Phil, c'est le stéréotype du vieux garçon névrosé, pas tout à fait méchant, mais fragile. Il suffirait d'un rien pour qu'il perde pied… Et ce'rien'arrive sous la forme d'un casque de réalité virtuelle. Monopovice, le dernier jeu à la mode, plonge ses utilisateurs dans un univers où tout est possible et où les vices sont rois. Sans hésiter, Phil se lance dans l'expérience. Mais dans ce monde virtuel, il devient la proie idéale. Ce qui devait être un simple divertissement tourne au cauchemar : Phil se réveille un matin avec de la cocaïne sur ses draps, la mort de Chloé sur la conscience, et les forces de l'ordre, ainsi que la mafia, à ses trousses. Pourtant, ce n'était qu'un jeu… rien qu'un jeu, non? Pauvre Phil est une histoire grinçante où la frontière entre réalité et illusion se brouille, et où un simple jeu peut entraîner des conséquences inattendues et dangereuses. À propos de l'auteur : John Tom Narfen a commencé sa carrière en enseignant les sciences à des collégiens en quête d'évasion. Un jour, il décide de leur raconter des histoires, réussissant ainsi à capter leur attention pour la première fois. Depuis, il n'a plus cessé d'écrire, troquant sa blouse de professeur pour la plume d'auteur.

Published
2024

48. MLPerf™ HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems

Author

Farrell, Steven, primary, Emani, Murali, additional, Balma, Jacob, additional, Drescher, Lukas, additional, Drozd, Aleksandr, additional, Fink, Andreas, additional, Fox, Geoffrey, additional, Kanter, David, additional, Kurth, Thorsten, additional, Mattson, Peter, additional, Mu, Dawei, additional, Ruhela, Amit, additional, Sato, Kento, additional, Shirahata, Koichi, additional, Tabaru, Tsuguchika, additional, Tsaris, Aristeidis, additional, Balewski, Jan, additional, Cumming, Ben, additional, Danjo, Takumi, additional, Domke, Jens, additional, Fukai, Takaaki, additional, Fukumoto, Naoto, additional, Fukushi, Tatsuya, additional, Gerofi, Balazs, additional, Honda, Takumi, additional, Imamura, Toshiyuki, additional, Kasagi, Akihiko, additional, Kawakami, Kentaro, additional, Kudo, Shuhei, additional, Kuroda, Akiyoshi, additional, Martinasso, Maxime, additional, Matsuoka, Satoshi, additional, Mendonca, Henrique, additional, Minami, Kazuki, additional, Ram, Prabhat, additional, Sawada, Takashi, additional, Shankar, Mallikarjun, additional, John, Tom St., additional, Tabuchi, Akihiro, additional, Vishwanath, Venkatram, additional, Wahib, Mohamed, additional, Yamazaki, Masafumi, additional, and Yin, Junqi, additional

Published
2021
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50. Thermal ablation in non-small cell lung cancer: a review of treatment modalities and the evidence for combination with immune checkpoint inhibitors

Author

Rangamuwa, Kanishka, primary, Leong, Tracy, additional, Weeden, Clare, additional, Asselin-Labat, Marie-Liesse, additional, Bozinovski, Steven, additional, Christie, Michael, additional, John, Tom, additional, Antippa, Phillip, additional, Irving, Louis, additional, and Steinfort, Daniel, additional

Published
2021
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