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2. Computing Power and the Governance of Artificial Intelligence

Author

Sastry, Girish, Heim, Lennart, Belfield, Haydn, Anderljung, Markus, Brundage, Miles, Hazell, Julian, O'Keefe, Cullen, Hadfield, Gillian K., Ngo, Richard, Pilz, Konstantin, Gor, George, Bluemke, Emma, Shoker, Sarah, Egan, Janet, Trager, Robert F., Avin, Shahar, Weller, Adrian, Bengio, Yoshua, and Coyle, Diane

Subjects
Computer Science - Computers and Society
Abstract

Computing power, or "compute," is crucial for the development and deployment of artificial intelligence (AI) capabilities. As a result, governments and companies have started to leverage compute as a means to govern AI. For example, governments are investing in domestic compute capacity, controlling the flow of compute to competing countries, and subsidizing compute access to certain sectors. However, these efforts only scratch the surface of how compute can be used to govern AI development and deployment. Relative to other key inputs to AI (data and algorithms), AI-relevant compute is a particularly effective point of intervention: it is detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain. These characteristics, alongside the singular importance of compute for cutting-edge AI models, suggest that governing compute can contribute to achieving common policy objectives, such as ensuring the safety and beneficial use of AI. More precisely, policymakers could use compute to facilitate regulatory visibility of AI, allocate resources to promote beneficial outcomes, and enforce restrictions against irresponsible or malicious AI development and usage. However, while compute-based policies and technologies have the potential to assist in these areas, there is significant variation in their readiness for implementation. Some ideas are currently being piloted, while others are hindered by the need for fundamental research. Furthermore, naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power. We end by suggesting guardrails to minimize these risks from compute governance., Comment: Figures can be accessed at: https://github.com/lheim/CPGAI-Figures

Published
2024

3. Visibility into AI Agents

Author

Chan, Alan, Ezell, Carson, Kaufmann, Max, Wei, Kevin, Hammond, Lewis, Bradley, Herbie, Bluemke, Emma, Rajkumar, Nitarshan, Krueger, David, Kolt, Noam, Heim, Lennart, and Anderljung, Markus

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence
Abstract

Increased delegation of commercial, scientific, governmental, and personal activities to AI agents -- systems capable of pursuing complex goals with limited supervision -- may exacerbate existing societal risks and introduce new risks. Understanding and mitigating these risks involves critically evaluating existing governance structures, revising and adapting these structures where needed, and ensuring accountability of key stakeholders. Information about where, why, how, and by whom certain AI agents are used, which we refer to as visibility, is critical to these objectives. In this paper, we assess three categories of measures to increase visibility into AI agents: agent identifiers, real-time monitoring, and activity logging. For each, we outline potential implementations that vary in intrusiveness and informativeness. We analyze how the measures apply across a spectrum of centralized through decentralized deployment contexts, accounting for various actors in the supply chain including hardware and software service providers. Finally, we discuss the implications of our measures for privacy and concentration of power. Further work into understanding the measures and mitigating their negative impacts can help to build a foundation for the governance of AI agents., Comment: Accepted to ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT 2024)

Published
2024

4. Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives

Author

Seger, Elizabeth, Dreksler, Noemi, Moulange, Richard, Dardaman, Emily, Schuett, Jonas, Wei, K., Winter, Christoph, Arnold, Mackenzie, hÉigeartaigh, Seán Ó, Korinek, Anton, Anderljung, Markus, Bucknall, Ben, Chan, Alan, Stafford, Eoghan, Koessler, Leonie, Ovadya, Aviv, Garfinkel, Ben, Bluemke, Emma, Aird, Michael, Levermore, Patrick, Hazell, Julian, and Gupta, Abhishek

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Software Engineering
Abstract

Recent decisions by leading AI labs to either open-source their models or to restrict access to their models has sparked debate about whether, and how, increasingly capable AI models should be shared. Open-sourcing in AI typically refers to making model architecture and weights freely and publicly accessible for anyone to modify, study, build on, and use. This offers advantages such as enabling external oversight, accelerating progress, and decentralizing control over AI development and use. However, it also presents a growing potential for misuse and unintended consequences. This paper offers an examination of the risks and benefits of open-sourcing highly capable foundation models. While open-sourcing has historically provided substantial net benefits for most software and AI development processes, we argue that for some highly capable foundation models likely to be developed in the near future, open-sourcing may pose sufficiently extreme risks to outweigh the benefits. In such a case, highly capable foundation models should not be open-sourced, at least not initially. Alternative strategies, including non-open-source model sharing options, are explored. The paper concludes with recommendations for developers, standard-setting bodies, and governments for establishing safe and responsible model sharing practices and preserving open-source benefits where safe., Comment: Official release at https://www.governance.ai/research-paper/open-sourcing-highly-capable-foundation-models

Published
2023

6. Towards best practices in AGI safety and governance: A survey of expert opinion

Author

Schuett, Jonas, Dreksler, Noemi, Anderljung, Markus, McCaffary, David, Heim, Lennart, Bluemke, Emma, and Garfinkel, Ben

Subjects
Computer Science - Computers and Society
Abstract

A number of leading AI companies, including OpenAI, Google DeepMind, and Anthropic, have the stated goal of building artificial general intelligence (AGI) - AI systems that achieve or exceed human performance across a wide range of cognitive tasks. In pursuing this goal, they may develop and deploy AI systems that pose particularly significant risks. While they have already taken some measures to mitigate these risks, best practices have not yet emerged. To support the identification of best practices, we sent a survey to 92 leading experts from AGI labs, academia, and civil society and received 51 responses. Participants were asked how much they agreed with 50 statements about what AGI labs should do. Our main finding is that participants, on average, agreed with all of them. Many statements received extremely high levels of agreement. For example, 98% of respondents somewhat or strongly agreed that AGI labs should conduct pre-deployment risk assessments, dangerous capabilities evaluations, third-party model audits, safety restrictions on model usage, and red teaming. Ultimately, our list of statements may serve as a helpful foundation for efforts to develop best practices, standards, and regulations for AGI labs., Comment: 38 pages, 8 figures, 8 tables

Published
2023

7. Exploring the Relevance of Data Privacy-Enhancing Technologies for AI Governance Use Cases

Author

Bluemke, Emma, Collins, Tantum, Garfinkel, Ben, and Trask, Andrew

Subjects
Computer Science - Artificial Intelligence, Computer Science - Cryptography and Security
Abstract

The development of privacy-enhancing technologies has made immense progress in reducing trade-offs between privacy and performance in data exchange and analysis. Similar tools for structured transparency could be useful for AI governance by offering capabilities such as external scrutiny, auditing, and source verification. It is useful to view these different AI governance objectives as a system of information flows in order to avoid partial solutions and significant gaps in governance, as there may be significant overlap in the software stacks needed for the AI governance use cases mentioned in this text. When viewing the system as a whole, the importance of interoperability between these different AI governance solutions becomes clear. Therefore, it is imminently important to look at these problems in AI governance as a system, before these standards, auditing procedures, software, and norms settle into place., Comment: arXiv admin note: text overlap with arXiv:2012.08347

Published
2023

9. Community dynamics and metagenomic analyses reveal Bacteroidota's role in widespread enzymatic Fucus vesiculosus cell wall degradation

Author

Jascha F. H. Macdonald, Pablo Pérez-García, Yannik K.-H. Schneider, Patrick Blümke, Daniela Indenbirken, Jeanette H. Andersen, Ines Krohn, and Wolfgang R. Streit

Subjects
Fucus vesiculosus, Cell wall degradation, Bacteroidota, α-L-fucosidases, Medicine, Science
Abstract

Abstract Enzymatic degradation of algae cell wall carbohydrates by microorganisms is under increasing investigation as marine organic matter gains more value as a sustainable resource. The fate of carbon in the marine ecosystem is in part driven by these degradation processes. In this study, we observe the microbiome dynamics of the macroalga Fucus vesiculosus in 25-day-enrichment cultures resulting in partial degradation of the brown algae. Microbial community analyses revealed the phylum Pseudomonadota as the main bacterial fraction dominated by the genera Marinomonas and Vibrio. More importantly, a metagenome-based Hidden Markov model for specific glycosyl hydrolyses and sulphatases identified Bacteroidota as the phylum with the highest potential for cell wall degradation, contrary to their low abundance. For experimental verification, we cloned, expressed, and biochemically characterised two α-L-fucosidases, FUJM18 and FUJM20. While protein structure predictions suggest the highest similarity to a Bacillota origin, protein–protein blasts solely showed weak similarities to defined Bacteroidota proteins. Both enzymes were remarkably active at elevated temperatures and are the basis for a potential synthetic enzyme cocktail for large-scale algal destruction.

Published
2024
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11. Deep Learning Analysis of Cardiac MRI in Legacy Datasets: Multi-Ethnic Study of Atherosclerosis

Author

Suinesiaputra, Avan, Mauger, Charlene A, Ambale-Venkatesh, Bharath, Bluemke, David A, Gade, Josefine Dam, Gilbert, Kathleen, Janse, Mark, Hald, Line Sofie, Werkhoven, Conrad, Wu, Colin, Lima, Joao A, and Young, Alistair A

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

The shape and motion of the heart provide essential clues to understanding the mechanisms of cardiovascular disease. With the advent of large-scale cardiac imaging data, statistical atlases become a powerful tool to provide automated and precise quantification of the status of patient-specific heart geometry with respect to reference populations. The Multi-Ethnic Study of Atherosclerosis (MESA), begun in 2000, was the first large cohort study to incorporate cardiovascular MRI in over 5000 participants, and there is now a wealth of follow-up data over 20 years. Building a machine learning based automated analysis is necessary to extract the additional imaging information necessary for expanding original manual analyses. However, machine learning tools trained on MRI datasets with different pulse sequences fail on such legacy datasets. Here, we describe an automated atlas construction pipeline using deep learning methods applied to the legacy cardiac MRI data in MESA. For detection of anatomical cardiac landmark points, a modified VGGNet convolutional neural network architecture was used in conjunction with a transfer learning sequence between two-chamber, four-chamber, and short-axis MRI views. A U-Net architecture was used for detection of the endocardial and epicardial boundaries in short axis images. Both network architectures resulted in good segmentation and landmark detection accuracies compared with inter-observer variations. Statistical relationships with common risk factors were similar between atlases derived from automated vs manual annotations. The automated atlas can be employed in future studies to examine the relationships between cardiac morphology and future events.

Published
2021
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12. Multicenter benchmarking of short and long read wet lab protocols for clinical viral metagenomics

Author

Lopez-Labrador, F. Xavier, Huber, Michael, Sidorov, Igor A., Brown, Julianne R., Cuypers, Lize, Laenen, Lies, Vanmechelen, Bert, Maes, Piet, Fischer, Nicole, Pichler, Ian, Storey, Nathaniel, Atkinson, Laura, Schmutz, Stefan, Kufner, Verena, van Boheemen, Sander, Mulders, Claudia E., Grundhoff, Adam, Blümke, Patrick, Robitaille, Alexis, Cinek, Ondrej, Hubáčková, Klára, Mourik, Kees, Boers, Stefan A., Stauber, Lea, Salmona, Maud, Cappy, Pierre, Ramette, Alban, Franze’, Alessandra, LeGoff, Jerome, Claas, Eric C.J., Rodriguez, Christophe, and de Vries, Jutte J.C.

Published
2024
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14. Challenges for machine learning in clinical translation of big data imaging studies

Author

Dinsdale, Nicola K, Bluemke, Emma, Sundaresan, Vaanathi, Jenkinson, Mark, Smith, Stephen, and Namburete, Ana IL

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

The combination of deep learning image analysis methods and large-scale imaging datasets offers many opportunities to imaging neuroscience and epidemiology. However, despite the success of deep learning when applied to many neuroimaging tasks, there remain barriers to the clinical translation of large-scale datasets and processing tools. Here, we explore the main challenges and the approaches that have been explored to overcome them. We focus on issues relating to data availability, interpretability, evaluation and logistical challenges, and discuss the challenges we believe are still to be overcome to enable the full success of big data deep learning approaches to be experienced outside of the research field.

Published
2021

15. Comparison of osteoclast differentiation protocols from human induced pluripotent stem cells of different tissue origins

Author

Alexander Blümke, Erica Ijeoma, Jessica Simon, Rachel Wellington, Medania Purwaningrum, Sergei Doulatov, Elizabeth Leber, Marta Scatena, and Cecilia M. Giachelli

Subjects
Human induced pluripotent stem cells, Osteoclasts, Osteoclastogenesis, Hematopoietic differentiation, Mesodermal differentiation, Mineral resorption, Medicine (General), R5-920, Biochemistry, QD415-436
Abstract

Abstract Background Ever since their discovery, induced pluripotent stem cells (iPSCs) have been extensively differentiated into a large variety of cell types. However, a limited amount of work has been dedicated to differentiating iPSCs into osteoclasts. While several differentiation protocols have been published, it remains unclear which protocols or differentiation methods are preferable regarding the differentiation of osteoclasts. Methods In this study, we compared the osteoclastogenesis capacity of a peripheral blood mononuclear cell (PBMC)-derived iPSC line to a fibroblast-derived iPSC line in conjunction with either embryoid body-based or monolayer-based differentiation strategies. Both cell lines and differentiation protocols were investigated regarding their ability to generate osteoclasts and their inherent robustness and ease of use. The ability of both cell lines to remain undifferentiated while propagating using a feeder-free system was assessed using alkaline phosphatase staining. This was followed by evaluating mesodermal differentiation and the characterization of hematopoietic progenitor cells using flow cytometry. Finally, osteoclast yield and functionality based on resorptive activity, Cathepsin K and tartrate-resistant acid phosphatase (TRAP) expression were assessed. The results were validated using qRT-PCR throughout the differentiation stages. Results Embryoid body-based differentiation yielded CD45+, CD14+, CD11b+ subpopulations which in turn differentiated into osteoclasts which demonstrated TRAP positivity, Cathepsin K expression and mineral resorptive capabilities. This was regardless of which iPSC line was used. Monolayer-based differentiation yielded lower quantities of hematopoietic cells that were mostly CD34+ and did not subsequently differentiate into osteoclasts. Conclusions The outcome of this study demonstrates the successful differentiation of osteoclasts from iPSCs in conjunction with the embryoid-based differentiation method, while the monolayer-based method did not yield osteoclasts. No differences were observed regarding osteoclast differentiation between the PBMC and fibroblast-derived iPSC lines.

Published
2023
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17. Deep Learning-based Automated Aortic Area and Distensibility Assessment: The Multi-Ethnic Study of Atherosclerosis (MESA)

Author

Jani, Vivek P., Kachenoura, Nadjia, Redheuil, Alban, Teixido-Tura, Gisela, Bouaou, Kevin, Bollache, Emilie, Mousseaux, Elie, De Cesare, Alain, Kutty, Shelby, Wu, Colin O., Bluemke, David A., Lima, Joao A. C., and Ambale-Venkatesh, Bharath

Subjects
Quantitative Biology - Quantitative Methods, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

This study applies convolutional neural network (CNN)-based automatic segmentation and distensibility measurement of the ascending and descending aorta from 2D phase-contrast cine magnetic resonance imaging (PC-cine MRI) within the large MESA cohort with subsequent assessment on an external cohort of thoracic aortic aneurysm (TAA) patients. 2D PC-cine MRI images of the ascending and descending aorta at the pulmonary artery bifurcation from the MESA study were included. Train, validation, and internal test sets consisted of 1123 studies (24282 images), 374 studies (8067 images), and 375 studies (8069 images), respectively. An external test set of TAAs consisted of 37 studies (3224 images). A U-Net based CNN was constructed, and performance was evaluated utilizing dice coefficient (for segmentation) and concordance correlation coefficients (CCC) of aortic geometric parameters by comparing to manual segmentation and parameter estimation. Dice coefficients for aorta segmentation were 97.6% (CI: 97.5%-97.6%) and 93.6% (84.6%-96.7%) on the internal and external test of TAAs, respectively. CCC for comparison of manual and CNN maximum and minimum ascending aortic areas were 0.97 and 0.95, respectively, on the internal test set and 0.997 and 0.995, respectively, for the external test. CCCs for maximum and minimum descending aortic areas were 0.96 and 0. 98, respectively, on the internal test set and 0.93 and 0.93, respectively, on the external test set. We successfully developed and validated a U-Net based ascending and descending aortic segmentation and distensibility quantification model in a large multi-ethnic database and in an external cohort of TAA patients., Comment: 25 pages, 5 figures

Published
2021

18. Beyond Privacy Trade-offs with Structured Transparency

Author

Trask, Andrew, Bluemke, Emma, Collins, Teddy, Drexler, Ben Garfinkel Eric, Cuervas-Mons, Claudia Ghezzou, Gabriel, Iason, Dafoe, Allan, and Isaac, William

Subjects
Computer Science - Cryptography and Security, Computer Science - Computers and Society
Abstract

Successful collaboration involves sharing information. However, parties may disagree on how the information they need to share should be used. We argue that many of these concerns reduce to 'the copy problem': once a bit of information is copied and shared, the sender can no longer control how the recipient uses it. From the perspective of each collaborator, this presents a dilemma that can inhibit collaboration. The copy problem is often amplified by three related problems which we term the bundling, edit, and recursive enforcement problems. We find that while the copy problem is not solvable, aspects of these amplifying problems have been addressed in a variety of disconnected fields. We observe that combining these efforts could improve the governability of information flows and thereby incentivise collaboration. We propose a five-part framework which groups these efforts into specific capabilities and offers a foundation for their integration into an overarching vision we call "structured transparency". We conclude by surveying an array of use-cases that illustrate the structured transparency principles and their related capabilities.

Published
2020

19. Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Author

Brundage, Miles, Avin, Shahar, Wang, Jasmine, Belfield, Haydn, Krueger, Gretchen, Hadfield, Gillian, Khlaaf, Heidy, Yang, Jingying, Toner, Helen, Fong, Ruth, Maharaj, Tegan, Koh, Pang Wei, Hooker, Sara, Leung, Jade, Trask, Andrew, Bluemke, Emma, Lebensold, Jonathan, O'Keefe, Cullen, Koren, Mark, Ryffel, Théo, Rubinovitz, JB, Besiroglu, Tamay, Carugati, Federica, Clark, Jack, Eckersley, Peter, de Haas, Sarah, Johnson, Maritza, Laurie, Ben, Ingerman, Alex, Krawczuk, Igor, Askell, Amanda, Cammarota, Rosario, Lohn, Andrew, Krueger, David, Stix, Charlotte, Henderson, Peter, Graham, Logan, Prunkl, Carina, Martin, Bianca, Seger, Elizabeth, Zilberman, Noa, hÉigeartaigh, Seán Ó, Kroeger, Frens, Sastry, Girish, Kagan, Rebecca, Weller, Adrian, Tse, Brian, Barnes, Elizabeth, Dafoe, Allan, Scharre, Paul, Herbert-Voss, Ariel, Rasser, Martijn, Sodhani, Shagun, Flynn, Carrick, Gilbert, Thomas Krendl, Dyer, Lisa, Khan, Saif, Bengio, Yoshua, and Anderljung, Markus

Subjects
Computer Science - Computers and Society
Abstract

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.

Published
2020

20. Identification and characterization of the anti-viral interferon lambda 3 as direct target of the Epstein-Barr virus microRNA-BART7-3p

Author

Juliane Blümke, Marcus Bauer, Christoforos Vaxevanis, Andreas Wilfer, Ofer Mandelboim, Claudia Wickenhauser, Barbara Seliger, and Simon Jasinski-Bergner

Subjects
EBV, EBV target genes, IFNL3, immune escape, microRNA, Immunologic diseases. Allergy, RC581-607, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

ABSTRACTThe human Epstein–Barr virus (EBV), as a member of the human γ herpes viruses (HHV), is known to be linked with distinct tumor types. It is a double-stranded DNA virus and its genome encodes among others for 48 different microRNAs (miRs). Current research demonstrated a strong involvement of certain EBV-miRs in molecular immune evasion mechanisms of infected cells by, e.g., the disruption of human leukocyte antigen (HLA) class Ia and NKG2D functions. To determine novel targets of EBV-miRs involved in immune surveillance, ebv-miR-BART7-3p, an EBV-encoded miR with high expression levels during the different lytic and latent EBV life cycle phases, was overexpressed in human HEK293T cells. Using a cDNA microarray-based comparative analysis, 234 (229 downregulated and 5 upregulated) deregulated human transcripts were identified in ebv-miR-BART7-3p transfectants, which were mainly involved in cellular processes and molecular binding. A statistically significant downregulation of the anti-proliferative and tumor-suppressive hsa-miR-34A and the anti-viral interferon lambda (IFNL)3 mRNA was found. The ebv-miR-BART7-3p-mediated downregulation of IFNL3 expression was due to a direct interaction with the IFNL3 3’-untranslated region (UTR) as determined by luciferase reporter gene assays including the identification of the accurate ebv-miR-BART7-3p binding site. The effect of ebv-miR-BART7-3p on the IFNL3 expression was validated both in human cell lines in vitro and in human tissue specimen with known EBV status. These results expand the current knowledge of EBV-encoded miRs and their role in immune evasion, pathogenesis and malignant transformation.

Published
2023
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27. Multiparametric Deep Learning Tissue Signatures for a Radiological Biomarker of Breast Cancer: Preliminary Results

Author

Parekh, Vishwa S., Macura, Katarzyna J., Harvey, Susan, Kamel, Ihab, EI-Khouli, Riham, Bluemke, David A., and Jacobs, Michael A.

Subjects
Physics - Medical Physics, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods, 68T05, 92C55, I.2.1, I.2.5, I.6.5, J.3, H.1.1
Abstract

A new paradigm is beginning to emerge in Radiology with the advent of increased computational capabilities and algorithms. This has led to the ability of real time learning by computer systems of different lesion types to help the radiologist in defining disease. For example, using a deep learning network, we developed and tested a multiparametric deep learning (MPDL) network for segmentation and classification using multiparametric magnetic resonance imaging (mpMRI) radiological images. The MPDL network was constructed from stacked sparse autoencoders with inputs from mpMRI. Evaluation of MPDL consisted of cross-validation, sensitivity, and specificity. Dice similarity between MPDL and post-DCE lesions were evaluated. We demonstrate high sensitivity and specificity for differentiation of malignant from benign lesions of 90% and 85% respectively with an AUC of 0.93. The Integrated MPDL method accurately segmented and classified different breast tissue from multiparametric breast MRI using deep leaning tissue signatures., Comment: Deep Learning, Machine learning, Magnetic resonance imaging, multiparametric MRI, Breast, Cancer, Diffusion, tissue biomarkers

Published
2018
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28. Native Top-Down Mass Spectrometry and Ion Mobility Spectrometry of the Interaction of Tau Protein with a Molecular Tweezer Assembly Modulator

Author

Nshanian, Michael, Lantz, Carter, Wongkongkathep, Piriya, Schrader, Thomas, Klärner, Frank-Gerrit, Blümke, Anika, Despres, Clément, Ehrmann, Michael, Smet-Nocca, Caroline, Bitan, Gal, and Loo, Joseph A

Subjects
Analytical Chemistry, Chemical Sciences, Physical Chemistry, Neurodegenerative, Aging, Acquired Cognitive Impairment, Alzheimer's Disease, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Brain Disorders, Underpinning research, 1.1 Normal biological development and functioning, Generic health relevance, Neurological, Binding Sites, Bridged-Ring Compounds, Hydrogen-Ion Concentration, Ion Mobility Spectrometry, Organophosphates, Phosphorylation, Spectrometry, Mass, Electrospray Ionization, tau Proteins, Electrospray ionization, Electron capture dissociation, Native mass spectrometry, Top-down mass spectrometry, Tau, Tweezer, Medicinal and Biomolecular Chemistry, Physical Chemistry (incl. Structural), Analytical chemistry
Abstract

Native top-down mass spectrometry (MS) and ion mobility spectrometry (IMS) were applied to characterize the interaction of a molecular tweezer assembly modulator, CLR01, with tau, a protein believed to be involved in a number of neurodegenerative disorders, including Alzheimer's disease. The tweezer CLR01 has been shown to inhibit aggregation of amyloidogenic polypeptides without toxic side effects. ESI-MS spectra for different forms of tau protein (full-length, fragments, phosphorylated, etc.) in the presence of CLR01 indicate a primary binding stoichiometry of 1:1. The relatively high charging of the protein measured from non-denaturing solutions is typical of intrinsically disordered proteins, such as tau. Top-down mass spectrometry using electron capture dissociation (ECD) is a tool used to determine not only the sites of post-translational modifications but also the binding site(s) of non-covalent interacting ligands to biomolecules. The intact protein and the protein-modulator complex were subjected to ECD-MS to obtain sequence information, map phosphorylation sites, and pinpoint the sites of inhibitor binding. The ESI-MS study of intact tau proteins indicates that top-down MS is amenable to the study of various tau isoforms and their post-translational modifications (PTMs). The ECD-MS data point to a CLR01 binding site in the microtubule-binding region of tau, spanning residues K294-K331, which includes a six-residue nucleating segment PHF6 (VQIVYK) implicated in aggregation. Furthermore, ion mobility experiments on the tau fragment in the presence of CLR01 and phosphorylated tau reveal a shift towards a more compact structure. The mass spectrometry study suggests a picture for the molecular mechanism of the modulation of protein-protein interactions in tau by CLR01. Graphical Abstract ᅟ.

Published
2019

32. Cardiovascular Magnetic Resonance for Patients With COVID-19

Author

Petersen, Steffen E., Friedrich, Matthias G., Leiner, Tim, Elias, Matthew D., Ferreira, Vanessa M., Fenski, Maximilian, Flamm, Scott D., Fogel, Mark, Garg, Ria, Halushka, Marc K., Hays, Allison G., Kawel-Boehm, Nadine, Kramer, Christopher M., Nagel, Eike, Ntusi, Ntobeko A.B., Ostenfeld, Ellen, Pennell, Dudley J., Raisi-Estabragh, Zahra, Reeder, Scott B., Rochitte, Carlos E., Starekova, Jitka, Suchá, Dominika, Tao, Qian, Schulz-Menger, Jeanette, and Bluemke, David A.

Published
2022
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33. {\Omega}-Net (Omega-Net): Fully Automatic, Multi-View Cardiac MR Detection, Orientation, and Segmentation with Deep Neural Networks

Author

Vigneault, Davis M., Xie, Weidi, Ho, Carolyn Y., Bluemke, David A., and Noble, J. Alison

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearance, orientation, and placement of the heart between patients, clinical views, scanners, and protocols makes fully automatic semantic segmentation a notoriously difficult problem. Here, we present ${\Omega}$-Net (Omega-Net): a novel convolutional neural network (CNN) architecture for simultaneous localization, transformation into a canonical orientation, and semantic segmentation. First, an initial segmentation is performed on the input image, second, the features learned during this initial segmentation are used to predict the parameters needed to transform the input image into a canonical orientation, and third, a final segmentation is performed on the transformed image. In this work, ${\Omega}$-Nets of varying depths were trained to detect five foreground classes in any of three clinical views (short axis, SA, four-chamber, 4C, two-chamber, 2C), without prior knowledge of the view being segmented. The architecture was trained on a cohort of patients with hypertrophic cardiomyopathy and healthy control subjects. Network performance as measured by weighted foreground intersection-over-union (IoU) was substantially improved in the best-performing ${\Omega}$- Net compared with U-Net segmentation without localization or orientation. In addition, {\Omega}-Net was retrained from scratch on the 2017 MICCAI ACDC dataset, and achieves state-of-the-art results on the LV and RV bloodpools, and performed slightly worse in segmentation of the LV myocardium. We conclude this architecture represents a substantive advancement over prior approaches, with implications for biomedical image segmentation more generally., Comment: First two authors contributed equally to this work, result for MICCAI Automated Cardiac Diagnosis Challenge (ACDC) dataset is added

Published
2017

34. Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization

Author

Vigneault, Davis M., Xie, Weidi, Bluemke, David A., and Noble, J. Alison

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an area of interest for quantification of regional cardiac function from balanced, steady state free precession (SSFP) cine sequences. However, currently available techniques lack full automation, limiting reproducibility. We propose a fully automated technique whereby a CMR image sequence is first segmented with a deep, fully convolutional neural network (CNN) architecture, and quadratic basis splines are fitted simultaneously across all cardiac frames using least squares optimization. Experiments are performed using data from 42 patients with hypertrophic cardiomyopathy (HCM) and 21 healthy control subjects. In terms of segmentation, we compared state-of-the-art CNN frameworks, U-Net and dilated convolution architectures, with and without temporal context, using cross validation with three folds. Performance relative to expert manual segmentation was similar across all networks: pixel accuracy was ~97%, intersection-over-union (IoU) across all classes was ~87%, and IoU across foreground classes only was ~85%. Endocardial left ventricular circumferential strain calculated from the proposed pipeline was significantly different in control and disease subjects (-25.3% vs -29.1%, p = 0.006), in agreement with the current clinical literature., Comment: Accepted to Functional Imaging and Modeling of the Heart (FIMH) 2017

Published
2017

35. Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study

Author

Yang, Jie, Angelini, Elsa D., Smith, Benjamin M., Austin, John H. M., Hoffman, Eric A., Bluemke, David A., Barr, R. Graham, and Laine, Andrew F.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Pulmonary emphysema is traditionally subcategorized into three subtypes, which have distinct radiological appearances on computed tomography (CT) and can help with the diagnosis of chronic obstructive pulmonary disease (COPD). Automated texture-based quantification of emphysema subtypes has been successfully implemented via supervised learning of these three emphysema subtypes. In this work, we demonstrate that unsupervised learning on a large heterogeneous database of CT scans can generate texture prototypes that are visually homogeneous and distinct, reproducible across subjects, and capable of predicting accurately the three standard radiological subtypes. These texture prototypes enable automated labeling of lung volumes, and open the way to new interpretations of lung CT scans with finer subtyping of emphysema., Comment: MICCAI workshop on Medical Computer Vision: Algorithms for Big Data (2016)

Published
2016

36. PySyft: A Library for Easy Federated Learning

Author

Ziller, Alexander, Trask, Andrew, Lopardo, Antonio, Szymkow, Benjamin, Wagner, Bobby, Bluemke, Emma, Nounahon, Jean-Mickael, Passerat-Palmbach, Jonathan, Prakash, Kritika, Rose, Nick, Ryffel, Théo, Reza, Zarreen Naowal, Kaissis, Georgios, Kacprzyk, Janusz, Series Editor, Rehman, Muhammad Habib ur, editor, and Gaber, Mohamed Medhat, editor

Published
2021
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38. Novel approach to identify putative Epstein–Barr–virus microRNAs regulating host cell genes with relevance in tumor biology and immunology

Author

Simon Jasinski-Bergner, Juliane Blümke, Marcus Bauer, Saskia Luise Skiebe, Ofer Mandelboim, Claudia Wickenhauser, and Barbara Seliger

Subjects
EBV, microRNA, EBV-driven disease, malignant transformation, transcriptomics, EBV target genes, Immunologic diseases. Allergy, RC581-607, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

The human Epstein–Barr virus is associated with several human solid and hematopoietic malignancies. However, the underlying molecular mechanisms including virus-encoded microRNAs (miRs), which lead to the malignant transformation of infected cells and immune evasion of EBV-associated tumors, have not yet been characterized. The expression levels of numerous known EBV-specific miRs and their suitability as diagnostic and/or prognostic markers were determined in different human EBV-positive tissues followed by in silico analyses to identify putative EBV-miR-regulated target genes, thereby offering a suitable screening strategy to overcome the limited available data sets of EBV-miRs and their targeted gene networks. Analysis of microarray data sets from healthy human B cells and malignant-transformed EBV-positive B cells of patients with Burkitt’s lymphoma revealed statistically significant (p

Published
2022
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41. Tool for Metamorphic Testing

Author

Bluemke, Ilona, Kamiński, Paweł, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Zamojski, Wojciech, editor, Mazurkiewicz, Jacek, editor, Sugier, Jarosław, editor, and Walkowiak, Tomasz, editor

Published
2020
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42. Tool for Assessment of Testing Effort

Author

Bluemke, Ilona, Malanowska, Agnieszka, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Zamojski, Wojciech, editor, Mazurkiewicz, Jacek, editor, Sugier, Jarosław, editor, and Walkowiak, Tomasz, editor

Published
2020
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44. Impact of COVID-19 Pandemic on Cardiovascular Testing in Asia: The IAEA INCAPS-COVID Study

Author

Einstein, Andrew J., Paez, Diana, Dondi, Maurizio, Better, Nathan, Cerci, Rodrigo, Dorbala, Sharmila, Pascual, Thomas N.B., Raggi, Paolo, Shaw, Leslee J., Villines, Todd C., Vitola, Joao V., Williams, Michelle C., Pynda, Yaroslav, Hinterleitner, Gerd, Lu, Yao, Morozova, Olga, Xu, Zhuoran, Hirschfeld, Cole B., Cohen, Yosef, Goebel, Benjamin, Malkovskiy, Eli, Randazzo, Michael, Choi, Andrew, Lopez-Mattei, Juan, Parwani, Purvi, Nasery, Mohammad Nawaz, Goda, Artan, Shirka, Ervina, Benlabgaa, Rabie, Bouyoucef, Salah, Medjahedi, Abdelkader, Nailli, Qais, Agolti, Mariela, Aguero, Roberto Nicolas, Alak, Maria del Carmen, Alberguina, Lucia Graciela, Arroñada, Guillermo, Astesiano, Andrea, Astesiano, Alfredo, Norton, Carolina Bas, Benteo, Pablo, Blanco, Juan, Bonelli, Juan Manuel, Bustos, Jose Javier, Cabrejas, Raul, Cachero, Jorge, Campisi, Roxana, Canderoli, Alejandro, Carames, Silvia, Carrascosa, Patrícia, Castro, Ricardo, Cendoya, Oscar, Cognigni, Luciano Martin, Collaud, Carlos, Cortes, Claudia, Courtis, Javier, Cragnolino, Daniel, Daicz, Mariana, De La Vega, Alejandro, De Maria, Silvia Teresa, Del Riego, Horacio, Dettori, Fernando, Deviggiano, Alejandro, Dragonetti, Laura, Embon, Mario, Enriquez, Ruben Emilio, Ensinas, Jorge, Faccio, Fernando, Facello, Adolfo, Garofalo, Diego, Geronazzo, Ricardo, Gonza, Natalia, Gutierrez, Lucas, Guzzo, Miguel Angel, Hasbani, Victor, Huerin, Melina, Jäger, Victor, Lewkowicz, Julio Manuel, López De Munaín, Maria Nieves A., Lotti, Jose Maria, Marquez, Alejandra, Masoli, Osvaldo, Masoli, Osvaldo Horacio, Mastrovito, Edgardo, Mayoraz, Matias, Melado, Graciela Eva, Mele, Anibal, Merani, Maria Fernanda, Meretta, Alejandro Horacio, Molteni, Susana, Montecinos, Marcos, Noguera, Eduardo, Novoa, Carlos, Sueldo, Claudio Pereyra, Ascani, Sebastian Perez, Pollono, Pablo, Pujol, Maria Paula, Radzinschi, Alejandro, Raimondi, Gustavo, Redruello, Marcela, Rodríguez, Marina, Rodríguez, Matías, Romero, Romina Lorena, Acuña, Arturo Romero, Rovaletti, Federico, San Miguel, Lucas, Solari, Lucrecia, Strada, Bruno, Traverso, Sonia, Traverzo, Sonia Simona, Espeche, Maria del Huerto Velazquez, Weihmuller, Juan Sebastian, Wolcan, Juan, Zeffiro, Susana, Sakanyan, Mari, Beuzeville, Scott, Boktor, Raef, Butler, Patrick, Calcott, Jennifer, Carr, Loretta, Chan, Virgil, Chao, Charles, Chong, Woon, Dobson, Mark, Downie, D'Arne, Dwivedi, Girish, Elison, Barry, Engela, Jean, Francis, Roslyn, Gaikwad, Anand, Basavaraj, Ashok Gangasandra, Goodwin, Bruce, Greenough, Robert, Hamilton-Craig, Christian, Hsieh, Victar, Joshi, Subodh, Lederer, Karin, Lee, Kenneth, Lee, Joseph, Magnussen, John, Mai, Nghi, Mander, Gordon, Murton, Fiona, Nandurkar, Dee, Neill, Johanne, O'Rourke, Edward, O'Sullivan, Patricia, Pandos, George, Pathmaraj, Kunthi, Pitman, Alexander, Poulter, Rohan, Premaratne, Manuja, Prior, David, Ridley, Lloyd, Rutherford, Natalie, Salehi, Hamid, Saunders, Connor, Scarlett, Luke, Seneviratne, Sujith, Shetty, Deepa, Shrestha, Ganesh, Shulman, Jonathan, Solanki, Vijay, Stanton, Tony, Stuart, Murch, Stubbs, Michael, Swainson, Ian, Taubman, Kim, Taylor, Andrew, Thomas, Paul, Unger, Steven, Upton, Anthony, Vamadevan, Shankar, Van Gaal, William, Verjans, Johan, Voutnis, Demetrius, Wayne, Victor, Wilson, Peter, Wong, David, Wong, Kirby, Younger, John, Feuchtner, Gudrun, Mirzaei, Siroos, Weiss, Konrad, Maroz-Vadalazhskaya, Natallia, Gheysens, Olivier, Homans, Filip, Moreno-Reyes, Rodrigo, Pasquet, Agnès, Roelants, Veronique, Van De Heyning, Caroline M., Ríos, Raúl Araujo, Soldat-Stankovic, Valentina, Stankovic, Sinisa, Albernaz Siqueira, Maria Helena, Almeida, Augusto, Alves Togni, Paulo Henrique, Andrade, Jose Henrique, Andrade, Luciana, Anselmi, Carlos, Araújo, Roberta, Azevedo, Guilherme, Bezerra, Sabbrina, Biancardi, Rodrigo, Grossman, Gabriel Blacher, Brandão, Simone, Pianta, Diego Bromfman, Carreira, Lara, Castro, Bruno, Chang, Tien, Cunali, Fernando, Jr., Cury, Roberto, Dantas, Roberto, de Amorim Fernandes, Fernando, De Lorenzo, Andrea, De Macedo Filho, Robson, Erthal, Fernanda, Fernandes, Fabio, Fernandes, Juliano, De Souza, Thiago Ferreira, Alves, Wilson Furlan, Ghini, Bruno, Goncalves, Luiz, Gottlieb, Ilan, Hadlich, Marcelo, Kameoka, Vinícius, Lima, Ronaldo, Lima, Adna, Lopes, Rafael Willain, Machado e Silva, Ricardo, Magalhães, Tiago, Silva, Fábio Martins, Mastrocola, Luiz Eduardo, Medeiros, Fábio, Meneghetti, José Claudio, Naue, Vania, Naves, Danilo, Nolasco, Roberto, Nomura, Cesar, Oliveira, Joao Bruno, Paixao, Eduardo, De Carvalho, Filipe Penna, Pinto, Ibraim, Possetti, Priscila, Quinta, Mayra, Nogueira Ramos, Rodrigo Rizzo, Rocha, Ricardo, Rodrigues, Alfredo, Rodrigues, Carlos, Romantini, Leila, Sanches, Adelina, Santana, Sara, Sara da Silva, Leonardo, Schvartzman, Paulo, Matushita, Cristina Sebastião, Senra, Tiago, Shiozaki, Afonso, Menezes de Siqueira, Maria Eduarda, Siqueira, Cristiano, Smanio, Paola, Soares, Carlos Eduardo, Junior, José Soares, Bittencourt, Marcio Sommer, Spiro, Bernardo, Mesquita, Cláudio Tinoco, Torreao, Jorge, Torres, Rafael, Uellendahl, Marly, Monte, Guilherme Urpia, Veríssimo, Otávia, Cabeda, Estevan Vieira, Pedras, Felipe Villela, Waltrick, Roberto, Zapparoli, Marcello, Naseer, Hamid, Garcheva-Tsacheva, Marina, Kostadinova, Irena, Theng, Youdaline, Abikhzer, Gad, Barette, Rene, Chow, Benjamin, Dabreo, Dominique, Friedrich, Matthias, Garg, Ria, Hafez, Mohammed Nassoh, Johnson, Chris, Kiess, Marla, Leipsic, Jonathon, Leung, Eugene, Miller, Robert, Oikonomou, Anastasia, Probst, Stephan, Roifman, Idan, Small, Gary, Tandon, Vikas, Trivedi, Adwait, White, James, Zukotynski, Katherine, Canessa, Jose, Muñoz, Gabriel Castro, Concha, Carmen, Hidalgo, Pablo, Lovera, Cesar, Massardo, Teresa, Vargas, Luis Salazar, Abad, Pedro, Arturo, Harold, Ayala, Sandra, Benitez, Luis, Cadena, Alberto, Caicedo, Carlos, Moncayo, Antonio Calderón, Gomez, Sharon, Gutierrez Villamil, Claudia T., Jaimes, Claudia, Londoño, Juan, Londoño Blair, Juan Luis, Pabon, Luz, Pineda, Mauricio, Rojas, Juan Carlos, Ruiz, Diego, Escobar, Manuel Valencia, Vasquez, Andres, Vergel, Damiana, Zuluaga, Alejandro, Gamboa, Isabel Berrocal, Castro, Gabriel, González, Ulises, Baric, Ana, Batinic, Tonci, Franceschi, Maja, Paar, Maja Hrabak, Jukic, Mladen, Medakovic, Petar, Persic, Viktor, Prpic, Marina, Punda, Ante, Batista, Juan Felipe, Gómez Lauchy, Juan Manuel, Gutierrez, Yamile Marcos, Menéndez, Rayner, Peix, Amalia, Rochela, Luis, Panagidis, Christoforos, Petrou, Ioannis, Engelmann, Vaclav, Kaminek, Milan, Kincl, Vladimír, Lang, Otto, Simanek, Milan, Abdulla, Jawdat, Bøttcher, Morten, Christensen, Mette, Gormsen, Lars Christian, Hasbak, Philip, Hess, Søren, Holdgaard, Paw, Johansen, Allan, Kyhl, Kasper, Norgaard, Bjarne Linde, Øvrehus, Kristian Altern, Rønnow Sand, Niels Peter, Steffensen, Rolf, Thomassen, Anders, Zerahn, Bo, Perez, Alfredo, Escorza Velez, Giovanni Alejandro, Velez, Mayra Sanchez, Abdel Aziz, Islam Shawky, Abougabal, Mahasen, Ahmed, Taghreed, Allam, Adel, Asfour, Ahmed, Hassan, Mona, Hassan, Alia, Ibrahim, Ahmed, Kaffas, Sameh, Kandeel, Ahmed, Ali, Mohamed Mandour, Mansy, Ahmad, Maurice, Hany, Nabil, Sherif, Shaaban, Mahmoud, Flores, Ana Camila, Poksi, Anne, Knuuti, Juhani, Kokkonen, Velipekka, Larikka, Martti, Uusitalo, Valtteri, Bailly, Matthieu, Burg, Samuel, Deux, Jean-François, Habouzit, Vincent, Hyafil, Fabien, Lairez, Olivier, Proffit, Franck, Regaieg, Hamza, Sarda-Mantel, Laure, Tacher, Vania, Schneider, Roman P., Ayetey, Harold, Angelidis, George, Archontaki, Aikaterini, Chatziioannou, Sofia, Datseris, Ioannis, Fragkaki, Christina, Georgoulias, Panagiotis, Koukouraki, Sophia, Koutelou, Maria, Kyrozi, Eleni, Repasos, Evangelos, Stavrou, Petros, Valsamaki, Pipitsa, Gonzalez, Carla, Gutierrez, Goleat, Maldonado, Alejandro, Buga, Klara, Garai, Ildiko, Maurovich-Horvat, Pál, Schmidt, Erzsébet, Szilveszter, Balint, Várady, Edit, Banthia, Nilesh, Bhagat, Jinendra Kumar, Bhargava, Rishi, Bhat, Vivek, Bhatia, Mona, Choudhury, Partha, Chowdekar, Vijay Sai, Irodi, Aparna, Jain, Shashank, Joseph, Elizabeth, Kumar, Sukriti, Girijanandan Mahapatra, Prof Dr, Mitra, Deepanjan, Mittal, Bhagwant Rai, Ozair, Ahmad, Patel, Chetan, Patel, Tapan, Patel, Ravi, Patel, Shivani, Saxena, Sudhir, Sengupta, Shantanu, Singh, Santosh, Singh, Bhanupriya, Sood, Ashwani, Verma, Atul, Affandi, Erwin, Alam, Padma Savenadia, Edison, Edison, Gunawan, Gani, Hapkido, Habusari, Hidayat, Basuki, Huda, Aulia, Mukti, Anggoro Praja, Prawiro, Djoko, Soeriadi, Erwin Affandi, Syawaluddin, Hilman, Albadr, Amjed, Assadi, Majid, Emami, Farshad, Houshmand, Golnaz, Maleki, Majid, Rostami, Maryam Tajik, Zakavi, Seyed Rasoul, Zaid, Eed Abu, Agranovich, Svetlana, Arnson, Yoav, Bar-Shalom, Rachel, Frenkel, Alex, Knafo, Galit, Lugassi, Rachel, Maor Moalem, Israel Shlomo, Mor, Maya, Muskal, Noam, Ranser, Sara, Shalev, Aryeh, Albano, Domenico, Alongi, Pierpaolo, Arnone, Gaspare, Bagatin, Elisa, Baldari, Sergio, Bauckneht, Matteo, Bertelli, Paolo, Bianco, Francesco, Bonfiglioli, Rachele, Boni, Roberto, Bruno, Andrea, Bruno, Isabella, Busnardo, Elena, Califaretti, Elena, Camoni, Luca, Carnevale, Aldo, Casoni, Roberta, Cavallo, Armando Ugo, Cavenaghi, Giorgio, Chierichetti, Franca, Chiocchi, Marcello, Cittanti, Corrado, Colletta, Mauro, Conti, Umberto, Cossu, Alberto, Cuocolo, Alberto, Cuzzocrea, Marco, De Rimini, Maria Luisa, De Vincentis, Giuseppe, Del Giudice, Eleonora, Del Torto, Alberico, Della Tommasina, Veronica, Durmo, Rexhep, Erba, Paola Anna, Evangelista, Laura, Faletti, Riccardo, Faragasso, Evelina, Farsad, Mohsen, Ferro, Paola, Florimonte, Luigia, Frantellizzi, Viviana, Fringuelli, Fabio Massimo, Gatti, Marco, Gaudiano, Angela, Gimelli, Alessia, Giubbini, Raffaele, Giuffrida, Francesca, Ialuna, Salvatore, Laudicella, Riccardo, Leccisotti, Lucia, Leva, Lucia, Liga, Riccardo, Liguori, Carlo, Longo, Giampiero, Maffione, Margherita, Mancini, Maria Elisabetta, Marcassa, Claudio, Milan, Elisa, Nardi, Barbara, Pacella, Sara, Pepe, Giovanna, Pontone, Gianluca, Pulizzi, Sabina, Quartuccio, Natale, Rampin, Lucia, Ricci, Fabrizio, Rossini, Pierluigi, Rubini, Giuseppe, Russo, Vincenzo, Sacchetti, Gian Mauro, Sambuceti, Gianmario, Scarano, Massimo, Sciagrà, Roberto, Sperandio, Massimiliano, Stefanelli, Antonella, Ventroni, Guido, Zoboli, Stefania, Baugh, Dainia, Chambers, Duane, Madu, Ernest, Nunura, Felix, Asano, Hiroshi, Chimura, Chimura Misato, Fujimoto, Shinichiro, Fujisue, Koichiro, Fukunaga, Tomohisa, Fukushima, Yoshimitsu, Fukuyama, Kae, Hashimoto, Jun, Ichikawa, Yasutaka, Iguchi, Nobuo, Imai, Masamichi, Inaki, Anri, Ishimura, Hayato, Isobe, Satoshi, Kadokami, Toshiaki, Kato, Takao, Kudo, Takashi, Kumita, Shinichiro, Maruno, Hirotaka, Mataki, Hiroyuki, Miyagawa, Masao, Morimoto, Ryota, Moroi, Masao, Nagamachi, Shigeki, Nakajima, Kenichi, Nakata, Tomoaki, Nakazato, Ryo, Nanasato, Mamoru, Naya, Masanao, Norikane, Takashi, Ohta, Yasutoshi, Okayama, Satoshi, Okizaki, Atsutaka, Otomi, Yoichi, Otsuka, Hideki, Saito, Masaki, Sakata, Sakata Yasushi, Sarai, Masayoshi, Sato, Daisuke, Shiraishi, Shinya, Suwa, Yoshinobu, Takanami, Kentaro, Takehana, Kazuya, Taki, Junichi, Tamaki, Nagara, Taniguchi, Yasuyo, Teragawa, Hiroki, Tomizawa, Nobuo, Tsujita, Kenichi, Umeji, Kyoko, Wakabayashi, Yasushi, Yamada, Shinichiro, Yamazaki, Shinya, Yoneyama, Tatsuya, Rawashdeh, Mohammad, Batyrkhanov, Daultai, Dautov, Tairkhan, Makhdomi, Khalid, Ombati, Kevin, Alkandari, Faridah, Garashi, Masoud, Coie, Tchoyoson Lim, Rajvong, Sonexay, Kalinin, Artem, Kalnina, Marika, Haidar, Mohamad, Komiagiene, Renata, Kviecinskiene, Giedre, Mataciunas, Mindaugas, Vajauskas, Donatas, Picard, Christian, Karim, Noor Khairiah A., Reichmuth, Luise, Samuel, Anthony, Allarakha, Mohammad Aaftaab, Naojee, Ambedhkar Shantaram, Alexanderson-Rosas, Erick, Barragan, Erika, González-Montecinos, Alejandro Becerril, Cabada, Manuel, Rodriguez, Daniel Calderon, Carvajal-Juarez, Isabel, Cortés, Violeta, Cortés, Filiberto, De La Peña, Erasmo, Gama-Moreno, Manlio, González, Luis, Ramírez, Nelsy Gonzalez, Jiménez-Santos, Moisés, Matos, Luis, Monroy, Edgar, Morelos, Martha, Ornelas, Mario, Ortga Ramirez, Jose Alberto, Preciado-Anaya, Andrés, Preciado-Gutiérrez, Óscar Ulises, Barragan, Adriana Puente, Rosales Uvera, Sandra Graciela, Sandoval, Sigelinda, Tomas, Miguel Santaularia, Sierra-Galan, Lilia M., Siu, Silvia, Vallejo, Enrique, Valles, Mario, Faraggi, Marc, Sereegotov, Erdenechimeg, Ilic, Srdja, Ben-Rais, Nozha, Alaoui, Nadia Ismaili, Taleb, Sara, Pa Myo, Khin Pa, Thu, Phyo Si, Ghimire, Ram Kumar, Rajbanshi, Bijoy, Barneveld, Peter, Glaudemans, Andor, Habets, Jesse, Koopmans, Klaas Pieter, Manders, Jeroen, Pool, Stefan, Scholte, Arthur, Scholtens, Asbjørn, Slart, Riemer, Thimister, Paul, Van Asperen, Erik-Jan, Veltman, Niels, Verschure, Derk, Wagenaar, Nils, Edmond, John, Ellis, Chris, Johnson, Kerryanne, Keenan, Ross, Kueh, Shaw Hua (Anthony), Occleshaw, Christopher, Sasse, Alexander, To, Andrew, Van Pelt, Niels, Young, Calum, Cuadra, Teresa, Roque Vanegas, Hector Bladimir, Soli, Idrissa Adamou, Issoufou, Djibrillou Moussa, Ayodele, Tolulope, Madu, Chibuzo, Onimode, Yetunde, Efros-Monsen, Elen, Forsdahl, Signe Helene, Hildre Dimmen, Jenni-Mari, Jørgensen, Arve, Krohn, Isabel, Løvhaugen, Pål, Bråten, Anders Tjellaug, Al Dhuhli, Humoud, Al Kindi, Faiza, Al-Bulushi, Naeema, Jawa, Zabah, Tag, Naima, Afzal, Muhammad Shehzad, Fatima, Shazia, Younis, Muhammad Numair, Riaz, Musab, Saadullah, Mohammad, Herrera, Yariela, Lenturut-Katal, Dora, Vázquez, Manuel Castillo, Ortellado, José, Akhter, Afroza, Cao, Dianbo, Cheung, Stephen, Dai, Xu, Gong, Lianggeng, Han, Dan, Hou, Yang, Li, Caiying, Li, Tao, Li, Dong, Li, Sijin, Liu, Jinkang, Liu, Hui, Lu, Bin, Ng, Ming Yen, Sun, Kai, Tang, Gongshun, Wang, Jian, Wang, Ximing, Wang, Zhao-Qian, Wang, Yining, Wang, Yifan, Wu, Jiang, Wu, Zhifang, Xia, Liming, Xiao, Jiangxi, Xu, Lei, Yang, Youyou, Yin, Wu, Yu, Jianqun, Yuan, Li, Zhang, Tong, Zhang, Longjiang, Zhang, Yong-Gao, Zhang, Xiaoli, Zhu, Li, Alfaro, Ana, Abrihan, Paz, Barroso, Asela, Cruz, Eric, Gomez, Marie Rhiamar, Magboo, Vincent Peter, Medina, John Michael, Obaldo, Jerry, Pastrana, Davidson, Pawhay, Christian Michael, Quinon, Alvin, Tang, Jeanelle Margareth, Tecson, Bettina, Uson, Kristine Joy, Uy, Mila, Kostkiewicz, Magdalena, Kunikowska, Jolanta, Bettencourt, Nuno, Cantinho, Guilhermina, Ferreira, Antonio, Syed, Ghulam, Arnous, Samer, Atyani, Said, Byrne, Angela, Gleeson, Tadhg, Kerins, David, Meehan, Conor, Murphy, David, Murphy, Mark, Murray, John, O'Brien, Julie, Bang, Ji-In, Bom, Henry, Cho, Sang-Geon, Hong, Chae Moon, Jang, Su Jin, Jeong, Yong Hyu, Kang, Won Jun, Kim, Ji-Young, Lee, Jaetae, Namgung, Chang Kyeong, So, Young, Won, Kyoung Sook, Majstorov, Venjamin, Vavlukis, Marija, Salobir, Barbara Gužic, Štalc, Monika, Benedek, Theodora, Benedek, Imre, Mititelu, Raluca, Stan, Claudiu Adrian, Ansheles, Alexey, Dariy, Olga, Drozdova, Olga, Gagarina, Nina, Gulyaev, Vsevolod Milyevich, Itskovich, Irina, Karalkin, Anatoly, Kokov, Alexander, Migunova, Ekaterina, Pospelov, Viktor, Ryzhkova, Daria, Saifullina, Guzaliya, Sazonova, Svetlana, Sergienko, Vladimir, Shurupova, Irina, Trifonova, Tatjana, Ussov, Wladimir Yurievich, Vakhromeeva, Margarita, Valiullina, Nailya, Zavadovsky, Konstantin, Zhuravlev, Kirill, Alasnag, Mirvat, Okarvi, Subhani, Saranovic, Dragana Sobic, Keng, Felix, Jason See, Jia Hao, Sekar, Ramkumar, Yew, Min Sen, Vondrak, Andrej, Bejai, Shereen, Bennie, George, Bester, Ria, Engelbrecht, Gerrit, Evbuomwan, Osayande, Gongxeka, Harlem, Vuuren, Magritha Jv, Kaplan, Mitchell, Khushica, Purbhoo, Lakhi, Hoosen, Louw, Lizette, Malan, Nico, Milos, Katarina, Modiselle, Moshe, More, Stuart, Naidoo, Mathava, Scholtz, Leonie, Vangu, Mboyo, Aguadé-Bruix, Santiago, Blanco, Isabel, Cabrera, Antonio, Camarero, Alicia, Casáns-Tormo, Irene, Cuellar-Calabria, Hug, Flotats, Albert, Fuentes Cañamero, Maria Eugenia, García, María Elia, Jimenez-Heffernan, Amelia, Leta, Rubén, Diaz, Javier Lopez, Lumbreras, Luis, Marquez-Cabeza, Juan Javier, Martin, Francisco, Martinez de Alegria, Anxo, Medina, Francisco, Canal, Maria Pedrera, Peiro, Virginia, Pubul-Nuñez, Virginia, Rayo Madrid, Juan Ignacio, Rey, Cristina Rodríguez, Perez, Ricardo Ruano, Ruiz, Joaquín, Hernández, Gertrudis Sabatel, Sevilla, Ana, Zeidán, Nahla, Nanayakkara, Damayanthi, Udugama, Chandraguptha, Simonsson, Magnus, Alkadhi, Hatem, Buechel, Ronny Ralf, Burger, Peter, Ceriani, Luca, De Boeck, Bart, Gräni, Christoph, Juillet de Saint Lager Lucas, Alix, Kamani, Christel H., Kawel-Boehm, Nadine, Manka, Robert, Prior, John O., Rominger, Axel, Vallée, Jean-Paul, Khiewvan, Benjapa, Premprabha, Teerapon, Thientunyakit, Tanyaluck, Sellem, Ali, Kir, Kemal Metin, Sayman, Haluk, Sebikali, Mugisha Julius, Muyinda, Zerida, Kmetyuk, Yaroslav, Korol, Pavlo, Mykhalchenko, Olena, Pliatsek, Volodymyr, Satyr, Maryna, Albalooshi, Batool, Ahmed Hassan, Mohamed Ismail, Anderson, Jill, Bedi, Punit, Biggans, Thomas, Bularga, Anda, Bull, Russell, Burgul, Rajesh, Carpenter, John-Paul, Coles, Duncan, Cusack, David, Deshpande, Aparna, Dougan, John, Fairbairn, Timothy, Farrugia, Alexia, Gopalan, Deepa, Gummow, Alistair, Ramkumar, Prasad Guntur, Hamilton, Mark, Harbinson, Mark, Hartley, Thomas, Hudson, Benjamin, Joshi, Nikhil, Kay, Michael, Kelion, Andrew, Khokhar, Azhar, Kitt, Jamie, Lee, Ken, Low, Chen, Mak, Sze Mun, Marousa, Ntouskou, Martin, Jon, Mcalindon, Elisa, Menezes, Leon, Morgan-Hughes, Gareth, Moss, Alastair, Murray, Anthony, Nicol, Edward, Patel, Dilip, Peebles, Charles, Pugliese, Francesca, Luis Rodrigues, Jonathan Carl, Rofe, Christopher, Sabharwal, Nikant, Schofield, Rebecca, Semple, Thomas, Sharma, Naveen, Strouhal, Peter, Subedi, Deepak, Topping, William, Tweed, Katharine, Weir-Mccall, Jonathan, Abbara, Suhny, Abbasi, Taimur, Abbott, Brian, Abohashem, Shady, Abramson, Sandra, Al-Abboud, Tarek, Al-Mallah, Mouaz, Almousalli, Omar, Ananthasubramaniam, Karthikeyan, Kumar, Mohan Ashok, Askew, Jeffrey, Attanasio, Lea, Balmer-Swain, Mallory, Bayer, Richard R., Bernheim, Adam, Bhatti, Sabha, Bieging, Erik, Blankstein, Ron, Bloom, Stephen, Blue, Sean, Bluemke, David, Borges, Andressa, Branch, Kelley, Bravo, Paco, Brothers, Jessica, Budoff, Matthew, Bullock-Palmer, Renée, Burandt, Angela, Burke, Floyd W., Bush, Kelvin, Candela, Candace, Capasso, Elizabeth, Cavalcante, Joao, Chang, Donald, Chatterjee, Saurav, Chatzizisis, Yiannis, Cheezum, Michael, Chen, Tiffany, Chen, Jennifer, Chen, Marcus, Clarcq, James, Cordero, Ayreen, Crim, Matthew, Danciu, Sorin, Decter, Bruce, Dhruva, Nimish, Doherty, Neil, Doukky, Rami, Dunbar, Anjori, Duvall, William, Edwards, Rachael, Esquitin, Kerry, Farah, Husam, Fentanes, Emilio, Ferencik, Maros, Fisher, Daniel, Fitzpatrick, Daniel, Foster, Cameron, Fuisz, Tony, Gannon, Michael, Gastner, Lori, Gerson, Myron, Ghoshhajra, Brian, Goldberg, Alan, Goldner, Brian, Gonzalez, Jorge, Gore, Rosco, Gracia-López, Sandra, Hage, Fadi, Haider, Agha, Haider, Sofia, Hamirani, Yasmin, Hassen, Karen, Hatfield, Mallory, Hawkins, Carolyn, Hawthorne, Katie, Heath, Nicholas, Hendel, Robert, Hernandez, Phillip, Hill, Gregory, Horgan, Stephen, Huffman, Jeff, Hurwitz, Lynne, Iskandrian, Ami, Janardhanan, Rajesh, Jellis, Christine, Jerome, Scott, Kalra, Dinesh, Kaviratne, Summanther, Kay, Fernando, Kelly, Faith, Khalique, Omar, Kinkhabwala, Mona, Iii, George Kinzfogl, Kircher, Jacqueline, Kirkbride, Rachael, Kontos, Michael, Kottam, Anupama, Krepp, Joseph, Layer, Jay, Lee, Steven H., Leppo, Jeffrey, Lesser, John, Leung, Steve, Lewin, Howard, Litmanovich, Diana, Liu, Yiyan, Magurany, Kathleen, Markowitz, Jeremy, Marn, Amanda, Matis, Stephen E., Mckenna, Michael, Mcrae, Tony, Mendoza, Fernando, Merhige, Michael, Min, David, Moffitt, Chanan, Moncher, Karen, Moore, Warren, Morayati, Shamil, Morris, Michael, Mossa-Basha, Mahmud, Mrsic, Zorana, Murthy, Venkatesh, Nagpal, Prashant, Napier, Kyle, Nelson, Katarina, Nijjar, Prabhjot, Osman, Medhat, Passen, Edward, Patel, Amit, Patil, Pravin, Paul, Ryan, Phillips, Lawrence, Polsani, Venkateshwar, Poludasu, Rajaram, Pomerantz, Brian, Porter, Thomas, Prentice, Ryan, Pursnani, Amit, Rabbat, Mark, Ramamurti, Suresh, Rich, Florence, Luna, Hiram Rivera, Robinson, Austin, Robles, Kim, Rodríguez, Cesar, Rorie, Mark, Rumberger, John, Russell, Raymond, Sabra, Philip, Sadler, Diego, Schemmer, Mary, Schoepf, U. Joseph, Shah, Samir, Shah, Nishant, Shanbhag, Sujata, Sharma, Gaurav, Shayani, Steven, Shirani, Jamshid, Shivaram, Pushpa, Sigman, Steven, Simon, Mitch, Slim, Ahmad, Smith, David, Smith, Alexandra, Soman, Prem, Sood, Aditya, Srichai-Parsia, Monvadi Barbara, Streeter, James, T, Albert, Tawakol, Ahmed, Thomas, Dustin, Thompson, Randall, Torbet, Tara, Trinidad, Desiree, Ullery, Shawn, Unzek, Samuel, Uretsky, Seth, Vallurupalli, Srikanth, Verma, Vikas, Waller, Alfonso, Wang, Ellen, Ward, Parker, Weissman, Gaby, Wesbey, George, White, Kelly, Winchester, David, Wolinsky, David, Yost, Sandra, Zgaljardic, Michael, Alonso, Omar, Beretta, Mario, Ferrando, Rodolfo, Kapitan, Miguel, Mut, Fernando, Djuraev, Omoa, Rozikhodjaeva, Gulnora, Le Ngoc, Ha, Mai, Son Hong, Nguyen, Xuan Canh, Lahey, Ryan, Henry Bom, Hee-Seung, Fazel, Reza, Karthikeyan, Ganesan, Keng, Felix Y.J., Rubinshtein, Ronen, Cerci, Rodrigo Julio, Vitola, João V., Choi, Andrew D., and Cohen, Yosef A.

Published
2021
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45. The relationship between systemic inflammation and increased left ventricular mass is partly mediated by noncalcified coronary artery disease burden in psoriasis

Author

Zhou, Wunan, Teklu, Meron, Bui, Vy, Manyak, Grigory A., Kapoor, Promita, Dey, Amit K., Sorokin, Alexander V., Patel, Nidhi, Teague, Heather L., Playford, Martin P., Erb-Alvarez, Julie, Rodante, Justin A., Keel, Andrew, Shanbhag, Sujata M., Hsu, Li-Yueh, Bluemke, David A., Chen, Marcus Y., Carlsson, Marcus, and Mehta, Nehal N.

Published
2021
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50. Metabolic syndrome and its factors are associated with noncalcified coronary burden in psoriasis: An observational cohort study

Author

Teklu, Meron, Zhou, Wunan, Kapoor, Promita, Patel, Nidhi, Dey, Amit K., Sorokin, Alexander V., Manyak, Grigory A., Teague, Heather L., Erb-Alvarez, Julie A., Sajja, Aparna, Abdelrahman, Khaled M., Reddy, Aarthi S., Uceda, Domingo E., Lateef, Sundus S., Shanbhag, Sujata M., Scott, Colin, Prakash, Nina, Svirydava, Maryia, Parel, Philip, Rodante, Justin A., Keel, Andrew, Siegel, Evan L., Chen, Marcus Y., Bluemke, David A., Playford, Martin P., Gelfand, Joel M., and Mehta, Nehal N.

Published
2021
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