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  1. AU="Gibbs, Tom"
  2. AU=Mezzabotta Federica
  3. AU="Jalas, Sören"
  4. AU="Suma, Rache"
  5. AU="Calderón, Alejandro"
  6. AU="Demertzi, Vasiliki"
  7. AU="Leonidov, A"
  8. AU="Luo, Suxin"
  9. AU="Thompson, Charlotte A S"
  10. AU="Dubbel, Polly"
  11. AU="Ten Bosch, Nora"
  12. AU="Giménez-Arnau, Ana Maria"
  13. AU=Maul Robert W.
  14. AU="Ivn Prez-MaldonadoauthorLaboratorio de Toxicologa Molecular, Centro de Investigacin Aplicada en Ambiente y Salud (CIAAS), Coordinacin para la Innovacin y Aplicacin de la Ciencia y la Tecnologa (CIACYT), Universidad Autnoma de San Luis Potos, MexicoFacultad de Medicina, Universidad Autnoma de San Luis Potos, San Luis Potos, MexicoFacultad de Enfermera, Universidad Autnoma de Zacatecas, Mexico"
  15. AU="Hansen, Kristian Schultz"
  16. AU="Davenport, Bennett"

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  1. Buch ; Online: Performance Evaluation and Acceleration of the QTensor Quantum Circuit Simulator on GPUs

    Lykov, Danylo / Chen, Angela / Chen, Huaxuan / Keipert, Kristopher / Zhang, Zheng / Gibbs, Tom / Alexeev, Yuri

    2022  

    Abstract: This work studies the porting and optimization of the tensor network simulator QTensor on GPUs, with the ultimate goal of simulating quantum circuits efficiently at scale on large GPU supercomputers. We implement NumPy, PyTorch, and CuPy backends and ... ...

    Abstract This work studies the porting and optimization of the tensor network simulator QTensor on GPUs, with the ultimate goal of simulating quantum circuits efficiently at scale on large GPU supercomputers. We implement NumPy, PyTorch, and CuPy backends and benchmark the codes to find the optimal allocation of tensor simulations to either a CPU or a GPU. We also present a dynamic mixed backend to achieve optimal performance. To demonstrate the performance, we simulate QAOA circuits for computing the MaxCut energy expectation. Our method achieves $176\times$ speedup on a GPU over the NumPy baseline on a CPU for the benchmarked QAOA circuits to solve MaxCut problem on a 3-regular graph of size 30 with depth $p=4$.
    Schlagwörter Quantum Physics ; Computer Science - Mathematical Software
    Erscheinungsdatum 2022-04-12
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning.

    Elnaggar, Ahmed / Heinzinger, Michael / Dallago, Christian / Rehawi, Ghalia / Wang, Yu / Jones, Llion / Gibbs, Tom / Feher, Tamas / Angerer, Christoph / Steinegger, Martin / Bhowmik, Debsindhu / Rost, Burkhard

    IEEE transactions on pattern analysis and machine intelligence

    2022  Band 44, Heft 10, Seite(n) 7112–7127

    Abstract: Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we ... ...

    Abstract Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids. The protein LMs (pLMs) were trained on the Summit supercomputer using 5616 GPUs and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw pLM-embeddings from unlabeled data captured some biophysical features of protein sequences. We validated the advantage of using the embeddings as exclusive input for several subsequent tasks: (1) a per-residue (per-token) prediction of protein secondary structure (3-state accuracy Q3=81%-87%); (2) per-protein (pooling) predictions of protein sub-cellular location (ten-state accuracy: Q10=81%) and membrane versus water-soluble (2-state accuracy Q2=91%). For secondary structure, the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without multiple sequence alignments (MSAs) or evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that pLMs learned some of the grammar of the language of life. All our models are available through https://github.com/agemagician/ProtTrans.
    Mesh-Begriff(e) Algorithms ; Computational Biology/methods ; Natural Language Processing ; Proteins/chemistry ; Supervised Machine Learning
    Chemische Substanzen Proteins
    Sprache Englisch
    Erscheinungsdatum 2022-09-14
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
    ISSN 1939-3539
    ISSN (online) 1939-3539
    DOI 10.1109/TPAMI.2021.3095381
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel: Innovation. Property is wealth.

    Plaistowe, Richard / Gibbs, Tom

    The Health service journal

    2010  Band 120, Heft 6217, Seite(n) 22

    Mesh-Begriff(e) Intellectual Property ; United Kingdom
    Sprache Englisch
    Erscheinungsdatum 2010-07-29
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 632799-0
    ISSN 0952-2271 ; 0300-8347
    ISSN 0952-2271 ; 0300-8347
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Buch ; Online: CodeTrans

    Elnaggar, Ahmed / Ding, Wei / Jones, Llion / Gibbs, Tom / Feher, Tamas / Angerer, Christoph / Severini, Silvia / Matthes, Florian / Rost, Burkhard

    Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing

    2021  

    Abstract: Currently, a growing number of mature natural language processing applications make people's life more convenient. Such applications are built by source code - the language in software engineering. However, the applications for understanding source code ... ...

    Abstract Currently, a growing number of mature natural language processing applications make people's life more convenient. Such applications are built by source code - the language in software engineering. However, the applications for understanding source code language to ease the software engineering process are under-researched. Simultaneously, the transformer model, especially its combination with transfer learning, has been proven to be a powerful technique for natural language processing tasks. These breakthroughs point out a promising direction for process source code and crack software engineering tasks. This paper describes CodeTrans - an encoder-decoder transformer model for tasks in the software engineering domain, that explores the effectiveness of encoder-decoder transformer models for six software engineering tasks, including thirteen sub-tasks. Moreover, we have investigated the effect of different training strategies, including single-task learning, transfer learning, multi-task learning, and multi-task learning with fine-tuning. CodeTrans outperforms the state-of-the-art models on all the tasks. To expedite future works in the software engineering domain, we have published our pre-trained models of CodeTrans. https://github.com/agemagician/CodeTrans

    Comment: 28 pages, 6 tables and 1 figure
    Schlagwörter Computer Science - Software Engineering ; Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; Computer Science - Machine Learning ; Computer Science - Programming Languages
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2021-04-06
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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  5. Artikel: ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing

    Elnaggar, Ahmed / Heinzinger, Michael / Dallago, Christian / Rihawi, Ghalia / Wang, Yu / Jones, Llion / Gibbs, Tom / Feher, Tamas / Angerer, Christoph / Steinegger, Martin / Bhowmik, Debsindhu / Rost, Burkhard

    Abstract: Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we ... ...

    Abstract Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112-times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure (3-states: Q3=76-84, 8-states: Q8=65-73), sub-cellular localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. The official GitHub repository: https://github.com/agemagician/ProtTrans
    Schlagwörter covid19
    Verlag ArXiv
    Dokumenttyp Artikel
    Datenquelle COVID19

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  6. Artikel ; Online: ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing

    Elnaggar, Ahmed / Heinzinger, Michael / Dallago, Christian / Rehawi, Ghalia / Wang, Yu / Jones, Llion / Gibbs, Tom / Feher, Tamas / Angerer, Christoph / Steinegger, Martin / Bhowmik, Debsindhu / Rost, Burkhard

    bioRxiv

    Abstract: Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we ... ...

    Abstract Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112-times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure (3-states: Q3=76-84, 8-states: Q8=65-73), sub-cellular localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs.
    Schlagwörter covid19
    Verlag BioRxiv
    Dokumenttyp Artikel ; Online
    DOI 10.1101/2020.07.12.199554
    Datenquelle COVID19

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  7. Buch ; Online: ProtTrans

    Elnaggar, Ahmed / Heinzinger, Michael / Dallago, Christian / Rihawi, Ghalia / Wang, Yu / Jones, Llion / Gibbs, Tom / Feher, Tamas / Angerer, Christoph / Steinegger, Martin / Bhowmik, Debsindhu / Rost, Burkhard

    Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing

    2020  

    Abstract: Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models taken from NLP. These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive models ( ... ...

    Abstract Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models taken from NLP. These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids. The LMs were trained on the Summit supercomputer using 5616 GPUs and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw protein LM-embeddings from unlabeled data captured some biophysical features of protein sequences. We validated the advantage of using the embeddings as exclusive input for several subsequent tasks. The first was a per-residue prediction of protein secondary structure (3-state accuracy Q3=81%-87%); the second were per-protein predictions of protein sub-cellular localization (ten-state accuracy: Q10=81%) and membrane vs. water-soluble (2-state accuracy Q2=91%). For the per-residue predictions the transfer of the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without using evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that protein LMs learned some of the grammar of the language of life. To facilitate future work, we released our models at https://github.com/agemagician/ProtTrans.

    Comment: 17 pages, 9 figures, 4 tables
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Computation and Language ; Computer Science - Distributed ; Parallel ; and Cluster Computing ; Statistics - Machine Learning
    Thema/Rubrik (Code) 612
    Erscheinungsdatum 2020-07-13
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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  8. Artikel: #COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol.

    Dommer, Abigail / Casalino, Lorenzo / Kearns, Fiona / Rosenfeld, Mia / Wauer, Nicholas / Ahn, Surl-Hee / Russo, John / Oliveira, Sofia / Morris, Clare / Bogetti, Anthony / Trifan, Anda / Brace, Alexander / Sztain, Terra / Clyde, Austin / Ma, Heng / Chennubhotla, Chakra / Lee, Hyungro / Turilli, Matteo / Khalid, Syma /
    Tamayo-Mendoza, Teresa / Welborn, Matthew / Christensen, Anders / Smith, Daniel Ga / Qiao, Zhuoran / Sirumalla, Sai K / O'Connor, Michael / Manby, Frederick / Anandkumar, Anima / Hardy, David / Phillips, James / Stern, Abraham / Romero, Josh / Clark, David / Dorrell, Mitchell / Maiden, Tom / Huang, Lei / McCalpin, John / Woods, Christopher / Gray, Alan / Williams, Matt / Barker, Bryan / Rajapaksha, Harinda / Pitts, Richard / Gibbs, Tom / Stone, John / Zuckerman, Daniel M / Mulholland, Adrian J / Miller, Thomas / Jha, Shantenu / Ramanathan, Arvind / Chong, Lillian / Amaro, Rommie E

    The international journal of high performance computing applications

    2022  Band 37, Heft 1, Seite(n) 28–44

    Abstract: We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of ... ...

    Abstract We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.
    Sprache Englisch
    Erscheinungsdatum 2022-10-02
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2017480-9
    ISSN 1741-2846 ; 1094-3420
    ISSN (online) 1741-2846
    ISSN 1094-3420
    DOI 10.1177/10943420221128233
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  9. Artikel: AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics.

    Casalino, Lorenzo / Dommer, Abigail / Gaieb, Zied / Barros, Emilia P / Sztain, Terra / Ahn, Surl-Hee / Trifan, Anda / Brace, Alexander / Bogetti, Anthony / Ma, Heng / Lee, Hyungro / Turilli, Matteo / Khalid, Syma / Chong, Lillian / Simmerling, Carlos / Hardy, David J / Maia, Julio D C / Phillips, James C / Kurth, Thorsten /
    Stern, Abraham / Huang, Lei / McCalpin, John / Tatineni, Mahidhar / Gibbs, Tom / Stone, John E / Jha, Shantenu / Ramanathan, Arvind / Amaro, Rommie E

    bioRxiv : the preprint server for biology

    2020  

    Abstract: We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the ... ...

    Abstract We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike's full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.
    Sprache Englisch
    Erscheinungsdatum 2020-11-20
    Erscheinungsland United States
    Dokumenttyp Preprint
    DOI 10.1101/2020.11.19.390187
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  10. Artikel: AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics.

    Casalino, Lorenzo / Dommer, Abigail C / Gaieb, Zied / Barros, Emilia P / Sztain, Terra / Ahn, Surl-Hee / Trifan, Anda / Brace, Alexander / Bogetti, Anthony T / Clyde, Austin / Ma, Heng / Lee, Hyungro / Turilli, Matteo / Khalid, Syma / Chong, Lillian T / Simmerling, Carlos / Hardy, David J / Maia, Julio Dc / Phillips, James C /
    Kurth, Thorsten / Stern, Abraham C / Huang, Lei / McCalpin, John D / Tatineni, Mahidhar / Gibbs, Tom / Stone, John E / Jha, Shantenu / Ramanathan, Arvind / Amaro, Rommie E

    The international journal of high performance computing applications

    2020  Band 35, Heft 5, Seite(n) 432–451

    Abstract: We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the ... ...

    Abstract We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike's full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.
    Sprache Englisch
    Erscheinungsdatum 2020-09-01
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2017480-9
    ISSN 1741-2846 ; 1094-3420
    ISSN (online) 1741-2846
    ISSN 1094-3420
    DOI 10.1177/10943420211006452
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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