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online data analysis at the ESRF bioSAXS beamline, BM29

JOURNAL OF APPLIED CRYSTALLOGRAPHY 2016年第1期49卷 203-212页

作者： Brennich, Martha Elisabeth Kieffer, Jerome Bonamis, Guillaume Antolinos, Alejandro De Maria Hutin, Stephanie Pernot, Petra Round, Adam European Synchrotron Radiat Facil 71 Ave MartyrsCS 40220 F-38043 Grenoble France European Mol Biol Lab Grenoble Outstn 71 Ave MartyrsCS 90181 F-38042 Grenoble France Univ Grenoble Alpes EMBL CNRS Unit Virus Host Cell Interact 71 Ave MartyrsCS 90181 F-38042 Grenoble France Keele Univ Fac Nat Sci Keele ST5 5BG Staffs England

High-throughput small-angle X-ray scattering on proteins in solution (bioSAXS) at synchrotron sources is a commonly used technique in structural biology, which relies on highly automated data acquisition. data reduction and primary analysis for bioSAXS experiments consist of a well defined series of individual tasks, the automation of which allows a first easy assessment of the quality of collected data and the adjustment of collection strategies if necessary. This article describes both the logic and the technical implementation of the automated processing pipeline for bioSAXS data at the ESRF BM29 beamline using the EDNA framework.

关键词： online data analysis solution scattering proteins

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OnDA: online data analysis and feedback for serial X-ray imaging

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JOURNAL OF APPLIED CRYSTALLOGRAPHY 2016年第3期49卷 1073-1080页

作者： Mariani, Valerio Morgan, Andrew Yoon, Chun Hong Lane, Thomas J. White, Thomas A. O'Grady, Christopher Kuhn, Manuela Aplin, Steve Koglin, Jason Barty, Anton Chapman, Henry N. DESY Ctr Free Electron Laser Sci Notkestr 85 D-22607 Hamburg Germany DESY Notkestr 85 D-22607 Hamburg Germany European XFEL GmbH Albert Einstein Ring 19 D-22761 Hamburg Germany SLAC Natl Accelerator Lab Linac Coherent Light Source 2575 Sand Hill Rd Menlo Pk CA 94025 USA Univ Hamburg Dept Phys Luruper Chaussee 149 D-22761 Hamburg Germany Ctr Ultrafast Imaging Luruper Chaussee 149 D-22761 Hamburg Germany

This article describes a free and open-source data analysis utility designed for fast online feedback during serial X-ray diffraction and scattering experiments: OnDA (online data analysis). Three complete real-time monitors for common types of serial X-ray imaging experiments are presented. These monitors are capable of providing the essential information required for quick decision making in the face of extreme rates of data collection. In addition, a set of modules, functions and algorithms that allow developers to modify the provided monitors or develop new ones are provided. The emphasis here is on simple, modular and scalable code that is based on open-source libraries and protocols. OnDA monitors have already proven to be invaluable tools in several experiments, especially for scoring and monitoring of diffraction data during serial crystallography experiments at both free-electron laser and synchrotron facilities. It is felt that in the future the kind of fast feedback that OnDA monitors provide will help researchers to deal with the expected very high throughput data flow at next-generation facilities such as the European X-ray free-electron laser.

关键词： online data analysis free-electron lasers serial femtosecond crystallography computer programs

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It takes two (seconds): decreasing encoding time for two-choice functional near-infrared spectroscopy brain-computer interface communication

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NEUROPHOTONICS 2023年第4期10卷 045005页

作者： Vorreuther, Anna Bastian, Lisa Andonegui, Amaia Benitez Evenblij, Danielle Riecke, Lars Luhrs, Michael Sorger, Bettina Maastricht Univ Dept Cognit Neurosci Maastricht Netherlands Univ Stuttgart Inst Human Factors & Technol Management IAT Appl Neurocognit Syst Stuttgart Germany Univ Tubingen Inst Med Psychol & Behav Neurobiol Tubingen Germany Int Max Planck Res Sch Grad Training Ctr Neurosci Tubingen Germany NIH MEG Core Facil Natl Inst Mental Hlth Bethesda MD USA Brain Innovat BV Res Dept Maastricht Netherlands

Significance: Brain-computer interfaces (BCIs) can provide severely motorimpaired patients with a motor-independent communication channel. Functional near-infrared spectroscopy (fNIRS) constitutes a promising BCI-input modality given its high mobility, safety, user comfort, cost-efficiency, and relatively low motion sensitivity. Aim: The present study aimed at developing an efficient and convenient two-choice fNIRS communication BCI by implementing a relatively short encoding time (2 s), considerably increasing communication speed, and decreasing the cognitive load of BCI users. Approach: To encode binary answers to 10 biographical questions, 10 healthy adults repeatedly performed a combined motor-speech imagery task within 2 different time windows guided by auditory instructions. Each answer-encoding run consisted of 10 trials. Answers were decoded during the ongoing experiment from the time course of the individually identified most-informative fNIRS channel-bychromophore combination. Results: The answers of participants were decoded online with an accuracy of 85.8% (run-based group mean). Post-hoc analysis yielded an average single-trial accuracy of 68.1%. analysis of the effect of number of trial repetitions showed that the best information-transfer rate could be obtained by combining four encoding trials. Conclusions: The study demonstrates that an encoding time as short as 2 s can enable immediate, efficient, and convenient fNIRS-BCI communication.

关键词： motor disability '' locked-in '' syndrome brain-based communication brain-computer interface fNIRS mental imagery online data analysis

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Raynals, an online tool for the analysis of dynamic light scattering

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ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY 2023年第8期79卷 673-683页

作者： Burastero, Osvaldo Draper-Barr, George Raynal, Bertrand Chevreuil, Maelenn England, Patrick Alai, Maria Garcia European Mol Biol Lab Notkestr 85 Hamburg Germany Ctr Struct Syst Biol Notkestr 85 D-22607 Hamburg Germany Univ Paris Cite CNRS UMR 3528 Inst Pasteur Plateforme Biophys Mol Paris France

Dynamic light scattering (DLS) is routinely employed to assess the homogeneity and size-distribution profile of samples containing microscopic particles in suspension or solubilized polymers. In this work, Raynals, user-friendly software for the analysis of single-angle DLS data that uses the Tikhonov-Phillips regularization, is introduced. Its performance is evaluated on simulated and experimental data generated by different DLS instruments for several proteins and gold nanoparticles. DLS data can easily be misinterpreted and the simulation tools available in Raynals allow the limitations of the measurement and its resolution to be understood. It was designed as a tool to address the quality control of biological samples during sample preparation and optimization and it helps in the detection of aggregates, showing the influence of large particles. Lastly, Raynals provides flexibility in the way that the data are presented, allows the export of publication-quality figures, is free for academic use and can be accessed online on the eSPC data-analysis platform at https://***/.

关键词： Raynals dynamic light scattering molecular biophysics online data analysis protein quality control hydrodynamic radius size distribution software

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online dependence clustering of multivariate streaming data using one-class SVMs

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第6期37卷 3682-3708页

作者： Lee, Geonseok Lee, Kichun Hanyang Univ Dept Ind Engn 222 Wangsimni Ro Seoul 04763 South Korea

online clustering of multivariate streaming data has attracted considerable interest in recent years due to the abundance of data sources. Numerous studies in this field have been performed, but they usually suffer from the practical problems associated with discovering arbitrary-shaped clusters, specifying major parameters in advance, and detecting aberrant observations. Addressing these issues is important for online-clustering tasks, where data arrive in continuous streams and group behaviors change simultaneously. In this paper, we propose a kernel-based online dependence clustering, namely, KODC, that not only estimates the cluster membership using one-class support vector machines (OC-SVMs), but also detects outliers distant from the identified clusters by aggregating OC-SVM decisions in a realtime basis. At the base level, we use a new measure of connective dependence that forms the graph connected via modified Markovian transitions to enable large-scale clustering. The proposed framework introduces the coherence threshold to extract data points, which can represent a cluster to which they belong, thus controlling the computational complexity without degrading the clustering performance. To track the pattern evolution over time, KODC also updates the classifier configuration maximizing the total group connective dependence. We evaluate this framework on both several synthetic and real-world data sets involving multivariate streaming data, and compare it experimentally with other popular online-clustering methods in terms of four evaluation metrics. The results show that our framework effectively identifies the clusters and outliers, especially in various shaped data subject to change over time, without requiring any prior knowledge of the data.

关键词： dependence clustering one-class support vector machine online data analysis outlier detection unsupervised learning

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New data analysis for BioSAXS at the ESRF

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JOURNAL OF SYNCHROTRON RADIATION 2022年第5期29卷 1318-1328页

作者： Kieffer, Jerome Brennich, Martha Florial, Jean Baptiste Oscarsson, Marcus Antolinos, Alejandro De Maria Tully, Mark Pernot, Petra ESRF European Synchrotron 71 Ave Martyrs F-38000 Grenoble France European Mol Biol Lab 71 Ave Martyrs F-38000 Grenoble France

The second phase of the ESRF upgrade program did not only provide a new storage ring (Extremely Brilliant Source, EBS) but also allowed several beamlines to be refurbished. The BioSAXS beamline (located on port BM29) was upgraded with a new wiggler source and a larger detector. All analysis software has been rewritten to cope with the increased data flux and continues to provide beamline users with reduced and pre-processed data in real time. This article describes FreeSAS, an open-source collection of various small-angle scattering analysis algorithms needed to reduce and analyze BioSAXS data, and Dahu, the tool used to interface data analysis with beamline control. It further presents the data-processing pipelines for the different data acquisitions modes of the beamline, using either a sample changer for individual homogeneous samples or an inline size-exclusion chromatography setup.

关键词： BioSAXS online data analysis solution scattering proteins biological small-angle X-ray scattering automation high brilliance structural biology high-throughput SAXS size-exclusion chromatography online purification

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RDMA data transfer and GPU acceleration methods for high-throughput online processing of serial crystallography images

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JOURNAL OF SYNCHROTRON RADIATION 2020年第5期27卷 1297-1306页

作者： Ponsard, Raphael Janvier, Nicolas Kieffer, Jerome Houzet, Dominique Fristot, Vincent ESRF European Synchrotron 71 Ave Martyrs F-38000 Grenoble France Grenoble Alpes Univ GIPSALAB 11 Rue Math F-38400 St Martin Dheres France

The continual evolution of photon sources and high-performance detectors drives cutting-edge experiments that can produce very high throughput data streams and generate large data volumes that are challenging to manage and store. In these cases, efficient data transfer and processing architectures that allow online image correction, data reduction or compression become fundamental. This work investigates different technical options and methods for data placement from the detector head to the processing computing infrastructure, taking into account the particularities of modern modular high-performance detectors. In order to compare realistic figures, the future ESRF beamline dedicated to macromolecular X-ray crystallography, EBSL8, is taken as an example, which will use a PSI JUNGFRAU 4M detector generating up to 16 GB of data per second, operating continuously during several minutes. Although such an experiment seems possible at the target speed with the 100 Gb s(-1) network cards that are currently available, the simulations generated highlight some potential bottlenecks when using a traditional software stack. An evaluation of solutions is presented that implements remote direct memory access (RDMA) over converged ethernet techniques. A synchronization mechanism is proposed between a RDMA network interface card (RNIC) and a graphics processing unit (GPU) accelerator in charge of the online data processing. The placement of the detector images onto the GPU is made to overlap with the computation carried out, potentially hiding the transfer latencies. As a proof of concept, a detector simulator and a backend GPU receiver with a rejection and compression algorithm suitable for a synchrotron serial crystallography (SSX) experiment are developed. It is concluded that the available transfer throughput from the RNIC to the GPU accelerator is at present the major bottleneck in online processing for SSX experiments.

关键词： online data processing RDMA RoCEv2 GPU SSX online data analysis

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Automated data collection and real-time data analysis suite for serial synchrotron crystallography

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JOURNAL OF SYNCHROTRON RADIATION 2019年第1期26卷 244-252页

作者： Basu, Shibom Kaminski, Jakub W. Panepucci, Ezequiel Huang, Chia-Ying Warshamanage, Rangana Wang, Meitian Wojdyla, Justyna Aleksandra Paul Scherrer Inst Swiss Light Source CH-5232 Villigen Switzerland EMBL Grenoble 71 Ave MartyrsCS 90181 F-38042 Grenoble France Cambridge Biomed Campus MRC Lab Mol Biol Francis Crick Ave Cambridge CB2 0QH England

At the Swiss Light Source macromolecular crystallography (MX) beamlines the collection of serial synchrotron crystallography (SSX) diffraction data is facilitated by the recent DA+ data acquisition and analysis software developments. The SSX suite allows easy, efficient and high-throughput measurements on a large number of crystals. The fast continuous diffraction-based two-dimensional grid scan method allows initial location of microcrystals. The CY+GUI utility enables efficient assessment of a grid scan's analysis output and subsequent collection of multiple wedges of data (so-called minisets) from automatically selected positions in a serial and automated way. The automated data processing (adp) routines adapted to the SSX data collection mode provide near real time analysis for data in both CBF and HDF5 formats. The automatic data merging (adm) is the latest extension of the DA+ data analysis software routines. It utilizes the sxdm (SSX data merging) package, which provides automatic online scaling and merging of minisets and allows identification of a minisets subset resulting in the best quality of the final merged data. The results of both adp and adm are sent to the MX MongoDB database and displayed in the web-based tracker, which provides the user with on-the-fly feedback about the experiment.

关键词： serial synchrotron crystallography protein crystallography online data analysis data merging data acquisition

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A Mean Model Based Incremental Learning Technique for Extreme Learning Machine 2nd

A Mean Model Based Incremental Learning Technique for Extrem...

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2nd International Conference on Recent Trends in Advanced Computing, Disruptive Innovation (ICRTAC-DTI)

作者： Vidhya, M. Aji, S. Univ Kerala Thiruvananthapuram Kerala India

Development of efficient methods for processing the online data is interesting as well as challenging in the computing scenario of data intensive applications, especially in streaming. Because of the inherent computational nature of Extreme Learning Machine, the non-iterative learning mechanism, it has undergone serous studies and exploitation especially in the domain of online data analysis. This paper gives a simple and effective strategy for updating the learned parameters, the knowledge of a machine learning system, in a computationally better perspective comparing with the existing state-of-the-art learning techniques. The performance of different measures for the central tendency is analyzed in this work. It is found that the results are comparable in respect of normal models. The overall performance of the proposed method with LIBSVM data sets gives an interesting direction for the improvement of learning machines for online data processing. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Extreme Learning Machine online data analysis Real-time Applications Central Tendency ELM Learning Knowledge Updating

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data acquisition architecture and online processing system for the HAWC gamma-ray observatory

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2018年 888卷 138-146页

作者： Abeysekara, A. U. Alfaro, R. Alvarez, C. Alvarez, J. D. Arceo, R. Arteaga-Velazquez, J. C. Solares, H. A. Ayala Barber, A. S. Baughman, B. M. Bautista-Elivar, N. Gonzalez, J. Becerra Belmont-Moreno, E. BenZvi, S. Y. Berley, D. Bonilla Rosales, M. Braun, J. Caballero-Lopez, R. A. Caballero-Mora, K. S. Carraminana, A. Castillo, M. Cotti, U. Cotzomi, J. de la Fuente, E. De Leon, C. DeYoung, T. Diaz-Cruz, J. Diaz Hernandez, R. Diaz-Velez, J. C. Dingus, B. L. DuVernois, M. A. Ellsworth, R. W. Fiorino, D. W. Fraija, N. Galindo, A. Garfias, F. Gonzalez, M. M. Goodman, J. A. Grabski, V. Gussert, M. Hampel-Arias, Z. Harding, J. P. Hui, C. M. Huntemeyer, P. Imran, A. Iriarte, A. Karn, P. Kieda, D. Kunde, G. J. Lara, A. Lauer, R. J. Lee, W. H. Lennarz, D. Leon Vargas, H. Linares, E. C. Linnemann, J. T. Proper, M. Longo Luna-Garcia, R. Malone, K. Marinelli, A. Marinelli, S. S. Martinez, O. Martinez-Castro, J. Martinez-Huerta, H. Matthews, J. A. J. McEnery, J. Mendoza Torres, E. Miranda-Romagnoli, P. Moreno, E. Mostafa, M. Nellen, L. Newbold, M. Noriega-Papaqui, R. Oceguera-Becerra, T. Patricelli, B. Pelayo, R. Perez-Perez, E. G. Pretz, J. Riviere, C. Rosa-Gonzalez, D. Ruiz-Velasco, E. Ryan, J. Salazar, H. Greus, F. Salesa Sanchez, F. E. Sandoval, A. Schneider, M. Silich, S. Sinnis, G. Smith, A. J. Woodle, K. Sparks Springer, R. W. Taboada, I. Toale, P. A. Tollefson, K. Torres, I. Ukwatta, T. N. Villasenor, L. Weisgarber, T. Westerhoff, S. Wisher, I. G. Wood, J. Yapici, T. Yodh, G. B. Younk, P. W. Zaborov, D. Zepeda, A. Zhou, H. Univ Utah Dept Phys & Astron Salt Lake City UT USA Univ Nacl Autonoma Mexico Inst Fis Mexico City DF Mexico Univ Autonoma Chiapas Tuxtla Gutierrez Chiapas Mexico Univ Michoacana Inst Fis & Matemat Morelia Michoacan Mexico Michigan Technol Univ Dept Phys Houghton MI 49931 USA Univ Maryland Dept Phys College Pk MD 20742 USA Univ Politecn Pachuca Pachuca Hidalgo Mexico NASA Goddard Space Flight Ctr Greenbelt MD USA Univ Rochester Dept Phys & Astron Rochester NY 14627 USA Inst Nacl Astrofis Opt & Electr Puebla Mexico Univ Wisconsin Dept Phys 1150 Univ Ave Madison WI 53706 USA Univ Nacl Autonoma Mexico Inst Geofis Mexico City DF Mexico Benemerita Univ Autonoma Puebla Fac Ciencias Fis Matemat Puebla Mexico Univ Guadalajara LAM Dpto Fis Dpto Elect CUCEI IT Phd CUCEAPhys Mat Phd CUVALLES Guadalajara Jalisco Mexico Michigan State Univ Dept Phys & Astron E Lansing MI 48824 USA Los Alamos Natl Lab Div Phys Los Alamos NM USA George Mason Univ Sch Phys Astron & Computat Sci Fairfax VA USA Univ Nacl Autonoma Mexico Inst Astron Mexico City DF Mexico Colorado State Univ Dept Phys Ft Collins CO 80523 USA NASA Marshall Space Flight Ctr Astrophys Off Huntsville AL USA Univ Calif Irvine Dept Phys & Astron Irvine CA USA Univ New Mexico Dept Phys & Astron Albuquerque NM 87131 USA Georgia Inst Technol Sch Phys Atlanta GA 30332 USA Georgia Inst Technol Ctr Relativist Astrophys Atlanta GA 30332 USA Ctr Invest Computac Inst Politecn Nacl Mexico City DF Mexico Penn State Univ Dept Phys 104 Davey Lab University Pk PA 16802 USA IPN Dept Phys Ctr Invest & Estudios Avanzados Mexico City DF Mexico Univ Autonoma Estado Hidalgo Pachuca Hidalgo Mexico Univ Nacl Autonoma Mexico Inst Ciencias Nucl Mexico City DF Mexico Inst Politecn Nacl Unidad Profes Interdisciplinaria Ingn & Tecnol Av Mexico City DF Mexico Univ New Hampshire Ctr Space Sci Durham NH 03824 USA IFJ PAN Inst Fizyki Jadrowej Henryka Niewodniczanskiego Polskiej Akad Nauk Krakow

The High Altitude Water Cherenkov observatory (HAWC) is an air shower array devised for TeV gamma-ray astronomy. HAWC is located at an altitude of 4100 m a.s.l. in Sierra Negra, Mexico. HAWC consists of 300 Water Cherenkov Detectors, each instrumented with 4 photomultiplier tubes (PMTs). HAWC re-uses the Front-End Boards from the Milagro experiment to receive the PMT signals. These boards are used in combination with Time to Digital Converters (TDCs) to record the time and the amount of light in each PMT hit (light flash). A set of VME TDC modules (128 channels each) is operated in a continuous (dead time free) mode. The TDCs are read out via the VME bus by Single-Board Computers (SBCs), which in turn are connected to a gigabit Ethernet network. The complete system produces approximate to 500 MB/s of raw data. A high-throughput data processing system has been designed and built to enable real-time data analysis. The system relies on off-the-shelf hardware components, an open-source software technology for data transfers (ZeroMQ) and a custom software framework for data analysis (AERIE). Multiple trigger and reconstruction algorithms can be combined and run on blocks of data in a parallel fashion, producing a set of output data streams which can be analyzed in real time with minimal latency (<5 s). This paper provides an overview of the hardware set-up and an in-depth description of the software design, covering both the TDC data acquisition system and the real-time data processing system. The performance of these systems is also discussed. (C) 2018 Elsevier B.V. All rights reserved.

关键词： data acquisition Real time data processing online data analysis Gamma-ray observatory

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