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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://spc.embl‐hamburg.de/. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. Implementation of the complete synchrotron serial crystallography (SSX) suite for real‐time data collection and processing available at the Swiss Light Source macromolecular beamlines is reported. A detailed description of the latest software extensions, i.e. the SSX user interface, as well as online data processing and merging routines, is also given. [ABSTRACT FROM AUTHOR]

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 ≈ 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. [ABSTRACT FROM AUTHOR]

Data acquisition software is an essential component of modern macromolecular crystallography (MX) beamlines, enabling efficient use of beam time at synchrotron facilities. Developed at the Paul Scherrer Institute, the DA+ data acquisition software is implemented at all three Swiss Light Source (SLS) MX beamlines. DA+ consists of distributed services and components written in Python and Java, which communicate via messaging and streaming technologies. The major components of DA+ are the user interface, acquisition engine, online processing and database. Immediate data quality feedback is achieved with distributed automatic data analysis routines. The software architecture enables exploration of the full potential of the latest instrumentation at the SLS MX beamlines, such as the SmarGon goniometer and the EIGER X 16M detector, and development of new data collection methods. [ABSTRACT FROM AUTHOR]

Significance: Brain-computer interfaces (BCIs) can provide severely motor-impaired 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-by-chromophore 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., (© 2023 The Authors.)

Reliability is an important metric for measuring the quality of software. Many methods have been proposed for online predicting and improving software reliability, but most of them have the following weakness: they are not able to predict software reliability on different time intervals and to locate the faulty components that cause the declining of the reliability either. This paper proposes a new method for online improvement of reliability of service composition. We use monitored failure data at ports of services to predict the reliabilities of service composition on different time intervals. If the predicted reliability is lower than the expected value, then we locate the faulty components that cause the declining of the reliability by using an improved spectrum-fault-localization (SFL) technique. The system can be automatically reconfigured to improve the system reliability by adding a component replica or replacing the faulty component. An Online Shop example is used to demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

While the potential benefits of participating in online learning communities are documented, so too are inequities in terms of how different populations access and use them. We present the online learning support roles (OLSR) framework, an approach using both automated analytics and qualitative interpretation to identify and explore online teaching roles. We analyze the OLSR using data logs ofiRemix, the online component of theDigital Youth Network, a face-to-face and online program for urban youth in underserved communities. In three middle-school classrooms, six educators usediRemixmost to interact with individual students, especially as a window into their work. Although many roles were documented, few were played regularly, raising questions about design and intentionality. To address participation inequities, our results suggest that the OLSR and related data can be used to support productive practice improvement conversations among educators and to inform the design of online social learning networks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recent advances in high throughput biological methods allow researchers to generate enormous amounts of data from a single experiment. In order to extract meaningful conclusions from this tidal wave of data, it will be necessary to develop analytical methods of sufficient power and utility. It is particularly important that biologists themselves be able to perform many of these analyses, such that their background knowledge of the experimental system under study can be used to interpret results and direct further inquiries. We have developed a web-based system, Magellan, which allows the upload, storage, and analysis of multivariate data and textual or numerical annotations. Data and annotations are treated as abstract entities, to maximize the different types of information the system can store and analyze. Annotations can be used in analyses/visualizations, as a means of subsetting data to reduce dimensionality, or as a means of projecting variables from one data type or data set to another. Analytical methods are deployed within Magellan such that new functionalities can be added in a straightforward fashion. Using Magellan, we performed an integrated analysis of genome-wide comparative genomic hybridization (CGH), mRNA expression, and clinical data from ovarian tumors. Analyses included the use of permutation-based methods to identify genes whose mRNA expression levels correlated with patient survival, a nearest neighbor classifier to predict patient survival from CGH data, and curated annotations such as genomic position and derived annotations such as statistical computations to explore the quantitative relationship between CGH and mRNA expression data.

This article reports collaborative research study from a small university. Twenty-seven faculty members were interviewed to determine attitudes and behaviors pertaining to using data in the classroom and whether easier access to secondary data allows for integration into a broader range of courses. The results of this study suggest a lack of awareness and skill deficits among faculty. These findings provide insights for librarians interested in working with faculty in supporting and promoting the use of open data online analysis, along with traditional quantitative data services in the social sciences and professional disciplines. Preliminary strategies developed in the year following this research study are also discussed. [ABSTRACT FROM PUBLISHER]

Abstract: PlanetServer is a WebGIS system, currently under development, enabling the online analysis of Compact Reconnaissance Imaging Spectrometer (CRISM) hyperspectral data from Mars. It is part of the EarthServer project which builds infrastructure for online access and analysis of huge Earth Science datasets. Core functionality consists of the rasdaman Array Database Management System (DBMS) for storage, and the Open Geospatial Consortium (OGC) Web Coverage Processing Service (WCPS) for data querying. Various WCPS queries have been designed to access spatial and spectral subsets of the CRISM data. The client WebGIS, consisting mainly of the OpenLayers javascript library, uses these queries to enable online spatial and spectral analysis. Currently the PlanetServer demonstration consists of two CRISM Full Resolution Target (FRT) observations, surrounding the NASA Curiosity rover landing site. A detailed analysis of one of these observations is performed in the Case Study section. The current PlanetServer functionality is described step by step, and is tested by focusing on detecting mineralogical evidence described in earlier Gale crater studies. Both the PlanetServer methodology and its possible use for mineralogical studies will be further discussed. Future work includes batch ingestion of CRISM data and further development of the WebGIS and analysis tools. [Copyright &y& Elsevier]

EDNA is a framework for developing plugin-based applications especially for online data analysis in the X-ray experiments field. This article describes the features provided by the EDNA framework to ease the development of extensible scientific applications. This framework includes a plugins class hierarchy, configuration and application facilities, a mechanism to generate data classes and a testing framework. These utilities allow rapid development and integration in which robustness and quality play a fundamental role. A first prototype, designed for macromolecular crystallography experiments and tested at several synchrotrons, is presented. [ABSTRACT FROM AUTHOR]

Demand for the newest and most elaborate Nuclear Medicine equipment is at an all time high. Never has there been more to offer than now with such improvements as large field of view, high resolution, whole-body capability and so on. The dedicated minicomputer systems that are available to go with these latest imaging devices are equally impressive. Still, the headaches associated with putting together a full capability camera/computer system are numerous. Things just do not seem to go together the way they ought to. If we are to truly get the most value out of our new equipment, we must put tremendous work loads on our clinical staff by expecting them to use very awkward and poorly configured systems in which several complicated steps are required to produce the finished product ready for the reading room. The relatively low usage factor which most clinical Nuclear Medicine computer systems experience is not surprising when we consider the ridiculous way in which these systems are configured. It is time for a little human engineering to be introduced into the design process. Unfortunately, this proliferation of equipment is wasteful of money as well as the operator's time. It would make more sense to consolidate the multitude of displays and controlling elements into a single console which would allow a single operator to perform all necessary data processing operations quickly and interactively. [ABSTRACT FROM AUTHOR]

Recent advances in high throughput biological methods allow researchers to generate enormous amounts of data from a single experiment. In order to extract meaningful conclusions from this tidal wave of data, it will be necessary to develop analytical methods of sufficient power and utility. It is particularly important that biologists themselves be able to perform many of these analyses, such that their background knowledge of the experimental system under study can be used to interpret results and direct further inquiries. We have developed a web-based system, Magellan, which allows the upload, storage, and analysis of multivariate data and textual or numerical annotations. Data and annotations are treated as abstract entities, to maximize the different types of information the system can store and analyze. Annotations can be used in analyses/visualizations, as a means of subsetting data to reduce dimensionality, or as a means of projecting variables from one data type or data set to another. Analytical methods are deployed within Magellan such that new functionalities can be added in a straightforward fashion. Using Magellan, we performed an integrated analysis of genome-wide comparative genomic hybridization (CGH), mRNA expression, and clinical data from ovarian tumors. Analyses included the use of permutation-based methods to identify genes whose mRNA expression levels correlated with patient survival, a nearest neighbor classifier to predict patient survival from CGH data, and curated annotations such as genomic position and derived annotations such as statistical computations to explore the quantitative relationship between CGH and mRNA expression data.