Automatic Data Processing Research Articles

SinGAN-Seg: Synthetic training data generation for medical image segmentation.

Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Therefore, artificial intelligence has become a popular tool for the automatic processing of medical data, acting as a supportive tool for doctors. However, the machine learning models used to build these tools are highly dependent on the data used to train them. Large amounts of data can be difficult to obtain in medicine due to privacy reasons, expensive and time-consuming annotations, and a general lack of data samples for infrequent lesions. In this study, we present a novel synthetic data generation pipeline, called SinGAN-Seg, to produce synthetic medical images with corresponding masks using a single training image. Our method is different from the traditional generative adversarial networks (GANs) because our model needs only a single image and the corresponding ground truth to train. We also show that the synthetic data generation pipeline can be used to produce alternative artificial segmentation datasets with corresponding ground truth masks when real datasets are not allowed to share. The pipeline is evaluated using qualitative and quantitative comparisons between real data and synthetic data to show that the style transfer technique used in our pipeline significantly improves the quality of the generated data and our method is better than other state-of-the-art GANs to prepare synthetic images when the size of training datasets are limited. By training UNet++ using both real data and the synthetic data generated from the SinGAN-Seg pipeline, we show that the models trained on synthetic data have very close performances to those trained on real data when both datasets have a considerable amount of training data. In contrast, we show that synthetic data generated from the SinGAN-Seg pipeline improves the performance of segmentation models when training datasets do not have a considerable amount of data. All experiments were performed using an open dataset and the code is publicly available on GitHub.

Automated Design and Rapid Manufacturing of Low-Cost Customized Upper Limb Prostheses

The paper presents results of studies on an innovative approach to mass customization of prostheses, namely automated design and rapid manufacturing (3D printing), on the basis of 3D scanning of upper limb of a given patient. Two different products are presented – a simple mechanical prosthesis and a cosmetic prosthesis. In both cases, intelligent CAD models, with adaptive geometry – changing automatically to the data from 3D scanning – were prepared. The digital models were built and tested using data of real patients. A special algorithm of 3D scanning data processing was prepared and implemented, as well as a set of algorithms of automated data processing for 3D printing processes. The test models were manufactured using low-cost 3D printing technology. The verification was successful – possibility of preparing customized prostheses was reached, using automated design.

A novel UHPLC-ESI-MS/MS method and automatic calculation software for regiospecific analysis of triacylglycerols in natural fats and oils

Regioisomeric analysis of triacylglycerols (TAGs) in natural oils and fats is a highly challenging task in analytical chemistry. Here we present a software (TAG Analyzer) for automatic calculation of regioisomeric composition of TAGs based on the mass spectral data from recently reported ultra-high performance liquid chromatography electrospray ionization tandem mass spectrometry (UHPLC−ESI−MS/MS) method for analyzing TAG regioisomers. The software enables fast and accurate processing of complex product ion spectra containing structurally informative diacylglycerol [M+NH4−RCO2H–NH3]+ and fatty acid ketene [RCO]+ fragment ions. Compared to manual processing, the developed software offers higher throughput with faster calculation as well as more accurate interpretation of chromatographically overlapping isobaric TAGs. The software determines results by constructing a synthetic spectrum to match the measured fragment ion spectrum, and by reporting the optimal concentrations of TAGs used to create the synthetic spectrum. This type of calculation is often extremely challenging for manual interpretation of the fragment ion spectra of isobaric TAGs with shared fragments, hence the need for automated data processing. The developed software was validated by analyzing a wide range of mixtures of regiopure TAG reference compounds of known composition and a commercial olive oil sample. Additionally, the method was also applied for regiospecific analysis of TAGs in human milk as an example of natural fats and oils with a highly complex TAG profile. The results indicate that the software is capable of resolving regioisomeric composition of natural TAGs even of the most complex composition. This novel calculation software combined with our existing UHPLC-ESI-MS/MS method form a highly efficient tool for regioisomeric analysis of TAGs in natural fats and oils.

Analysis of Ti- and Pb-based particles in the aqueous environment of Melbourne (Australia) via single particle ICP-MS

The analysis of natural and anthropogenic nanomaterials (NMs) in the environment is challenging and requires methods capable to identify and characterise structures on the nanoscale regarding particle number concentrations (PNCs), elemental composition, size, and mass distributions. In this study, we employed single particle inductively coupled plasma-mass spectrometry (SP ICP-MS) to investigate the occurrence of NMs in the Melbourne area (Australia) across 63 locations. Poisson statistics were used to discriminate between signals from nanoparticulate matter and ionic background. TiO2-based NMs were frequently detected and corresponding NM signals were calibated with an automated data processing platform. Additionally, a method utilising a larger mass bandpass was developed to screen for particulate high-mass elements. This procedure identified Pb-based NMs in various samples. The effects of different environmental matrices consisting of fresh, brackish, or seawater were mitigated with an aerosol dilution method reducing the introduction of salt into the plasma and avoiding signal drift. Signals from TiO2- and Pb-based NMs were counted, integrated, and subsequently calibrated to determine PNCs as well as mass and size distributions. PNCs, mean sizes, particulate masses, and ionic background levels were compared across different locations and environments.Graphical abstract

Multi-pronged approach for monitoring sedimentation processes at multi-barrel culverts

This paper describes a case study that employs an in-situ methodology for monitoring sedimentation processes at a multi-barrel culvert site. This area is seldom discussed in the scientific and engineering literature. The methodology integrates ground and aerial surveys from Real-Time Kinematic (RTK) and drone-based photogrammetry, with traditional hydro-geomorphological monitoring techniques (e.g., on-site photography, webcams, soil coring, and in-stream measurements) to acquire data continuously or for specific weather events. The paper focuses on presenting how experimental methods and their associated protocols can be assembled to complement each other to enable a comprehensive and detailed investigation of culvert sedimentation processes. The investigation entails the inception of the sediment deposits in the culvert vicinity and the progression of the depositional pattern over time (e.g., individual storms, seasons, and annual cycles). The data and information produced by our combined observational system lead to practical insights regarding the interplay between local hydraulic and geomorphological variables, which can be otherwise difficult to obtain. A automated data processing technique is applied in this study to automatically connect aerial photogrammetry results with the RTK-based ground survey to improve the accuracy of the monitoring.

A Database of Teleseismic Shear-Wave Splitting Measurements for the Ordos Block and Adjacent Areas

Abstract The Ordos block of the north China craton and its surrounding regions are affected by the India–Eurasia collision to the southwest and the subduction of the Pacific beneath the Eurasian plates to the east. To provide the foundation for delineating lithospheric deformation and asthenospheric flow beneath this tectonically diverse region, we have created a database of individual shear-wave splitting (SWS) parameters by applying a uniform set of data processing procedures. After automatic data processing and manual checking, a total of 16,228 pairs of well-defined PKS, SKKS, and SKS splitting parameters (fast orientations and splitting times) are obtained from 1023 broadband seismic stations that recorded data between 2007 and 2019. Along the western and southern margins of the Ordos block, the observed seismic anisotropy is attributable to mantle flow deflected by the relatively thick lithospheric root of the Ordos block. A clear back-azimuthal dependence of the fast orientations is observed at some of the stations located in the Alxa block, Ordos block, and Sichuan basin, indicating possible existence of complex anisotropic structures. The new SWS database can be employed by researchers in various fields to study lithospheric deformation and asthenospheric flow beneath the Ordos block and surrounding regions.

Evaluation of IMERG and ERA5 Precipitation-Phase Partitioning on the Global Scale

The precipitation phase (i.e., rain and snow) is important for the global hydrologic cycle and climate system. The objective of this study is to evaluate the precipitation-phase partitioning capabilities of remote sensing and reanalysis modeling methods on the global scale. Specifically, observation data from the National Centers for Environmental Prediction (NCEP) Automated Data Processing (ADP), from 2000 to 2007, are used to evaluate the rain–snow discrimination accuracy of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) and the fifth-generation reanalysis product of the European Centre for Medium Range Weather Forecasts (ERA5). The results show that: (1) the ERA5 performs better than the IMERG at distinguishing rainfall and snowfall events, overall. (2) The ERA5 has high accuracy in all continents except for South America, while the IMERG performs well only in Antarctica and North America. (3) Compared with the IMERG, the ERA5 can more effectively capture snowfall events at high latitudes but shows worse performance at mid-low latitude regions. Both the IMERG and ERA5 have lower accuracy for rain–snow partitioning under heavy precipitation. Overall, the results of this study provide references for the application and improvement of global rain–snow partitioning products.

Sistem Informasi Pengolahan Data Pembelian Dan Penjualan Produk Usaha Pada Toko Mentari Berbasis Website

The development of technology is growing rapidly and there is many oportunity people could gain from that development, which also been implemented in the small and micro business development in various field to achieve its goals. Mentari Store is one of them. To ensure that data processing and report making of goods could be carried out properly, they need to change their management method from conventional into computerized and automated data processing methods. The data for this program is collected through interviews with the store owner, obvervation at the store, and through library studies. Using the Waterfall methodology and the Blackbox testing, this research is focused on software functionally to ensure that all parts already tested and successfully executed.

PERANCANGAN MEDIA PEMBELAJARAN INTERAKTIF MATA PELAJARAN IPA KELAS VI SD BERBASIS WEBSITE

The library is a center of knowledge that must be managed properly and correctly in an educational institution. SLBN 1 Linggo Sari Baganti is a school where all kinds of activities get more attention from the government, one of which is through the library. various assistance, facilities and infrastructure in the form of a collection of books are always provided. However, the operation of the library at SLBN 1 Linggo Sari Baganti is currently still managed in a conventional way. Starting from the data collection process to borrowing books is still done manually. Causing activities to be slow, inefficient and errors tend to be repeated frequently. So the Design of a Library Information System at SLBN 1 Linggo Sari Baganti Using Visual Basic 2010 Programming Language and Supported by Mysql Database is the right solution to overcome these problems. The results of the evaluation of the library system that has been designed show that the library system has features of computerized automation of data processing, transactions and the creation of important reports that have made it easier for librarians to manage the library.

Land objects size approximation informational technology

Despite rapid development of Big Data and data processing technologies, it is still quite a challenge to implement efficient pipelines for obtaining reliable and reasonable results. In re-mote sensing, thousands of gigabytes of data is acquired by satellite and aircraft vehicles, but only the modest amount of data is processed. Among the data processed, even less results re-mains reliable overtime due to the nature of satellite imagery. Nowadays, machine learning area affects nearly every domain of knowledge including remote sensing. A toolset of machine learning suggests ways to automate data processing which makes data pipelines constructure much more effective. Deep neural networks demonstrate the best results so far in image pro-cessing field. However, applying deep learning achievements in Remote Sensing still remain challenging in regard to multi-channel satellite imagery of a very-high resolution. Despite astonishing results deep learning field demonstrates in remote sensing competitions such as Open Street Map, these are applicable on regular RGB images of popular image formats such JPEG and similar. In current paper, a technology is suggested to apply deep neural network toolset in multispectral images of a very high resolution acquired from a commercial WorldView-3 satellite vehicle. The suggested approach covers topics of dataset preparation to fully replicate the results of the suggested neural network training. The proposed neural network solution output are segmentation maps. In addition to replicatable neural network structure, a particular use case is considered in detail which implements a fully automated technology of highly precise approximation of physical sizes of the land objects. The neural network performance is measured and compared to modern similar neural network solutions.

Pollen observations at four EARLINET stations during the ACTRIS-COVID-19 campaign

Abstract. Lidar observations were analysed to characterize atmospheric pollen at four EARLINET (European Aerosol Research Lidar Network) stations (Hohenpeißenberg, Germany; Kuopio, Finland; Leipzig, Germany; and Warsaw, Poland) during the ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure) COVID-19 campaign in May 2020. The reanalysis (fully quality-assured) lidar data products, after the centralized and automatic data processing with the Single Calculus Chain (SCC), were used in this study, focusing on particle backscatter coefficients at 355 and 532 nm and particle linear depolarization ratios (PDRs) at 532 nm. A novel method for the characterization of the pure pollen depolarization ratio was presented, based on the non-linear least square regression fitting using lidar-derived backscatter-related Ångström exponents (BAEs) and PDRs. Under the assumption that the BAE between 355 and 532 nm should be zero (±0.5) for pure pollen, the pollen depolarization ratios were estimated: for Kuopio and Warsaw stations, the pollen depolarization ratios at 532 nm were of 0.24 (0.19–0.28) during the birch-dominant pollen periods, whereas for Hohenpeißenberg and Leipzig stations, the pollen depolarization ratios of 0.21 (0.15–0.27) and 0.20 (0.15–0.25) were observed for periods of mixture of birch and grass pollen. The method was also applied for the aerosol classification, using two case examples from the campaign periods; the different pollen types (or pollen mixtures) were identified at Warsaw station, and dust and pollen were classified at Hohenpeißenberg station.

A BIM Based Framework for Damage Segmentation, Modeling, and Visualization Using IFC

Paper-based data acquisition and manual transfer between incompatible software or data formats during inspections of bridges, as done currently, are time-consuming, error-prone, cumbersome, and lead to information loss. A fully digitized workflow using open data formats would reduce data loss, efforts, and the costs of future inspections. On the one hand, existing studies proposed methods to automatize data acquisition and visualization for inspections. These studies lack an open standard to make the gathered data available for other processes. On the other hand, several studies discuss data structures for exchanging damage information among different stakeholders. However, those studies do not cover the process of automatic data acquisition and transfer. This study focuses on a framework that incorporates automatic damage data acquisition, transfer, and a damage information model for data exchange. This enables inspectors to use damage data for subsequent analyses and simulations. The proposed framework shows the potentials for a comprehensive damage information model and related (semi-)automatic data acquisition and processing.

Optical Coherence Tomography for 3D Weld Seam Localization in Absorber-Free Laser Transmission Welding

Quality and reliability are of the utmost importance for manufacturing in the optical and medical industries. Absorber-free laser transmission welding enables the precise joining of identical polymers without additives or adhesives and is well-suited to meet the demands of the aforementioned industries. To attain sufficient absorption of laser energy without absorbent additives, thulium fiber lasers, which emit in the polymers’ intrinsic absorption spectrum, are used. Focusing the laser beam with a high numerical aperture provides significant intensity gradients inside the workpiece and enables selective fusing of the internal joining zone without affecting the surface of the device. Because seam size and position are crucial, the high-quality requirements demand internal weld seam monitoring. In this work, we propose a novel method to determine weld seam location and size using optical coherence tomography. Changes in optical material properties because of melting and re-solidification during welding allow for weld seam differentiation from the injection-molded base material. Automatic processing of the optical coherence tomography data enables the identification and measurement of the weld seam geometry. The results from our technique are consistent with microscopic images of microtome sections and demonstrate that weld seam localization in polyamide 6 is possible with an accuracy better than a tenth of a millimeter.

Impact of NOTAM on security and efficiency performance of flights (overview)

The purpose of this work is to analyze and assess the impact of NOTAM on the flight safety and efficiency. The main problems associated with NOTAM were considered: number of NOTAMs, the practical use of NOTAM information, technical limitations of the current NOTAM system. Examples of the negative impact of NOTAM on the quality of air navigation support, safety and efficiency of flights are presented. Also, the best practice of solving problems, related to NOTAM in the world, is presented with using: Q-code and flight planning systems (on the example of Lido Flight 4D). The concept of the European AIS Database (EAD), developed on the basis of the Aeronautical Information Exchange Model (AIXM), is presented. The concept of Digital NOTAM, implemented on the basis of AIXM and intended for the exchange, automatic processing and interpretation of the dynamic aeronautical data, is considered. The research studies the new modernized NOTAM system – Federal NOTAM System (FNS – Federal NOTAM System), developed by the Federal Aviation Administration (FAA) of the United States, which allows encoding the Digital NOTAM. ICAO's plans for the transition from the concept of Aeronautical Information Service (AIS) to the Aeronautical Information Management (AIM), applying the principles of the System Wide Information Management (SWIM) concept, were analyzed. As a result of the analysis of the current NOTAM system and the modernized NOTAM system (FNS from FAA), conclusions were drawn that the implementation of Digital NOTAM should solve the technical part of the problems associated with NOTAM, brought about by the use of modern communications (internet) and new data exchange standards (AIXM), which, as a result, will lead to an increase in the level of safety and efficiency of flights. At the same time, problems with NOTAM caused by human factors remain unresolved, due to incorrect use of the NOTAM instrument.

A reconstruction method to determine crack length based on virtual extensometers

The laboratory measurement accuracy of crack length has an influence on the study of material properties and behaviors. There are some defects in the existing methods for determining crack length, such as strong subjectivity, complex experimental process and difficult data processing. To solve the above problems, based on the surface deformation data recorded by DIC, a method of reconstructing the virtual extensometers and measuring the crack length is proposed by fixing the virtual extensometers along the crack growth path. The fracture process is analyzed based on the plastic hinge hypothesis, and the whole deformation data of the specimen is obtained by analyzing the length change of the virtual extensometers. The rigid components of virtual extensometers deformation along the crack growth path are reconstructed and compared with the original elongation curve to determine the crack length. It shows that the reconstruction method has a high reliability to compare the prediction results with those of the curvature method, the direct-reading method and the Martinelli-Venzi model method. The characteristic of virtual extensometers fixed after experiment and programming design of analysis process are applied to simplify the experiment and data processing. On the basis, the influence of the initial length of the virtual extensometer and the decomposition process on the results is discussed. The results show that reconstruction method has good adaptability to tunneling effect, and it is expected to realize automatic data collection and processing based on this method.

Non-target analysis and stability assessment of reference materials using liquid chromatography‒high-resolution mass spectrometry

Development and characterization of biological and environmental matrix certified reference materials (CRMs) for organic analytes typically relies heavily on targeted analytical methods, such as liquid chromatography (LC) with triple-quadrupole mass spectrometry detection. LC with high-resolution mass spectrometry (LC‒HRMS) can also provide high quality data for both targeted and non-targeted analytes, with the potential for retrospective data analysis. Here, we demonstrate the utility of non-target analysis (NTA) using LC‒HRMS for profiling and stability assessment of a mussel tissue matrix CRM certified for several classes of marine algal toxins (CRM-FDMT1). First, the NTA method was developed using data-dependent MS/MS acquisition and commercial metabolomics software for data processing. Of 128 toxin analogues previously reported in CRM-FDMT1, 125 were detected by LC-HRMS, with 97 triggered for MS/MS by data dependant acquisition. Automated data processing detected 119 of these compounds and 109 were retained after automated filtering of results for putative toxin analogues. Those analogues not detected were low abundance ions, or poorly resolved isomers. The method was then used to demonstrate new strategies for CRM stability assessment considering the stability of certified analytes, related toxin analogues, and unrelated matrix compounds. Several analogues from each toxin class in CRM-FDMT1 as well as other unrelated matrix compounds were observed to be significantly less stable than the certified toxins. Using this method, no instability was measured for any compounds at conditions ≤4 °C, providing a greater degree of confidence in CRM stability than could be achieved using conventional approaches to stability assessment targeting only the certified analytes. The NTA method and stability assessment approach presented are applicable to future CRM development with other matrices and organic analyte classes.

Physical Activity, Sedentary Behavior, and Sleep on Twitter: Multicountry and Fully Labeled Public Data Set for Digital Public Health Surveillance Research.

BackgroundAdvances in automated data processing and machine learning (ML) models, together with the unprecedented growth in the number of social media users who publicly share and discuss health-related information, have made public health surveillance (PHS) one of the long-lasting social media applications. However, the existing PHS systems feeding on social media data have not been widely deployed in national surveillance systems, which appears to stem from the lack of practitioners and the public’s trust in social media data. More robust and reliable data sets over which supervised ML models can be trained and tested reliably is a significant step toward overcoming this hurdle. The health implications of daily behaviors (physical activity, sedentary behavior, and sleep [PASS]), as an evergreen topic in PHS, are widely studied through traditional data sources such as surveillance surveys and administrative databases, which are often several months out-of-date by the time they are used, costly to collect, and thus limited in quantity and coverage.ObjectiveThe main objective of this study is to present a large-scale, multicountry, longitudinal, and fully labeled data set to enable and support digital PASS surveillance research in PHS. To support high-quality surveillance research using our data set, we have conducted further analysis on the data set to supplement it with additional PHS-related metadata.MethodsWe collected the data of this study from Twitter using the Twitter livestream application programming interface between November 28, 2018, and June 19, 2020. To obtain PASS-related tweets for manual annotation, we iteratively used regular expressions, unsupervised natural language processing, domain-specific ontologies, and linguistic analysis. We used Amazon Mechanical Turk to label the collected data to self-reported PASS categories and implemented a quality control pipeline to monitor and manage the validity of crowd-generated labels. Moreover, we used ML, latent semantic analysis, linguistic analysis, and label inference analysis to validate the different components of the data set.ResultsLPHEADA (Labelled Digital Public Health Dataset) contains 366,405 crowd-generated labels (3 labels per tweet) for 122,135 PASS-related tweets that originated in Australia, Canada, the United Kingdom, or the United States, labeled by 708 unique annotators on Amazon Mechanical Turk. In addition to crowd-generated labels, LPHEADA provides details about the three critical components of any PHS system: place, time, and demographics (ie, gender and age range) associated with each tweet.ConclusionsPublicly available data sets for digital PASS surveillance are usually isolated and only provide labels for small subsets of the data. We believe that the novelty and comprehensiveness of the data set provided in this study will help develop, evaluate, and deploy digital PASS surveillance systems. LPHEADA will be an invaluable resource for both public health researchers and practitioners.

Intelligent Fuzzy Optimization Algorithm of Data Mining Based on BP Neural Network

This article aims to explore the intelligent fuzzy optimization algorithm for data mining based on BP neural network. Although the database technology has been improved with the increase of the amount of data, facing the explosive growth of the amount of data, the previous database management methods have been unable to meet and analyze the hidden knowledge in this scale of data. Therefore, it is important to find better automated data processing methods to satisfy the classification and analysis of massive data. However, the current BP neural network is not yet perfect. This method has some problems, such as slow convergence speed. The problem is reflected in the problem of pattern recognition and insufficient generalization ability and stability. Based on the above description, the research content of this paper is an intelligent fuzzy optimization algorithm for data mining based on BP neural network. Considering that the training of the BP algorithm is based on the weight correction principle of error gradient descent, the genetic algorithm is good at global search, but it does not have accurate local searchability. Therefore, this paper uses the weight of the genetic algorithm. This paper improves BP neural network based on a genetic algorithm. The experimental simulation results of Iris show that the quantity of hidden nodes usually increases with the number of training samples. ACBP algorithm can construct a better network structure based on the number of training samples. And through the experimental comparison of the traditional BP neural network algorithm, it is concluded that the improved algorithm can allow data mining technology to mine relatively more ideal data from complex environments.

Automatic organ-level point cloud segmentation of maize shoots by integrating high-throughput data acquisition and deep learning

• High-throughput data acquisition platform was used to build high-quality dataset. • Automatic maize shoot segmentation was realized using deep learning. • Six phenotypes of maize shoot can be extracted with satisfactory accuracy. • An pipeline was established from data acquisition to phenotypes extraction. • The pipeline can deal with large amount of 3D data processing for plant phenomics. Point cloud segmentation is essential for studying the 3D spatial characteristics of plants. Notably, the segmentation accuracy greatly impacts subsequent 3D plant phenotypes extraction and 3D plant reconstruction. Automated segmentation approaches for plant point clouds are a bottleneck in achieving big data processing of 3D plant phenotypes. Using maize as a representative crop, this study developed DeepSeg3DMaize, a technique for plant point cloud segmentation that integrates high-throughput data acquisition and deep learning. A high-throughput data acquisition platform for individual plants and an association mapping panel containing 515 inbred lines were used to construct the training dataset. Specifically, the MVS-Pheno platform was used to acquire high-throughput data, and Label3DMaize was used for point cloud data labeling. Based on the dataset, PointNet was introduced to implement stem-leaf and organ instance segmentation, and six phenotypes were extracted. According to the results, the mean precision and F1-Score of stem-leaf segmentation were 0.91 and 0.85, respectively. Meanwhile, the mean precision and F1-Score for organ instance segmentation were 0.94 and 0.93, respectively. The correlations of the six parameters (leaf length, leaf width, leaf inclination, leaf growth height, plant height, and stem height) extracted from the segmentation results with the measured values were 0.90, 0.82, 0.94, 0.95, 0.99, and 0.94, respectively. High-throughput data acquisition, automatic organ segmentation, and phenotypic data extraction form an automatic phenotypic data processing pipeline, which is practical for dealing with large amounts of initial data. Besides, it provides a systematic reference for the automated analysis of 3D phenotypic features at the individual plant level.

User capabilities at the GM/CA@APS Structural Biology Facility at the Advanced Photon Source

The General Medical Sciences and Cancer Institutes’ structural biology facility at the APS (GM/CA@APS) operates a National User Facility for crystallographic structure determination of biological macromolecules, with an emphasis on challenging, high-impact projects. The facility includes canted-undulator beamlines, 23ID-B and 23ID-D, that provide stable, intense X-ray beams of user-selectable size down to 5-micron, an intuitive user interface for experiment control, and an automated data processing pipeline.