dataesr/scientific-paragraphs-categorization huggingface dataset:
If these images do not exist, the system reduces the angular separation required in increments of 5 until images are found. The paths to the images are assembled by parsing the XDS OSCILLATION_RANGE and STARTING_ANGLE keywords. This additional image range is usually only relevant in full processing mode (x2. 4), since in most cases the second wedge of images will not yet exist in fast processing mode. After the integration run of XDS, a rejection file (REMOVE. HKL) Architecture of the GrenADeS automatic data processing system. Components for which Python, Perl or Java is the predominant language are coloured blue, green or red, respectively, in the electronic version of the journal (dark grey, mid-grey and graduated, respectively, in the print version). XMLRPC is a method for interprocess communication via the exchange of XML-formatted data.
This simple expression overestimates the resolution slightly, possibly owing to the assumption of a linear dependence of hI/(I )i as a function of resolution. It should be noted however that this cutoff is chosen in order to rapidly provide a reasonable estimate of the resolution and quality of the data, and the final resolution cutoff should still be chosen by the user. Future versions will support an additional criterion for resolution cutoffs: the CC*  (Karplus & Diederichs, 2012). Once the resolution limit has been determined, the CORRECT step of XDS is run twice, either merging or keeping separate reflections making up anomalous pairs (FRIEDEL'S_LAW = TRUE and FRIEDEL'S_LAW = FALSE), with the resolution limits for the latter also calculated using equation (  1 ). This is a branch point in the program flow, and all subsequent steps are performed in parallel with intensities for anomalous pairs either merged or unmerged.
Automatic grouped processing An increasingly common strategy for obtaining a higher-quality dataset than would otherwise be possible by collecting from a single position in a crystal is through the exploitation of small X-ray beams and high-precision goniometers  (Perrakis et al. , 1999; Hilgart et al. , 2011) , where radiation damage is limited by exposing fresh crystal volumes. Examples of this include collection of 'helical' datasets in which the sample is translated with simultaneous oscillation  (Flot et al. , 2010) ; the collection of multiple small datasets ('sub-datasets') from different positions on single crystals  (Amunts, 2007) ; or the collection of sub-datasets from multiple crystals  (Cockburn et al. , 2004). During such experiments, proper experiment logging and consistent indexing can be error prone and a time-consuming manual task, particularly when a large number of sub-datasets are used.
We have therefore automated the collection and processing of datasets from multiple positions, whether they have been collected from multiple positions on single crystals, multiple individual crystals or a combination of both. In MxCuBE, prior to an experiment, one can choose more than one position for data collection, and for each position specify data collection parameters such as oscillation width, number of images, exposure time and resolution. These can be different for each sub-dataset, and are all submitted into the MxCuBE data collection queue (Fig. 2a ). When all positions have been specified, the user selects 'Collect Queue'. The sample centring motors then successively move to each position and sub-datasets are collected in accordance with the parameters in the queue. The subdatasets are automatically merged into a single dataset in the following manner (Fig.
2 ) using the results of the integration in P1 from fast processing (see above). All sub-datasets are used as input for POINTLESS to determine the space group. The Bravais lattice of each sub-dataset is checked for consistency with the POINTLESS lattice. XDS is then run with JOB = CORRECT for all fast processing results with the 'correct' lattice, or with JOB = ALL, specifying the space group and unit cell from POINTLESS, if the lattices do not match. The first dataset successfully integrated with the POINTLESS Bravais lattice is used as the reference for all subsequent datasets, using the REFERENCE_DATA_SET card. All the sub-datasets are then merged with XSCALE and converted to MTZ format. Bar graphs of the incremental improvement of the merged datasets are automatically generated with gnuplot (http://gnuplot.
Presentation of results Once uploaded to ISPyB, the results of data processing are immediately available via a pull-down menu in the database web computer programs interface  (Delageniere et al. , 2011). The selected processing runs can be filtered and viewed using various criteria, including the Bravais lattice, hI/(I )i values, merging R value, and whether anomalous pairs are merged or unmerged. A summary of relevant statistics can also be exported in PDF or Microsoft Excel formats. Scaled intensity data in SCALEPACK or MTZ format can be downloaded directly from the web interface in a compressed form. A more complete set of processing files is also available to users on the ESRF's central shared file system. Additionally, data processing summaries obtained with XDSSTAT  (Diederichs, 2006)  and gnuplot may be displayed on a monitor above the beamline control computer as soon as they are available (Fig.
Automatic structure solution In order to further enhance the tools available to users of the ESRF MX beamlines, a prototype automatic structure determination pipeline using the single-wavelength anomalous diffraction (SAD) technique, based on autoprocessed diffraction data, has also been implemented. In the first step SHELXC  (Sheldrick, 2010)  is used to determine if any anomalous signal is present in the full processing mode (POINTLESS-determined space group) dataset. This analysis is done for every dataset collected at the ESRF JSBG beamlines. As merged data are currently used for this analysis, hd 00 /(d 00 )i is used as the criterion for determining the strength of the anomalous signal, rather than correlation coefficients between anomalous differences. Subsequent versions will use unmerged data.
If a resolution bin has hd 00 /(d 00 )i > 1. 3, the data are submitted to the EMBL Auto-Rickshaw server  (Panjikar et al. , 2005). In parallel, anomalous scattering substructures are determined locally on the ESRF cluster using SHELXD  (Sheldrick, 2010) , with subsequent solvent flattening and automatic model building in SHELXE  (Sheldrick, 2010). While efforts are underway to provide intuitive data entry fields for information such as the identity, the number of anomalous scatterers and the solvent content of the crystal via MxCuBE and ISPyB, there is currently no such mechanism. The success of both SHELXD and SHELXE is therefore significantly hindered. In the absence of such information, the program calculates a tentative molecular weight for the molecule, assuming the unit cell is 47% solvent, as is the case for most proteins  (Matthews, 1968).
Three values of solvent content are then used in SHELXE: 37, 47 and 57%. Both enantiomorphs are also evaluated. Manual assessment of the success of this method is made difficult by the sheer number of datasets collected at the ESRF MX beamlines. Therefore, a semi-automatic reporting system has been developed in order to identify solved structures. In order to reduce false positives, the cut-off for flagging a successful structure determination is high (CC of partial model > 25% and average fragment length > 10 residues). It is therefore likely that solved structures with relatively poor electron density are overlooked. In spite of this and since its introduction, the method implemented has on average determined and built nine structures per month without any user intervention or any additional information about the contents of the asymmetric unit.
Conclusions We have developed an automatic data processing system, which has been available and in use on all JSBG MX beamlines since January 2010. A stable version with ISPyB implementation has been used from late 2010. Unless disabled by the user, it is run on all datasets collected and has become an important part of the software portfolio offered to both academic and industrial users. Data can be processed in a timely fashion to provide users with rapid at-beamline feedback on the quality of the datasets collected, thus allowing informed decisions to be made as to whether further optimization of the experiment in hand is necessary or whether experimenters should move on to the next project for which data collection is required. Metrics of data quality are presented to the user in various media, including a web front end to the ISPyB system, a graphical 'heads up display' on the beamline control computer and log files in the data processing directories.
When anomalous signal is present, in some cases useful experimental phases can be automatically obtained even though information such as amino acid sequence, space group, solvent content, the number and kind of anomalous scatterers, and the number of molecules in the asymmetric unit is unavailable (see x4 below for plans for making this available to the autoprocessing system). While it is tempting to use the results thus far obtained using the automatic processing system described here for data mining, our initial analyses have made it clear that several improvements and changes to the existing ISPyB data model must be implemented in order to obtain a complete and accurate picture of the ways in which data are collected at the ESRF, the resulting data quality and how these collected data are used in the determination of crystal structures.
Future perspectives The autoprocessing system we have described here is the subject of almost continuous development in order to provide improved functionality. Some of the current projects include efforts to explicitly link PDB entries (Protein Data Bank; wwPDB;  Berman et al. , 2000)  to specific datasets, a detailed study of the effect of advanced data collection strategy software on data quality  (Bourenkov & Popov, 2010; Incardona et al. , 2009; Paithankar & Garman, 2010) , the incorporation of hierarchical cluster analysis  (Giordano et al. , 2012)  into the grouped data merging (x2. 5), more extensive reporting of results, particularly those pertaining to phasing, in ISPyB, and, most importantly, an improved and unified method for the user to provide information about their samples (i.
e. sequence information, anomalous scatterer type and number) that can be used to improve the success rate of automatic structure determination. We also plan to implement the automatic rejection of diffraction images at the end of data collections if radiation damage is detected. Initial trials in this area have focused on the removal of images towards the end of datasets where a significant decrease in hI/(I )i or significant increases in image scale factors, R merge or B factors obtained during scaling procedures are detected. However, two problems have been discovered with this approach. The first is that, particularly when dealing with datasets collected using fine ' slicing employing the current generation of pixel detectors, these metrics can vary significantly from diffraction image to diffraction image.
The second is that other problems with data quality, for example a poorly centred crystal or a crystal with anisotropic diffraction, could be mistaken for radiation damage. Thus rather than implement a method for the detection of radiation damage that may result in false positives we currently prefer to present, for any given dataset, the results of autoprocessing 'as collected' (i. e. integrated intensities from all diffraction images making up the dataset are included). Nevertheless, to aid the automatic removal of diffraction images affected by radiation damage, we are currently exploring the use of combined data metrics, such as mosaicity and Wilson B factors across single images, wedges and/or sliding windows of data, but no satisfactory solution has yet been obtained. Once a reliable metric can be found, it will be incorporated into the automatic data processing system.
While spot overlap during data collection can normally be avoided if an experimenter uses software such as the EDNA/BEST combination  (Incardona et al. , 2009; Bourenkov & Popov, 2010)  to obtain optimized data collection strategies, full data processing can reveal errors in diffraction pattern indexing at the characterization stage. In these cases the correct unit-cell dimensions and experimentally determined diffraction pattern point group could be provided, via ISPyB, to EDNA/BEST and used in any characterization (including data collection strategy calculations) of crystals of the same type. This is also an area that will be addressed in the future. Although the current performance of the autoprocessing system described here is already excellent (xx2. 3 and 2. 4) we plan to further reduce the time between data collection and user feedback on processing statistics.
This can be done both at the implementation level -the program flow can be further optimized -and via continuous upgrades to the associated network and computing infrastructure. The ultimate goal is to implement a data processing and analysis platform that provides almost immediate feedback and which will allow users to make informed decisions while the current sample is still mounted on the diffractometer. Critical to this is the availability of more detailed information about each step in autoprocessing, including failures. Currently, failures in XDS due to indexing, weak diffraction or overlapping spots can only be identified by examining the XDS-generated log files directly. The ISPyB data model is currently being modified and web services created to parse this information, add it to the database and display it to the user.
Once intensities have been correctly integrated, numerous automated downstream applications can be developed. We have presented one example here, with the automatic determination of structures by SAD, but other methods could also be explored. Examples include automatic phasing by molecular replacement (MR), followed by ligand fitting (if present) as well as phase combination and validation approaches using orthogonal phase sets (MR and SAD, for example). The results from all of these pipelines should eventually be stored in ISPyB. To enable such features, the ISPyB data model and user interface, although already quite rich, will have to be extended even further. Such developments will allow for a more systematic analysis of the metrics required for a successful MX experiment.
808Figure 3 3 Figure 3An example of an automatically generated plot from the grouped processing routine. The 7 Â 11 mm beam on ESRF beamline ID23-2 was used to collect multiple sub-datasets from five positions on a single large cubic insulin crystal. Each cluster of bins reflects the addition of a new sub-dataset to the final dataset. As datasets are added, completeness, signal-to-noise ratio and multiplicity improve.
Immediately after exposure to EdU, cells were trypsinized (Trypsin-EDTA (8 min, 37 C)) and fixed (PFA 4%, 15 min at RT, in PBSþ) and then stained with the Click-it EdU detection reagent (30 min, RT, protected from light) in order to detect EdU with the Alexa Fluor azide 555 dye according to the manufacturer's instructions. Centrifugation (5 min/600 g/RT) was then performed, followed by a wash with PBS and 3% BSA (Bovine Serum Albumin). The supernatant was discarded and the pellet was re-suspended in PFA 2% in PBSþ and kept at 4 C before FACS analysis. FACS (Becton Dickinson LSR II) flow cytometer was used to scan and collect the cell data. The FACS Diva software was used to generate flow cytometry data and to estimate the proportion of proliferative cells at 72 h. Statistically significant differences between three independent experiments were identified using the ManneWhitney test.
Twelve fields of view were imaged per well using a 20Â/0. 4NA objective. Image analysis was performed using the Morphology Explorer Ò Bioapplication from ThermoFisher (supplied with the Cellomics Ò ArrayScan Ò VTI HCS Reader). Segmentation was used in the Morphology Explorer Ò software to resolve and identify individual objects of the images (see Suppl. Info. Fig. S-3 ). Multiparametric and quantitative measurements were related to individual cell morphology (area and roundness), intra-cellular parameter (nuclei area) as well as to inter-cellular morphology (multicellular clusters) and to discrete objects analysis at the level of cell membrane (number of focal adhesion points). Roundness was defined as the perimeter-to-area ratio and the minimal distance (between the centres of the objects) to nearest neighbour was used to quantify the clustering behaviour of cells.
Growth model For each film or control, the number of cells was measured by phase contrast microscopy, at 40Â magnification, in triplicate at time-points 3, 24, 48, 72 and 96 h of culture in order to report the kinetics of proliferation. Cell counting was both performed manually and semi-automatically using ImageJ software, after contrastenhancement of the images. Typical random counting errors were evaluated by recounting the cells four times (on different days). These errors depended on cell type, due to differences of contrast. For example, the PC3 cells were easy to observe and count and hence the counting error was less than 2% (i. e. less than 10 cells in a total of more than 500), whereas PNT-2 cells were more difficult to see and hence the counting error was typically 10%. A table of 558 lines was then created containing cell number as a function of cell type, time, coating, field of view and replicate.
A model was developed for cell growth analysis and the statistical R language was applied as followed. Data were fitted using the statistical R language  [14]  and the package lme4  [15]  for linear models with mixed effects (LMME). Models with both fixed and mixed effects were compared with their maximum likelihood using analysis of variance (ANOVA). The p-value gave the probability that the two models were equal. However, estimation of parameters was performed with optimization of restricted maximum likelihood (REML)  [15]. Linear models with mixed effects were particularly useful here to study the effective initial cell number N 0 as a function of the upper layer. As for classical linear regression, estimation of fixed effects, such as growth speed b, was Gaussian and the mean and standard deviation can be given.
From this segmentation, the number of focal adhesion points was quantified (see also Fig. S-3 ) and reported in C (left). Shape parameters such as the cell roundness were determined by the perimeter to area ratio (P2A) and are reported (middle). If the object is a perfect circle, its P2A is equal to 1. As it deviates from roundness, its perimeter gets large relative to its area and thus its P2A becomes larger than 1. A significant effect was only observed between PAH and FN-coated surface. Cell average area (in mm 2 ) is reported (left) and does not show any significant effect of PE. ***indicates p < 0. 1%, **p < 1%, *p < 5%,. :p < 10%. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) as expected. Data show also that the doubling time of PC3 on PAH is similar to that of PNT-2 in all conditions, namely around 30 h.
After all, how much use would BLAST  (1)  be without an E-value? The ModFOLD4 server is the latest version of our popular independent server for the prediction of the global and local quality of 3D protein models. The server allows researchers to make value judgements about the credibility of models through the provision of accurate Quality Assessment (QA) data and intuitive graphical visualisations. As the first models of protein structures were made researchers have developed tools to determine their quality. Early tools focused on basic stereochemical checks such as WHAT-CHECK (2), PROCHECK (3) and, more recently, MolProbity  (4). Although these are good tools for providing a useful 'reality check' and identifying unusual geometric features in models, they do not produce single scores that allow you to rank numerous alternative models.
Such criticisms prompted a change of focus at CASP10 to rebalance the QA assessment using smaller bespoke data sets. The ModFOLD4 server was independently benchmarked at CASP10 and was found to be among the top performing methods in the QA category. The ModFOLD4 server can make use of the availability of large data sets containing hundreds of models, or it can evaluate single models with comparable performance, thereby providing accurate evaluations of model quality while addressing the real-life needs of researchers investigating protein structures.
Inputs The only required inputs to the server are the amino acid sequence for the target protein (on which the model is based) and a single 3D model [in Protein Data Bank format] for evaluation. However, users may optionally upload the following: multiple alternative models (as a tarred and gzipped directory of PDB files), a name for their protein sequence and their email address. The time taken for a prediction to complete will depend on the length of the sequence, the number of models submitted and the load on the server  (20). Typically, users should expect to receive results back for a single model within a few hours and certainly within the same day. If several hundred models have been submitted for a single target, then it may take several days before the results are returned.
displays the measured intermediate scattering function S(Q, t)/S(Q) of the PEG-OH matrix (a) for times up to 25 ns and (b) of the PBO matrix for longer times. The solid lines represent the full Rouse model description of the datasets while the dashed lines single out the contribution of the internal chain relaxation modes (S(Q, t) modes ). The dynamic parameters of the Rouse model ts are summarized in Table.
23 With this setup, HAXPES data from a Ge film buried under 10-nm-thick oxide and metal layers  20  and even from a thin LaCaMnO 3 layer buried under 53 nm of SrTiO 3 24 could be obtained. X-ray reflectivity (XRR) data, as shown in Fig. 1 , were measured at the beamline and fitted using the ESRF-developed software tool Xop in order to evaluate the MOS layer thicknesses and interface roughnesses. 20, 25   he obtained HfO 2 layer thicknesses were 5. 9 ± 0. 2 and 11. 9 ± 0. 3 nm, and the W and Pd layer thicknesses were 5. 9 ± 0. 4 and 5. 4 ± 0. 1 nm, respectively. These values agree very well with the expected values. The roughness of the InAs/HfO 2 and HfO 2 /metal interfaces were 0. 4 and 0. 2 nm, respectively, indicating a sharp interface extending over only one to two monolayers. In 2p 3/2 , As 2p 3/2 , Hf 2p 3/2 , Pd 2p 3/2 , W 2p 3/2 , and O 1s core level spectra were recorded.
ACKNOWLEDGMENTS The experiments were performed on beamline BM25 at the  European Synchrotron Radiation Facility (ESRF), Grenoble, France. We would like to thank the  SpLine  staff for their assistance in using beamline BM25. This work was supported by the  Nanometer Structure Consortium at Lund University  (nmC@LU), the  Swedish Research Council (VR) , the  Knut and Alice Wallenberg Foundation , and the  European Research Council  under the  EU FP7  Grant Agreement No. 259141. One of the authors (R. T. ) acknowledges support from the  European Commission  under a  Marie Curie Intra-European Fellowship.