dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on manufacturer recommendations, a 95% or greater training accuracy was required for each classifier in order to proceed with image analysis of each tissue region. Classifiers were combined with appropriate image analysis algorithms for each marker for the automated quantitative assessment of immune infiltrates within the specific regions of tissue, across whole-slide sections. DPRIA provides an accurate output of average percent positive cells for a cell type within a region of interest. All images were quality controlled for accuracy of the tissue recognition software, in addition to accuracy of the image analysis algorithm. Pathologist, CW, further confirmed that the tumour and stroma were being identified correctly. Statistical analysis. Data were collated and displayed as mean percent positive cells (±s.
RESULTS Tissue pattern recognition platform. The combined workflow of the DPRIA platform is illustrated in Figure  1. Digital images were acquired using a ScanScope digital scanner (Figure  1A ). Genie pattern recognition software was utilised to distinguish different areas of tissue. Specific tissue 'classifiers' were trained to identify distinct spatial-spectral features that discriminate image pixels for each region of tissue. This was achieved by the operator manually marking up example regions of segregated tissue to provide a training set of images for the pattern recognition software. Tissue regions included defined areas of tumour (red), stroma (yellow), lymphoid structures (green) and background glass (blue; Figure  1B ). The assessment and validation of classifiers was achieved across example tissue images (Figure  1C ).
Trained Genie classifiers were then combined with appropriately tuned image analysis algorithms for the quantification of different markers within each specific area (Figure  1D ). A nuclear algorithm (Aperio ePathology Solutions) was modified to quantify the number of immune cells and data expressed as a percentage of the total number of cells within the area of interest (percentage positive cells). A micro-vessel density (MVD) algorithm (Aperio ePathology Solutions) was tuned to assess the number of vessels per mm 2 of each tissue region. Implementation of pattern recognition image analysis: the importance of the tumour IM. DPRIA distinguished areas of tumour (CT), tumour-associated stroma (TS), IM, DNN and DNN-associated stroma (DNNS). Primary CRC tumour with matched distant normal tissue (n ¼ 40) was evaluated for CD8 þ cytotoxic T lymphocytes (CTLs), Foxp3 þ Tregs, CD68 þ macrophages and CD31 þ vascular endothelium (Figure  2 ).
ACKNOWLEDGEMENTS This work was supported by the  University of Nottingham , the  EPSRC  ( EP/D501849/1 ) and  AstraZeneca. We acknowledge the third party providers to the  Alderley Park Biobank ,  Indivumed GmbH  and the  Manchester Cancer Research Centre (MCRC) Biobank. We acknowledge the scientific guidance of her PhD supervisors  Professor David Pritchard  and  Dr Robert Wilkinson. This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 3. 0 Unported License.
Here, the method proposed could be applicable for the detection and quantification of nanoparticles in cells. This was made possible by the use of ImageStream, a device combining both flow cytometry and high resolution imaging. It couples the advantages of acquiring a large among of cells (up to 5000 events/sec) -and benefits of statistical data and high resolution images of each cell at the same time (0. 5 µm spatial resolution). Generally, images are collected using a CCD camera and are a two dimensional grey scale representation of the cell. The light is quantified for each pixel in the image and identifies both the intensity and the location of either fluorescence or components with a subcellular size (in BF for example), allowing thus a powerful ability to discriminate cells based on their appearance 9,10.
RegaDB is a free and open source data management and analysis environment for infectious diseases. RegaDB allows clinicians to store, manage and analyse patient data, including viral genetic sequences. Moreover, RegaDB provides researchers with a mechanism to collect data in a uniform format and offers them a canvas to make newly developed bioinformatics tools available to clinicians and virologists through a user friendly interface. Availability and implementation: Source code, binaries and documentation are available on http://rega. kuleuven. be/cev/regadb. RegaDB is written in the Java programming language, using a webservice-oriented architecture.
INTRODUCTION Advances in infectious diseases research require efficient collaboration and exchange of clinical and virological data. Researchers need access to large amounts of data to test hypotheses or extract valuable information through data mining  (Sloot et al. , 2008 (Sloot et al. , , 2009)). For this purpose, RegaDB was developed as a free and open source data management and analysis environment for infectious diseases  (Libin et al. , 2007). RegaDB runs on Windows, Linux or Mac OS X. The system can be installed within a hospital or institute so that the data stays within the clinical environment. RegaDB follows the idea of an integrated environment for bioinformatics analysis, such as the Genetic Data Environment  (de Oliveira et al. , 2003) , ViroLab  (Assel et al. , 2009)  and Geneious  (Drummond et al.
, 2011). The difference is that RegaDB uses a relational database, and can be locally or remotely accessed. This allows RegaDB to be used for clinical management and/or research in one locality or for longterm data-sharing collaborations between different institutes. attributes, tests, events, therapies and viral isolates. Attributes annotate a patient with information, which is typically of a clinical or epidemiological nature, e. g. the gender or transmission risk group. RegaDB implements tests as values that are obtained at a given moment in time, i. e. there is only one date associated with it. The results can be in vivo or in vitro measurements, appointments, as well as computational results obtained from a web service. General tests are used to store data extracted from patient samples, e.
g. cell counts and viral loads. Tests can also be linked to viral isolates, e. g. typing and subtyping results, to drugs, e. g. therapeutic drug monitoring, or to a combination of an isolate and a drug, e. g. phenotypic and genotypic resistance interpretations. Events cover a specific time interval in the patient's history, i. e. have a start and end date, e. g. AIDS-defining illnesses or pregnancy. The default list of attributes, tests and events available in the system can be extended via the user interface. In this way, RegaDB can be tailored to the user's needs or research interests. Attributes, tests and events are annotated with a data type  (numbers, strings, nominal values, etc. ) , which allows the user interface and data access layer to maintain data integrity. The therapy entity allows users to store the medication history of a patient.
The alignment procedure finds the protein reading frames encoded by the sequences that make up the isolate. This information, together with all detected point mutations, insertions and deletions, is stored in the database. The alignment web service implements the Needleman-Wunsch algorithm in Cþþ  (Needleman and Wunsch, 1970)  to analyse large sequences efficiently. Depending on the pathogen determination returned by the web service, the viral isolate is directed to a typing web service  (Alcantara et al. , 2009; de Oliveira et al. , 2005)  and/or resistance interpretation web service  (Liu and Shafer, 2006). Table  1  shows detailed information on reference sequences and bioinformatics tools available for the supported pathogens. RegaDB supports the use of bioinformatics tools published on the web as web services.
All data can be viewed and edited through a web-based interface. Key parameters of a patient's clinical history are visualized in a patient chart as a time-line annotated with viral loads, CD4 counts, regimens and viral isolate time points. RegaDB can export patient details into a report document by replacing variables in a user-designed RTF template. Several tools are already available or are being developed, some of which by the users. Drug resistance interpretation can be performed according to several algorithms. For HIV, various versions of the Stanford algorithms (HIVdb,  Liu and Shafer, 2006) , the Rega algorithms  (Van Laethem et al. , 2002)  and the ANRS algorithms  (Meynard et al. , 2002)  are implemented. For each algorithm, a cumulative overview is available, whereby resistance detected in a patient is taken forward to the last sample.
Evolution of a virus isolate is tabulated as amino acid changes compared with the previous isolate from the same patient. Another tool allows plotting a phylogenetic tree constructed from a set of sequences with a pre-defined similarity to a query sequence. To ensure the quality of the sequence database, a tool was developed that can be used to flag potential contaminations, errors in sampling or data entry, super infection or transmission chains, by detecting unusual intra-or inter-patient evolutionary distances. Attributes are synchronized with a central repository to ensure compatibility between different RegaDB instances. The central repository contains a collection of standardized data fields and corresponding values such as demographic information (country of origin, transmission risk group, etc.
OPPORTUNITIES FOR RESEARCHERS When the development of RegaDB started, several custom-made databases were available that allowed users to enter ambiguous representations of data, for example, different representations for the same medical compound. However, to facilitate efficient data exchange and to make the execution of aggregate queries possible, it is important that data are available in a structured format. By providing support for explicit data types and enforcing these data types through the user interface, RegaDB circumvents many difficulties that might complicate the exchange of data. RegaDB allows data to be exported in XML format from local data sources (hospitals, institutes), and these exports can be combined in a research database. Data from other databases can be imported via a generic import tool.
RegaDB also provides a programming interface, A research database will generally be accessed via the Internet; therefore, authentication is an important security aspect. RegaDB supports password-based authentication by default. The authentication module abstraction allows for a straightforward implementation of alternative authentication back-ends (OpenId, Kerberos, etc. ), which makes it possible for RegaDB to connect to existing user management systems. The application will only allow registered users to access the system. Once granted access to the system, a user is only able to access patient information that belongs to a dataset connected to the user's profile. The owner of the dataset can configure the access of users to this dataset, and revoke the access after a certain analysis or assignment is finished.
Researchers can query RegaDB using the visual query tool, which allows users to define complex queries guided by a user interface. Query definitions can be saved and re-run every time an update of the data becomes available. Work is in progress to support the use of predefined SQL-based queries via the user interface. Query results can be exported to a CSV and/or FASTA file. It is possible to set-up an analysis workflow by configuring a query to execute a python post processing script. If the script generates statistical data in a graphical format, this is visualized in the query user interface after the query has been executed. When researchers make their tools available as web services, they can be easily integrated in RegaDB, lowering the threshold for clinicians and virologists to use such tools.
RegaDB has been used in several collaborations including the Virolab EC project (virolab. org). Data from several European hospitals were stored in one RegaDB instance, resulting in a combined dataset of 48000 sequences. During the last phase of the project, we were able to combine our efforts with another EC project, EUResist (euresist. org), resulting in a combined database of 455 000 sequences. Another example of the utility of RegaDB is the collaborative database used within the Southern African  Treatment and Resistance Network (SATuRN). This network has 24 member institutions working in Southern Africa, the region at the epicentre of the HIV epidemic. Currently there are 410 institutions using the SATuRN RegaDB for patient data management, data curation and research. Under SATuRN, 47000 genotypes with treatment and monitoring data have been collected.
AVAILABILITY AND USAGE RegaDB is a software application that can be downloaded from the Internet and installed in a health care or research institute. Documentation, source files and binaries are available on http://rega. kuleuven. be/cev/regadb. Because of its modular and flexible design, RegaDB can be used in many different contexts and settings, from managing patient data in a clinical environment to setting up large-scale research collaborations. Currently, all RegaDB instances are private instances that can only be accessed by a restricted user base. Some of these instances are accessible on the Internet; others are only accessible from within the institute's intranet. The current version of the software is already used for storing genetic data of HIV-1, HIV-2, HTLV  (Araujo et al. , 2012)  and HCV isolates and related patient and clinical information.
ACKNOWLEDGEMENTS The authors would like to thank the  AIDS Reference Laboratory of Leuven  that receives support from the  Belgian Ministry of Social Affairs  through a fund within the  Health Insurance System, the 'SPIRALES' program  from  IRD  and the mybiodata company. has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under the project ' Collaborative HIV and Anti-HIV Drug Resistance Network (CHAIN) '-grant agreement n  223131.
In the present work, we investigate this relationship using biochemical and structural biology approaches. We show that native PhtD and AdcAII proteins interact at the surface of S. pneumoniae. The 137 amino acid-long N-terminal domain of PhtD (t-PhtD, amino acids 30 to 166), that contains a single histidine triad motif, is studied by NMR spectroscopy. We demonstrate that Zn 2+ is bound by the histidine triad of t-PhtD and that, in presence of apo-AdcAII, the metal ion is transferred from Zn 2+ -t-PhtD to AdcAII. We solve the high-resolution NMR structure of the Zn 2+bound form of t-PhtD. This structure provides new key structural data for this protein family for which only a structure of a short, 55-residue fragment was known. Detailed comparison of the two available structures of Pht proteins reveals a short conserved motif characteristic of streptococcal HxxHxH-containing proteins.
8 mM) for 1 h (x-IP). The cross-linking reaction was quenched by adding 250 mL of 2 mM Tris (pH 7). Samples (Mb: 2 mL, the third wash (W): 5 mL, IP and x-IP: 15 mL) were loaded onto a sodium dodecylsulfate polyacrylamide 12. 5% gel for electrophoresis (SDS-PAGE) and were immuno-blotted using specific primary polyclonal antibodies from rabbit or mouse. Secondary a clean Blot IP Detection Agent 1/400 (Pierce) was used to reveal primary rabbit antibodies, whereas the horseradish peroxydaseconjugated goat anti-mouse IgG (1/100) was used to detect PhtD mouse primary antibody. The immuno-reactive bands were revealed using chemiluminescence detection with the ECL system (GE Healthcare). Signals recorded on autoradiography were quantified using the Image J software and used to calculate the ratios of immuno-precipitated proteins.
05, 0. 09, 0. 15, 0. 25, 0. 4, 0. 6, 0. 9, 1. 2, 1. 5, 1. 8 s and 0. 01, 0. 02, 0. 03, 0. 05, 0. 07, 0. 09, 0. 11, 0. 13, 0. 17, 0. 25, 0. 4 s were used to explore the magnetization decay for longitudinal and transverse relaxation, respectively. For the heteronuclear { 1 H}-15 N-NOE, the amide proton signals were saturated with a 1. 7 kHz WALTZ16 decoupling scheme centered at the amide proton frequency. The saturation and recycle delays were set to 3 and 5 s, respectively. NMRView was used to quantify peak intensities and relaxation rates were extracted with the Curvefit program (http://cpmcnet. columbia. edu/dept/gsas/biochem/labs/ palmer/software/curvefit. html). Experimental data were fitted to a two-parameter single exponential and errors were estimated from a Monte-Carlo simulation, taking into account twice the root-mean-square noise of the spectra.
Structure calculation of Zn 2+ -t-PhtD The UNIO'10 software  [55]  was used for automatic peak picking, initial NOE assignment, and distance restraint extraction. TALOS+-derived dihedral restraints  [56]  together with manually assigned long-distance restraints calculated from a 3D-methylselective 13 C-NOESY experiment were used as additional input data. Initial structures were calculated using the Cyana molecular dynamics algorithm  [57, 58]  in the UNIO'10 software. In the initial calculations the Zn 2+ ion and the definition of the Zn 2+binding site were omitted. Final structures were calculated using the ARIA 2. 3. 1/CNS 1. 1 software  [59] , allowing for reassignment of the peaks in the lists initially provided by UNIO'10. A Zn 2+ ion was introduced by constructing a non-standard residue involving coordination to three histidines (residues H83, H86, H88) and one glutamic acid (E63), as described in the Supporting Information (see Protocol S1).
A tetrahedral geometry of the Zn 2+site was assumed with bond-lengths of 2. 0 A ˚for N-Zn 2+ and O-Zn 2+ in accordance with published crystal structures (Protein Data Bank, PDB codes 3KS3 and 2CS7). 1000 structures were calculated in the 8 th ARIA iteration from which 20 structures with the lowest total energy were selected and submitted to further refinement in explicit water to give the final structural ensemble.
Exposure times of the films varied from 2 to 30 min depending on the antibody used. (B) Quantification of the ratio of immuno-precipitated proteins. Signals recorded on autoradiography were quantified using the Image J software. Absolute density values from the autoradiographies from the membrane (Mb) and the immuno-precipitated (IP and x-IP) fractions were used to calculate the percentage of enrichment in the immuno-precipitated fractions from a given membrane volume. doi:10. 1371/journal. pone. 0081168. g001 Zn 2+ ion (Fig. 2A ). On the other hand, titration of U-15 N-labeled Zn 2+ -t-PhtD with unlabeled substoechiometric quantities of apo-AdcAII induced formation of apo-t-PhtD (Fig. 2B ). Within the limits of errors in the determination of protein and Zn 2+ concentrations, the results of these experiments concordantly show that at AdcAII:t-PhtD:Zn 2+ ratios of 1:1:1 between 80 and 100% of the Zn 2+ ions is bound to AdcAII at equilibrium, whereas a five-fold molar excess of Zn 2+ -AdcAII over apo-t-PhtD is needed to convert roughly 20% of apo-t-PhtD to Zn 2+ -t-PhtD.
Comparing the data for the two different forms of t-PhtD, it can be seen that the R 2 /R 1 ratio is systematically higher for apo-t-PhtD than for the Zn 2+ -form (Fig. 4B ). The program Tensor2  [54]  was used for a quantitative analysis of the relaxation rate constants measured for each of the two forms and the rotational diffusion tensor was determined using the molecular coordinates of the representative Zn 2+ -t-PhtD  suggesting that Zn 2+ -t-PhtD is a monomer in solution. According to Fig. 4A , residues 92-146 do not undergo significant chemical shift changes between the apo and Zn 2+ -bound form. These residues were therefore used in the Tensor2 program for the analysis of apo-t-PhtD dynamics. The principal values of the rotational diffusion tensor of the apo form are slightly smaller (D xx = 1.
PhtD:40-158 and PhtA:166-220 share common structural features The solution structure of Zn 2+ -t-PhtD is composed of two threestranded b-sheets and two helices. The three histidines (H83, H86, and H88) of the histidine triad are located in the first sheet. Structural comparison with all protein structures deposited in the Protein Data Bank (PDB) using the DALI server  [67]  gave a single hit, the 55-residue fragment of the closely related pneumococcal PhtA protein (PDB code 2CS7  [43] ). This fragment corresponds to PhtA residues 166-220, involves the second histidine triad, and is conserved in the PhtD protein (see sequence alignment, Supporting Fig. S2 ). The PhtA crystal structure also contains a single Zn 2+ ion chelated by Nd1 of H194, Ne2 of H197, Ne2 of H199 and the carboxylate of D173.
On the other hand, current identification of histidine triad-containing proteins uses the HMM (Hidden Markov Models) built from sequence alignments that form the basis either of the PFAM  [69]  family entry PF04270 or the TIGRFAM  [70]  entry TIGR01363. Both signatures are also referenced in the InterPro  [71]  domain prediction database under the identifiers IPR006270 and IPR023832, respectively. Whereas the underlying sequence alignment of PF04270 is based on the crystal structure of PhtA, and includes more than 10 residues of low sequence complexity that form the C-terminal helix in PhtA (not conserved in the t-PhtD structure), the one of TIGR01363 includes the N-terminal half (348 residues) of streptococcal histidine triad proteins. Based on the structural alignment presented in Fig. 5 , we looked for an alternative motif for the identification of protein sequences containing the streptococcal histidine triad.
Retained criteria concern sequence conservation in the shorter protein fragment including conserved residues from the Zn 2+ -binding site and the surrounding b-sheet and led to the development of a new PROSITE  [72] motif: [DEH]-G-[YILVTFMW]-x(12,16)- [YILVTFMW]-[YILVTFMWA]-[YILVTFMW]-x-H-x(2)-H-x-H- [YILVTFMW]-[YILVTFMW] (for the PROSITE pattern syntax see http://prosite. expasy. org/scanprosite/scanprosite_doc. html). This motif was used to scan the UniProtKB/Swiss-Prot (release 2012_10 of 31-Oct-12), UniProtKB/TrEMBL (release 2012_10 of 31-Oct-12) databases after addition of a criterion excluding sequences with less than two hits. This search yielded identification of 3922 hits in 1035 sequences. The taxonomic distribution of the resulting sequences reveals 4 sequences from the Genus Gemella and 1031 sequences from Lactobacillales, involving 2 and 3 sequences from Aerococcus and Carnobacteriaceae families, respectively, and 1026 sequences from the different serotypes of streptococci.
The number of recovered sequences in the latter search is higher than the 915 sequences associated with PFAM PF04270 family. As already stated before, the majority of these sequences are Pht-like proteins (group 1 and group 3 in the classification of Plumptre et al. [32] ) and internalin A-like proteins, containing leucine-rich repeats at the C-terminal, such as the previously described Blr and Slr proteins  [38, 39]  (see Table  2 ). Two S. suis proteins annotated as DNA gyrase/topo II, topoisomerase IV A subunits (SSU05_1267 and SSU98_1281) were identified to be Pht-like proteins due to their sequence similarity with other Pht-like proteins. Moreover, scanning the complete UniProtKB/Swiss-Prot and UniProtKB/ TrEMBL databases against the newly designed PROSITE motif exclusively yielded sequences from firmicutes, suggesting its robustness against false positives.