dataesr/scientific-paragraphs-categorization huggingface dataset:
Both quantities are difficult, i. e. error prone, to determine for low resonance frequency, soft cantilevers in liquids, as has been used here. Thus, we relied here on the spring constant as determined by a vibrometer. An alternative variation of the Sader method for calibration of cantilever spring constants has been recently proposed which overcomes these issues and might represent an alternative to the use of a vibrometer  33. However, we propose using accurate force constants determined by interferometry, since this direct method promises better accuracy for soft cantilevers in liquid. Figure  1B  shows data of elastic modulus obtained on the same 8 gels presented in Fig. 1A , but recorded and analysed following our procedure from one user in one location. These data prove that the proposed procedure strongly reduces the variability of the measurements.
However, the aim of this work was to test the procedure among different users and instruments, therefore successively, one single gel was independently measured with three different instruments (from Asylum, Bruker and JPK) in different locations but with the same kind of colloidal probe. Figure  2  shows a comparison of elastic modulus obtained from force maps recorded and analysed following the herein proposed SNAP, as well as with the other previous conventional methods; in fact, AFM data were analysed with the Hertz model using each time a force constant extracted following a different approach. In particular, by using the deflection sensitivity from force curves on a rigid substrate and force constant values calculated from the AFM thermal or measured with the vibrometer, a large variation in elastic moduli can be seen.
info. 5 and 8). SNAP leads to a substantial increase in consistency; in fact, implementing this correction the elastic modulus has been determined to be 655 +/-171 Pa in comparison to 758 +/-373 of the conventional procedure (Fig. 3 ). So, with the help of SNAP the consistency has been increased by a factor of 2. Without SNAP the variation between experiments (labs) is very high. On average, i. e. averaging several independent measurements from different locations, we got similar values for the Young's moduli. However, since Elastic moduli of the same gel measured with three different instruments using the same kind of colloidal probes. The data were then analysed with different methods numbered from 1 to 4. In 1 to 3 ones, the deflection sensitivity was calibrated with a force curve on a stiff substrate and the force constant was determined from the analysis of the thermal with the AFM software (1); from the analysis of the thermal with the JAVA applet (2) and from the vibrometer measurements (3).
However, even for complex samples like cells, the model is generally accepted and it is usually applied for data analysis. AFM experiments on PA gels. Preliminary experiments were performed on PA gels, with elasticity values in a comparable range to those of cells. Initially, gels were prepared and measured in Bremen, then sent to the other locations and circulating from lab to lab. Data were recorded with the respective local AFM using colloidal probes: spherical SiO 2 beads with a diameter of 6. 62 µm attached to a tipless PNP-TR-TL cantilever (nominal k = 0. 08 N/m; CP-PNP-SiO-C-5 NanoAndMore, Karlsruhe, Germany). Force constants were measured for each cantilever by the manufacturer with a vibrometer and found to be around 50 mN/m. The vibrometer used was a Polytec (Waldbronn, Germany) OFV-5000 system with a DD-600 digital displacement decoder on a Mitutoyo (Japan) upright microscope with a 20x objective.
A careful calibration of the deflection signal has been done on a stiff substrate (glass slide). Several force curves were recorded to achieve a reasonable average for the deflection sensitivity. Force volume maps were recorded with a scan rate of 1 Hz, travel range of 4 µm, tip velocity of 8 µm/s and trigger deflection of 50 nm (corresponding to a trigger force of 2. 5 nN) in water at room temperature. The data were recorded in the centre of the gel; typically, 100 force curves were collected over an area of 10 × 10 µm². For a second round of experiments, in order to test the proposed procedure, one same gel was investigated by using three different AFMs (Asylum, Bruker and JPK). Several techniques to analyse data and to calibrate deflection sensitivity and force constant were compared with SNAP.
5 nN) in HEPES-Ringer buffer at room temperature. Since cells show viscoelastic behaviour it is of utmost importance to maintain the same settings for all data compared within this study. 100 curves were acquired over an area of 20 × 20 µm² in the form of force volumes. For each samples three force volumes were recorded at different areas, for a total of 300 curves for each experiment, hence for each laboratory. Data were analysed with and without applying SNAP to assess the actual benefits from the proposed method. The experimental steps have been standardized as much as possible: first, thermal tunes were recorded in the buffer afterwards a careful calibration of the deflection signal has been done on a stiff substrate (glass slide). A force curve was recorded to determine the deflection sensitivity.
From this point, LASER position and photo detector were kept unchanged. Thermal tunes were analysed with the built-in software of each instrument and/or by custom protocols. Briefly, thermal fluctuation data of free cantilevers were recorded, the deflection signal was corrected for the differences in amplitude and deflection sensitivities (see supporting information 3), transformed in frequency space by the Fourier transform, and then a model function (single harmonic oscillator or Lorentzian) was fitted to obtain the total power, and hence the force constant (see supporting information 1). In addition, data were exported to be analysed in a custom-made Java applet, developed within this initiative. This applet is open source and publicly available  43. We recommend here using a Lorentzian for thermals in liquids, since the fluctuations of a cantilever in water is highly over damped.
Standardized nanomechanical AFM procedure (SNAP). Usually in AFM, the deflection sensitivity is determined from a force curve on a stiff substrate (which can be difficult in cell samples, since even bare regions will be covered with extracellular matrix components secreted by neighbouring cells). After deflection sensitivity is determined, the thermal fluctuations of the free cantilever are recorded, and by analysing the power spectral density (PSD) of these fluctuations the force constant is determined. If the deflection sensitivity is wrong (and our experience with cell samples shows that it will be wrong by 5 to 20%), the fluctuation data will be erroneously calibrated and the force constant will be wrong by roughly twice the error in the deflection signal. Thus, we propose an inverse procedure: determine the force constant by using some highly accurate method (like a vibrometer); record the thermal fluctuations and then adjust the deflection sensitivity such that the thermal results in the same exact force constant.
This can either be done before the experiment, by applying a correction factor to the deflection sensitivity, or during offline analysis, if the software allows to re-calibrate the data. Most software packages provided by the AFM manufacturer do not allow re-calibrating data after acquiring, thus we developed software solutions using the data analysis package Igor (Wavemetrics, Lake Oswego, OR, USA). We also provide an open source JAVA applet that allows to re-calibrate and analyse single force curves. A step-by-step description of SNAP can be found in supporting information 5 and 8.
Acknowledgements We thank  NanoAndMore (Karlsruhe, Germany) and sQUBE (Bickenbach, Germany)  for generously providing the colloidal probes used for studying for mechanical properties of soft gels. We thank  Bruker  ( Santa Barbara, CA, USA ) for supporting this study by vibrometer calibration of colloidal probes. The idea of this standardization effort has been inspired and promoted during several meetings of the  COST Action TD 1002 AFM4BioNanoMed. This effort was only possible because this  COST Action  provided the infrastructure to start and promote the discussion process needed here. Initially, the correcting procedure described here was known as the "Dubrovnik procedure". This work was supported by grants to D. N. , T. L. , N. C. , and  J. J. U. ( Spanish Ministry of Economy and Competitiveness   FIS-PI14/00280 ), M.
L. and J. B. (National Science Center  NCN DEC-2011/01/M/ST3/00711 ), H. O. ( Deutsche Forschungsgemeinschaft ,  Koselleck 68/1  and  Centre of Excellence, Cells in Motion (CIM), University of Münster, Germany ), S. B. and F. L. ( ANR-10-EQPX-04-01 ) and A. D. , E. T. H. , R. G. ( CSD2010-00024 ,  ERC-AdG-340177 ). A. P. thanks the  Dept. of Physics of the University of Milano  for financial support under the project " Piano di Sviluppo dell' Ateneo per la Ricerca 2014 ", actions: " A. Upgrade  of instrumentation", and  "B. Support  to young researchers". We acknowledge the support of the  International AFMBioMed Conference Association.
, 2016). A global PPR control and eradication strategy has been developed by the Food and Agriculture Organization of the United Nations (FAO) and the World Organisation for Animal Health (OIE)  (OIE and FAO, 2015; Raj et al. , 2015). Different scenarios are constructed to estimate the resource requirements associated with PPR vaccination. In the following sections, the VacciCost tool is described in detail along with the data required to use it. The data collected in collaboration with Senegalese Directorate of Veterinary Services are described and scenarios reflecting different vaccination productivity levels (number of animals vaccinated per day per vaccination team) are constructed to assess the influence of some parameters on the cost evaluation. Next, results are presented and discussed, as well as the tool's limitations.
Cost components The resource requirements are computed according to the following components: vaccines, injection supplies, staff, transport, maintenance and overhead, training, social mobilization, and surveillance and monitoring. Additional incentives to increase the farmers' willingness to vaccinate (such as monetary incentives or free deworming while vaccinating) can be considered when needed. Table  1  summarizes the data required as input for each component. The productivity of vaccination teams is a crucial parameter for the cost estimation. However, it is not easy to estimate. To account for its uncertainty, productivity of vaccination teams is considered as a random variable described by a PERT distribution. The parameters of such distribution are the minimum, maximum, and the most likely value; a 95% confidence interval can be computed around the average cost.
Data collected from Senegal In collaboration with the National Directorate of Veterinary Services (DVS), we analyzed the weekly reports corresponding to the 2012-2013 livestock vaccination campaign. The information available was not completely exhaustive and/or detailed, so it was complemented using data from the report of the DCI-Food EU-funded activity "Vaccines for the Control of Neglected Animal Diseases in Africa" (VACNADA), a project implemented during the 2010-2011 vaccination campaign in the northern region of Saint-Louis with the objective of increasing the vaccination coverage against PPR. The distribution of the small ruminant population according to the farming systems was estimated using the database Gridded Livestock of the World  (Robinson et al. , 2014). Some information, such as staff wages, vaccine cost and small ruminant market prices, was obtained during a workshop organized in October 2015 in Dakar and completed with requests addressed to the Senegalese Institute for Agricultural Research (ISRA), which produces and sells the PPR vaccine.
Acknowledgments This work was supported by the  European project ,  FP7-REGPOT-2012-2013-1 , "EPIGENESIS",  One Health  approach to integrate Guadeloupe research on vector-borne and emerging diseases in the ERA: from the characterization of emergence mechanisms to innovative approaches for prediction and control (post-doctoral grant to  D. Tago ). We thank the  Senegalese Direction of Veterinary Services of the Ministry of Livestock and Animal Production  for the provision of data, and the members of the  Caribbean Animal Health Network (CaribVET)  for their input on the development of the tool.
One of the strategies for curing viral HIV-1 is a therapeutic vaccine involving the stimulation of cytotoxic CD8-positive T cells (CTL) that are Human Leucocyte Antigen (HLA)-restricted. The lack of efficiency of previous vaccination strategies may have been due to the immunogenic peptides used, which could be different from a patient's virus epitopes and lead to a poor CTL response. To counteract this lack of specificity, conserved epitopes must be targeted. One alternative is to gather as many data as possible from a large number of patients on their HIV-1 proviral archived epitope variants, taking into account their genetic background to select the best presented CTL epitopes. In order to process big data generated by Next-Generation Sequencing (NGS) of the DNA of HIV-infected patients, we have developed a software package called TutuGenetics.
This tool combines an alignment derived either from Sanger or NGS files, HLA typing, target gene and a CTL epitope list as input files. It allows automatic translation after correction of the alignment obtained between the HxB2 reference and the reads, followed by automatic calculation of the MHC IC 50 value for each epitope variant and the HLA allele of the patient by using NetMHCpan 3. 0, resulting in a csv file as output result. We validated this new tool by comparing Sanger and NGS (454, Roche) sequences obtained from the proviral DNA of patients at success of ART included in the Provir Latitude 45 study and showed a 90% correlation between the quantitative results of NGS and Sanger. This automated analysis combined with complementary samples should yield more data regarding the archived CTL epitopes according to the patients' HLA alleles and will be useful for screening epitopes that in theory are presented efficiently to the HLA groove, thus constituting promising immunogenic peptides for a therapeutic vaccine.
Some studies have shown that the viruses recovered from archived DNA are different from pre-ART circulating viruses and  [6]. Therefore the characterization of CTL epitopes encoded by archived DNA in patients according to their HLA alleles should generate information on epitopes that could be used for therapeutic vaccination in future trials. Provir Latitude 45 is a project aiming at identifying CTL epitopes in archived HIV-1 DNA in patients during ART, taking into account their HLA A and B genotype. It was initiated in Bordeaux University Hospital and then extended to other groups (Montreal, Canada and Lima, Peru). It aims at gathering as broad a dataset as possible on archived epitope sequences from a large number of patients and at identifying epitopes that are conserved and potentially used for vaccination, taking into account the patients' genetic background.
The present study concerned the sequence analysis of archived HIV-1 DNA using Sanger technology and Next-Generation Sequencing (NGS). As previously detailed  [7] , the lack of frame awareness in the alignments in the Amplicon Variant Analyzer (AVA) software provided by Roche and the frequent shifts due to homopolymers complicates the analysis of amino acid sequences generated by 454 Roche sequencing. Therefore, we have developed software able to perform an automatic analysis of nucleic acid sequences of Gag and Pol among other genes, leading to the identification of CTL epitopes and MHC IC 50 values calculated from the Immune Epitope DataBase (IEDB) according to patients' specific HLA A and B alleles. This software can be used for the population analysis of archived epitopes using either Sanger or NGS technology.
Gag and Pol Ultra Deep Pyro Sequencing (UDPS) Gag and Pol ultra-deep-sequencing were performed by using the Roche 454 technology. Amplicons for Gag (obtained with the same primers as for Sanger sequencing) and Pol  [4, 5]  were pooled at equimolar concentrations. Clonal amplification on beads (EmPCR) was performed by using the 454 Life Science Reagents that enable bi-directional sequencing, composed of 30 cycles of PCR amplification. DNA-containing beads were recovered and UDPS was performed on the GS Junior Sequencer (454 Life Science Roche). Analysis of the obtained data is detailed below. Raw data are available in GenBank under accession numbers SRP114367.
HLA class I typing The method used for the molecular characterization of HLA alleles A and B in the Bordeaux and Montreal cohorts has been described previously  [4, 5]. For the patients from Lima, highresolution HLA typing was performed by Sequence-Based Typing (SBT) at the University of Oklahoma Health Science Center, a CLIA/ASHI-accredited HLA typing laboratory, using inhouse methods. Briefly, genomic DNA was extracted from PBMCs using a QIAamp DNA blood kit (QIAGEN). After confirmation, the PCR product was purified using an ExoSAP-IT kit (USB) and was sequenced using BigDye1 Terminator v3. 1 (APPLIED BIOSYSTEMS) chemistry. Dye was removed by ethanol precipitation. Sequencing reactions were performed on a 3730 Capillary Electrophoresis DNA Sequencer (APPLIED BIOSYSTEMS). Four-digit HLA types were determined using the HLA typing program Assign SBT (Conexio Genomics).
Immune recognition tools Viral epitopes were selected from the Los Alamos HIV Immunology Database using the most recent update of the optimal CTL epitope list  [8]. Peptide binding to its restricting HLA class I molecule was predicted by using the NetMHCpan 3. 0 software from the Immune Epitope Database (IEDB)  [9]. A value of 500 nM for the MHC IC50 (IC50) was used as the threshold between binder and non-binder epitopes and their variants according to the recommendation given by IEDB and other groups  [10] [11] [12] [13] [14].
TutuGenetics software TutuGenetics is a command line software tool written in C taking as inputs numerous files "Fig  1" : • The BDCEL which list HIV-1 epitopes described for given HLA alleles. • The patient's HLA alleles A and B. • HxB2 and the patients' sequences alignment. • The specified target gene required to determine the correct open reading frame for the amino-acid translation. As stated earlier, the UDPS alignment cannot be translated directly. Prior to translation, TutuGenetics is able to rearrange the UDPS alignment according to some filtering criterion such as a minimum reads length and alignment correction heuristic. The alignment issued from a Sanger sequencing or the corrected UDPS are then translated into amino acid sequences given the open reading frame (ORF) of the gene specified by the user.
The last column indicates the presence or absence of the different epitopes in the sequences obtained by the Sanger method. The threshold of 1% for the detection of variants was determined according to previous data from our group  [17]. Globally we analyzed 133 Gag or Pol epitopes. The results obtained with Sanger sequencing and AVA combined with TutuGenetics data were concordant for 120/133 epitopes (90. 2%). Results obtained for 8 samples are presented in Table  2. We then analyzed for each sample the variability of the epitope sequences and their predicted binding to the patient HLA allele. B03_Ba expresses the HLA-B Ã 27:05 allele which allows presentation to CTL of the Gag p17 (19-27) epitope; the reference antigen sequence according to the Los Alamos database is noted in the first line.
Discussion TutuGenetics allows the rapid and reliable analysis of NGS data with direct and HLA alleledependent identification of the CTL epitope according to a reference HIV-1 epitope list and predication of epitope binding affinity. The correlation between the quantitative results of NGS and Sanger was high and in accordance with a threshold of variant detection around 20 to 30% for NGS quantitation/Sanger detection, as generally admitted in the field  [18]. This tool is thus able to facilitate the identification of archived CTL epitopes according to the patients' HLA alleles for their potential inclusion in therapeutic vaccine immunogens. As already pointed out by Deng et al  [6] , a large number of archived HIV-1 DNA genomes carry CTL epitope escape mutations even in patients close to primary infection.
It renders infected cells insensitive to CTL directed at conserved/wild type epitopes when ART is interrupted. It is therefore crucial to identify CTL epitopes that are non-mutated or, if they do show sequence mutations, that have retained their ability to bind to the presenting HLA class I molecule. The pattern is very complicated since data indicate that a process of viral adaptation to high-frequency HLA alleles can occur in a given population  [19] , therefore conferring an advantage to some rare HLA supertypes in HIV disease progression  [20]. TutuGenetics may be able to overcome this issue by generating big data from a large number of patients with the hypothesis that some archived CTL epitopes cannot vary for viral functional reasons, even though they are under pressure from the most frequent HLA alleles in a population.
One example of a study similar to Provir but devoted to the analysis of a preventive vaccine trial (RV144) has shown that there is an association between vaccine efficacy and HLA A Ã 02:01  [21]. TutuGenetics has the advantage to be modular. Therefore it accepts as input data files generated by other NGS platforms (PGM Ion Torrent and Illumina). Also, the search of epitopes may not be restricted to the BDCEL list and can be used to identify novel epitopes for other viruses. Some important issues remain to be solved. One is the variability of these epitopes within the quasi-species. This variability, if present, is mainly measurable by NGS; if there is a high variability of the CTL epitopes within the quasi-species with some variants outside the range of theoretical presentation, it will be more complicated to design a peptide cocktail reacting with the most frequent HLA alleles.
Acknowledgments Membership of the Provir/Latitude 45 project:  Mark Wainberg  (+),  Jean Pierre Routy  (jeanpierre. routy@mcgill. ca), McGill University, Montreal ,  Canada ;  Michel Dupon  (michel. dupon@chu-bordeaux. fr), Didier Neau  (didier. neau@chu-bordeaux. fr), Service des Maladies Infectieuses et Tropicales ,  Hopital Pellegrin ,  CHU de Bordeaux, Bordeaux, France ;  Jorge Sanchez  (jsanchez@citbm. pe), Centro de Investigationes Tecnologicas ,  Biomedicas y Madioambiantales, Lima, Peru and Department of Global Health, University of Seattle, Seattle, WA, USA ;  Christian Brander  (cbrander@irsicaixa. es),  IrsiCaixa AIDS Research Institute, Hospital Universitari Germans Trias i Pujol, Badalona (Barcelona)  and  University of Vic, Central University of Catalonia, Barcelona  and  Institucio Catalana de Recerca i Estudis Avancats (IRCEA) ,  Pg Lluis Companys 23, 08005 Barcelona, Spain.
Many studies, in the literature, have affirmed a low level of user satisfaction concerning the understandability and readability of large-scale Linked Data visualizations offered by current available tools. This issue is especially problematic for inexperienced users. To address these requirements, we have extended our previous work Memo Graph, an ontology visualization tool, to provide a user-centered interactive solution for extracting and visualizing Linked Data. It takes aim to provide comprehensible and legible visualization. To manage scalability, it is built on an incremental approach to extract descriptive summarization from a given Linked Data endpoint where it becomes possible to generate a "summary graph" from the most important data (middleout navigation approach). It offers user interfaces that reduce task complexity for users, especially the inexperienced ones.