dataesr/scientific-paragraphs-categorization huggingface dataset:
Testing for virus control over infection outcome while conditioning on host's IL28B status Estimates of infection outcome correlation could have been confounded by the presence of SNPs at the IL28B locus that correlate with increased clearance rate. In these cases, we wanted to obtain estimates of the trait correlation while accounting for external factors. To achieve this, we calculated switching rates and log-likelihood values using the BAYESTRAITS package (available from www. evolution. rdg. ac. uk), which executes the maximum-likelihood analysis of  Pagel (1994)  while accounting for secondary traits that can affect the evolution of the main trait. We first checked that the same results as the 'ace' package for R were obtained for the switching rates if we only investigate one trait, which they did.
BayesTraits can thus be used to calculate the estimated transition rates of infection outcome, given that there is a possible covariation with the host's IL28B genetic status. From these estimates, we recalculated the estimated virus control over the trait, given the new transition probabilities, from the simulation data as outlined above. We then tested for significance by randomizing the tips 1000 times, while ensuring that the infection outcome and host IL28B factor for a specific tip remain linked together. Trait correlation estimates were then recalculated for each of these 1000 new tip sets and tested for significance as before.
Correlation between virus genomes and infection outcome for all genome sequences Genetic data obtained from the HITS-p cohort mostly resolved itself into one of the two clades, specifically HCV genotypes 1 (including subtypes 1a and 1b), and subtype 3a (Fig. 1 ). These data sets are labelled as 'Genotype 1' and 'Genotype 3', respectively. As a first test of our method, we investigated whether we could detect evidence of virus control over infection outcome for both genotypes analysed together (a joint phylogeny is shown in Fig. S2 ). Results are presented in Fig. 4A , which shows that a fairly high level of virus control was observed (0. 63), which was significant (P = 0. 044 following a randomization test). A lower value (0. 59) was observed for the birth-death model, which was also marginally significant (P = 0.
5D  for Genotype 1, 6D for Genotype 3). We found a significant virus control value of 0. 63 for Genotype 1; this estimate was not significantly different from the value obtained without correcting for host effects (two-sided t = 1. 66, P = 0. 0961). However, despite a higher control value being obtained for Genotype 3 than before (0. 64), it was nonsignificant following the randomization test. The estimated control value using the birthdeath phylogeny was higher for Genotype 1 (0. 69) and remained significant. Although a similarly high value was found for the Genotype 3 birth-death phylogeny (0. 66), it was not significant due to the high estimates obtained from randomized tips (Figs  S4D  and S5D ). Overall, this analysis suggests that the phylogenetic signal obtained for the complete data set was strongly driven by viruses of Genotype 1.
Power calculations and analysis of simulated data In the previous analysis, one of the more glaring outcomes is that even randomized tips gave rather high estimates of virus control, lying at around 40%. The randomization test determined that our data from Genotype 1 showed evidence of phylogenetic signal compared with randomized tips, but the question remained as to whether the magnitude of the estimates of virus control we obtained are meaningful. To determine the accuracy and the potential power of our method, we simulated 100 different tip outcomes along the Genotype 1 phylogeny, for a known virus control value (c), with each outcome produced along a separate tree from the posterior distribution. We then used our method to estimate the level of virus control from these simulated data sets as before, except that we tested for significance using 100 randomized tips as these tests were computationally intensive.
Acknowledgements We would like to thank  Roger Kouyos  and  Sebastian Bonhoeffer  for discussions, and two anonymous reviewers for comments on the manuscript. The HITS investigators include  Andrew Lloyd ,  Fabio Luciani ,  Michael Levy ,  Kate Dolan ,  Peter White ,  Bill Rawlinson ,  Paul Haber ,  Carla Treloar ,  Greg Dore  and  Lisa Maher. The HITS-p cohort was supported by an  NHMRC   Partnership Grant  (No. 1016351 ). FL and RB were supported by  NHMRC Early Career Fellowships  (No. 510428  and  630733 , respectively) and AL by a  NHMRC Practitioner Fellowship  (No. 510246 ). M. H. is funded by an  ATIP-Avenir  grant from  CNRS  and  INSERM  to S. A. ; S. A. and M. H. acknowledge additional support from the  CNRS  and the  IRD.
The LD61 whole genome was sequenced by using an Illumina HiSeq 2000 (Fasteris, Geneva, Switzerland). Base calling was performed using the HiSeq Control software version 1. 4. 8. After barcode selection, 9. 2 million paired-end reads of 100 bases in length were obtained. The sequence reads were de novo assembled using the Edena assembler version 3. 131028  (9, 10). Assembly resulted in 132 contigs (sum, 2. 60 Mbp; N 50 , 48. 2 Kbp; max, 252. 7 Kbp; min, 200 bp) with a GϩC content of 36. 4%. A total of 2,601 coding sequences were detected by using the NCBI Prokaryotic Genomes Automatic Annotation Pipeline (PGAAP)  (11). Over 45% of the genes were assigned to specific subsystem categories by RAST  (12). A more detailed analysis of this genome and comparative analyses with other L. lactis subsp.
SELDI-TOF-MS Profiling SELDI-TOF-MS analysis was used to profile samples collected for the selection of potential biomarkers of remote conditioning. Samples were profiled with eight spot format ProteinChip Arrays CM10 (Weak Cation Exchanger) or H50 (Reverse Phase) (Bio-Rad Laboratories). Arrays were prepared with 5 mL samples diluted 10-fold in binding buffer, which was sodium acetate 100 mmol/L pH4 for CM10 and ACN10%, TFA 0. 1% and NaCl 150 mmol/L for H50 array. Native and CPLL treated forms of all samples were analyzed using the two types of ProteinChip and each of them was tested in duplicate and randomly distributed on arrays, as previously described  [25]. All data were processed with the ProteinChip Data Manager software. For the peak selection of potential biomarkers, peaks were detected automatically: groups of peaks of similar mass across spectra were assembled into clusters, according to two-step parameter settings as previously described  [25].
Protein Identification of the Peaks Differentially Expressed between the Three Groups of Rats We followed the same strategy for purification and mass spectrometry identification of SELDI peaks corresponding to lowabundance proteins that has been described previously  [26]. The first step was the choice of the appropriate sample containing the selected peaks. From our databank recording all the m/z peaks detected from samples in different conditions, we excluded 12 peaks of our proteomic profiling to be purified (Table  1 ) due to low abundance in the sample or too much high intensity of peaks surrounded the peak to be purified. For the 6560, 7017, 7227, 8405, 8527, 9566, 13038, 13156, 13794, 21120, 21182, 21354, 21390 and 26026 m/z peaks, we used the strategy described previously  [26]  but we were unsuccessful to purify them and identify the corresponding protein.
Conclusion Our data shows that RIPC by a short episode of limb I/R in vivo was highly related to proteomic modulation, with up-and downregulation of plasma protein expression. We identified seven significantly modulated proteins as potential blood-borne RIPC factors. This data supports the involvement of circulating mediators in signal transduction from remote organs to the heart. RIPC 109 rat. Results are presented as intensities of SELDI-TOF reading (arbitrary units). B: The 9420 and 15870-15980 m/z peak were respectively found to be differentially expressed on the H50 and CM10 arrays as calculated by the Mann-Whitney test. Scattergram showing the significant differences in intensity of 9420 (left panel), 15870 (middle panel) and 15980 (right panel) m/z peaks in plasma samples derived from Control, RIPC 59, and RIPC 109 rats.
Finally, the drilled part goes to a third machine for surface finishing to produce the finished part. The part is the enterprise object and "blank part", milled part", "drilled part" and "finished part" are four object views of the part object. According to CIMOSA and ISO 19440, the two fundamental constructs of the Information View are: Enterprise Object and Object View. Templates proposed for these constructs are given in the Appendix. An Enterprise Object is any entity of the enterprise having a life cycle of its own. It must be uniquely identifiable (i. e. , it has an identity) and is described by a set of descriptive properties. Each property is defined by a name and a data type that can itself be an object or a set or list of objects. Two abstraction mechanisms borrowed from the information system modeling theory can be used to represent semantic links among enterprise object classes: (1) the generalization/specialization hierarchy (or Is-a link) to relate a super-class to its more specialized classes, and (2) the aggregation mechanism (or Part-of link) to relate a compound class to its component classes.
Indeed, any customer order is the relationship between a customer (an enterprise object) with a product (another enterprise object) ordered in some quantity on a certain date. The object view construct template therefore distinguishes between the so-called Leading Object (the main object on which the object view is defined) and Related Objects (the other enterprise objects providing properties of the object view). Last but not least, two fundamental types of object views must be distinguished: physical object views and information object views. Physical object views represent the physical manifestations of the object, i. e. , the ones that can be situated in shape, space and time. Information object views are purely descriptive and are usually computer or database representations of the objects.
g. , a tool, a cart, a truck, a telephone, etc. ). Functional entities can be further partitioned into three fundamental sets: Humans, Machines and Applications. Humans are all kinds of human agents. Machines are machines with a controller (e. g. , robots, CNC machines, a truck with its driver equipped with a GSM or cellular phone, a printer connected to a computer network, etc. ). Applications are IT systems or computer applications including database systems. Each category can be further decomposed. To model resources and their charateristics, at least two essential constructs are needed: Resource and Capability Set. Templates of these constructs are given in the Appendix. The Resource construct will be used to model either functional entities or components as well as aggregations of these in the form of resource sets or cells as found in manufacturing systems.
Organization View The organization view is used to describe the organizational structure (i. e. , administrative and functional structures) of the enterprise as a collection of organizational entities (or decision centers) related to one another either by hierarchical (i. e. , decisional) links or co-lateral (i. e. , network) links. An organization entity may be a workposition, a work-station, a team, a department, a division, a plant or a branch, a direction or the entire enterprise. Organizational entities are usually assigned responsibilities and authorities on tasks or processes, systems, data or resources and people of the enterprise. CIMOSA and ISO 19440 provide two basic constructs for the organization view (See Appendix): Organization Unit and Organization Cell. An Organization Unit is a single decision centre reduced to one functional entity assigned to a specific job (defined by its job description) and provided with well-defined responsibilities and authorities.
A responsibility is a duty for which a person is accountable (e. g. , the order O1 of customer X must be delivered by the end of the week) while an authority is a right (e. g. , to hire someone, to sign a contract, to delete data in corporate databases, etc. ). An Organization Cell is an aggregation of organization units and/or cells to form a higher level decision center in the organization structure. It is placed under the management of one functional entity (a person, who is the manager) and has a set of well-defined responsibilities and authorities on components or people of the enterprise. This way, jobs can be aggregated in teams, teams in departments, departments in divisions, divisions in directions and directions in the entire enterprise to reflect the organization chart of the enterprise.
-GRAI-GIM: It is a methodology and enterprise architecture framework containing enterprise modeling tools, including IDEF0 for activity modeling. It covers data (i. e. , information), process (i. e. , function) and operational aspects (mixing resource and organization aspects). The major advantage of GRAI-GIM is its ability to analyze the decision system of an enterprise for which it provides the GRAI grid to identify decision centers and the GRAI nets to model operational and decision activities. In addition, GRAI-GIM has been provided with a performance evaluation framework for economic evaluation  (Doumeingts & Vallespir, 1995; Doumeingts & Ducq, 2001). -ARIS ToolSet: ARIS (Architecture for Information Systems) was originally, as its name suggests, architecture framework for building IT systems (programs and databases).
It has then been proposed as an enterprise architecture framework  (Scheer, 1992 (Scheer, , 1999)). Although made of four views (data, control, function and on top organization), it mostly covers function and information aspects and very partially organization and resource aspects. Each view comes with its modeling constructs. Especially, business processes are modeled as sequences of events and activities called Event-Process Chains (EPC). EPC has been a popular business process language because it has been promoted by ARIS ToolSet, the modeling tool of ARIS which is number one sales in the world, and recommended by SAP for implementing the SAP ERP. Recently, the EPC notation has been replaced by BPMN and its use is declining in industry. -UML: Although clearly announced as a modeling method for software-based systems by OMG (Object Management Group), UML (Unified Modeling Language) can be useful in enterprise modeling, especially for building models of enterprise objects and object views in the information view but also to specify computer and IT systems to be implemented in the particular architecture of an enterprise.
UML provides object class diagrams, activity diagrams, sequence diagrams and collaboration diagrams that can be useful to analyze, design and specify IT components of the enterprise. Starting with version 1. 3  (OMG, 2000) , the current version of UML is 2. 4 (OMG, 2011a). Version 2. 5 is now available in beta version. -BPMN: Because there were just too many similar but different and non interoperable formalisms, languages and specific tools to model business processes, there was a need for a de facto standard language or notation. BPMN (Business Process Model and Notation), coming from IT, has become the Esperanto in the field. Most commercial systems for modeling business processes provide a BPMN interface, including ARIS ToolSet. As the name says, it is a business process description notation to be used to capture process structure and behavior.
Conclusion Enterprise Modeling emerged in the late 80's as a technique for describing various aspects of an enterprise, especially for the purpose of analysis, design, reengineering, optimization, performance evaluation and even control of a given business entity. At that time, CIMOSA demonstrated that this can be achieved in an integrated and uniform way by developing modeling language constructs and templates. These templates can then easily be implemented in the form of object classes to stored in relational or object databases and used by advanced modeling and simulation tools. This what most EA and EM commercial tools now do. Since then, EM has evolved and has been constantly enriched with new constructs to capture more details or cover additional aspects. In the paper, fundamental principles and constructs of EM have been first reminded before presenting recent extensions and a short panorama of well-known modeling techniques to make a state of the art.
SPOT 5 DEMs Two SPOT 5 DEMs were used in this study. First, a DEM covering 30 km × 60 km and including the entire Mont-Blanc area was computed in a previous study from two SPOT 5-HRG images acquired 19 and 23 August 2003  (Berthier et al. , 2004). The ground sampling distance of a SPOT 5-HRG image is 2. 5 m and the DEM pixel size is 10 m. This DEM was subtracted from the Pléiades 19 August 2012 Pléiades DEM to measure the geodetic mass balance for the entire Mont-Blanc area. Second, a DEM including the Astrolabe Glacier was extracted from the SPIRIT (SPOT 5 stereoscopic survey of polar ice: reference images and topographies) database  (Korona et al. , 2009). The ground sampling distance of a SPOT 5-HRS (high-resolution sensor) image is 10 m across track and 5 m along track  (Berthier and Toutin, 2008; Bouillon et al.
Validation data: GNSS and lidar Table  1  includes the type, date and precision of the reference data available to evaluate the Pléiades DEMs. Most of the reference data are differential GNSS (global navigation satellite system) measurements including the US GPS and Russian GLONASS constellations. They are processed relative to the closest available base station. A decimetric precision can be reached in the best cases (the Mont-Blanc area), whereas, in the worst case, the precision is closer to ±0. 5 m in the Andes due to the fact that the base station (TOLO) is located 100 km away from Agua Negra Glacier. For Iceland, the Pléiades DEM was also compared to an airborne lidar DEM with a 2 m pixel size. The lidar DEM was acquired 7 and 8 August 2011, slightly more than 2 years before the acquisition of Pléiades images  (Jóhannesson et al.
4. 2). All elevation differences between the Pléiades DEMs and the reference data (GNSS surveys or lidar DEM) are assumed to be due to errors in the Pléiades DEMs. In fact the total error budget also includes (i) on glaciers only, real and spatially varying elevation changes over the time span of a few days or weeks separating the acquisition of the Pléiades images and the reference data and (ii) everywhere, errors in the reference data themselves. Error (i) can also matter off glacier if snow was present during the acquisition of the Pléiades images or the reference data. Therefore, the present study provides an upper limit for the errors of the Pléiades DEMs. When the time between satellite acquisition and field measurements exceeds a few weeks (e. g. the lidar DEM in Iceland), only reference data off glaciers are considered for the evaluation of the DEM, and data on glaciers are used only for the study of glacier elevation changes.
Glacier outlines Glaciers outlines are needed for the Mont-Blanc area and the Icelandic study site to perform separate statistics on/off glaciers and to compute the glacier-wide mean elevation changes. For Mont Blanc, glacier outlines were drawn manually on a SPOT 5 2. 5 m orthoimage acquired 23 August 2003. Very little seasonal snow remained when this image was acquired due to the heatwave that baked Europe in early August 2003. For Tungnafellsjökull, the margin of the ice cap was digitised manually from a shaded relief image of the August 2011 lidar DEM. The surrounding snow patches were obtained through semi-automatic classification applied to the corresponding lidar intensity image. For others study sites, GNSS data points were overlaid on the Pléiades orthoimages and visually classified as on/off glaciers so that no glacier outline was necessary.
The parameter settings used to generate all the DEMs are as follows: DEM detail is low, type of terrain is mountainous, pixel size is 4 m, data gaps are not filled. The number of pixels used in these statistics is over 3 000 000. a after horizontal co-registration; b after horizontal and vertical co-registration, i. e. correction of the mean horizontal and vertical shifts between the Pléiades and lidar DEMs estimated on the ice-free terrain.
Evaluation of the Pléiades DEMs from all study sites Table  4  summarises the results of the evaluation of the Pléiades DEMs with GNSS measurements for all study sites. In the Andes and for Mont Blanc, 5-13 GCPs were available to compute the DEMs. For Mera Glacier in Himalaya no accurate GCPs, i. e. measured in the field using static GNSS positioning, were available at the time of processing. Instead, a set of 22 GCPs was derived from a coarser resolution SPOT 5 data set (2. 5 m orthoimage and 40 m DEM). These SPOT 5 data were previously co-registered to GNSS data acquired along the trails of the Everest base camp, outside of the Pléiades images (see  Wagnon et al. , 2013 , for a complete description). Because of the coarse resolution of the SPOT 5 DEM we tried as much as possible to select GCPs over flat terrain.
Necessity of tie points We already mentioned above that TPs had no influence on the coverage and the precision of the DEM of the Tungnafellsjökull ice cap (Table  3 ). However, this does not hold for the Mont-Blanc area and the Astrolabe Glacier, two other sites where DEMs were generated without GCPs. In both cases, the automatic collection of about 20 TPs provided far better coverage probably by improving the relative orientation of the two images of the stereo pairs. The necessity of collecting TPs will depend on the accuracy of the navigation data (position and attitude of the satellite) provided with the images. Given that the latter accuracy is not known a priori, we recommend collecting TPs between the images.
Added value of a tri-stereo There is a moderate added value of a tri-stereo instead of a simple stereo pair. In general, among the three possible pair combinations of the three images, the largest percentage of data gaps is observed for the front/back pair, due to the stronger distortion between these images. This is especially true when the base-to-height ratio is high (> 0. 5), for example in the case of the Mont Blanc 2013 tri-stereo where the data voids represent as much as 30 % for the nadir/back pair and only 15 % for the front/nadir and nadir/back pairs. For the latter two pairs, we combined the two DEMs in a merged DEM as follows: (i) for pixels where both DEMs were available, the mean of the two values was calculated; (ii) for pixels where only one DEM was available, this single value was retained; (iii) for pixels corresponding to data voids in both DEMs, a data void was kept in the merged DEM (i.
e. no gap filling was used). The percentage of data voids in the Mont Blanc 2013 merged DEM dropped from 15 to 6 %, showing that data voids were not at the same location in the front/nadir and nadir/back DEMs. For Agua Negra Glacier (Andes), the same merging reduced the percentage of data voids by only 1 % (but the initial coverage in the DEMs was higher, over 96 %) with no significant improvements in vertical precision.
Acknowledgements. We thank  I. Howat  (editor), three anonymous referees,  R. Marti ,  S. Gascoin  and  B. Raup  for their constructive comments that led to an improved manuscript. We acknowledge all our colleagues who helped collect the GNSS data in the field (including  F. Couvreux ,  Þ. Jónsson. ,  S. Steinþórsson ,  C. Wild  and  M. Sacchettini ) and acquire the Pléiades images ( C. Tinel ,  D. Fontannaz ,  D. Giaccobo ,  M. Bernard  and  S. Hosford ) within the framework of the CNES Pléiades thematic commissioning phase and the  Airbus Defence and Space "Pléiades User Group ". Images from Iceland and Antarctica were purchased at research price thanks to the  CNES ISIS program  (http://www. isis-cnes. fr/). GNSS reference data from the TOLO base station (Chile) was provided by  C. Vigny  and the " Laboratoire International Associé Montessus de Ballore ".