dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Analysis Raw files generated from mass spectrometry analysis were combined and processed with Proteome Discoverer 1. 3 (Thermo Fisher Scientific). This software was used for extraction of MGF files. Protein identification and quantification were carried out using ProteinPilot version 4. 0 (Applied Biosytems). The search was performed against the mixed database containing 56478 sequences (Mus musculus (54080 sequences from Uniprot December 13 rd , 2011)+West Nile virus (2242 sequences), and some classical contaminant proteins (156 sequences)). Data were processed as described previously  [33].
Ingenuity Pathway Analysis A dataset containing differentially regulated proteins obtained from the 2D-DIGE and iTRAQ analysis and their corresponding expression values (fold-change and p-values) were uploaded into the IPA software, Inc. (http://www. ingenuity. com). In this analysis, the evolution of protein expression was taken into account according to the onset of clinical symptoms. Proteins whose expression was significantly differentially regulated (foldchange $1. 3, p-value # 0. 05) were selected for the analysis. The IPA program uses a knowledgebase (IPA KB) derived from the scientific literature to connect genes or proteins based on their relationships and functions. Ingenuity Pathway Analysis generates biological networks, canonical pathways and functions that are relevant to the uploaded dataset.
A right-tailed Fisher's exact test is used for calculating p-values to determine if the probability that the association between the proteins in the dataset and the functional and canonical pathway can be explained by chance alone. The scores are derived from a p-value (score = 2log (pvalue)) and indicate the likelihood that the proteins of interest (i. e. , the identified proteins within a network)) are clustered together. Thus, these proteins and their association with the IPA KB were used to generate networks and to perform functional canonical pathway analyses.
Detection of Differentially Expressed Proteins Following WNV Infection by 2D-DIGE Analysis To determine the proteins that were differentially regulated following WNV infection, half whole brain hemispheres from WNV-infected mice were sampled at early (E) and late (L) time-points, and protein extracts were analyzed by 2D-DIGE (Tables  S1  and S2 ). Brains from non-infected mice (mock) were used as controls. Six mice per group were tested. Using the Progenesis SameSpot v. 2 software, the abundance of 37 protein spots was found to be significantly changed (ANOVA, p#0. 05) between the three groups (mock, WNV-E and WNV-L) with a fold-change $30% (|FC| $1. 3) in the pH range 3-10 (Figure  2A ). The major portion of the protein spots were significantly altered at the late time-point compared to both the mock condition (85%, n = 32; 25 up-and 7 down-regulated, Figure  2D ) and the early-groups (81%, n = 34; 25 up-and 9 downregulated, Figure  2C ).
The comparison of protein profiles between the early-and mock-groups allowed the detection of six protein spots representing significant changes (4 up-and 2 downregulated, Figure  2B ). Because the determination of host proteome alterations prior to the appearance of clinical symptoms after WNV infection was one of the main aims of the present study, 2D-DIGE analyses were performed on early-infected samples compared to mock-infected samples using narrower pH range IPG strips (pH 4-7 and 6-11) to enhance and increase protein resolution. Using pH 4-7 IPG strips for the IEF, 19 protein spots were found to be significantly altered between early WNV-and mock-infected samples (fold-change $1. 3, p#0. 05) (14 up-and 5 down-regulated, Figure  S1 ). Although no protein spots were found to be significantly differentially regulated using pH 6-11 IPG strips (data not shown), the use of a narrower pH range allowed a three-fold increase in the number of differentially regulated protein spots at the early time-point compared to the mock group.
Identification of Altered Protein Abundance Following WNV Infection from 2D-DIGE Analysis The 56 protein spots of interest were excised from gels, subjected to in-gel digestion, and then analyzed by tandem MS for identification. The resulting fragment ion spectra were searched against Mus musculus and West Nile virus protein databases (SwissProt). Among the 37 protein spots selected from the 3-10 pH range analysis, 34 (91. 9%) were successfully identified with a high degree of confidence, corresponding to 25 distinct proteins according to their accession number (Table  1 ). These proteins were grouped into functional categories according to gene ontology (GO). Multiple proteins were present in some of these spots; one spot contained a mixture of viral and human proteins (e. g. , spot ID: 710), and some proteins were present in multiple spots (e.
g. , Dynamin-1 was detected in spot IDs: 475 and 476 in the pH range 3-10, Figure  2A  and Table  1 ). As expected, the most important average fold-change corresponded to WNV proteins (mean 6 SD: 3. 861. 8, Table  1 ). Interestingly, despite a detection of viral RNA in the brain of all animals prior to and after clinical onset (Figure  1 ), viral proteins were identified only at the late timepoint, indicating that at the early time-point, viral proteins are below the detection limit of this method. Three protein spots could not be identified, most likely due to an insufficient amount of protein and/or low MS spectra quality. Among the 19 protein spots selected from the 4-7 pH range analysis, 17 (89. 5%) were identified corresponding to 10 distinct host proteins according to their SwissProt accession number (Table  S4 ), and four of them were identified in more than one spot.
Identification of Differentially Expressed Proteins Following WNV Infection using iTRAQ Labeling To complete the overview of mouse brain proteome changes after WNV infection, an off-gel quantitative proteomic analysis was performed using iTRAQ reagents allowing the labeling and the comparison of 8 distinct samples. Then, the six brain samples from each experimental condition (i. e. , WNV-E, WNV-L and mock) were pooled and separated into four mock, two WNV-E and two WNV-L replicates, each labeled with a specific iTRAQ reagent (from 113 to 121 as indicated in Table  S3 ). Labeled samples were mixed and separated by an off-gel system into 12 fractions prior to subjecting each fraction to tandem MS analysis. Data generated were analyzed with Protein Pilot software using the parameters described above.
Proteins from mock-and late WNV-infected brain samples were labeled with Cy5 and Cy3 cyanine dyes, respectively. As determined by Progenesis SameSpot software, protein spots that were differentially regulated between the three experimental conditions (|ratio| $1. 3 and p#0. 05), were submitted to mass spectrometry for identification. The numbers annotated on the gel correspond to master gel numbers of differentially regulated protein spots. Spots identified as Mus musculus (numbers#) and WNV (numbers*) proteins are listed in Table  1. Spots identified as a mixture of both species are indicated by numbers#*. Spots that were differentially modified between WNV-early and mock (B), WNV-late and WNV-early (C) and WNV-late and mock (D) infected samples are represented by red (up-regulated) or blue (down-regulated) dots.
Networks, Biological Pathways and Functions Involved in the Clinical Evolution of WNV Infection at the Brain Level To understand and highlight the relationship of these differentially regulated proteins and the consequences of these modifications in the context of their cellular function, during the course of WNV infection in the brain, before and after the appearance of clinical symptoms, a bioinformatics analysis using a web-based entry tool developed by Ingenuity Systems, Inc. (http//www. ingenuity. com) was performed. The 148 unique significantly differentially regulated host proteins were uploaded into IPA to statistically determine the functions and pathways that were most strongly associated with the protein list and to establish interactions with other proteins in known networks. At the early time-point after WNV-infection compared to the mock-group, the mapping of our protein dataset (n = 93) onto biological pathways and disease networks allowed us to identify a total of five relevant networks.
The identities of the spots, their SwissProt accession numbers, and the theoretical molecular masses and pI values as well as the number of peptide sequences, the corresponding percent sequence coverage, and the Mascot score are listed for MS/MS analysis. Protein scores greater than 34 were considered as significant (p,0. 05). Paired average volume ratio and p values (ANOVA) between the three experimental groups were defined using Progenesis Samespot software. Missing data correspond to paired average volume ratio not significantly changes. n. i. , no identification. doi:10. 1371/journal. pone. 0068318. t001 canonical pathways were generated, among which 23 presented a significant association (2Log (p-value). 2. 0, Table  S7 ). The most relevant pathways were related to entry of the virus including clathrin/caveolar-mediated endocytosis, as observed at the early time-point, neurological disorders, and the protein ubiquitination pathway.
Considering the 65 differentially regulated molecules between the late and early time-points, the IPA system generated four networks. The top three networks (Table  2 ) contained at least 16 focus proteins that have functions related to cell morphology, cell assembly and organization and cell function and maintenance (network 1); organismal injury and abnormalities, respiratory disease, and hematological disease (network 2); and infectious disease, endocrine system development and function, and energy production (network 3). The highest scoring network was composed of 19 proteins from our dataset and mainly contained up-regulated proteins that are related to cytoskeleton organization, such as actin, tubulin and dynamin, and down-regulated proteins that are related to nervous system development, including APP, DPYSL proteins, MAP1B and MAP2, the latter of which is associated with neurogenesis and axonogenesis.
Discussion The current study analyzed the kinetic changes of protein expression in mouse brain tissue samples that were collected at early and late time-points post-viral infection, corresponding to the absence and the appearance of neurological clinical symptoms, respectively, using comprehensive quantitative proteomic approaches. A total of 148 unique proteins were found to be differentially regulated following WNV infection, indicating a profound host proteome modification at the brain level. The bioinformatics analysis (Ingenuity Pathway Analysis) of each protein dataset of the different time-point comparisons revealed that four major functions were altered during the course of WNVinfection in the mouse brains: i) modifications of cytoskeleton maintenance associated with virus circulation; ii) deregulation of the protein ubiquitination pathway; iii) modulation of the inflammatory response; and iv) alteration of neurological development and neuronal cell death.
i) Modifications of Cytoskeleton Maintenance and Virus Circulation Virus entry via clathrin-mediated endocytosis (CME). As viruses are obligate intracellular pathogens, viral entry into target cells is necessary to initiate replication and infection. The use of CME for virus entry has been described for several viruses, including influenza  [42] , hepatitis B  [43] , orthobunyavirus  [44]  and WNV  [45]. Bioinformatic analysis of our datasets revealed that CME was one of the most significant canonical pathways generated by IPA, involving several host proteins that are differentially regulated at both the early and late time-points. Several key factors participating in the CME pathway were identified (Figure  5A ). Our data support previous in vitro experiments suggesting that WNV uses the CME pathway to gain entry into host cells  [45].
The research leading to these results has received funding from the  European Community 's  Seventh Framework Program  ( FP7/2007-2013) under the project ''VECTORIE'', EC  grant agreement number  261466. This study was also supported by  Delegation Generale pour l'Armement (DGA ,  ArthroSer 10Ca 401 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
416 [Mus musculus] Glycyl-tRNA synthetase [Mus musculus] 82. 624 Serum albumin [Mus musculus] 70. 700 Heat shock 70 kDa protein 12A [Mus musculus] 75. 167 Heat shock cognate 71 kDa protein [Mus musculus] 71. 055 Peroxiredoxin-6 [Mus musculus] 24. 969 T-complex protein 1 subunit alpha [Mus musculus] 60. 867 T-complex protein 1 subunit gamma [Mus musculus] 61. 162 Accession number (SwissProt) Viral proteins POLG_KUNJM POLG_WNV Host proteins Metabolic/biosynthertic process ACLY_MOUSE BACH_MOUSE CAH2_MOUSE CATA_MOUSE GPDM_MOUSE SYG_MOUSE Host response/protein folding ALBU_MOUSE HS12A_MOUSE HSP7C_MOUSE PRDX6_MOUSE TCPA_MOUSE TCPG_MOUSE.
41 6. 90 9. 45 5. 29 7. 61 4. 78 5. 95 6. 04 5. 27 6. 94 5. 57 Accession number Molecular weight (SwissProt) Protein name (kDa) Transcription/translation regulation EF2_MOUSE Elongation factor 2 [Mus musculus] 96. 222 FUBP2_MOUSE Far upstream element-binding protein 2 [Mus 77. 184 musculus] SFPQ_MOUSE Splicing factor, proline-and glutamine-rich [Mus 75. 508 musculus] Cytoskeleton maintenanvce ACTB_MOUSE Actin, cytoplasmic 1 [Mus musculus] 42. 052 DYN1_MOUSE Dynamin-1 [Mus musculus] 98. 140 TBB5_MOUSE Tubulin beta-5 chain [Mus musculus] 50. 095 Nervous system developement DPYL2_MOUSE Dihydropyrimidinase-related protein 2 [Mus musculus] 62. 638 DPYL3_MOUSE Dihydropyrimidinase-related protein 3 [Mus musculus] 62. 296 GFAP_MOUSE 49. 927 Glial fibrillary acidic protein [Mus musculus] Transport TRFE_MOUSE 78.
Survey and temperature measurements The data analysed in this paper are derived from the Carbon Reduction in Buildings Home Energy Survey (CaRB HES), the first national survey exclusively focused on energy use in English homes, that commenced in early 2007 (for details, see Ref. [9] ). Households were selected by stratified random sample drawn from the Postcode Address File. Sampling and face-to-face interviews in 427 homes were carried out by the National Centre for Social Research (NatCen). During the interview, householders answered questions on the building characteristics of their home, heating practices, and socio-demographics. For a subset of homes, temperatures were monitored in the bedroom and living room from mid July 2007 to early February 2008. HOBO UA 001-08 sensors are self-contained data loggers that recorded spot temperature every 45 min, resulting in 32 measurements per day.
These sensors have a manufacturer reported accuracy of AE0. 47 C at 25 C, and were placed in the home by the interviewer and/or the homeowner with instructions on correct placement, i. e. between knee and head height, away from any heat sources or direct sunlight. Calibration measurements were taken of each sensor in a climate chamber at 25 C before placement in the home and used to correct the readings after the logged data had been extracted. The calibration error from all sensors was found to be minimal with an average error of M error ¼ 0. 19 C (SD ¼ 0. 11).
It should be noted however that for some homes on some days close to 15. 5 C, incidental gains may maintain internal temperatures without the heating system being on leading to false positive results. The recorded internal temperature data was screened for outliers, i. e. for recorded temperatures below 10 C or above 35 C, and for changes of more than 10 C in 45 min (indicating possible placement close to a heating source or in direct exposure to sunlight). Those potentially erroneous data points occurred on less than 0. 2% of days and were excluded from further analysis. The dataset was managed and analysed using MS Access, SPSS, STATA, and MatLab.
Probability of the heating system being on over the course of the day The averaged probability over the course of the day did not differ significantly between homes who reported having additional heating in the main living room and those who had no other forms of heating in the main living room. Therefore, in all following analysis the full dataset is considered. 6 Fig. 1  shows that average probability (i. e. averaged over the 248 dwellings) for the heating system being on at all of the 32 temperature-difference intervals. Both for weekdays and weekends, the probability of the heating system being on is about 0. 1 from midnight onwards, and then starts to increase at about 4:30, and reaches a morning peak between 6:45 and 7:30 on weekdays and between 7:30 and 8:15 on weekends. For weekdays, the probability then declines by just over 0.
Acknowledgements This work has been done under the " People Energy and Buildings: Distribution, Diversity and Dynamics " project ( EP/H051112/1 ) in partnership with  EDF European Energy Efficiency Research Centre (ECLEER)  and funded by the  Engineering and Physical Sciences Research Council (EPSRC)  and  EDF Energy. The data analysed in this paper is drawn from the  CaRB Home Energy Survey conducted as part of the Carbon Reduction in Buildings project  ( GR/ S94377/01 ). The authors would like to thank  Kevin Lomas ,  Steven Firth ,  Andrew Wright , and  Mike Gentry  for their contribution in.
The device also comprised a displacement gauge, placed on the top of the piston (Figure  1 ) to check the rigidity of the cell (absence of deformation during the experiments). A fluid circulation could be established in the cell through capillaries in the bottom and top of the cell. An initial pressure of 1. 8 MPa was applied for each trial. Then, after pressure stabilization, different solutions were percolated through the resins and the pressure variations were monitored via the strength gauge connected to a data logger. The initial loading of the sample made it possible to detect pressure decrease due to shrinkage of the resin grains, or pressure increase due to swelling of the resin grains. To clarify the influence of different parameters on the volume changes of IERs, several tests were performed.
Data reduction and analysis To derive the transmission factor and capture yield from the TOF spectra, the software package AGS (Analysis of Geel Spectra)  [16]  was used. Within its operations it performs the most important actions, such as dead-time correction, background subtraction etc. , while it fully propagates uncertainties, starting from counting statistics. The transmission factor T(tn), as a function of the neutron time-of-flight tn, was obtained as the ratio of the time-integrated spectra of the sample in and out, Cin and Cout, after been corrected for the incident neutron flux and the dead-time effects. The shape of background was determined from fitting the black resonance dip points in the TOF spectrum. During the capture measurement, data were stored in cycles of 1 hour, both for the scintillators and the flux chamber.
A FReLoN CCD detector with an effective pixel size of 14. 9614. 9 mm 2 with 102461024 pixels was used. The field of view was limited horizontally by the number of pixels of the detector, and vertically by the X-ray beam size; this resulted in a field of 15. 3 mm width and 13. 7 mm height. For each tomographic dataset four phase step projections with an exposure time of 0. 5 seconds each were collected for 701 angles evenly distributed over 360 degrees. This resulted in a total net exposure time of 24 minutes. Due to the large field-of-view four samples could be measured simultaneously. The data was reconstructed using a Hilbert-filter-based phase-contrast filtered back-projection algorithm  [29]. A phase unwrapping procedure was applied prior to reconstruction to suppress cupping artifacts.
Data Analysis and Statistics Each image was converted to DICOM format and imported into the Inveon Software (Siemens Medical Solutions, Inc. , Knoxville, TN, USA) for analysis by two experts in Medical Imaging. The study was blinded and retrospective. Since the technique was new and the images unlike prior absorption x-ray images, the experts who analyzed the scans first looked at a test set of three images and set their diagnosis, then they were given the correct diagnosis before completing the rest of the image analysis. This was done in order to know which features to look for in the benign and malignant lymph nodes respectively. The reference for metastatic disease was histopathology diagnosis set by a pathologist who was blinded to the x-ray phase-contrast results. For comparison between groups for diameter and weight an independent-samples t-test was used.
015 and p = 0. 005, respectively). All of the 10 metastatic lymph nodes had a longest diameter that was above 10 mm, which is the criterion for considering the lymph node suspicious of malignancy on CT. Two lymph nodes were however close to 10 mm with the longest diameter of 12 and 13 mm respectively, which could be within the range of measurement inconsistence on CT images, and the lymph nodes could be considered non-malignant on conventional CT or not be detected at all. The raw data and image reconstruction was carried out by experts in x-ray physics. The final images were analyzed by two experts in nuclear medicine and both the physicists and the physicians were blinded to the diagnosis of the lymph nodes. Each of the 2 experts in Nuclear Medicine who analyzed the images had 1 false positive result and no false negative results.
Financial support is given through  Faculties of Science and Health at the University of Copenhagen , through the  European Synchrotron Radiation Facility (ESRF) , and through the  DFG Cluster of Excellence Munich-Center for Advanced Photonics  ( DFG EXC-158 ) and the  European Research Council  ( FP7 , Starting Grant No. 24012 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Micro-Raman analysis Two slices of the same section of each animal were analyzed by Raman using a inVia spectrometer (Renishaw, UK) system equipped with charge-coupled device (CCD) detector of 1040 Â 256 pixels and coupled to a Leica microscope (DM-2500 model) (50x, 0. 75 NA) with a computer-controlled x-y-z stage. A diode laser line (785 nm) was used as excitation source in combination with a grating of 1200 grooves/mm. The laser power was kept below 10% to avoid sample damage, employing one 30-sec acquisition for each data point. The spectral resolution was 4 cm À1 , and the spectra were taken from 300 to 1800 cm À1. For each bone sample, two sets of spectra were collected: one near the implant in the newly formed bone zone, where the implant was only in contact to the marrow, and other in the existing cortical bone.
Each set was comprised of a 4 Â 5 grid of spectra that were averaged and statistically analyzed. For better understanding of the chemical and compositional changes at the interface, line scans were collected starting from the metallic implant, passing through the newly formed bone and finishing in the marrow. The total length of the spectra was 200 lm, taking a spectrum every 10 lm. The measurement conditions were the same as for the previous scans (785 nm laser, 300-1800 cm À1 scan, resolution 4 cm À1 , 30 s acquisition time). Background fluorescence in the spectra was subtracted by a modified polynomial fitting algorithm. Peak intensities were recorded for amide I (1650 cm À1 ), amide III (1245 cm À1 ), B-type carbonate (1071 cm À1 ), m1 phosphate (960 cm À1 ), and m2 phosphate (430 cm À1 ) using a custom developed Matlab program.
Acknowledgments The authors would like to thank  A. Cisilino ,  L. Escalada ,  Y. Liu  and  I. Santana , for the grateful help. To the  National Research Council of Argentina (CONICET) ,  National Agency for Science and Technology Promotion  ( ANPCyT -PICT 0917 ) and  National University of Mar del Plata  ( UNMdP -15/G331 ) for financial support. Also would like to acknowledge  SOLEIL-France  for provision of synchrotron radiation facilities,  Cooperation program  MinCyT-SOLEIL and  Anne-Marie Flank  for assistance in application for using beamline.