dataesr/scientific-paragraphs-categorization huggingface dataset:
Microbiota composition analysis with HuGChip Microbial diversity was determined using the HuGChip (GEO: GSE44752), an explorative DNA phylogenetic microarray composed of 3 × 4441 probes targeting 66 bacterial families  [18]. DNA was extracted from fermentation samples and the total 16S bacterial gene was amplified using universal primers, labeled with Cy3 or Cy5 dye, and hybridized to the DNA microarray probes as stated previously  [18]. After scanning, the data were retrieved using Agilent Feature Extraction software 10. 5. 1. 1 and signals were normalized (Agilent platform GPL16731). The results of the hybridization were deposited in the NCBI database under GEO record GSE77327. Taxonomic assignment of bacterial families was performed as previously described  [18] , with the help of PhylInterpret software (http://g2im.
Amplification of microbes from fresh starting material The ECSIM model was inoculated with the 1 mL aliquot that was untreated and not frozen (see above). After 7 RT, several analyses were performed (gas, SCFA, and microbial contents) and compared to the fresh fecal sample (see Supplementary Figure  S1  for complete comparisons with initial stools). Data at this stage corresponded to the initial point (T0), and were then used for comparison in identical experiments reproduced 3 or 6 months later using -80°C preserved 1 mL aliquots as the starting inoculum. Preservation of metabolic behavior assessed with gas and SCFA production The ECSIM system allows chemostatic conditions under anaerobia caused by the microbiota fermentative metabolism itself. Similar patterns of gas were obtained for all the preservation conditions (Supplementary Table  S1 ).
Funding This work was supported by two PhD scholarships, one from the  French Ministère de l'Enseignement Supérieur etde la Recherche  to NG [2012-MESR] and another from the  European Union (UE) /the  Auvergne Council to WT (FEDER)  [2011-Auv]; PPC was funded by a grant from the  Université d'Auvergne (UdA)  [2014-UdA].
H. alexandrinus strain Arc-hr is a Gram-negative archaeon, highly pleomorphic and strictly aerobic, with optimal growth observed at 37°C, pH 7. 5 and 100 g/ L NaCl. Sequencing the 16S ribosomal DNA confirmed the affiliation of the strain Arc-hr (GenBank accession no. HG931927) to the genus Haloferax and yielded a 99. 9% sequence similarity with the reference H. alexandrinus JCM 10717 T (GenBank accession no. NR_113438). Strain Arc-hr therefore represents the first H. alexandrinus isolate cultured from the human gut. The complete genome of H. alexandrinus strain Arc-hr was sequenced on the MiSeq Technology (Illumina, San Diego, CA, USA) with the mate-pair strategy as previously described  [4]. Open reading frames (ORFs) were predicted using Prodigal  [5]  with default parameters, but the predicted ORFs were excluded if they spanned a sequencing gap region (containing N)  [6].
Noncoding genes and miscellaneous features were predicted using RNAmmer  [7] , ARAGORN  [8] , Rfam  [9] , PFAM  [10]  and Infernal  [11]. Coding DNA sequences were predicted using Prodigal  [5] , and functional annotation was achieved using BLAST+  [6]  and HMMER3  [12]  against the UniProtKB database  [13]. The genome the strain Arc-hr is 3 893 626 bp long with 66. 00% GC content. It is composed of one scaffold (composed of five contigs); of the 3770 predicted genes, 3679 were protein-coding genes and 58 were RNAs (two genes are 5S rRNA, three genes are 16S rRNA, two genes are 23S rRNA and 51 genes are tRNA genes). A total of 2019 genes (65. 38%) were assigned a putative function. The remaining genes were annotated as hypothetical proteins (924 genes, 29. 92%). Moreover, the H. alexandrinus strain Arc-hr genome contains 37 genes identified as ORFans, seven genes associated to halocin and 11 genes associated with polyketide synthases or nonribosomal peptide synthetases.
Image analysis All image processing was performed in VivoQuant 2. 0 (inviCRO) and iPACS (inviCRO). Preprocessing included SPECT/CT coregistration at a voxel size of (0. 4 mm) 3  and splitting into individual mouse images. An iPACS script facilitated entering injection doses and body weights simultaneously for automatic conversion of image data into absolute radioactivity, percent injected dose (ID) or standardized uptake value (SUV). A Vivo-Quant script generated and saved rotating maximum intensity projection (MIP) movies and single-slice images in all three orientations centred on the tumour ROI with a chosen colour scale (here 0. 2 to 20% ID/mL). Tissue uptake data from SPECT images were generated via multi-atlas segmentation: a reference library of ROIs is built in usually 10 to 20 animals to create an ROI atlas, which is co-registered to SPECT/CT images.
γ-Counting and analysis Animals were dissected after imaging. Tissue sample weights were transferred by push-button from the balance into a spreadsheet template. Decay-corrected counts-perminute data of the daily batch of tissue samples (≈100 for 2 compounds) were measured on a γ-counter LKB Wallac 1282 Compugamma within 2 h. All accumulated data of the whole study were then imported into MATLAB by a custom-written script linking data points from different sources via compound and mouse identifiers. Uptake data (% ID, % ID/g and SUV) were tabulated and plotted. Further details including tissue-specific calculations are described in the Additional file 1.
Statistical analysis and data plotting Basic descriptive statistical calculations were performed in MATLAB R2015b (The MathWorks) and Excel 2013 (Microsoft). Data are reported as mean ± standard deviation (SD) if not noted otherwise. Intersubject variability was expressed as % COV (coefficient of variation) for each group of three mice, then taking the median for each uptake measure over all time points, compounds and organs. Details and all organ-specific data are given in the Additional file 1. Bland-Altman analysis and plotting were conducted in Prism 5 (GraphPad Software). All other plots were prepared in MATLAB.
Still, for the voxel size and tumour sizes in this study, erosion of or dilation by a single voxel layer from the surface of a 3D ROI changes volume estimates by tens of percents. Therefore, ROI volume estimates were compared to ex vivo tumour weights as QC. In cases of large discrepancy, the tumour ROI was revisited and, if necessary, edited. Multi-atlas segmentation has proven useful for automated region identification especially in clinical neuroapplications  [24] [25] [26] [27]. To overcome the limitation of many available software packages designed for clinical brain imaging, this approach has been implemented and employed for full and subregion segmentation of other organs (for whole body distribution and radiation dosimetry) in several non-human species. Data variability-due to factors such as animal positioning, the non-rigidity of non-brain regions, general shape/distribution outliers and fluctuating contrast-to-noise-may occasionally cause segmentation failures, thus requiring strict QC and manual corrections.
Ex vivo Ex vivo data were acquired to validate the biodistribution results from imaging which was successful as Bland-Altman analysis showed. One observation was, however, that the SPECT tumour ROI volume tended to be significantly larger than the ex vivo tumour weight. This discrepancy is likely due to an overestimation of the tumour volume on the images, and deviation from the assumed 1 g/mL mass density may also play a role. Such an overestimated ROI volume likely captures spillover of the tumour SPECT signal just outside the tumour, an occasional artefact from image reconstruction or smoothing. This conjecture would explain the good match of tumour uptake values (% ID) between ex vivo and in vivo measurements and also explain the observed non-equivalent measures of uptake concentration in tumour (ex vivo % ID/g versus imaging % ID/mL).
Throughput All experiments from animal model setup to final analysis took 3 months. This included time for the animal model setup which could be eliminated in other studies when using naïve animals, or which might take longer for certain disease models. This study design for large proteins included imaging up to 48 h pi. In hindsight, this time point adds only limited information to that obtained at 24 h and could be omitted in a future study of such compounds, making space in the schedule available for other experiments. Image analysis of incoming data was done within a day to create updated study plots. A fortunate benefit was that image analysis was performed in a later time zone than data acquisition (GMT→EST), effectively stretching the working day. γ-counters are more sensitive than SPECT cameras.
Acknowledgements We are indebted to  Dr. Emmanuelle Vigne  ( Sanofi-Aventis ) for providing the compounds and to  Dr. Jane Sosabowski  ( QMUL ) for making the excellent preclinical imaging facility at QMUL available for contract research. Many thanks to  Dr. Roxana Kashani  ( QMUL ) for radiochemistry support, to  Ed Waters  (now  KCL ) for technical support and to  Dr. Julie Foster  and the  Animal Technician Service team (QMUL)  for biology support.
Acknowledgements. The  Swiss National Science Foundation  granted financial support under project Nr. 200020-146540. Hans Christian Steen-Larsen  was supported by the  AXA Research Fund. The authors thank  Koji Fujita ,  Edwin Waddington  and an anonymous reviewer for the suggestions and critical review. Matthias Jaggi ,  Sascha Grimm ,  Alessandro Schlumpf  and  Sarah Berben  gave technical support. The data for this paper are available by contacting the corresponding author. Edited by: Benjamin Smith Reviewed by: two anonymous referees.
1 kb, encoding a polyprotein that is processed into 11 mature peptides. Rhinovirus A genotype 22 (HRV-A22) has one complete genome sequence in GenBank, that of the ATCC VR-1132 reference strain (accession no. FJ445122). Eleven other fragments have been deposited, ranging in size from 207 to 1,713 bases. Six volunteers with COPD were recruited from a general practice surgery in Lancaster, United Kingdom (54. 05°N, 2. 80°W). RNA was obtained from nasopharyngeal swabs taken between 5 January 2015 and 25 February 2015. One patient was asymptomatic at time of sampling, and the other five patients exhibited symptoms consistent with respiratory tract infection. Ethical approval.
0% divergence, and is 9. 7% divergent from the other HRV-A22 strains, in VP1. Lancaster/2015 and KM4 thus represent an outlier group within HRV-A22 but are insufficiently divergent to constitute a novel genotype (BAM files, alignments, and phylogenetic trees are available from https://doi. org/10. 17635/lancaster/researchdata/123). Accession number(s). The genome sequence of HRV-A22/Lancaster/2015 has been deposited in GenBank under the accession number KY342346.
Acknowledgments This study was done in the frame of the  EcoPotential  project. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  641762. We would like to thank all PA managers, and scientists for their contribution to the tables by filling in the survey, and especially the PA manager of Samaria,  Antonios Barnias , the PA manager of  Gran Paradiso ,  Ramona Viterbi , and the scientist responsible for Samaria,  Dr. Nektarios Chrysoulakis.
RESULTS The diagnosis of RT is hard to ascertain when histological, clinical and genetic features are not all typical for that diagnosis. In order to base our comparisons on a robust dataset, we decided to first analyse a "training set" composed by tumors for which all criteria, i. e. histological, clinical and genetic features converged to ascertain the diagnosis of RT. We then analysed a series of samples with uncertain diagnosis (thereafter referred to as "study cohort") based on the results obtained from the training set.
All these methods are based on the expressed and variable genes obtained after elimination of background (background threshold: 3. 5, fold change threshold: 1. 2) and invariant genes using RIQR (threshold: 0. 9; max(Q3-Q2,Q2-Q1)/ Q2). Hierarchical clustering with silhouette was applied on gene sets which characterize differential subgroups by NMF. All clustering were performed using average linkage and Pearson correlation distance. Raw data are deposited with the following access number GSE94321.
RT-PCR validation We quantified the mRNA expression level of five different human genes (DNMT3B, FGF9, FGFR2, HHIP, and HOTAIR) in five RT and five SD-NRT to validate the expression data assessed by transcriptome micro-arrays. cDNA synthesis and subsequent qPCR reaction were performed on total RNA. We also quantified transcripts TBP as an endogenous RNA control gene. Real-time quantitative RT-PCR were performed using the ABI Prism 7900 Sequence Detection System (Perkin-Elmer Applied Biosystems) and the results were expressed as N-fold differences in target gene expression relative to the TBP control gene based on the 2 -ΔΔCT method. Primers for TBP and the target genes are available on demand.
Exome sequencings Whole Exome Sequencing (WES) was performed on 6 matched pairs of blood and tumors, and 6 tumors with no germline DNA, using Illumina Hi-Seq2500 leading to paired-ends 100x100bp with 100X expected coverage. Alignments were performed on human reference sequence (hg19) using Bowtie2-v2. 1. 0. Reads with mapping quality under 20 and reads that were marked as duplicates by Picard-v1. 97 (http://broadinstitute. github. io/picard/) were excluded from further analysis. Variant calling was performed in parallel using 3 variant callers: Unified Genotyper and Haplotype Caller from GenomeAnalysisTK-v3. 1. 1 and Samtools-v0. 1. 18. Annovar-v2013-08-23 with cosmic-v65 and dbsnp-137 were used for the annotation and RefSeq for the structural annotation. Single nucleotide variants (SNVs) and InDels with a quality under 20, a depth of coverage under 10 or with less than 5 reads supporting the variant were filtered out.
ACKNOWLEDGMENTS AND FUNDING RTOP is supported by  SiRIC of Curie Institute. INSERM U830  is supported by the  Institut National de la Sante et de la Recherche Medicale , the  Institut Curie  and the  Ligue Nationale Contre Le Cancer. This study was supported by a grant from the  French Ministry of Health (Direction Generale de l'Organisation des Soins)  and the  French National Cancer Institute  ( RTK-2012 , no. 2012-1-RT-02-IC-1 ). We thank the Associations who support our research on RTs: Abigael,  Marabout de Ficelle ,  Franck un Rayon de Soleil ,  Les Torocinelles ,  Couleur Jade ,  Au  nom d'Andrea and ADAM. We are also indebted to the  SFCE, Enfants et Sante  and  Etoile de Martin  for their financial support. F. B. is a recipient of the fellowship Enfance-et-Cancer/Hubert Gouin association.
We thank the French pathologists and oncologists who refer the RTs samples to our laboratory; in particular, we thank the  Pr Aurore Coulomb ,  Dr Louise Galmiche ,  Pr Michel Peuchmaur ,  Dr Elisabeth Cassagnau ,  Dr Francoise Lemoine ,  Dr Emmanuel Bompas ,  Dr Guillaume Muller ,  Dr Sebastien Lepreux ,  Dr Guillaume Muller ,  Pr Marie Wislez ,  Pr Dominique Plantaz ,  Dr Christophe Bergeron ,  Dr Laurence Brugieres  and  Dr Nadege Corradini. Highthroughput  sequencing has been performed by the  ICGex NGS platform of the Institut Curie  supported by the grants  ANR-10-EQPX-03  ( Equipex ) and  ANR-10-INBS-09-08  ( France Genomique Consortium ) from the  Agence Nationale de la Recherche  (" Investissementsd'Avenir" program ), by the  Canceropole Ile-de-France  and by the  SiRIC-Curie program -SiRIC Grant  ( INCa-DGOS-4654 ).
2015). Nevertheless, some progress is being made in the atmospheric chemistry field to miniaturise such sensors into autonomous data loggers that could eventually be used to study olfactory components of the life of individuals. In the future, sound and video could also be recorded by loggers to better apprehend the environmental context around the focal animal to better explain its behaviour. Although we can use the sensing system mentioned above to infer the internal physiological state of individuals in many cases, there is still a major lack of technological developments applying the existing physiological sensors to work autonomously in free-roaming animals  (Ropert-Coudert and Wilson 2005; Ponganis 2007; Cooke et al. 2014). In human and veterinary medicine many sensors already exist that could potentially be combined with bio-logging devices; however, there is still little concrete progress in this field with some notable exceptions  (Schobel et al.
Methods Bird capture and data recording 18 non-breeding Himalayan vultures were captured in Bhutan between November 2014 and February 2015 using wire mesh-mesh cage traps, weighed and measured. All birds included in the study were immature individuals at an estimated age of 1-2 years. Individuals were equipped with high-resolution GPS data loggers (45 g, cell phone link, e-Obs GmBH), using an approximately 30 g Teflonnylon harness. Birds were then filmed before release to calibrate their behaviour against 3D-acceleration data that were collected by the tags (see below). Daily between 02. 00 and 20. 00 hour UTC, loggers were set to periodically collect 1 Hz GPS fixes for 10 min, whenever solar charge allowed. Whenever there was low sunlight charge of the tag, the GPS fix rate was lowered to 30 min.
We collected a total of 2,923,736 GPS fixes between February 1, 2015 and January 31, 2016. The tags were also set to collect 3D-acceleration data at 15 Hz per axis for a duration of 4 s at 2 min intervals throughout the day. These data were also transmitted through the GPRS connection of the tags. Data are available through the Movebank archive (https://www. movebank. org/node/15294).
Environmental data annotation, acceleration analysis, and circumstances of disappearance In our analysis we focused on non-breeding individuals (N = 18) as they all face the same life history constraints, i. e. , the survival analysis is not confounded by the potentially differing demands of reproduction. GPS tracks and ACC data transmitted from bio-loggers on Himalayan vultures in the field are stored in the Movebank online database  (Fiedler and Davidson 2012)   (Dodge et al. 2013). These environmental data were annotated in space and time and interpolated as needed to the GPS data. We used the 3D-acceleration data to determine the behaviour of the birds throughout their annual cycle. For this, we plotted the data using the Movebank accelerationviewer annotation extension (https://www. movebank.
org/ node/5920) and visually annotated them from our observation and photograph-calibrated 3D-acceleration data, prior to the release of the birds in a wire mesh cage trap where they were feeding, preening, sitting, and interacting with other vultures (for similar methods, see also  (Resheff et al. 2014). Combined with the GPS data that gave the spatial and movement (speed, height, and directional heading) context, we used the behavioural classes resting, flying (further distinguishing between flapping flight and gliding flight), and forage to characterise the birds' actions throughout the year. For the 5 birds that died during the observation year, we detailed the location and acceleration information within the last two weeks of their disappearances. We also sent field teams to three of the carcasses to confirm the most likely cause of death.
Statistical analysis We used statistical package SPSS 22 for analysis. We included one entire year of data, starting from February 1, 2015, for all birds except for the five birds that died. For the dead birds we did not include the last two weeks before death into our analyses to ensure that we do not bias the mortality analysis by factors that preceded death (e. g. , if birds that sit on the ground continuously are more likely to be poisoned or killed by hunters). For each bird we calculated, for its entire lifetime (minus the last two weeks), or for a maximum of one year, average values for their behavioural parameters (such as flight altitude above the surface, % time resting, % time thermalling, etc. ) and the associated environmental parameters (such as NDVI, temperature, humidity, etc.
To assess the environmental and behavioural parameters that could affect individual mortality, we selected 7 individual performance variables of the vultures: flight altitude above the surface, absolute flight height, proportion of sitting on the ground versus flying, proportion of flight in a straight line versus thermalling (circling) flight, and the strength of the thermal uplift or of orographic uplift (as calculated from the Movebank data models). We also included 7 environmental variables: sunshine duration, precipitation, NDVI, ambient temperature, relative humidity, northerly winds versus easterly or westerly winds, as well  Vultures that often flew against headwinds from the north did not survive their first year (Independent samples t test, F = 0. 27, t = 3. 4, df = 16, p < 0. 001; Fig.
comm. ). The specific causes of death included starvation and poisoning, which was confirmed on the ground in two of the five cases. The use of acceleration data combined with GPS tracks collected in the two weeks before death was useful to give cues about the likely cause of death. Thus, even for the Himalayan vultures that are much less exposed to diclofenac poisoning, the apparent poisoning of mammalian carcases may turn out detrimental for the survival of these birds  (Acharya et al. 2009; Das et al. 2011). With the rapid technological advancement and miniaturisation of devices in animal tracking as well as the inclusion of multiple sensors (such as high-resolution GPS and ACC, pitot pressure, gyroscope, magnetometer) we can now track annual movements and soon the entire life history of an animal in high-definition.
2014). Such bio-logging devices combining GPS and ACC data transmitted by GSM could help finding and rescuing several vulture species (G. barbatus, A. monachus, N. percnopterus) that are the focus of reintroduction or rehabilitation programmes in France (O. Duriez, unpublished results). Remote sensing environmental data (weather, habitat, and human land use) can now be commonly used in animal tracking. For example, the global data repository Movebank  (Fiedler and Davidson 2012)  offers open access environmental data that can be easily annotated to GPS data with the Env-DATA system  (Dodge et al. 2013). In our analysis, 13 environmental variables were used in the survival model. While several environmental parameters significantly contribute to the survival and death of Himalayan vultures, NDVI parameters failed to provide evidence of significance.
Acknowledgements Open access funding provided by  Max Planck Society. This research was supported by the  Max-Planck Society. We thank  Kamran Safi ,  Nawang Norbu ,  Jesko Partecke , and  Christian Ziegler  for discussions that started this work and  Gil Bohrer  for advice on large scale atmospheric processes. Thanks to  Yachang Chen  for tag recovery, and the  UWICE  staff for support in the field.
They also found that the road transport sector in the Middle East is as significant of a contributor to the global emissions of CO and NO x as road transport in western Europe and North America. These findings are consistent with results reported by  Uherek et al. (2010)  for a larger range of pollutants (CO 2 , CO, NO x , and NMVOCs, non-methane volatile organic compounds). The same picture is presented by emission inventories. Figure  1  compares the annual total anthropogenic emissions of NMVOCs from three reference emission inventories between the Middle Eastern (MEA) region, Europe, and North America: ACCMIP (Atmospheric Chemistry and Climate Model Intercomparison Project), EDGARv4. 2 (Emissions Database for Global Atmospheric Research), and RCPs (Representative Concentrations Pathways) (ECCAD, 2015, http://eccad.