dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequencing was carried out in a ABI Prism 3100 sequencer (Applied Biosystems). Using the Basic Local Alignment Search Tool (BLAST, http://blast. ncbi. nlm. nih. gov/ Blast. cgi), most closely related CytB sequences were identified and only bats identified as E. serotinus were kept for the further analysis. The resulting sequences were assembled with Sequencher (GeneCodes) aligned with ClustalX (http://www. clustal. org) and trimmed with BioEdit (http://www. mbio. ncsu. edu/bioe dit/bioedit. html) to create a 1,064-and 424-bp alignment for CytB and D-loop regions, respectively. (Accession numbers are available in supplementary table S2, Supplementary Material online).
Acknowledgments The author want to acknowledge  Karin Eiglmeier  from  Unit of Insect Vector Genetics and Genomics  for access and help on ABI Prism 3730 for microsatellite genotyping. We would like to thank the  SFEPM group  ( Societe Francaise pour l'Etude et la Protection des Mammiferes, groupe Chiropteres ), and especially the bat naturalists, for their collaboration in the surveillance of bat rabies in France. This work was supported by  European Union Seventh Framework Programme  ( FP7/ 2007-2013) PREDEMICS  (under grant agreement number  278433 ),  SantePublique France  and the  Institut Pasteur. SD is funded by the  Fonds Wetenschappelijk Onderzoek  ( FWO, Flanders, Belgium ). PL acknowledges the  Research Foundation -Flanders  ( G0D5117N & G066215 ) and the  Research Fund KU Leuven  ( OT/14/115 ).
Introduction: Quantitative assessment of heterogeneity by histogram analysis (HA) of tumor images can potentially provide a non-invasive prognostic biomarker. We assessed the prognostic value of HA and evaluated a correlation with molecular signature. Results: CT scans performed between July 2009 and January 2015 from 692 patients were reviewed. HA was performed on scans from 313 patients in the training dataset and 108 in the validation dataset. Median follow-up were 33. 7 months [range: 1. 7 -65. 5] and 29 months [range: 1. 1 -35. 6] with a median overall survival (OS) of 11. 7 months [95%CI: 10. 7 -13. 1] and 9. 5 months [95%CI: 7. 9 -12. 7] respectively. Primary mass entropy in coarse texture with spatial filter 3. 3 was prognostic for OS in a multivariate Cox analysis (HR: 1. 3 [95%CI: 1.
1 -1. 5], p=0. 001). Results were not reproduced in our validation set and no correlation with molecular signature was identified. Materials and Methods: HA using filtration-histogram method was applied to the region of interest on the primary tumor in enhanced-CT acquired as diagnostic/ staging routine, from a cohort of patients with advanced non-small cell lung cancer (NSCLC) treated with platinum-based chemotherapy. The resultants parameters were prospectively applied to a validation dataset. CT scans, clinical and molecular data were retrospectively collected. Cox proportional hazard models were used for survival analysis and Wilcoxon test for correlations. Conclusion: Primary mass entropy was significantly associated with survival in the training set but was not validated in the validation cohort, raising doubt over the reliability of published data from small cohorts.
RESULTS Out of 692 patients with contrast-enhanced CT scans screened, 421 (60. 8%) were suitable for histogram analysis. Patients were divided into the training dataset (n=313) and the validation dataset (n=108). The main reason for exclusion from the training dataset were screening failure (due to < stage IV at inclusion, absence of platinum-based treatment, other histology than NSCLC and baseline CT not available) followed by errors in the transferring process and non-evaluable primary tumor. Screening failure was also the main reason for exclusion in the validation dataset (Figure  1 ).
HA prognosis markers in the training dataset Primary mass entropy in coarse texture with spatial filter 3. 3 (HR: 1. 3 [95%CI: 1. 1 -1. 5], p=0. 001) was significantly associated with OS in the univariate analysis, and there was a trend toward better survival for the coarse texture with spatial filter 2. 8 (HR: 1. 26 [95%CI: 1. 1 -1. 5], p=0. 007) and medium texture with spatial filter 2. 2 (HR: 1. 3 [95%CI: 1. 1 -1. 6], p=0. 006). No statistically significant associations were identified for primary mass entropy with the finest textures (spatial filter 1. 7: HR: 1. 2 [95%CI: 0. 9 -1. 5], p=0. 248; spatial filter 1. 1: HR: 1. 2 [95%CI: 0. 9 -1. 6], p=0. 143). None of the other parameters analyzed achieved significant associations with OS. Smoker status (HR: 1. 9 [95%CI: 1. 3 -2. 7], p=0. 001), PS (HR: 2.
DISCUSSION This study aimed to determine the potential of imaging analysis as an independent predictor of survival and the potential to associate tumor heterogeneity with gene alterations in patients with NSCLC. In the training dataset primary mass entropy was significantly associated with OS in the univariate analysis and remained an independent prognostic factor in a multivariate analysis. This finding is coherent with two published studies in early-stage and locally-advanced NSCLC in which primary mass entropy was reported to be prognostic for OS  [16, 18]. The prospective application of significant parameters in the validation dataset was intended to increase statistical robustness. However we were unable to validate the prognostic value of primary mass entropy. A literature search revealed only one study using an independent validation dataset cohort, however different parameters were analyzed between sets, limiting the validity of their results  [19].
g. restricting the analysis population to primary tumor) and differences concerning settings and protocol CT acquisitions cannot be excluded; histogram analysis assessed on contrast-enhanced CT scans can be affected by several variables, such as interscanner variability, pixel values, imaging parameters and contrast media injection rate  [23, 24]. The impact of this variability on HA features was not evaluated in this study; prospective studies are needed to measure the true impact of HA features. Patient variables (e. g. , cardiac output and body mass index) may influence tumor enhancement. In addition, only a single largest cross section was used for analyses, which may not have been representative of tumor heterogeneity  [25]. The inability to reproduce primary mass entropy as an independent prognostic factor in the validation patient cohort may have been due to the smaller size of the validation dataset, although statistical power was adequate to detect a reasonable effect size.
Few publications address the possibility of false discovery rates in image analysis despite that investigation of multiple variables in single datasets may increase this phenomenon  [26]. Although the analysis of large quantities of variables may have increased the risk of false positives findings, we decreased this risk by strictly adjusting the p-value cut-off (p<0. 005); nonetheless, our validation results did not confirm the ones from training dataset. Verifying outcomes within a properly selected prospective validation dataset should help clarify this issue. Variations within the tumor (necrosis, angiogenesis, hypoxia, etc. ) at a cellular level may potentially be translated into variations within pixel distribution, however this must be confirmed with a software that integrates other aspects of the tumor images such as shape, wavelet among others, more uniform CT scan acquisition and large patient cohorts before it can be implemented into routine clinical practice.
Patients The overall study population consisted of 692 patients with advanced NSCLC (stage IIIB not amenable to locoregional treatment or stage IV) included in the MSN-LUNG study (CSET: 2007/1363) who received platinum-based chemotherapy at Gustave Roussy (France) between July 2009 and January 2015. All patients had to have a diagnosticquality contrast-enhanced CT scan with an evaluable primary tumor, acquired for diagnostic/staging routine. Molecular profiling was performed for the MSN study over patient's archived diagnostic tissue. The MSN study was approved by the institutional ethics committee board and all patients signed an inform consent for tumor analysis. CT scans, clinical and molecular data (KRAS, EGFR and ALK status) were retrospectively collected for correlation with HA parameters.
Statistical analysis The primary objective was the assessment of the prognostic value of CTHA in a cohort of advanced NSCLC patients treated with platinum-based chemotherapy in terms of overall survival (OS). The secondary objective was the correlation of CTHA parameters with gene alterations. Statistical analyses were performed using SAS version 9. 3 (SAS institute INC. Cary NC, USA). OS was defined from the first platinum-line treatment to death from any cause or the last follow-up and curves were estimated with the Kaplan-Meier method. The prognostic value of the following covariates was measured in a univariate analysis: sex, age at inclusion (>70 years-old), smoker status (never/passive smoker versus ex/current smoker), performance status (PS) (≤1 versus >1), primary size tumor (≤ vs.
As was the case in the Training Dataset, no histogram analysis parameters significantly discriminated the mutational status for the EGFR-mutated versus nonmutated, ALK+ versus ALK-and KRAS mutated versus non-mutated profiles. Training dataset Validation dataset p-Value * n=313 (%) n=108 (%) EGFR 26 (11) 7 (9) KRAS 49 (20) 12 (15) ALK/EGFR 1 (0. 4) - ALK/KRAS 2 (0. 8) - EGFR/KRAS 1 (0. 4) - ALK/Other * 2 (0. 8) - EGFR/Other * 1 (0. 4) 1 (1) KRAS/Other * 11 (5) 10 (12) Other * 28 (11) 22 (27) any other parameters identified as significantly associated with survival, although were not planned for this analysis. Furthermore the prognostic factors significantly associated with OS differ from those found in the Training Set. In the validation cohort, female sex (HR: 0. 5 [95%CI: 0. 3- 0. 8], p=0.
To further exclude the possibility of early contamination of the blood samples, we performed replicate experiments at different times using different library preparation protocols. Comparison of repertoire overlaps from such replicate experiments for the same set of twins shows no difference and rules out experimental contamination as a confounding effect (see SI). We also observed the same effects in previously and independently collected datasets  [8] , further excluding the possibility of experimental artefacts (Fig. S8 ). Cord blood sharing between twin embryos could have important implications on twin immunity: they could share and respond with private clonotypes, which would otherwise not be likely to be produced independently. This could possibly include sharing of malignant  [19] [20] [21]  or autoimmune clones, leading to disease in both individuals.
This mechanism of sequence sharing is very different from sharing by convergent recombination  [6] , because it also implies the sharing of the second TCR chain and of the cell phenotype. Paired repertoires studies, which combine alpha and beta chains together  [22, 23] , could be used to track clones shared between twins more precisely, and distinguish them from convergently recombined ones. We conclude that fetal clonotypes are long-lived based on the analysis of over-abundant zero-insertion clonotypes. Invariant T-cells, MAIT (Mucosal-Associated Invariant T-cells) and iNKT (Invariant Natural Killer Tcells) are intrinsically insertion-less, have restricted VJ usage for alpha chain, and are often abundant. These cells are produced in adulthood and could in principle constitute a substantial fraction of our zero-insertion dataset, confounding our analysis.
In general, the observed abundance of large zero-insertion clonotypes and their persistence through significant part of our life should have important consequences for the adaptive immunity regulation both in pre-and post-fetal period. Lastly, our general framework for analyzing the overlap between different repertoires has far-reaching practical implications for the tracking of T-cell clonotypes in the clinic. In particular, the analysis of overlap between preand post-treatment repertoires using probabilistic characteristics of clonotypes sharing could help determine the host or donor origin of clonotypes after hematopoietic stem cell transplantation (HSCT), and also increase reliability of malignant clones identification in minimal residual disease follow-up. TRB/TRA libraries sequencing, raw sequencing data processing and reconstruction of TRB/TRA repertoires were supported by Russian Science Foundation grant 15-15-00178.
Work was also partially supported by Russian Science Foundation project 14-14-00533 (to DMC, molecular barcoded data analysis). This work was partially supported by European Research Council Starting Grant n. 306312. We want to thank Dr. I. V. Zvyagin for his help in finding appropriate donors for this study, and S. A. Kasatskaya for productive discussion of the manuscript. Part of experiments were conducted using equipment provided by the CKP IBCH RAS core facility. Blood samples were collected from 3 pairs of monozygotic twin female donors, 23 (donors S1 and S2), 23 (donors P1 and P2) and 25 (donors Q1 and Q2) years old respectively. We also collected blood from two 19 and 57 year old male donors, along with a 51 year old female donor for memory and naive T-cells isolation, and a cord blood sample from a female newborn.
G. Distribution of insertions for each beta chains abundance class We applied the procedure described in the previous section separately for each abundance class of the betachain sequences. However, given the small size of the datasets (2000 or 3000 sequences), we did not learn the full model for each class. Instead, we used a previously inferred universal beta-chain recombination model  [11]  for the V,D,J gene usages and their deletion profiles, and we learned the insertion distributions (P (insV D) and P (insDJ)) for each class separately, while keeping the other parameters constant. The distribution of insertions thus inferred are used to plot the results of Figs. 3  and 4  of the main text.
I. Data analysis Analysis of the shared clonotypes was performed using the R statistical programming language  [31]  and the tcR package  [32]. To predict sharing for each individual, we generated sequences using our recombination model P gen (alpha or beta), with individually inferred model parameters. Normalized sharing of the TCR sequences between two clonesets is defined as the number of the same unique TCR nucleotide sequences observed in both of them, divided by the product of the total numbers of unique TCR nucleotide sequences in the two datasets. We calculated sharing of either whole chains, or of their CDR3, defined as the sub-sequence going from the conserved cystein at the end of the V region, to the conserved phenylalanine in the J region. The alpha chain results for whole-chain sharing are plotted in the main text in Fig.
1 , and the data shows good agreement with the model. The results for CDR3 sharing are shown in Fig. S2. The model systematically underestimates the normalized sharing by a common multiplicative factor of 1. 7 for non-twins, with a Pearson correlation coefficient of 0. 8 between the data and the model prediction. Absolute numbers of shared CDR3 sequences for alpha chains varied from 400 to 1200. For beta chain sequences, the prediction of out-offrame sharing is more difficult because of the low numbers of out-of-frame sequences in the RNA data, which, combined to a lower mean P gen , results in a much lower number of shared out-of-frame sequences. We also identified and removed from the dataset 26 out-of-frame sequences shared between more than two individuals. These sequences are likely to arise due to reproducible aligner errors or technology artifacts -some of them contained intronic sequences, etc.
This fact could be explained by the much higher number of clonotypes shared due to convergent recombination in the alpha in-frame dataset than in the beta in-frame and alpha and beta out-of-frame datasets. Excess of shared CDR3 nucleotide sequences due to biological contamination in twins is lower than the amount of convergent recombination noise in the alpha in-frame shared CDR3 nucleotide sequences. Absolute numbers of shared in-frame CDR3 sequences for alpha chains varied from 30000-50000 sequences depending of the pair, and 5000-9000 for beta chains.
We then separated out the sequences consistent with zero insertions from the others, and created, for each abundance class C, and artificial dataset with a fraction F 0 (C) of such sequences, and 1 -F 0 (C) of the other sequences (not consistent with zero insertions), where F 0 (C) is given by the data. We then calculated normalized sharing in the synthetic data by including an increasing number of abundance classes, starting with the most abundant ones, and com-pared to data in Fig. 5.
It was previously shown that mucosal-associated invariant T-cells (MAIT) and natural killer T-cells (NKT) have an invariant alpha chain with very low diversity  [33]. Specific V-J combinations are chosen (TRAV10/TRAJ18 for NKTs and TRAV1-2/TRAJ33 for MAIT) and no nucleotides are inserted in the recombination process of these clonotypes. To see whether these clonotypes could potentially confound our analysis, we searched for published NKT and MAIT sequences in our datasets. 25 out of the 27 known MAIT sequences were found in the datasets at least once (21 out of them in the all six individuals), and 8 out of the 13 known NKT sequences (2 of them in the all six individuals). MAIT and NKT sequences are present in our data, but only a few shared sequences could be explained by them. The majority of shared zero insertion sequences could thus not be attributed to known MAIT or NKT subsets.
To be able to compare datasets of different sizes, the sharing number was normalized by the product of the two cloneset sizes. The three outlying red circles represent the twin pairs, while the black circles refer to pairs of unrelated individuals. Diagonal is equality line. Error bars show one standard deviation. To be able to compare datasets of different sizes, the sharing number was normalized by the product of the two cloneset sizes. The three red circles represent the twin pairs, while the black circles refer to pairs of unrelated individuals. Diagonal is equality line.
To be able to compare datasets of different sizes, the sharing number was normalized by the product of the two cloneset sizes. The three outlying red circles represent the twin pairs, while the black circles refer to pairs of unrelated individuals. Diagonal is equality line. Error bars show one standard deviation. To be able to compare datasets of different sizes, the sharing number was normalized by the product of the two cloneset sizes. The three red circles represent the twin pairs, while the black circles refer to pairs of unrelated individuals. Diagonal is equality line. 4e-07 Normalized sharing, data Normalized sharing, data 1. 0e-08 2. 0e-08 1e-07 2e-07 3e-07 0 0 1. 0e-08 Normalized sharing, model 2. 0e-08 FIG. S4: Number of shared in-frame beta TCR CDR3 clono-types reported between all 15 pairs of 6 donors consisting of 3 twin pairs (ordinate) compared to the model prediction (ab-1e-07 2e-07 3e-07 4e-07 Normalized sharing, model FIG.
Statistical Analysis The pgirmess package in the R statistical environment  [37]  was used to evaluate changes in edaphic properties within reference and burn soils across the various time points using Kruskal-Wallis contrasts. Enzyme activity was also analyzed as a BG: NAG ratio and tested for statistical differences across time within both burned and reference forest soils. Pearson product moment correlations were calculated between environmental factors of total C, total N, pH, C:N ratio, and percent moisture and BG/NAG activity both in burned and reference plot samples across all time points. Data were checked for normality and if nonconforming were transformed to achieve normality before correlation analysis.
Acknowledgments: This work was supported by the  National Science Foundation of the USA  through grant  DEB-1258160  to D. R. N. and S. K. S. We also acknowledge support from the  Microbiomes in Transition (MinT) Initiative  at  Pacific Northwest National Laboratory , operated by  Battelle for the U. S. Department of Energy  ( DE-AC05-76RL01830 ). We thank  Duaba and Sean O'Neill  for assistance in the establishment of the field site and  Janet Prevéy  for botany insights at the field site. We appreciate the expertise of  Holly Hughes  in analytical chemistry and the comments of two anonymous reviewers on the manuscript. Diana Nemergut-a superbly creative and innovative scientist-guided and worked on this manuscript. Diana continues to impact not only the scientific community and ongoing research but also far wider, diverse communities of people who she forever shines upon with her generous and beautiful life.
Acknowledgements The authors certify that they comply with the ethical guidelines for authorship and publishing of the Journal of Cachexia, Sarcopenia and Muscle: update 2015. 22We thank  M. Stijntjes ,  J. H. Pasma ,  A. Y. Bijlsma ,  Y. Barnouin ,  T. Maden-Wilkinson ,  K. S. van Schooten  for their contributions to collect the data. This study was supported by the seventh framework programme  MYOAGE  ( HEALTH-2007-2. 4. 5-10 ),  050-060-810 Netherlands Consortium for Healthy Aging  and by the  Dutch Technology Foundation STW , which is part of the  Netherlands Organisation for Scientific Research , and partly funded by the  Ministry of Economic Affairs, Agriculture, and Innovation. This study was also supported by the  PANINI programme (Horizon 2020 ,  Marie Curie, Sklodowska, Innovative Training Network, No 675003 ) and by  PreventIT (European Union 's  Horizon 2020 Research and Innovation Programme , No  689238 ).
Selection of primers specific to ankA clusters II, III and IV To select primers conserved within each ankA cluster but discriminating the A. phagocytophilum variants according to their clusters, the 5′ region of the ankA gene was selected, from nt 1 to 1650, as a highly variable region of the gene but outside the region where recombination has been previously reported  [7, 21]. ankA sequences covering the selected region and belonging to the 5 clusters were selected from the studies  [6, 7, 21]. They were then retrieved from GenBank, aligned using clustalomega and a consensus sequence (highlighting conserved and variable positions) was established for each cluster. The cluster I consensus sequence was established by aligning 95 ankA complete sequences from 43 dogs, 12 humans, 12 sheep, 10 horses, 9 European bisons, 5 red deer, 3 cows and 1 cat  [6] , and 30 partial sequences (523 bp) from hedgehogs  [7].
Accession number of nucleotide sequences A selection of 10 ankA partial sequences, representing each different variant and the different geographical regions, was deposited in GenBank (KY618918-KY618927). For example, all sequences for cluster IV were identical so only one was deposited (KY618927). Sequences of 4 variants of cluster II can be found under accession numbers KY618918-KY618921, as well as sequences of 5 variants of cluster III (KY618922-KY618926). For details about the variants, see Additional file 1: Table  S1.