dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors would like to express their sincere thanks to  Karine Marin ,  Lucille Cameleyse ,  Romain Pizzatto ,  Corinne Lyonnais ,  Laure Ridel ,  Thibaud Alain ,  Niklas Frieling ,  Melanie Hoffman ,  Marion Moreux , for supporting sample manufacturing and performing analyses of samples. We thank  Christian Airiau ,  Denis Hurlin ,  Cecilia Xi-Kramer ,  Silvere Petit ,  Laurent Esteve ,  Emmanuelle Vetter ,  Caroline Leveder ,  Didier Caudron  and  Fernando Ausar  for fruitful exchanges and supporting stability modeling activities.
Therefore, the role of CCL21-CCR7 pathway in GBM is not yet fully understood. Particularly, it remains unclear if this pathway is active in GBM patient samples and patient-derived cell lines and how it impacts the tumor microenvironment in preclinical models. In the present study, we investigated the expression and function of CCL21-CCR7 in GBMs, through the analysis of GBM patient datasets, patient-derived cell lines, and by manipulating CCL21-CCR7 signaling in preclinical models of GBM.
CCR7 expression increases with glioma grades and is associated with decreased patient survival To investigate CCR7 expression in malignant gliomas in silico, we used multiple data sources from The Cancer Genome Atlas (TCGA) database (TCGA LGGGBM microarray and GBM RNAseq), a tissue microarray (TMA) of 153 primary CNS tumors and 11 normal brain samples, and the RNA database from a primary glioma patient cohort (25 patients, 14 GBMs and 11 LGGs). We observed increased CCR7 expression in GBM compared with all other low-grade glioma (LGG) subtypes (Fig. 1a ). CCL21 expression was not detected in this dataset. By QPCR, we observed elevated expression levels of both CCR7 and CCL21 in a cohort of 14 WHO grade IV GBM compared to  11  LGG patients (WHO grade I, II, and III gliomas) (Fig. 1b , Table  1 ).
Bioinformatic analysis For 'The Cancer Genome Atlas' (TCGA) datasets, TCGA-GBMLGG microarray data of 688 glioma patients and RNAseqV2 normalized data of 151 primary glioblastoma multiforme and associated clinical data were downloaded from the cBioPortal and GlioVis data portals (https:// gliov is. bioin fo. cnio. es and https:// www. cbiop ortal. org/ study/ summa ry? id= gbm_ tcga)  [64]. For survival analysis, in each dataset the cohort was split into two groups of patients defined by the mean level of target gene expression. Overall survival (in months) was used to estimate survival distributions using the Kaplan-Meier method, and the distributions were compared using the log-rank test.
Human brain tumor RNA extraction RNA was purified from frozen tissue samples in liquid nitrogen using the RNeasy kit (Qiagen). 0. 5 μg of RNA was reverse transcribed using SuperScript IV reverse transcriptase and random primers (Invitrogen). Quantitative PCR was performed as described for sorted cells from the tumor microenvironment using Quantitec qPCR primers (Qiagen). The data were first normalized to the actin level in each sample, and the relative expression levels of the different genes were calculated using the comparative Ct method.
Wound-healing assay The wound healing assay was performed as previously described, with minor modifications  [67] : Briefly 1. 5 × 10 5 tumor cells were seeded on 24 well plates and after 8hs, the cells were treated with Mitomycin C overnight to prevent proliferation. Cell monolayers were then scraped into straight lines with a p10 pipette tip, and debris was removed by washing the monolayers with fresh serum-free culture medium. Images were captured using a Nikon Eclipse T300 microscope before treatment with CCL21 and after 24 h of treatment. ImageJ software (v1. 46; National Institutes of Health, Bethesda, MD, USA) was used to analyze the images, and the scratched areas were determined using the "Polygon Selection Tool" for each time-point and treatment. The results were normalized to the scratched areas at 0 h.
Transwell assay To evaluate chemotactic migration of GBM cells or macrophages in the direction of CCL21, Transwell assays were performed:1. 0 × 10 5 cells were plated in the top chambers in 150 μL of serum-free medium, with increasing concentrations of CCL21 (from 50 to 1000 ng/mL) or GBM conditioned medium in the bottom chambers. Cells were incubated overnight at 37 °C and 5% CO 2 , the medium was removed from both chambers, and inserts were fixed with 70% ethanol for 20 min at room temperature before staining with Giemsa or live stained with Calcein AM (Thermo Fisher). Then, the wells were washed, and 10 pictures per well were acquired at 10 × magnification using a Nikon Eclipse T300 epifluorescence microscope. Migrated cells per field were counted using the ImageJ software.
Viability assay GBM cells were treated for 24 h with different concentrations of Temozolomide (TMZ) alone or in combination with 5 μg/mL anti-CCR7. The viability of GBM cells was determined by trypan blue staining. Briefly, after 24hs treatment, cells were detached from 24-well plates with 0. 5 mM EDTA in PBS, centrifuged, and resuspended in 50uL of fresh medium. Then, a 1:1 dilution with 0. 4% Trypan Blue solution was made, and viable (unstained) and non-viable cells (blue) were counted using a hemocytometer. The curve for the calculation of the TMZ concentration necessary to inhibit cell proliferation by 50% was determined using GraphPad Prism 5 (version 5. 00; GraphPad Software, Inc. , San Diego, CA, USA).
RT-qPCR on cultured cells After siRNA transfection and/or treatment, RNA from RAW264. 7 or primary cells were purified using a RNeasykit (Qiagen). RNA (1000 ng) was reverse transcribed using SuperScript II reverse transcriptase and random primers (Invitrogen). Quantitative PCR was performed as described for sorted cells from the tumor microenvironment using Quantitec qPCR primers (Qiagen). The data were first normalized to the actin level in each sample, and the relative expression levels of the different genes were calculated using the comparative Ct method.
After washing with TBS-T, the membranes were incubated with the appropriate HRP-conjugated secondary antibodies for 3 h at room temperature under agitation. Western blots were developed with a chemiluminescence HRP substrate (Bio-Rad) on a luminescent image analyzer, ChemiDoc XRS + (Bio-Rad). Densitometric analyses were performed using ImageJ 1. 49v software (Wayne Rasband, National Institutes of Health, Bethesda, MD, USA).
All the authors have edited the manuscript. Funding This study was supported by the Brazilian Funding agencies Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ), Instituto Nacional de Neurociência Translacional (INNT), and Programa de Pós-Graduação em Ciências Morfológicas (PCM) from Universidade Federal do Rio de Janeiro (UFRJ). This study was also supported by the European Research Council (ERC) (grant agreement No. 834161 to A. E. ) and the Agence Nationale de la Recherche (ANR-20-CE16-0027-01, JLT). L. H. G. was founded by fellowships of the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), European Society of Cardiology (ESC Basic Research Fellowship) and American Heart Association (AHA Postdoctoral Fellowship).
Acknowledgements We thank the  Paris Cardiovascular Research Center (PARCC) Flow and Image Cytometry facility, FDG PET  and  MRI imaging platform (France Life Imaging network  (grant  ANR-11-INBS-0006 ), ' Infrastructures Biologie Santé ' (IBISA), and multiphoton imaging platform (Leducq Foundation RETP: Visualization des pathologies vasculaires). We thank  Dr. Holger Gerhardt  for providing GL261 and  RAW264. 7 cells used in this study.
A deeper understanding of HIV-1 transmission and drug resistance mechanisms can lead to improvement in current treatment policies. However, the rates at which HIV-1 drug resistance mutations (DRMs) are acquired and at which transmitted DRMs persist are multi-factorial and vary considerably between different mutations. We develop a method for estimation of drug resistance acquisition and transmission patterns, which refines the method we described in Mourad et al. AIDS 2015. The method uses maximum likelihood ancestral character reconstruction informed by treatment roll-out dates and allows for analysis of very large data sets. We apply our method to transmission trees reconstructed on the data obtained from the UK HIV drug resistance database to make predictions for known DRMs. Our results show important differences between DRMs, in particular between polymorphic and non-polymorphic DRMs, and between the B and C subtypes.
The fitness was measured as a ratio between transmission rates of hosts infected by drug resistant strains and transmission rates of hosts infected by sensitive strains. They applied this model to the data from the Swiss HIV cohort study. They reconstructed a maximum likelihood tree for 5 638 pol-gene sequences from the Swiss HIV cohort study and 4 284 closely related sequences from the Los Alamos HIV database. On this tree, for each of 15 major DRMs present in the Swiss cohort sequences, they identified its transmission clusters of up to 250 sequences each, containing > 80% of Swiss sequences and at least one sequence with the mutation. Kühnert et al. [9]  estimated the model parameters on all the clusters for each mutation separately, in a Bayesian setting. To account for the fact that DRMs appear under aniretroviral (ARV) selective pressure, they put the rates of state change (from sensitive to resistant and vice versa) to zero before significant usage of the related drug(s) in Switzerland.
The study showed that some of the mutations (RT:D67N, RT:K70R, RT:M184V, RT:K219Q) decreased the fitness, one (PR:L90M) seemed to increase the fitness, while the others did not have a significant effect. The models above, and more generally the family of multi-type birth-death models  [7]  with a Bayesian birth-death skyline plot (allowing the parameters to change in a piece-wise constant manner)  [12]  it belongs to, define ODEs for fine-tuned parameter estimation. However, their complexity prevents resolving them analytically. Numerical solution of the ODEs, on the other hand, takes long computational time and prevents the application of these models to larger datasets (dozens of thousands of sequences), while larger data sets are desirable for more accurate parameter estimation. A compromise between the model complexity and computational speed when applied to large datasets needs to be found.
Moreover, we account for the changes in treatment policies by allowing for separate ACR for different time intervals (e. g. before and after the first DRM-provoking ARV introduction). Once the reconstruction is performed we visualize the results with PastML and calculate various statistics for transmission patterns. We apply our approach to analyze the patterns of DRM emergence, transmission and loss in HIV-1-infected individuals in the UK, using sequences and metadata from the UK HIV Drug Resistance Database  [14].
Materials and Methods The UK HIV Drug Resistance Database provides HIV protease (PR) and reverse transcriptase (RT) sequences extracted during the resistance tests and the corresponding metadata (e. g. , treatment status of the patient before the test: treatment-experienced, -naive, or unknown; and date of the test). In response to our request for data from the database we obtained 88 009 sequences for 60 846 different patients, sampled between 1996 and 2016.
Transmission tree reconstruction We reconstructed phylogenetic trees for B and C sequences separately, using RAxML-NG (v0. 9. 0, evolutionary model GTR+G4+FO+IO, for detailed options see Appendix A)  [17]  and rooted them with the outgroup sequences, which we then removed. For tree reconstruction, the positions of surveillance DRMs were removed from the alignment, as they are influenced by treatment-selection forces unlike the other positions, and could bias the reconstruction by grouping together the sequences that share the same DRMs. We then dated each tree with LSD2  [18]  (v2. 3: github. com/tothuhien/lsd2/tree/v1. 4. 2. 2, under strict molecular clock with outlier removal, for detailed options see Appendix A) using tip sampling dates.
Ancestral character reconstruction For each DRM (surveillance or accessory) listed in the Stanford HIV Drug Resistance Database  [19]  we extracted its presence/absence in the sequences of our data sets and the ARVs that can provoke it with Sierra, the Stanford Algorithm  [20]  web service. We then analyzed the DRMs that were found in at least 0. 5% of sequences (after filtering by patient and temporal outlier removal) of our dataset (either B or C, analyzed separately). Each DRM name (e. g. RT:T215D) contain 2 pieces of information: the DRM position (e. g. RT:T215, which in turn contains the protein name: RT (reverse transcriptase) or PR (protease), the reference position of the amino acid, e. g. 215, and its wildtype amino acid, e. g. T), and its mutated amino acid, associated with resistance (e.
g. D). We analyzed each DRM position independently by reconstructing its states in the ancestral nodes, based on the tip states. The DRMs with prevalence > 0. 5% found in this position in the dataset of interest (e. g. B) were analyzed together. For the majority of the DRM positions, only one DRM with prevalence > 0. 5% was found (e. g. PR:L90M for the position PR:L90), however for the positions RT:T215 and RT:K219 in the B data set and for the position RT:V179 in the C data set, several DRMs were found (RT:T215D/F/S/Y, RT:K219E/N/Q, and RT:V179D/E). Possible states for ancestral character reconstruction (ACR) corresponded to DRM presence (i. e. the resistant state) or absence (sensitive state) for DRM positions with only one DRM. For instance, for PR:L90M the resistant state corresponds to the amino acid M, and the sensitive state to any other amino acid; in practice, the sensitive state is almost uniquely L.
For each ARV we extracted the dates of their acceptance with Wikipedia python package (https://github. com/goldsmith/Wikipedia). We cut the time-scaled tree at the earliest of the dates of acceptance of ARVs that can provoke the DRM (e. g. for PR:L90M, saquinavir (SQV) was accepted in 1995). We hence obtained the pre-treatment-introduction tree and a forest of post-treatment-introduction subtrees. For the trees in the forest we added additional one-child root nodes (as parents of the corresponding tree roots, at distances that corresponded to the differences between the root dates and the ARV acceptance date), which we marked as sensitive in the PastML input annotation file. We performed ACR with PastML on the forest, and then combined it with the all-sensitive annotation for the pre-treatment-introduction tree nodes.
Times of DRM loss We estimated the loss times for non-polymorphic DRMs, using survival analysis with an exponential (constant hazard) model (Weibull model with β = 1), implemented in Python3 package SurPyval (github. com/derrynknife/SurPyval, v0. 10. 10). This model takes as input observations about event durations and estimates the rate at which the event occurs. The input data might be left-, right-or interval-censored. Left-censored data represent times that are longer than the event occurrences, e. g. if the DRM loss occurred in exactly 2 years, but the observation was only made after 3 years, the 3-year duration represents a left-censored data point. Right-censored data represent times that are shorter than the event occurrences, e. g. if for the same DRM loss the only observation was made after 1 year (and observed no DRM loss yet), the 1-year duration represents a right-censored data point.
Interval-censored data represents cases when both a left-and a right-censored data point are available, e. g. for the same DRM loss an interval-censored data might state that it occurred sometime between 1 and 3 years. For each individual represented in our data set we extracted at most one data point for the loss survival analysis, as described below. A right-censored data point represents the maximal observed duration during which a mutation loss did not occur. We extracted such points for the individuals who had several consecutive treatment-naive samples with the DRM of interest (and of the subtype of interest) in our metadata: we took the difference in sampling times of the last such sample and the first one. A left-censored data point represents a duration that is longer than the mutation loss time.
If resistance is the reason for a treatment failure, the treatment is changed. Therefore, in the case of treatment failure, there is a window of opportunity for the virus to be transmitted: between the time the viral load increases to transmittable levels and the time when the clinician realizes it and changes treatment. The probability of transmission varies across patients, and depends on various factors  [5]. The information collected from the HIV drug resistance tests carried out in the UK since 1996 is available in the UK HIV Drug Resistance Database. The database stores protease (PR) and reverse transcriptase (RT) sequences for about 50% of infected individuals in the UK.
UK HIV data set We used the data from the UK HIV Drug Resistance Database containing samples from 1996 to 2016 to estimate transmission mechanisms for different common DRMs. Out of 88 009 initial sequences obtained from the database, the majority were of subtypes B (58 569 sequences, 66. 5%) and C (27 151 sequences, 30. 1%), we also detected 8 D, 1 F, 2 G, and 3 K (< 0. 0001%) sequences, and 2 276 potentially recombinant sequences (2. 6%, in particular 494 A,B,G and 446 B,K-recombinants (0. 5%)). We report these and other data set statistics in Table  1. Table  1. Statistics on the B and C data sets. The "with DRM(s)" statistics count samples with at least one unambiguous resistant amino acid at any DRM position. Samples that contained either non-resistant or ambiguous amino acids at all DRM positions were considered as "without DRMs".
B We detected 161 DRMs found in at least one sequence of the B data set, and 146 DRMs for C. 31. 4% of B and 27. 4% of C sequences had at least one of these DRMs present, 18. 5% of B and 16. 9 % of C sequences had only one mutation, while the others had multiple DRMs present. While the subtypes B and C are different, as well as are the locations where these subtypes are most prevalent (African countries for C versus the UK and other European countries for B), we did not detect major differences in DRM distribution in the B and C data sets. Hence, while more C than B sequences correspond to imported cases, the UK health policies must play an important role on their DRM patterns, independently of the subtype. In a recent study Blassel et al. [27]  compared DRMs in a UK and an African data sets.
They reported that the median number of DRMs in resistant sequences differed between the two datasets (3 in the African sequences versus 1 in the UK sequences). In our case, there was no difference between B and C data sets if all DRMs were considered (median number of 1 DRM for both B and C data sets in resistant sequences); if we considered only non-polymorphic DRMs, a slight difference appeared (1 for B vs 2 for C). Detailed statistics on DRM number distributions are shown in Table  A1. There was however a significant difference in the TDR distribution: more TDR could be suspected among the B samples (12% of treatment-naive sequences had non-polymorphic DRMs present, while in the C samples there were only 7% of such sequences).
Drug resistance analyses We reconstructed time-scaled phylogenetic trees for B and C data sets and performed ancestral character reconstruction for each of the selected DRMs and positions to look at their transmission patterns. Consistently with what was previously reported in HIV-1 group M studies (of the pol gene  [28]  and of the full-genome  [29] ), we estimated a faster mutation rate (1. 9 • 10 -3 [mutations per site per year]) and a more recent root date  (1965)  for subtype B than for subtype C (1. 4 • 10 -3 ; 1944). More details on B and C datasets can be found in Table  1. On the time-scaled trees we analyzed the transmission patterns of the DRMs found in at least 0. 5% of sequences: 31 DRMs (on 26 different positions) for B and 21 (on 20 different positions) for C. The major drug resistance patterns found in B and C data sets are visualized in Figures  3  and 4.
The statistics on these DRMs and their loss times are shown in Tables  2 3 4. While some of the DRMs (e. g. RT:M184V) are comparably prevalent in B and C (4. 8% of resistant cases vs 5. 4%), others are very subtype-specific. For instance, the nonpolymorphic mutation PR:L90M is present in 2. 2% of B resistant cases and only in 0. 6% of C. Another example is the mutations in position RT:106. In the B dataset the polymorphic DRM RT:V106I is present in 3. 9% of resistant cases and is 30 times more prevalent than the non-polymorphic DRM RT:V106M, which was not selected for our analysis due to its low prevalence; while for the C data set we have the opposite distribution: RT:V106M is present in 2% of resistant cases and is 16 times more prevalent than RT:V106I, which was non selected for our analyses.
RT:T215D/S area form of reversion and are are often developed in patients primarily infected with strains with RT:T215Y/F, and hence have a higher fitness  [30]. It is further confirmed by our estimation of the loss times: the loss times of RT:T215D/S are long (9. 3 and 6. 8 years versus 1. 1 and 1. 8 years for RT:T215Y/F, see Table  4 ), which may explain their prevalence in treatment-naive patients. Similarly, we estimated a rather long loss time for RT:K219N (3. 7 years). We did not have enough data to estimate the loss time of RT:V179E. While this mutation is generally considered as non-polymorphic  [31] , its natural presence in treatment-naive patients has been reported for the HIV-1 common recombinant form CRF55_01B  [32]. Using the information from the tree, we refined the mutation statistics further, classifying resistant mutations sources into TDR vs ADR, and detecting DRM loss events (see Tables  2  and 3 ).
DRM loss times We estimated the times of DRM loss for non-polymorphic DRMs in our data sets and compared them to the estimates previously reported by Castro et al. [5]. Castro et al. [5]  analyzed 313 patients from the UK Drug Resistance database, who were treatment-naive and had a DRM present in their first resistance test (performed between 1997 and 2009), mixing all the subtypes and using survival analysis. We also used survival analysis, but had a larger data set, included the information not only from the metadata, but also from the tree, and analyzed the subtypes separately. Out results and the comparison are shown in Table  4. Overall, out estimates are compatible with those by Castro et al. [5] : the CIs of the two studies intersect for all the DRMs but RT:K103N in the C dataset. The difference for RT:K103N could be explained by the fact that Castro et al.
Discussion We proposed fast maximum-likelihood ACR methods for investigation of drug resistance patterns in large sequence data sets. Their application to ∼ 40 000 subtype B and ∼ 20 000 subtype C sequences from the UK HIV Drug resistance database allowed us to investigate the trends in drug resistance patterns between 1996 and 2016 and to estimate the loss times for 25 common non-polymorphic DRMs. An important advantage of our methods is their applicability to very large data sets (dozens of thousands of sequences). Previous studies had to face an uncomfortable choice between using more complex models on filtered data  [9]  or using less accurate (e. g. parsimony) approaches on full data sets  [4]. Our approach uses a robust maximum likelihood framework, and permits the extraction of global drug resistant patterns from all the available data.