dataesr/scientific-paragraphs-categorization huggingface dataset:
We note that when we applied this method to the dataset reported here, we did not detect any statisticallysignificant association. This is not a discrepancy: we showed earlier that PTLMAPPER and QTL mapping of dispersion traits have different sensitivities and are therefore complementary. So it is not surprising that one of the two methods could detect an association that the other approach missed  11. What is the biological implication of the genetic modulation of CD23, CD55, CD63, and CD86 expression dispersion? Although the inter-individual differences in dispersion that we report here are subtle they may be important for two reasons. First, a slight probabilistic effect is negligible over small numbers, but becomes clearly substantial over millions of 'trials'. Given the lifetime of a human individual, its high number of B cells and the known activity and reactivity of these cells, the number of times surface proteins are used-the number of 'trials'-is enormous.
Reads quality was assessed with FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/, last accessed November 2015). Overlapping paired-end reads were merged using PEAR  40. We then used bioawk (https://github. com/lh3/bioawk) to extract assembled reads with an average Phred score ≥ 33 and unassembled reads were discarded. FASTQ files were converted into FASTA files using a custom bash function. For mapping, we downloaded Fasta files from http://www. ncbi. nlm. nih. gov/igblast/showGermline. cgi in November 2015 (now migrated at http://www. imgt. org/) and we used standalone IgBLAST version 1. 4. with commands C1-C4 of Supplementary Table  3  to create a database of V, D, and J human segments and to assign each read to a specific gene segment, as defined in the IMGT ontology (http://www.
1 ). Single-cell flow cytometry acquisitions. Plates were analyzed on a BD FACS-Canto II (BD Biosciences) flow cytometer equipped with a High Throughput Sampler (HTS). Acquisitions parameters were configured as follows: area scaling values of forward scatter (FSC) and side scatter (SSC) channels were optimized with photomultiplier tubes (PMT) voltages at 295 mV and 415 mV, respectively. Fluorescent PMT voltages were adapted for each antibody. DNA content was estimated from DAPI signal acquired in a Pacific Blue filter with PMT set at 280 mV. For each sample, 20,000 cells were acquired. Analysis of flow cytometry data: dataset structure. The study included three sets of experiments that were performed independently and that we treated separately. Set 1 covered the expression levels of 19 proteins (CD2, CD5, CD9, CD19, CD20, CD22, CD23, CD37, CD38, CD40, CD46, CD53, CD55, CD59, CD63, CD79b, CD80, CD86, ROR1) and one isotype control (IgKappa) in 6 LCLs of the 1000Genome project (GM18519B, GM19238E, GM18489A, GM18486A, GM18505D, GM19239C).
Acknowledgements We thank  Mirko Francesconi  and  Olivier Gandrillon  for critical reading of the manuscript,  Thierry Defrance ,  Evelyne Manet ,  Jacqueline Marvel  for discussions,  Abhishek Sarkar  and two anonymous referees for peer reviewing,  Laura Presti  and  Celia Joseph  for technical contribution,  Véronique Barateau  and  SFR Biosciences Gerland-Lyon Sud  ( UMS344/US8 ) for access to flow cytometers and technical assistance, the 1000  Genome  Project Consortium for access to cell lines and genotypes, the  Pôle Scientifique de Modélisation Numérique  for computing resources,  BioSyL Federation  and  Ecofect Labex  for inspiring scientific events,  Laurent Modolo  for guidelines and tools related to code versioning, developers of IgBLAST, PLINK, R, git, bioconductor, and  Ubuntu  for their software.
Data availability The full sequencing data of the clonality assay are available from the European Nucleotide Archive (https://www. ebi. ac. uk/ena/) under study accession number PRJEB37875. Single-cell expression data and SNPs genotypes are available in the BioStudies database (http://www. ebi. ac. uk/biostudies) under accession number S-BSST382. Summaries of expression data, including numbers of cells, as well as GMM parameter values for CD23 are provided in Supplementary Data 1. Complete results of linkage analysis are provided as Supplementary Data 2. Source data of main figures are provided as Supplementary Data 3.
A total of 36 projects were tendered for consideration to be part of the DWL programme and 13 of these were selected. The projects were selected based on their potential benefits to the applying organisation, the government as a whole, and the citizens/residents of Dubai Emirate. The commitment of the government organisations, including their mandatory presence at all programme events, was also a consideration. The selected projects were then monitored by the research team over a one-year period at the end of which the project teams were required to submit a benchmarking report showing how the project was conducted and the results achieved. The research team had direct access to the project teams and project data at all times.
Data sources. The strand-specific RNA Sequencing (ssRNA-seq) datasets used in this study were obtained from a previous publication  9. Briefly, strand-specific (ss)RNA-seq reads were mapped to the P. tricornutum genome using TopHat (version 2. 0. 8)  33 and then assembled using Cufflinks (v2. 1. 1)  34. Each ssRNA-seq sample was then merged and annotated using Cuffcompare (v2. 1. 1)  34. Expression levels of each gene were then calculated by the fragments per kilobase of exons per million fragments mapped (FPKM) using Cuffdiff (v2. 1. 1)  34. The Pearson correlation coefficients (PCC) were calculated between the FPKM of 2-3 replicates of each 5 physiological states corresponding to 2-3 independent experiments. In order to increase the depth of the reads for increased detection of noncoding RNA transcripts, and since the PCCs were high (> 0.
For quantitative RT-PCR analysis, cDNA was amplified using SYBR Premix ExTaq (Takara) with specific primers (Table  S2 ) picked from a random sample of identified NATs. Primers were designed with the Primer-Blast program (https ://www. ncbi. nlm. nih. gov/tools /prime r-blast /) defining PCR amplicon size between 70-150 bp and Phaeodactylum tricornutum CCAP 1055/1 (taxid: 556484) as the reference organism to check for primer pair specificity. Quantitative 2-step PCR conditions were set as following: 95 °C for 10 s followed by 40 cycles of 95 °C for 5 s and 60 °C for 30 s and a final cycle of 95 °C for 10 s and 60 °C for 5 s in a Bio-Rad CFX96 real-time system or a Roche LightCycler 480 in a 384 well. CDKA and HISTONE 4 mRNA levels were used for normalization  35. Identification of NATs and phosphate-regulated NAT-mRNA pairs.
All assembled intergenic transcription units were collected as lncRNA candidates. Candidates with length ≥ 200 nt, predicted ORF ≤ 100 amino acids and with at least 50 nt overlap with annotated gene models were defined as NATs  30, 36. Low abundance NATs (FPKMmax < 1) were excluded from the analysis. The length of transcripts was used to compare transcript length distribution. For ORF predictions, the spliced transcripts were used and sent to GenScan  37. To detect putative NAT-mRNA pairs (expression correlation between sense-antisense NAT and overlapping gene models), the PCCs were calculated using FPKMs of all the phosphate-responsive NATs versus mRNAs encoded by their overlapping genes. We classified phosphate-regulated NAT pairs into two groups: concordant regulation and discordant regulation.
Results Genome wide identification of NATs in Phaeodactylum tricornutum. Using high-depth strandspecific RNA sequencing (ssRNA-seq) data from at least 10 independent cDNA libraries from polyadenylated [poly(A) +] RNAs under normal or phosphate stress conditions (Accession no. GSE669970)  9  , we assembled all the reads to reconstruct the P. tricornutum transcriptome. After removing transcripts that were previously annotated as protein coding, transcripts with short length (< 200 nt), low abundance (FPKMmax < 1) and putative open reading frames (ORFs) of more than 100 amino acids, we identified a total of 4,138 lncRNAs. Amongst these were the 1,510 previously annotated lincRNAs 9 plus 2,628 newly identified transcripts belonging to the natural antisense transcript family (NATs) (Supplementary Table  S1 ), having at least 50 nt overlapped with annotated gene models (Phatr3).
tricornutum lincRNAs which have a median size of 482 nt were significantly shorter than NATs (Supplementary Fig. S1 ). Another feature of functional lncR-NAs is lower GC content than mRNAs  38  , which was noted previously in P. tricornutum lincRNAs  9  and was also found in P. tricornutum NATs (Fig. 1c ). The expression levels of NATs and mRNAs, estimated by FPKM using Cuffdiff  34  , revealed that the average expression levels of NATs was much lower than those of protein coding mRNAs (P-value < 0. 0001, paired t test) (Fig. 1d ). Interestingly, significant differences in expression levels were found between NATs and lincRNAs in P. tricornutum, with NATs having the lowest expression levels of the three transcript categories (NATs, lincRNAs, mRNAs) (Supplementary Fig. S1 ). NATs are highly abundant in the P.
tricornutum phosphate responsive transcriptome. Diatoms are frequently subjected to nutrient fluctuations in their environment, and phosphate has been shown to be amongst the major nutrients affecting primary production in the ocean, namely in coastal areas where P. tricornutum is predominantly present  39, 40. In order to assess the functional relevance of the identified NATs in P. tricornutum we sought to investigate their pattern of expression under Pi fluctuations. In order to do this we calculated the FPKM from the ssRNA-seq datasets obtained from diatom cultures under different Pi regimes (Pi starved for 4 and 8 days, Pi resupplemented for 4 days after 4 days depletion and control cultures grown for 4 and 8 days in full media)  9. Of the 2,628 NATs identified, 1,481 NATs were differentially expressed (DE) (fold change ≥ 2 or ≤ 0.
Acknowledgements The authors wish to thank  Hai-Xi Sun ,  Federico Ibarbalz  and  Ouardia Ait-Mohamed  for the bioinformatic support and suggestions, and  Damien Le Moigne  for his help with some of the RT-qPCR reactions. This work was supported by the  Seventh Research Program  of the European Union FP7/2007-2013 under  Marie Curie Grant   PIOF-GA-301466  (to MHCC) and also benefited from funding from the  DiaLincs award  from the  French Government  ( ANR 19-CE43-0011-01 ) (to MHCC). CB acknowledges funding from the  European Research Council Advanced Award Diatomite and Diatomic  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement Nos. 294823 and 835067), as well as the  French Government "Investissements d' Avenir" programs MEMO LIFE  ( ANR-10-LABX-54 ),  Université de Recherche Paris Sciences et Lettres (Université PSL)  ( ANR-1253 11-IDEX-0001-02 ), and  OCEANOMICS  ( ANR-11-BTBR-0008 ).
Quantitative Proteomics Provides Insights into Glycosylation Mechanisms Involved in High MGL Binding We next investigated the potential mechanism(s) responsible for the differential MGL binding to CRC cell lines, such as (i) variable expression of proteins carrying the MGL epitope (ii) alteration in N-/O-glycosylation pathways (e. g. , levels of glycosyltransferases or transcription factors). Therefore, we performed comparative bottom-up quantitative proteomics. For this purpose, protein extracts from HCT116, HT29, and LS174T cells (three biological replicates for each) were digested with trypsin, isotopically labeled (9-plex TMT labeling (Tandem Mass Tags)), mixed, fractionated and analyzed by LC-MS/MS. Overall, this approach resulted in the identification of 6126 proteins. For 5141 of these, quantitative data was obtained (Table  S2 ).
This was also true for most of the other major MGL-binding proteins that we previously identified. Hence, the MGL-binding of specific proteins in HT29 and HCT116 cells compared to LS174T cannot solely be explained by the higher levels of these proteins in these cells, even though some differences were observed. For example, HCT116 and HT29 cells have higher levels of the MGL binder ITGA3 in comparison to LS174T. Next, we compared the levels of proteins involved in N-and O-glycosylation in our cell lines. We limited the analysis to 245 glycosylation-related proteins as annotated in Gene Ontology (filtering for the term "protein glycosylation"). Overall, we covered 30% of these in our dataset (Figure  3 ), probably indicating that many others are present, but at very low levels. The top 20 MGL-binding proteins that we previously identified  [16]  are shown (in green, PNGase F -(white indicates not identified in that cell line)).
This was also true for most of the other major MGL-binding proteins that we previously identified. Hence, the MGL-binding of specific proteins in HT29 and HCT116 cells compared to LS174T cannot solely be explained by the higher levels of these proteins in these cells, even though some differences were observed. For example, HCT116 and HT29 cells have higher levels of the MGL binder ITGA3 in comparison to LS174T. Next, we compared the levels of proteins involved in Nand O-glycosylation in our cell lines. We limited the analysis to 245 glycosylation-related proteins as annotated in Gene Ontology (filtering for the term "protein glycosylation"). Overall, we covered 30% of these in our dataset (Figure  3 ), probably indicating that many others are present, but at very low levels. The initial step of mucin type O-glycosylation is mediated by a family of 20 polypeptide N-acetylgalactosaminyltransferases (GALNTs).
Seven of these (GALNT 1-5, 7 and 12) were found in our dataset, four of which could be reliably quantified. Although the levels of GALNT2, 4, and 7 were comparable in the three cell lines, GALNT3 was found at higher levels in HT29 compared to HCT116 and LS174T (Figure  3 ). This is in line with recent evidence of higher GALNT3 mRNA expression in BRAFV600E mutated cell lines, as HT29, which could be responsible for higher Tn expression in this cell line  [21] , and the relatively high MGL binding, even after N-glycan release (Figure  1A ). The elongation of Tn is mediated by T-synthase (C1GALT1) and its chaperone Cosmc (C1GALT1C1). These two proteins were not observed in our dataset. However, their mRNA expression was not reported to be different in BRAFV600E CRC cell lines in the study mentioned above  [21].
However, none of these mucins was identified as MGL binder in our previous experiments  [16]  and our current experiments demonstrate an inverse correlation between the expression of these proteins and the degree of MGL binding. Interestingly, when we looked more in general for proteins that were different between the high binding cell lines (HT29 and HCT116) and the low MGL-binding cell line (LS174T), we observed the cluster of mucins (Figure  S4 ) in close proximity to the transcription factor CDX-2. Together with CDX-1, this protein acts as a transcription factor that in cooperation with HNF4A and 1A, is involved in the regulation of multiple intestinal specific genes  [26] , as well as fucosyltransferases  [27]  and α2,6-sialyltransferase  [28]. CDX-1 is very similar to CDX-2, and these proteins share several tryptic peptides, but we did not observe a unique CDX-1 peptide in our dataset.
Briefly, fractions were loaded onto a homemade precolumn and eluted via a homemade analytical nano-HPLC column (for 20 min), followed by electrospray injection into the mass spectrometer. The MS was operated in data-dependent MS/MS (top-10 mode) with a normalized collision energy of 32% and recording of the MS 2 spectrum in the Orbitrap (parameters specified in  [33] ). For protein identification, raw data was converted to mzXML using Proteowizard software. Peptide and protein identification as well as the after statistical validation were performed in Trans Proteomics Pipeline version 5. 1. 0 using included software pipeline: X! Tandem Jackhammer TPP (version 2013. 06. 15. 1-LabKey, Insilicos, ISB) search engine, PeptideProphet, and ProteinProphet. The parameters were set as follows: precursor mass error of 10 ppm, fragment mass error of 0.
MS 2 analysis consisted of collision-induced dissociation (quadrupole ion trap analysis; AGC 1 × 10 4 ; normalized collision energy (NCE) 35; maximum injection time 50 ms). The isolation window for MS/MS was 0. 7 Da. Following acquisition of each MS 2 spectrum, the MultiNotch MS 3 spectrum was recorded using an isolation window for MS 3 of 2 Da. MS 3 precursors were fragmented by high energy collision-induced dissociation (HCD) and analyzed using the Orbitrap, NCE 65; AGC 1 × 10 5 ; maximum injection time 105 ms, resolution 60,000). In a post-analysis process, raw data were first converted to peak lists using Proteome Discoverer version 2. 4 (Thermo Electron, Waltham, MA, United States), and then submitted to the Uniprot Homo sapiens minimal database (20205 entries), using Mascot v. 2. 2. 04 (www.
In  [17] , a comparison between a LDPL-based approach and fingerprinting-based approaches is introduced, showing that fingerprinting-based approaches have better performance. The accuracy of the fingerprinting-based approaches is not only dependent on the learning model but also on the quality of the mapping in the first phase. For the practicality of the system, the time and cost of the site-survey phase are important factors  [18]. There are various works that aim to reduce the complexity of the site-survey phase in many aspects. In  [19] , the authors introduce a passive collection method that collects unlabeled fingerprints of the Wi-Fi signal. In the context of multiple users, Wi-Fi probes information can be used to build a survey-free fingerprinting database, as presented in  [20]. As different devices often have different Wi-Fi signal characteristics,  [21]  proposes a Bayesian approach for dealing with device heterogeneity-related issues in indoor positioning.
The drawback of Wi-Fi Direct technology is that it does not allow regular Wi-Fi scanning. Therefore, the proposed Social-Loc is more suitable for improving the Dead Reckoning tracking than the Wi-Fi fingerprinting tracking. In  [30] , a graph-based optimization process is used to combine different trajectories from multiple users. The graph is built by using the Personal Dead Reckoning data from the phone as moving constraints. It is then used to correct and extend the existing fingerprinting database.
The updated positions of users i and j are then calculated from r by using the two mentioned constraints: g(r) = r 2 δ 2 w + ( dij -2r -l ij ) 2 2δ 2 b. (15) The time interval of validity for the incoming Wi-Fi and Bluetooth data makes the error probability able to be approximated by function g. However, it introduces several drawbacks. The first one is due to the users' movement, i. e. , the distances estimated using Wi-Fi and Bluetooth data may vary in different time windows. The second drawback is the difficulty to determine the time interval length ∆t for grouping consecutive Wi-Fi and Bluetooth data. The later Temporal approach is designed to address these problems of the Non-temporal approach.
(16) For each user i, assume that K within T time indices corresponds to the Wi-Fi scan records. Denote the timestamps for Wi-Fi events as u 1 , u 2 ,. , u K , then for each u k , the corresponding Wi-Fi scan information is denoted as w i u k. The function W is used to estimate the likelihood probability W( x i u k , y i u k w i u k ). Then, the Wi-Fi component F W i of user i is established from all the K available Wi-Fi scans: F W i = K k=1 W( x i u k , y i u k w i u k ). ( 17 ) The Bluetooth evolves for a pair of users i and j. Suppose that there are L Bluetooth data that arrive at timestamps v 1 , v 2 ,. , v L , it is possible to chain the error function over the L timestamps as follows: F B ij = L l=1 B( x i v l , y i v l , x j v l , y j v l rss ij v l ). (18) By joining the three functions, the total likelihood function F could be rewritten as: F =        N i=1 F M i               N i=1 F W i                N i=1 N j=1 F B ij        .
, C D. To compute the coordinates of the centers, a K-means clustering approach is used on all the appeared positions using the available fingerprinting data. Nearby positions are grouped into separated clusters. The center position of each cluster is the average of all the points that belong to each group. Figure  5  illustrates three centers with their boundaries. The boundaries are then used to transform the positions into appropriate indices. A 2D point (x, y) can be assigned to label d if the center C d is the nearest center to (x, y). With this mapping, it is possible to transform the Wi-Fi fingerprinting model from a standard regression problem into a classification problem  [16]. Assume the training samples in the fingerprinting database have the form rss, P where rss is the RSS vector and P is the coordinates where the RSS is collected.
Data Collection To evaluate the performance of the two proposed approaches, an experiment in an office environment that includes two floors is set up. All recordings follow one common trajectory, which is composed of the corridors, office rooms and stairs located in two consecutive floors. This trajectory is defined by several checkpoints. The path is illustrated in Figure  7. The length of the path is approximately 200 m, which usually takes about 300 s of walking at an average speed. Samsung Galaxy Tab Tablet.
At the distance of 20 m, there are only 10 inquiry samples. Figure  8  illustrates the experiment results. The distance  of 0 m has the highest RSS value and the lowest standard deviation. The Different scenarios are designed in such a way that the additional Bluetooth scanning data can provide useful information for smoothing the Wi-Fi positioning output. Each of them involves groups of two to four users. The users were instructed to carry the devices and move along the designed path. While all the users walk along the same path, their relative distances are variable. In the recordings, we use a time interval of 0. 5 s for tracking the users. Each time a checkpoint is reached, the time is recorded. As the checkpoint coordinates and their reaching timestamps are known, they can later be used to interpolate the ground truth for training and evaluation.
Baseline Wi-Fi Fingerprinting Model A Wi-Fi fingerprinting model is trained as a baseline model for the later fusion step with Bluetooth data. For building the Wi-Fi fingerprinting database, Device 1 (Samsung Galaxy Note 3) and Device 3 (Asus ME) are used. The database is built by using interpolation. First, one user is asked to carry the device and walk along the test path. The test path is predefined by several checkpoints (see Figure  7 ). Every time a checkpoint is reached, a timestamp is recorded and then used to interpolate the full moving path in a later stage. The database building process therefore does not take much time. There are six training log files in total. Given the specific testing path, all the data are collected in almost 1 h. Table  3  gives an overview of the collected Wi-Fi database.
A time threshold of 5 s is used to group RSS values into one complete fingerprint scan. There are around 400 fingerprints in six training files. The number of completed Wi-Fi scans per user's walks varies between 50 and 100. Besides, the number of visible Wi-Fi access points also varies depending on the user location and the device in use. In our database, the average number of visible Wi-Fi access points per scan is almost 7, while the minimum is 3 and maximum is 12. The distributions of RSS values from two devices is plotted Figure  10. A variation between the distributions of the two devices can be noticed. The variation is more significant in the range of signal strength from -90 dBm to -55 dBm. In a multiple-device context, this variation may affect the performance of fingerprinting models.