dataesr/scientific-paragraphs-categorization huggingface dataset:
Ultrasequencing. A sample of all indexed libraries was prepared at 4 or 10 nM in 10 µl H 2 O. All libraries were subjected to quality control using a Bioanalyzer High Sensitivity DNA Assay chip (Agilent). double-stranded DNA samples were cluster-amplified and sequenced in the HiSeq 2500 platform (Illumina) at the CRG Genomics Core facility. Proteomics data analysis. Proteome Discoverer software suite (v2. 0, Thermo Fisher Scientific) and the Mascot search engine (v2. 5, Matrix Science 51 ) were used for peptide identification. Samples were searched against a M. pneumoniae database 52 with a list of common contaminants and all the corresponding decoy entries (87,059 entries). Trypsin was chosen as enzyme and a maximum of three miscleavages were allowed. Carbamidomethylation (C) was set as a fixed modification, whereas oxidation (M) and acetylation (N terminal) were used as variable modifications.
Searches were performed using a peptide tolerance of 7 ppm, a product ion tolerance of 0. 5 Da. Resulting data files were filtered for false discovery rate (FDR) < 5%. Protein Top 3 areas were calculated with unique peptides per protein. Analysis and filtering of Illumina sequencing data. Raw reads were filtered for both the common Dam-coding sequence and the varying upstream sequence according to the specific study. Since the raw reads are reverse complements, we used the following reverse complementary sequences for filtering: 1. Dam coding sequence: TGCCCACTTCAAAAAAGCGCGATTTTTCTTCAT. 2. Promoter study: GACCGGAACTTCTATGATCGAGATCGAGATCGA-GATCGCGGCCGCAAC and the TATAAT motif in the middle of the promoter sequence. 3. 5′-UTR study with strong promoter: AGTTTATATTATAACACTTTAACC-TATGGC. 4. 5′-UTR study with weak promoter: TGCAATTATTCTAACAAACCC CAAACTTATTTCAA.
pw function of the "mpmi" package in R (version 2. 7), which calculates the MI between a set of discrete variables (nucleotides) between two vectors. To eliminate random selection bias, we averaged out 10,000 random selections of 10,000 samples. Naive Bayes classification and support vector prediction. The naive Bayes classifier is a well-known statistical learning method based on Bayes' theorem. It is based on the very simple assumption that all feature variables are independent. Despite its simplicity, Bayes classifier has been successfully used to study promoter recognition  54. We used naive Bayes classifier to evaluate the independent contributions of individual bases in promoter sequences. This method requires vectors of real numbers in feature space. To convert a sequence into vectors of feature space, we coded the input data set as a binary, orthonormal set of four dimensions.
With these converted vectors, we used support vector regression (SVR) to train the model to predict continuous values (DAMRatio). Basically, the SVR solves this: Minimize 1 2 w k k 2 þ C P n i¼1 ξ i þ ξ Ã i À Á subject to y i À w; x i h iÀ b ε þ ξ i w; x i h iþ b À y i ε þ ξ Ã i ξ i ; ξ Ã i ! 0 8 > > > > > > > > < > > > > > > > > : where x i is an input data sample and y i is the log 10 -transformed DAMRatio value. The inner product plus intercept <w, x i > + b is the prediction for that sample, and ε is a free parameter that serves as a boundary threshold (we set it as 0. 2). ξ i and ξ i * in the formula are non-negative slack variables that allow a certain violation of boundary error. C is the regularization term that balances the training error. We used SVR function "sklearn" package with default parameters and radial basis function (rbf) kernel to transform input data to kernel space.
In order to check how local hairpin structures affect translation (Fig. 4h ), we used the folding region (-25 to 30) that gives maximum correlation between folding energy and DAMRatios. We assessed the secondary structures from the NUPACK output and calculated whether or not each position was located inside secondary structures. In order to build a prediction model for 5′-UTRs, we used the same SVR and coding (binary, orthonormal set of four dimensions) as for the promoters to convert sequences into numerical vectors. Furthermore, we used the predicted folding energy (ΔG) as an input with the sliding window method (up to 30 bases in coding regions). We used the regions having a correlation between ΔG and DAMRatio greater than 0 in order to remove the insignificant features, thereby reducing the problem of having too many input data features  57.
We examined whether translation efficiency prediction tools commonly used and optimized for Escherichia coli can be applicable for M. pneumoniae. We evaluated the prediction power of two predictors-the RBS Calculator  18  and the UTRDesigner  17  -and obtained an AUC of 0. 66 for both of them. We used the default parameters and 5′-UTR and coding region sequences required for these predictors (Supplementary Fig. 10  and Supplementary Data 6). Gene-specific RNA-seq data analysis. Gene-specific RNA-seq was used to determine the TSS of the Dam translation screen libraries. The raw sequencing reads were filtered for the dam-coding sequence (ATGAAGAAAAATCG). The resulting reads were aligned with the final confident 5′-UTR library sequences (94,945 5′-UTR sequences with the strong promoter and 71,478 5′-UTR sequences with the weak promoter) after trimming six barcode bases from their 5′ end (see "Gene-specific RNA-seq" and Supplementary Note 1).
For RNA abundance, we summed up TPM i,l from all the different lengths of 5′-UTRs mapped to a specific construct. To normalize the RNA abundances with DNA abundance, we used the log10-transformed DNA read counts from the "no restriction enzyme" treatment ("uncut" DNA data). Statistics. Otherwise specified, we tested the statistical significance of the correlation coefficient r, using the stats. pearsonr function of the "scipy" python package. It converts a Pearson's correlation coefficient to an appropriate t-value, and statistical significance (two-tailed P value) is then calculated using approximation of Student's t-distribution with degrees of freedom n-2. We also used Spearman rank-order correlation coefficient with stats. spearmanr function of the same package. It gives two-tailed P value for null hypothesis that two sets of data are uncorrelated.
For the t-test, we used the two-tailed P value from the stats. ttest_ind function of the same package. As for the Fisher test, we used the stats. fisher_exact function in "scipy" package. AUC of receiver operating characteristic curves was calculated using the metrics. auc function of the "sklearn" python package. To correct the multiple testing errors, we used Bonferroni-corrected P values calculated with a python script to multiply the obtained P value by the number of testing trials.
Acknowledgements We acknowledge the staff of the  Genomics core facility  for their assistance, especially to  Jochen Hecht  for fruitful discussions on the screening designs. Also, we thank the  CRG/ UPF  Proteomics Unit that is part of the  Plataforma de Recursos Biomoleculares y Bioinformáticos (Instituto de Salud Carlos III) , supported by grant  PT13/0001. The project was supported by funds from the  Fundación Marcelino Botin  and the  Spanish Ministerio de Economía y Competitividad  ( BIO2007-61762 ). This project was financed by  Instituto de Salud Carlos III  and co-financed by  Federación Española de Enfermedades Raras  under grant agreement  PI10/01702  and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program , under grant agreement Nos 634942 (MycoSynVac) and 670216 (MYCOCHASSIS).
Code availability. Computer code is available at https://github. com/lionking0000/ ELMSeq/. Data availability. The raw data of DNA sequencing of random promoter and 5′-UTR libraries, as well as of RNA-seq of the 5′-UTR libraries and the UTR pool internal control, have been submitted to the ArrayExpress short read database (http://www. ebi. ac. uk/arrayexpress) and assigned the identifiers E-MTAB-5365 and E-MTAB-5363, respectively. Proteomics data have been submitted to ProteomeXchange via the PRIDE database (http://www. ebi. ac. uk/pride) and assigned the identifier PXD005606.
The experiments were performed under ultrahigh vacuum conditions at pressures <3 × 10 -8 mbar. Pair Distribution Function (PDF) Analysis. PDF data were collected at beamline ID22 at the European Synchrotron Radiation Facility using a two-dimensional detector (PerkinElmer XRD 1611CP3) and an incident X-ray beam with λ = 0. 19057 Å (Q max = 24 Å -1 ). The data were further processed with the help of GudrunX software 8a and the computational analysis was achieved using the PDFGui software. 8b  High Resolution X-ray Powder Diffraction Data. High resolution synchrotron X-ray powder diffraction (PXRD) data were collected at Beamline I11 of Diamond Light Source using multianalyzing crystal-detectors (MACs) and monochromated radiation [λ = 0. 827136(2)Å]. The powder samples were loaded into capillary tubes of 0.
7 mm diameter and the data collection was carried out at room temperature. Terahertz Spectroscopy. The THz spectra were measured with a liquid He cooled silicon bolometer detector from IR Laboratories at the MIRIAM beamline B22 of Diamond Light Source. The spectrometer was an in vacuum Bruker Vertex 80 V equipped with a beamsplitter in the FTIR of 6 μm thick multilayer mylar with a nominal transmission range from 600 to 100 cm -1. Typically 512 scans were averaged per spectrum with an FTIR scanner velocity of 40 kHz at the reference laser wavenumber (15 799 cm -1 ). The spectral resolution was 2 cm -1. Raman Spectroscopy. Raman spectra were obtained using an Xplora PLUS Raman microscope (Horiba Company) with a 785 nm laser and a 1200 lines/mm grating. The acquisition time was 10 s and accumulated for 5 cycles.
Inelastic Neutron Scattering (INS). INS experiments were undertaken at the beamline TOSCA at the ISIS Facility. The bare and I 2 -loaded MFM-300(Sc) materials were loaded into two 11 mm diameter vanadium sample cans. The sample can was loaded into a closed cycle refrigerator (CCR) He cryostat and cooled to 11 K for data collection. Structure Determination of MFM-300(Fe) and I 2 -Loaded MFM-300(Sc). Rietveld refinements of PXRD data of the samples at various I 2 loadings were performed with Topas Academic 5 software package (Table  S1 ). The structure of MFM-300(Fe) was determined by considering the structural model of MFM-300(Al). 7a  The crystallographic sites of adsorbed iodine atoms within MFM-300(Sc) were determined by electron density peak analysis of sequential difference Fourier maps.
The appearance of the single characteristic peak at 620. 1 eV is indicative of only one type of adsorbed I 2 species in the pore and all adsorbed I 2 molecules remain neutral. In other words, there is little difference in the binding environment of the I 2 molecules in MFM-300(Sc). The   absence of morphological changes in the crystallites of MFM-300(Sc) upon adsorption of I 2 was confirmed by SEM (Figure  S10 ), and EDX data showed homogeneous distribution of I 2 throughout MFM-300(Sc) and the absence of any bulk surface adsorbed I 2 (Figure  S11 ). PXRD and PDF Analysis of I 2 -Loaded MFM-300(Sc). The stability and potential evolution in the host materials were monitored by PXRD as a function of I 2 loading. All MFM-300 samples showed similar trends by PXRD upon I 2 adsorption and the details for MFM-300(Sc) are described here.
Interestingly, similar changes of the PDF plots (i. e. , reduction of the peaks associated with atoms in the MOFs structure and appearance of new peaks at ca. 2. 7 and 4. 5 Å) have been observed in HKUST-1 upon adsorption of I 2. 6a Determination of the Binding Domains for Adsorbed I 2 in MFM-300. To determine the binding domains of adsorbed I 2 in MFM-300, high resolution synchrotron PXRD data have been collected for MFM-300(Sc) with moderate and high I 2 loadings (1. 11 and 2. 62 I 2 /formula, respectively). The I 2 -loaded samples were annealed at 70 °C for 3 days prior to the data collection to ensure full diffusion of I 2 into and within the channels and to reduce structural disorder. Fourier difference map analysis of the PXRD data for the desolvated MOF indicates no residual electron density peak within the pore, thus confirming the complete activation and structural stability of the desolvated host.
An estimated production of 9000 t/year has been documented in the US in 2004 for all BZTs  [2, 12]  and from the most recent data in the USEPA Chemical Data Reporting (CDR) database, 850 t of BTR was used in the US in 2012; no data were available for 5MeBTR and 5ClBTR  [13]. BZTs are characterized by a low vapor pressure, high water solubility, high polarity, and low octanol-water partition coefficient (log Kow: 1. 23 to 2. 17; Fig  1 )  [2] , which confers mobility in the aqueous environment. BZTs have been detected ubiquitously in raw and treated wastewaters as well as in surface and ground waters, as recently reviewed in Herrero et al. [4] , Cantwell et al. [7]  and Careghini et al. [11]  (Table  1 ). Moreover, BZTs are resistant to photochemical and biological degradation, have limited sorption tendency, and are only partially removed by conventional wastewater treatments  [2, 5, 12] ; wastewater treatment plants (WWTPs) are therefore one of the most important sources of BZTs into aquatic environments Table  1.
Ten neonates (<24-h) were exposed for 48-h without feeding to increasing concentrations of test solution made in MHRW. No solvent was used for BZT solution preparation due to proper water solubility. Endpoints of death were monitored at the end of two individual acute exposures. The LC 50 was estimated by the Spearman-Karber method (ToxStats, USEPA software). Chronic toxicity assay. Five replicate groups of 12 D. magna neonates (<24-h) were exposed to two sublethal concentrations of BTR, 5MeBTR and 5ClBTR for 21-d following OECD guidelines  [50]. The lowest dose of 2 μg/L was based on the range of environmental concentrations reported in surface waters worldwide and measured in surface water samples from the Hamilton harbor in Lake Ontario, Canada  [31]. The higher dose of 2 mg/L corresponds to 1000 × the environmental concentration and falls below the 1/10 th of the lowest measured LC 50  (28.
73 mg/L for 5ClBTR; Table  2 ). Culture medium was used as a control group. New stock solutions were used at every 48-h media renewal where water temperature, conductivity, dissolved oxygen, pH, and hardness were monitored. Spiked culture media and stock solutions were analyzed at the start of the exposure (T0-h) and after 48-h to 96-h of the 21-d chronic exposure experiment to evaluate the chemical stability of BZTs between media renewals (see S1 Protocol). The number of offspring was counted and compared between treatments using a Poisson regression with ordinal model (JMP 9. 0. 0, SAS Institute Inc. ). Body length (n = 6-16/treatment) was defined as the distance from the upper edge of the compound eye to the base of the tail spine and evaluated using a digital image analyzing system (Leica M165c Stereo microscope, Wetzlar, Germany).
A significant difference in body length across treatments was tested using ANOVA and Tukey's HSD test (JMP 9. 0. 0, SAS Institute Inc. ). Organisms used for growth measurements were not used for biochemical or transcriptomic analysis. The number of molts was determined each day and the total number of molts after 21-d of exposure was compared between experimental conditions using ANOVA and Tukey's HSD test (JMP 9. 0. 0, SAS Institute Inc. ). For each replicate, individual pools of 2-3 individuals were adequately stored at -80˚C at the end of the 21-d exposure for further transcriptomic and enzyme activity analyses. Early response assay. Five replicate groups of 12 D. magna neonates (<24-h) were exposed to 2 mg/L of BTR, 5MeBTR and 5ClBTR for 8-d following the protocol described in the chronic toxicity assay.
RNA-sequencing cDNA libraries preparation, sequencing and de novo transcriptome assembly. For each of the BZTs (BTR, 5MeBTR, 5ClBTR), 4 biological replicates were used for each treatment condition (control and 2 mg/L BZT-exposure; N = 24 samples) for genomic analyses after 21-d of exposure. cDNA libraries were generated from 250 ng of each RNA samples using the TruseqTM Stranded mRNA sample prep kit (Illumina) following manufacturer's instructions. The 24 individually tagged libraries were randomly pooled in equal amounts and sequenced on 3 lanes at the McGill University and Genome Quebec Innovation Centre (Montreal, QC, Canada). Multiplex sequencing of 100 paired-end (PE) reads was performed on the Illumina HiSeq2000 instrument. The RNA-seq reads are available through NCBI's Sequence Read Archive (SRA) under the accession number SRP076999.
Reads were trimmed from the 3' end to have a phred score of at least 30. Illumina sequencing adapters were removed from the reads, and all reads had a minimum length of 50 bp. Trimming and clipping were performed using Trimmomatic  [51]. Data were normalized by reducing the number of reads using the Trinity normalization utility inspired by the Diginorm algorithm  [52]. De novo assembly of the transcriptome was realized following the protocol described in  [53]  and using the Trinity assembler software suite  [54]. Differential gene transcription analysis and annotation. Gene abundance estimation was performed using RSEM (RNA-Seq by Expectation Maximization)  [55]  and differential gene transcription analysis was done using the DESeq Bioconductor package in R  [56]. Fold changes (FC) in abundance of transcripts in D.
magna exposed to 2 mg/L of BTR, 5MeBTR and 5ClBTR were determined relative to control individuals exposed to culture medium only. Based on the negative binomial distribution implemented in DESeq, only transcripts whose abundance was significantly (p<0. 05) 4-fold greater or lesser than in the control samples (i. e. absolute log 2 FC of 2) were treated as differentially transcribed genes for each experimental condition. Differentially transcribed genes were annotated by retrieving the closest protein homolog annotation using translated BLAST searches (blastx-http://blast. ncbi. nlm. nih. gov/Blast. cgi) restricted to the arthropod database and with an e-value cut-off of 1e-05. Sequences homologous to unknown proteins or without known homologues were further annotated by blastn searches in the interactive D.
Quantitative real-time PCR (qRT-PCR) Quantitative RT-PCR analyses were conducted to validate RNA-sequencing data for selected transcripts and to study BZTs mode of action for a suite of candidate genes involved in molting and other endocrine-mediated processes. Total RNA (1 μg) was reverse transcribed using the QuantiTect 1 Reverse transcription kit (QIAGEN, Toronto, On, Canada) following manufacturer's instructions. qRT-PCR analyses were then carried out on a CFX96 Touch™ real-time PCR detection system using iQ SYBR green Supermix (Bio-Rad) with a final concentration of 300 nM for each primer in a total reaction volume of 13 μL. The PCR conditions were as follows: 95˚C for 2 min, followed by 40 cycles of 95˚C for 15 s, 60˚C for 15 s, and 68˚C for 15 s. Primers were either retrieved from published sequences when available or designed using Primer-BLAST (http://www.
ncbi. nlm. nih. gov/tools/primer-blast/). Name and symbol of genes as well as primer-specific efficiency and sequence are listed in S1 Table. Each reaction was run in technical duplicate and the mean of five independent biological replicates was calculated. All results were normalized using mRNA level of three reference genes (tbp, ub and gapdh) and relative transcription values were calculated in R using an in-house qPCR analysis package based on the qBase relative quantification software  [59]  and developed by the Sophia Agrobiotech Institute (INRA Sophia-Antipolis, France) as detailed in Hilliou and Tran  [60].
RNA-seq de novo assembly Transcriptome sequencing was performed using an Illumina HiSeq2000 sequencer for 24 libraries from D. magna exposed to 0 or 2 mg/L of BTR, 5MeBTR and 5ClBTR. The transcriptome assembly produced a total of 629,397,113 clean paired reads after quality filtering and removing of low quality reads (S3 Table ). Using the Trinity assembly program, a total of 41,538 putative transcripts clustered into 14,666 components was generated, with a mean length of 2,385 bp and 50% of the assembly were contained in transcripts larger than 3,200 bp (N50 = 3,263) (S4 Table ). These numbers are consistent with a recent study in D. pulex, suggesting the robustness of the present transcriptome data  [83].
This work was supported by the  Natural Sciences and Engineering Research Council's Visiting Fellowship in Canadian Government Laboratories program  to MG, http://www. nserccrsng. gc. ca/Students-Etudiants/ PD-NP/Laboratories-Laboratoires/index_eng. asp, and was funded by grants awarded to MH from  Environment and Climate Change Canada's Chemical Management Plan (CMP) , http://www. chemicalsubstanceschimiques. gc. ca/plan/indexeng. php, and the  Strategic Technology Application of Genomics in the Environment (STAGE)  program http://www. ec. gc. ca/scitech/default. asp?lang= En&n=BA7C049C-1. Funders approved the study design.
Strain identification After a failed identification of all five strains using matrixassisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) on a Microflex spectrometer (Bruker Daltonics, Bremen, Germany)  [3, 4] , the 16S rRNA gene was sequenced using fD1 and rP2 primers  [5]  as well as the rpoB gene for strain mt20 T  [6]. Kim et al. suggested a 98. 65% and a 95% similarity level threshold to define a new species and a new genus, respectively, without performing DNA-DNA hybridization  [7]  using the 16S rRNA gene while a 97. 7% threshold was set for identification using the rpoB gene  [6]. The 16S rRNA gene of strain Mt13 T (Accession number LN850733) showed a 94. 23% similarity level with Hathewaya histolytica strain ATCC 19401 T (Accession number AB566416)  [8].
This classifies strain Mt13 T as a putative new genus within the family Clostridiaceae (Fig. 1 ), for which we suggest the name Khelaifiella (Khe. lai. fi. el'la N. L. fem. n. composed of Khelaifia, in honour of the microbiologist Saber Khelaifia, and bacterium, meaning rod) with the type species being 'Khelaifiella massiliensis' (mas. si. li. en'sis; L. fem. adj. massiliensis for Massilia, the Roman name of Marseille, where strain Mt13 T was first isolated). Strain Mt13 T is the type strain of the species 'Khelaifiella massiliensis'. The 16S rRNA gene sequences of strain Mt14 T (Accession number LN850735) revealed a 92. 18% similarity level with Cellulosilyticum lentocellum strain RHM5 T (Accession number X71851)  [9] , confirming strain Mt14 T as a new genus within the family Lachnospiraceae (Fig.
2 ). The name Niameybacter (Nia. mey. bac'ter for Niamey, the capital of Niger where the stool sample was collected and bacter for bacterium) is proposed for this new genus as well the type species 'Niameybacter massiliensis' (mas. si. li. en'sis; L. masc. adj. massiliensis for Massilia, the Roman name of Marseille, where strain Mt14 T was first isolated). Strain Mt14 T is the type strain of the species 'Niameybacter massiliensis'. The 16S rRNA sequence of strain MT5 T (Accession number LN906631) presented a 98. 18% similarity level with Brachybacterium saurashtrense strain JG 06 T (Accession number EU937750)  [10]. Strain MT5 T was therefore classified as a new species within the genus Brachybacterium (Fig. 3 ) for which we suggest the name 'Brachybacterium massiliense' (mas. si. li. en'se; L.