dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , several low-energy collisions during a longer time than nonresonant), which enables to produce fragmentation trees beyond MS 2. Generally, these fragmentation trees cover the full range of possible fragmentation pathways, and are therefore specific identifiers for the corresponding molecules, which can be stored in databases (e. g. , mzCloud  [47] ). With ion trap MS 2 , only parts of the entire range of possible fragmentation reactions are covered, even when applying higher collision energies  [58, 59]. Such spectra match the lower energy part of spectral series acquired on tandem-in-space instruments (Orbitrap-HCD and QqTOF-CID) well. There is, however, limited overlap with spectra acquired at higher collision energies. Another problem of ion trap fragmentation is related to the "low mass cut-off ", or the so-called "1/3 rule"  [59].
Another limitation of tandem mass spectral databases is highlighted in Fig. 8. It is well recognized that stereoisomers can hardly be distinguished from each other by tandem mass spectral fragmentation  [13, 60]. But even the differentiation of constitutional isomers can be challenging. Fragmentation of such compounds may lead to identical products. In the worst-case scenario, tandem mass spectra will be identical. One such example of a pair of constitutional isomers comprises phentermine and methamphetamine. At higher collision energy levels, the fragmentation mass spectra of these two compounds show two identically intense fragment ions (Fig. 8 ), leading to ambiguous library search results.
QA recommendations for acquisition and processing of tandem mass spectral reference data The results of the interlaboratory study, as well as the available experience and knowhow in building tandem mass spectral libraries, formed the basis for drafting and adopting the following recommendations: Fig. 7  The influence of the "1/3 rule" for ion trap spectra, exemplified with desipramine using a spectrum acquired on a LIT-Orbitrap instrument with CID and a QqTOF spectrum taken from the WRTMD. The black dot indicates the precursor mass isolated for MS/MS fragmentation (hollow dot). Due to the "low mass cut-off" observed on LIT-Orbitrap instruments, an abundant fragment ion is missing in the corresponding fragment ion mass spectrum Fig. 8  An example of near identical tandem mass spectra: phentermine-and methamphetamine spectra acquired on a QqTOF instrument.
Conclusions Acquisition of tandem mass spectral data requires the physical availability of reference substances and sufficient experimental capacities for acquiring fragment ion mass spectra. Even by joining forces, for instance within HBM4EU and NORMAN initiatives, the acquisition of reference data for ten thousands of compounds is a multi-annual project requiring significant resources. With this document, an outline for harmonized acquisition of suitable spectra for the expansion of public resources is proposed, which balances the consideration of individual instruments and methods at individual laboratories and the comparability of the resulting data. As a result, it is hoped that the next few years will see an increase in the number of environmentally relevant spectra in (open) mass spectral libraries.
Funding The authors acknowledge financial support by the HBM4EU project. HBM4EU has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 733032. This text has been modified and extended for publication from Deliverable AD16. 4 as part of WP16 within the HBM4EU project. ELS acknowledges funding from the Luxembourg National Research Fund (FNR) for project A18/BM/12341006. MS acknowledges financial support by the University of Innsbruck.
Methods Parasite isolates and sequencing L. (V. ) peruviana isolate PAB-4377 was kindly provided by the U. S. Naval Medical Research Unit No. 6 (NAMRU-6) and the LEM1537 (MHOM/PE/84/LC39) isolate was obtained from the Montepellier reference center. PAB-4377 was confirmed as L. (V. ) peruviana by Multilocus Enzyme Electrophoresis (MLEE) and sequencing of the Manose Phosphate Isomerase and 6-phosphogluconate dehydrogenase genes. LEM1537 is a L. (V. ) peruviana reference strain (MHOM/PE/84/LC39) and has been widely characterized by MLEE. Libraries consisting of 350 bp fragments were obtained and 100 bp paired end reads were generated at the Genome Technology Access Center (GTAC) at Washington University in St. Louis by Illumina HiSeq 2000. The version 6 of the L. (V. ) braziliensis M2904 genome was obtained from the Tritryp database (http://tritrypdb.
Genome assembly and annotation L. (V. ) peruviana reads were filtered by quality using Trimmomatic  [27]  with a minimum base quality cutoff of 30, leading and trailing base qualities of 28, five bases sliding window with minimum per base average quality of 20 and a minimum read length of 70 bp. A combined De novo and reference based assembly approach (Baptista et al. , in preparation) was used to generate a draft assembly for each sample. Briefly, De Novo assemblies were generated using the Velvet optimizer perl script under Velvet version 1. 2. 10  [28]. Draft assemblies were extended by iterative mapping using IMAGE  [29]  and corrected using iCORN2  [30]. For reference-based assembly, reads from each sample were mapped against the L. (V. ) braziliensis M2904 genome using Bowtie2  [31].
SNP and pseudogene analysis To detect SNPs between L. (V. ) peruviana and L. (V. ) braziliensis and determine their potential effects on coding sequences, L. (V. ) peruviana reads were mapped onto the L. (V. ) braziliensis M2904 reference genome using Bowtie2 and analyzed using the recommended parameters of GATK  [36]. Briefly, mapped bam files were filtered for redundant reads and local realignment was performed around indels in order to remove potential mapping artifacts. SNPs were called using the haplotype caller module and raw variants were filtered using GATK's variant quality score recalibration selecting sites with a minimum raw coverage of 10, Root Mean Square mapping quality lower than 40, quality by depth greater than 2 and haplotype score greater than 13. The same method was employed to call variants between both L.
(V. ) peruviana isolates. To analyze the effects of SNPs in coding regions of the L. (V. ) peruviana genome, we filtered variant calls of PAB-4377 and LEM1537 selecting only SNPs shared by both isolates to limit the potential impact of within-species SNP variability and minimize incorrect SNP calling. The combined variant called was used as input for SnpEff  [37]  to annotate and predict the effects of variants of genes. To find potential targets sequences to accurately discriminate L. (V. ) peruviana from L. (V. ) braziliensis we employed a custom Perl script to screen the genes with variant calls. These genes were analyzed using a sliding window of 1000 nucleotides to report the region with the highest SNP density and the number of SNP that it presented. Genes with significant SNP calls were detected using the ROUT test under Graph Pad Prism V5  [38]  We downloaded L.
(V. ) braziliensis pseudogenes from the Tritryp database and compared them against L. (V. ) peruviana to detect potential pseudogenes that remained functional in L. (V. ) peruviana. Briefly, L. (V. ) peruviana amino acid fasta sequences were generated using SAMtools Mpileup and translated into amino acids for sequence alignment against L. (V. ) braziliensis pseudogenes in ClustalΩ  [39].
Acknowledgements We thank  Nick Dickens  for his help with FPKM copy number calculations and the  Genome Technology Access Center in the department of Genetics at Washington University School of Medicine  for their support with next-generation sequencing. The Center is partially supported by  NCI Cancer Center Support  Grant # P30 CA91842  to the  Siteman Cancer Center  and by  ICTS/CTSA  Grant # UL1 TR000448  from the  National Center for Research Resources (NCRR). Daniella C. Bartholomeu  research was supported by  Fundação de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG) ,  Instituto Nacional de Ciência e Tecnologia de Vacinas (INCTV) - Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). DCB is a CNPq research fellow.
HOV, JLRC, GFRL received scholarships from  CAPES  and RPB received a scholarship from  CNPq. Stephen Beverley  and  Deborah Dobson  research was supported by  NIH  grants  R01-AI29646  and  R56-AI099364. Francine Pratlong  and  Patrick Bastien  research was funded by the  Institut de Veille Sanitaire, France.
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statistical analysis All statistical analyses were performed by using the GraphPad Prism 4 software and the R software (v. 3. 0. 2). Data were expressed as mean ± standard deviation (SD). Differences in survival curves were compared by using the Mantel-Haenszel test. For other data, the differences in the mean values between groups were analyzed with the two-tailed Student t test. Differences were considered significant when p < 0. 05. Pearson correlation coefficient was used to compare gene expression profiles in different organs. All supplementary materials are available online at www. molmed. org.
aCkNoWleDGmeNTs This work was supported by grants from the Bulgarian-Swiss Research Programme (BSRP IZEBZO_142967), the Bulgarian National Science Fund (DFNI B02/29), the Pasteur Institute (ACIP A07-2012), the NATO Science for Peace Program (SfP 982158), and the German Federal Ministry of Education and Research (BMBF; grant 01EO1002) and grant 03Z2J521 (Meta-ZIK), both to the Centre for Sepsis Control and Care. We thank D Himsel for technical assistance.
The analysis was performed at the level of transcript clusters. We also used the MetaCore Analytical Suite to perform the transcription factor and the pathway and toxicity process analysis based on network objects recognized by MetaCore. Hybridisation cocktails were applied to Affymetrix Rat Gene 2. 0 microarrays, and processed in accordance with the manufacturer's recommended procedure using the GCS3000 microarray system (Affymetrix). Array data was exported as cell intensity (CEL) files for further analysis.
Microarray data analysis CEL files were normalised together in the Expression Console software package (Affymetrix), using the Robust Multi-array Average (RMA) sketch algorithm (gene-level). Data was quality control assessed by using standard metrics and guidelines for the Affymetrix microarray system. Normalised data files (CHP files) were imported into Omics Explorer 3. 0 (Qlucore) for further quality control and statistical analysis. It was decided to include all genes in the tests as there is no simple measure of presence/ absence with RMA normalized data from Rat Gene 2. 0 ST arrays. Although it risks a higher false discovery rate, all data collected were subjected to the statistical tests so as not to inadvertently filter out important genes based on an arbitrary detection threshold.
Data used for the functional analysis were selected at the cut off values of p < 0. 01 with FC >1. 1 to use large gene lists as recommended  [19]. The q-values, an estimate of the false discovery rate, were the p-values corrected using the Benjamini-Hochberg procedure and were below 8 %. Gene ontology, pathways, gene networks, transcription factor binding and disease ontology were analyzed using the Thomson Reuters MetaCore Analytical Suite recognizing network objects (proteins, protein complexes or groups, peptides, RNA species, compounds among others) and/or the NIH Database for Annotation, Visualization and Integrated Discovery Bioinformatics Resources 6. 7, recognizing individual coding genes, using recommended analytical parameters  [19]. These microarray data have been submitted to Gene Omnibus and are accessible through accession number GSE66060.
Microarray data validation by real-time qPCR In order to validate the list of differentially expressed/ regulated genes revealed by microarray analysis, we randomly selected a subset of 18 genes for analysis by realtime quantitative PCR (RT-qPCR) using the original RNA samples as starting material. Total RNA (2 μg) was converted to cDNA with the High Capacity RNA-to-cDNA kit (Applied Biosystems, Life Technologies Ltd, Paisley, UK), and gene expression measurements were assessed by RT-qPCR using TaqMan Gene Expression assays and TaqMan Universal PCR Master Mix (Applied Biosystems). Housekeeping genes used as endogenous controls were of two types: 1) three standard historically used housekeeping genes (Gapdh, Hprt1, Actb) (from the GeNorm list) as being among the most invariant transcript clusters from microarray datasets; that is, genes with the lowest standard deviation across all relevant samples, and with high or comparable expression level, and 2) Pes1, a gene which is not sex-and/or multihormone-regulated in rat studies  [20].
Transcriptome patterns segregate liver and kidney samples based on Roundup treatment In an attempt to confirm the anatomopathological effects in a more quantitative manner and to gain insight into the gene expression profiles that are associated with the signs of pathology observed in the liver and kidneys, we conducted a full transcriptome microarray analysis of these organs. We began by undertaking an unsupervised Principal Component Analysis (PCA) of the dataset, which reduces a high-dimensional expression profile to single variables (components) retaining most of the variation (Fig. 2a ). As control and Roundup-treated animals were sacrificed at various ages, we initially conducted a PCA analysis where individual animals were sorted by age (oldest vs youngest), to ascertain if differences in transcriptome profiles correlate with this parameter (Additional file 2).
Data used in functional analysis were selected at the cut off values of p < 0. 01 and q < 0. 08 with FC >1. 1 for use with large gene lists as previously recommended  [19]. A Venn diagram comparing liver and kidney transcript cluster expression profiles at an FC >1. 1 (Fig. 3a ) indicates that even if most of the disturbances were tissue specific, 1319 transcript clusters were commonly disturbed between the two organs. A comparison at a frequently selected cut off threshold of FC > 2 again results with most changes in gene expression being specific to either liver or kidney but with a total of 20 genes being commonly disturbed in both organs (Fig. 3b ). The FC, p-value and q-value for all genes whose expression is commonly disturbed in both liver and kidney are shown in Additional file 5. Out of these, 18 genes were randomly selected for validation of the microarray results by RT-qPCR (Additional file 6).
The overall pattern of the RT-qPCR results confirmed the microarray data with 86 % of the genes found to be similarly up-or down-regulated by both methods. Gene function alterations involved in mitochondrial respiration, spliceosome function and chromatin structure modification is associated with Roundup treatment We next conducted an ontology analysis of the 1319 transcript clusters commonly deregulated in liver and kidney using the DAVID gene functional classification tool to reveal the most affected gene categories. As a result 868 genes were recognised. The 8 clusters of functional disturbances having enrichment scores over 2 are presented in Table  2. All have significant p-values and Benjamini corrected p value (q-values). A total of 3 major affected gene networks were identified. First, two clusters were related to spliceosome function.
Alterations in transcriptome profile suggest liver and kidney anatomorphopathogy Scoring maps for pathways and toxicity processes (Fig. 5 ) indicates that multiple cellular functions could be involved. Out of the 4224 liver and 4447 kidney transcript clusters found to be altered, 2636 and 2933 network objects were respectively recognized by GeneGO Metacore. Mapped pathways related to inflammatory responses,   2 ) derived from the alteration in gene expression patterns commonly disturbed in liver and kidneys from Roundup treated female rats (Figs. 2  and 3 ) with respect relative to mRNA splicing via spliceosome (GO:0000398, in blue), histone modification (GO:0016570, in yellow) and cellular respiration and TCA cycle (GO:0045333 and GO:0006099, in pink), were grouped on organ-specific heatmaps using hierarchical clustering of samples (C, control; R, Roundup) and variables (gene symbols).
Instrumentation The lidar instrument used during the campaign is similar to the one previously described by  Royer et al. (2011). It operates at 355 nm with 16 mJ pulse energy, and has three acquisition channels for elastic, perpendicularly polarized and N 2 -Raman backscatters. The signals were recorded with an initial resolution of 25 s (500 laser shots) and 0. 75 m, both in analog and photon-counting mode. During daytime, when the photodetectors are saturated by the sky background light, only the analog mode is used, whereas during nighttime the analog and photon-counting signals are merged to optimize both dynamic range and signal-to-noise ratio. After correction for the platform inclination (measured using a Xsens MTi-G GPS/inclinometer attached to the optical head) and after cloud screening, data are averaged over 5 or 30 min and 7.
5 m in altitude. The 30 min averaging period was chosen because it makes the signal from the N 2 -Raman channel exploitable up to 700 m, even during daytime, without mixing data recorded in too distant locations (∼ 50 km given the speed limits). The overlap functions of the lidar channels were assessed before the trip using horizontal profiles, when the lower atmosphere could be considered as homogeneous along the line of sight. Once attached to the van, it was not possible to tilt the lidar to retrieve the overlap function from a horizontal profile. It was instead checked using fixed observations below fair weather afternoon cumulus clouds (i. e. in a supposedly homogeneous boundary layer). The overlap function retrieved at different points of the journey (Riga, Irkutsk, Istomino) is remarkably similar, which confirms the optical stability and validates the well-mixed boundary layer hypothesis.
Systematic data processing To analyse the variability of aerosols along the journey, we wish to obtain a set of aerosol optical thicknesses (AOT), LR (extinction-to-backscatter ratio) and PDR values using a systematic processing performed on the 30 min average profiles from the whole campaign (day-and nighttime). However, as the range of the N 2 -Raman channel is limited by the sky background light during daytime, this processing can only rely on a partial AOT between 300 m (complete overlap) and 700 m a. g. l. (range limit of the N 2 -Raman channel at noon). The partial AOT from the Raman channel serves to constrain the lidar ratio used in a standard Klett inversion E. Dieudonné et al. : Lidar profiling of aerosol optical properties  (Klett, 1985) , which is achieved through a convergent process described in Appendix A.
3. 1); for the others, it is necessary to choose an arbitrary LR value in order to compute the extinction profile, total AOT, and subsequently the PDR. In order to avoid introducing discontinuities in the AOT and PDR data sets between profiles that converged or not, the same LR value is used to invert all profiles through a standard Klett procedure. The chosen LR (58 sr) is the mean value of the LR distribution obtained from the valid profiles (see Sect. 3. 1).
Case study data processing The case studies presented in Sect. 4 rely on fixed measurements, with longer time averaging. Nighttime observations, added to this longer averaging, make the N 2 -Raman channel exploitable up to a purely molecular layer (above 6 km a. g. l). In this case, a complete lidar ratio profile can be retrieved using either the standard Raman inversion method described in  Ansmann et al. (1990)  or a constrained Klett method similar to the one used for the systematic processing, but applied on a sliding window browsing the full altitude range. More details about both inversion processes are given in Appendix A. After the LR profile has been retrieved from the average profile over the whole period, it is used to process more frequent 5 min average profiles and invert the time-dependent extinction profile and AOT.
Distribution of lidar ratios in the boundary layer Data recorded during the whole campaign produced 547 cloudless 30 min average profiles. Because of sometimes insufficient aerosol load or due to the presence of elevated aerosol layers, only 106 profiles (∼ 19 %) can be considered as "fully convergent", i. e. they give the best quality LR values (see Sect. 2. 3. 1). Among those 106 convergent profiles, 30 (∼ 28 %) are located in Istomino village as several days of observations were recorded there between 29 June and 7 July 2013. In order not to give the Baikal region an excessive weight, the LR distribution is computed on the 76 profiles recorded elsewhere than Istomino village (Fig. 2 ). LR values during the campaign range from 32 to 106 sr, with an average and standard deviation of 63 ± 17 sr; the distribution is slightly skewed towards high values (median LR is 61 sr and first/last quartiles are 51/74 sr).
Temporal representativeness of the observations The lidar-derived AOT values presented in Sect. 3. 1 were compared with the AOT measured by MODIS Terra. A multiyear average was computed from the monthly 1 • × 1 • gridded product (MOD08_M3) using the months of June from years 2000 to 2013  (years 2001, 2003 and 2012  were removed because, due to intense fire events, those years are too far from the conditions encountered during the campaign). MODIS data from the grid pixel where the lidar was located were extracted without any spatial interpolation. For the four AERONET stations located close to the transect (Palaiseau, Mainz, Moscow and Irkutsk), monthly averages were computed from the daily averages including at least four observations, then the multi-year June average was computed from years 2006 to 2013 (the time period is shorter than for MODIS because Mainz and Irkutsk records started in 2006).
Acknowledgements. The authors would like to thank  Frederik Paulsen ,  Honorary Consul  for the  Russian Federation in the canton of Vaud, Switzerland , both for his financial support and for obtaining the permission to operate in Russia. The authors are also very grateful to  Alexander Ayurzhanaev  from the  Siberian Branch of the Russian Academy of Sciences, Laboratory of Physics of Atmospheric Processes ,  Ulan-Ude , for his vital help with the logistics of the journey while he was aboard the van. We also thank  Yoann Chazette  for his help during the trip. Finally, the authors thank  Cyril Moulin , head of the  Laboratoire des Sciences du Climat et de l'Environnement , for his support and assistance in the administrative part of the project. Edited by:  A. Petzold.