dataesr/scientific-paragraphs-categorization huggingface dataset:
After antibody staining, samples were washed and sorted with a BD FACS Aria (BD Biosciences). Unstained cells as well as FMO stainings were used as negative controls. Single-stained controls were used for compensation. Data were analyzed using FlowJo software (FlowJo, Ashland, OR, USA). Sorted cells were centrifuged at 300 × g for 5 min for further processing.
Microarray expression Profiling Preparation of biotin-labeled cRNA samples from 500 ng total RNA, and hybridization on Illumina Mouse Sentrix-6 BeadChip arrays (Illumina, San Diego, CA, USA) were performed according to the manufacturer's instructions. Scanning was performed on a Beadstation array scanner (Illumina). The raw microarray data are available in the ArrayExpress database  1  under accession number E-MTAB-3693. Beads with a value >20 (bead level) were selected, and outliers with values >2. 5 median absolute deviation (MAD) were removed. All remaining data points were used for the calculation of the mean average signal for a given probe. Intensity values were normalized by quantile normalization using Chipster Software (CSC, Espoo, Finland). Probe annotation was according to MouseWG-6_V2_0_R1_11278593_A.
Principal component analysis was performed with TM4 MeV software on log2-transformed values using median centering  (14). Twogroup significance tests as indicated were performed in Chipster without pre-filtering of probes on log2-transformed values (empirical Bayes with Bonferroni-Holm p-value adjustment). Gene set enrichment analysis [GSEA  (15) ] was performed on the complete probe dataset based on the MouseWG-6_V1_1_ R4_11234304_A annotation. The following gene set collections from the Molecular Signatures Database (MSigDB)  2  were used: The c2. cp. kegg. v3. 0. symbols. gmt gene set collection, derived from http://www. genome. jp/kegg/pathway. html, and the c3. tft. v3. 0. symbols. gmt gene set collection, in with gene sets contain genes that share a transcription factor binding site defined in the TRANSFAC database  3.
One hundred nanogram of each labeled DNA probe together with 7 μg Cot-1 mouse DNA and 5 μg sonicated salmon sperm DNA were used per coverslip. Cells were examined by Nikon Ti-E/B epifluorescence microscope, equipped with a HG Intensilight ® illumination source, a CCD Orca R2 camera (Hamamatsu ® ) and imaged through an NIKON oil-immersion objective 60× (Plan APO 1. 4). The devices were controlled by NIS-elements ® 3. 2. Three-dimensional images were captured at 200 nm intervals in the z-axis, using an objective fitted with a piezo nano Z100 Ti. Analysis of nuclear position of the detected fluorescent signals was performed using NEMO software  (17). The radial localizations of loci were then calculated in Microsoft Excel. Three shells of equal area eroded from the center (shell 1) to the periphery (shell 3) of the nucleus were used.
The resulting PCR products were separated on a 1. 2% agarose gel and extracted from the gel with a QIAquick Gel Extraction kit (Qiagen). The purified PCR products were subjected to library preparation using a GS Amplicon Library Preparation Kit (Roche), emulsion PCR and pyrosequencing with GS Junior Titanium kits strictly following the manufacturer's instructions (Roche). The pyrosequencing was performed with a GS Junior 454 sequencer (Roche). The data obtained were processed as described previously  [12]. Briefly, fasta and qual files generated as an output of the pyrosequencing were uploaded into Qiime software  [15]. Trimming criteria consisted of none or one mismatch in primer sequences and no mismatch in MID sequences. Sequences with a quality score higher than 20 were shortened to the same length of 350 bp and classified with RDP Classifier and RDP Seqmatch with an OTU discrimination level set to 97%.
Statistics To achieve normal distribution and to stabilize the variance, real-time PCR data from the abundance of antibiotic resistance genes were transformed using logarithmic transformation and Box-Cox transformation, respectively. In the next step, data were analyzed by ANOVA followed by Tukey HSD post-hoc test (Statistica v. 12. 0, module ANOVA). Transformed data were also analyzed by principal component analysis (PCA; Statistica v. 12. 0, module Multivariate exploratory techniques).
Data and methods In this work, MERRA results, available data from NASA's Global Modeling and Assimilation Office  (Global Modeling and Assimilation Office 2011 ), are examined from year 2003 to 2009 (7 yr). This reanalysis has a horizontal resolution of 1. 258 3 1. 258 and 42 vertical levels, spanning up to 0. 1 hPa. The MERRA system is based on the GEOS-5 atmospheric general circulation model, which includes both an orographic and a nonorographic gravity wave drag parameterization  (Rienecker et al. 2011). The total forcing from the gravity wave drag parameterizations is available in the MERRA archive. The MERRA assimilation system employs a three-dimensional variational data assimilation (3DVAR) algorithm and incorporates analysis innovations through the incremental analysis update (IAU) approach  (Bloom et al.
Acknowledgments. This work was partially funded by an  Agencia Nacional de Promoción Científica y Tecnológica  Grant  PICT 2011-2452  and a  CONICET  Grant  PIP 112-20120100414CO. This work was performed using computational facilities at  CECONEA (UNNE). We thank the Editor,  Rolando Garcia ,  Naftali Cohen , and an anonymous reviewer for insightful comments that helped to improve the manuscript. This work was, in part, motivated by an  ISSI  gravity wave team workshop.
DNA extraction, PCR and sequence analysis Genomic DNA was extracted from 75 frozen canine MCT tissue samples using QIAamp DNA Mini Kit (Qiagen France, Paris, France), according to manufacturer's protocol. In the 23% of the cohort samples, different c-KIT mutations were previously identified in exons 8, 9 and 11  [8]. Among them, internal tandem duplications of exon 11 represented 36% of total mutations registered. In the present study, PCR amplifications of all TET2 coding exons and the hot-spot regions of IDH1 (exon 2), IDH2 (exon 1), SF3B1 (exons 13, 14, 15, 16), SRSF2 (exon 1), NRAS (exons 1, 2) and KRAS (exon 1) were executed. Primer oligonucleotide sequences were designed using the AmplifX software (http://crn2m. univ-mrs. fr/AmplifX) and CanFam3. 1 genome sequences available http://www.
2 pmol of oligo F or R, 1μL of Big Dye Terminator V1. 1, 1X reaction buffer and water (in a final volume of 10 μL). The thermal protocol consisted in: an initial denaturation (1 min at 96°C) followed by 25 cycles of 10 sec at 96°C, 5 sec at 50°C and 2 min at 60°C. Blood samples of 39 healthy dogs were collected and DNA extraction was performed as previously reported  [26]. Around 30 ng of genomic DNA were used in PCR reaction to amplify TET2 exon 11 and the products obtained were subsequently sequenced as described above. Sequences were analyzed and aligned by using the SeqScape software v3. 0 (Life Technologies, Carlsbad, USA) and identity percentage between dog protein sequences and mouse, rat, dog and cat were assessed through BLAST (https://blast. ncbi. nlm. nih. gov/Blast).
The amino acids residues considered hot spot sites for mutations in humans as R132 for IDH1, R140 and R170 for IDH2, G12 and Q61 for NRAS and G12 for KRAS were recognized in dog. Since in humans TET2 mutations occur almost all over the sequence, all the corresponding canine coding exons were amplified; the two sequences shared the 84% of amino acid identity (Table  2 ). On the other hand, the canine SRSF2 partial sequence obtained in this study differed, either in exons and introns, from NCBI release. Anyway, the analog of human hot spot site (P95) was conserved also in dog. The updated partial sequence was submitted to NCBI with the following accession number: KT072629.
These alterations, with the relative allelic frequencies and population distribution, are collected in Table  3. Deepening in sequence analysis, other genetic variations were detected, in two specific regions of the gene. The protein database Uniprot (http://www. uniprot. org/) recognized them as polyglutamine rich-regions because they are rich in glutamine residues: one is located in exon 3 and another one in exon 11. The alignment of canine sequence with the same human, cat, mouse and rat sequence portion showed that poly-glutamines residues were quite conserved among species and dog possessed the highest number of glutamine repetitions (Fig  1 ). In details, in canine exon 3, 65 out of 75 MCTs (86,66%) showed the deletion of glutamine 753 (c. 2250_2252del; p. Gln753del). In exon 11, different rearrangements in the number of glutamine repetitions were detected among samples.
Salmonella enterica Serotype Enteritidis in French Polynesia, South Pacific, 2008-2013 profile. MLVA typing (  7 ) on a subset of 60 strains showed main diversity in the SENTR4 and SENTR5 loci in isolates with the JEGX01. 0004 PFGE profile. MLVA types 2-10-8-5-2 and 2-10-8-6-2 dominated in strains isolated from humans and laying hens. The CRISPR1 and CRIS-PR2 polymorphisms in 83 selected strains were studied by PCR amplification and sequencing as described elsewhere  (9). The spacer content was determined by submitting the DNA sequences to the Institut Pasteur CRISPR database for Salmonella (http://www. pasteur. fr/recherche/genopole/ PF8/crispr/CRISPRDB). The 83 strains from French Polynesia had the same CRISPR1 allele (A14) but 2 different CRISPR2 alleles (B20 or B21), differing by the presence of a single spacer, EntB9 (online Technical Appendix 1, http://wwwnc.
cdc. gov/EID/ article/21/6/14-1103-Techapp1. xlsx; online Technical Appendix 2, http://wwwnc. cdc. gov/EID/article/21/6/14-1103-Techapp2. xlsx). Both CRISPR2 alleles contained a triplication of the EntB8 spacer, which had not been observed in our database (194 Salmonella Enteritidis strains from France and Europe during 1920-2014)  (9). However, this particular A14-B21 CRISPR profile is displayed by 37 Salmonella Enteritidis genomes deposited in the GenBank public database and originating in poultry or humans from North America  (8, 11, 12)  (online Technical Appendix 3, http://wwwnc. cdc. gov/EID/article/21/6/14-1103-Techapp3. xlsx). Locally, in the month after the outbreak associated with consumption of the tuna dish, different control measures were implemented, depending on whether eggs were contaminated.
†SENTR7-SENTR5-SENTR6-SENTR4-SE3. The Unité des Bactéries Pathogènes Entériques belongs to the Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence funded by the French government's Investissement d'Avenir programme (grant no. ANR-10-LABX- 62-IBEID). Emerging Infectious Diseases • www. cdc. gov/eid • Vol. 21, No. 6, June 2015 1047 *.
2014). The Advisory Group has made a number of recommendations on further improvements in the Monographs process specifically related to conflict of interest, transparency, and the use of the systematic review procedures in data gathering and evaluation. Thus, possible changes to the process are periodically considered by IARC Background: Recently, the International Agency for Research on Cancer (IARC) Programme for the Evaluation of Carcinogenic Risks to Humans has been criticized for several of its evaluations, and also for the approach used to perform these evaluations. Some critics have claimed that failures of IARC Working Groups to recognize study weaknesses and biases of Working Group members have led to inappropriate classification of a number of agents as carcinogenic to humans.
P. Comba, P. Forastiere, E. Merler, F. Merletti, R. Pirastu, B. Terracini, and P. Vineis have acted as consultants to prosecutors and judges in a number of court case trials. F. Mirer has received compensation as a consultant to the AFL-CIO and the UAW in support of litigation. E. Weiderpass is currently a member of the IARC Scientific Council. The other authors declare they have no actual or potential competing financial interests. Received: 14 September 2014; Accepted: 20 February 2015; Advance Publication: 24 February 2015; Final Publication: 1 June 2015. governing groups (Scientific Council and Governing Council) and Advisory Groups. Here, we focus on current IARC processes and practices because these have been the focus of recent criticisms. The authors of this Commentary are scientists from a wide range of disciplines who are involved in designing and conducting studies that provide data used in hazard evaluations, such as those performed by IARC.
The procedures used to evaluate the scientific evidence are described in the Preamble to the Monographs  (IARC 2006). It is important to stress that only Working Group members conduct the actual evalua tion  (Wild and Cogliano 2011; Wild and Straif 2011). IARC staff facilitate the evalua tion process and ensure that the procedures described in the Preamble are followed; however, they do not determine the outcomes. IARC assessments of carcinogenicity are based on, and necessarily limited to, scientific evidence available at the time of the review. The evidence comes from epidemiologic studies, animal bioassays, pharmacokinetic/mechanistic experiments, and surveys of human exposure. The aim is to include all relevant papers on cancer in humans and experimental animals that have been published, or accepted for publication, in peer-reviewed scientific journals and also any publicly available government or agency documents that provide data on the circumstances and extent of human exposure.
To that end, the search of the literature takes a comprehensive approach. Papers that are found not to provide useful evidence can be excluded later in the process. IARC staff first use previous IARC Monographs (if available), database searches using relevant text strings, and contact with investigators in the field to identify potentially relevant material. Thus, the initial assembly of the literature is performed by individuals who are not engaged in the actual evaluation. Working Group members are then assigned various writing tasks and are instructed to perform their own literature searches to identify any further papers that might have been missed. In addition, all of the papers assembled by IARC are made available to the full Working Group before they meet, and any member can recommend other papers not previously identified that they think should be considered.
For some agents, this reevaluation has resulted in progression toward greater certainty regarding their human carcinogenicity, whereas for others the progress has been moved toward less certainty. Such movements are expected in an open, transparent, and evidence-based process. A comprehensive update of all Group 1 carcinogens was recently accomplished in Volume 100 A through F (http:// monographs. iarc. fr/ENG/Monographs/ PDFs/index. php). Usually, several agents are evaluated in a single meeting lasting more than 1 week. After discussing the evidence fully, the Working Group members follow the published IARC procedures for combining information from epidemiologic studies and bioassays to arrive at a preliminary classification (IARC 2014a). Mechanistic data are then considered in order to determine whether they warrant a change from the preliminary classification.
There is evidence that carcinogenicity in human and animal studies is often concordant, although data may differ as to the affected cancer site  (Haseman 2000; Maronpot et al. 2004; Tomatis 2002). A major effort to evaluate the concordance between animal and human results is currently under way; two Working Groups were convened at IARC in 2012, and a systematic evaluation of the correspondence between human and animal data was undertaken (a report is not yet publicly available).
Background: The spatial distribution of many genes has been visualized during the embryonic development in the starlet sea anemone Nematostella vectensis in the last decade. In situ hybridization images are available in the Kahi Kai gene expression database, and a method has been developed to quantify spatial gene expression patterns of N. vectensis. In this paper, gene expression quantification is performed on a wide range of gene expression patterns from this database and descriptions of observed expression domains are stored in a separate database for further analysis. Methods: Spatial gene expression from suitable in situ hybridization images has been quantified with the GenExp program. A correlation analysis has been performed on the resulting numerical gene expression profiles for each stage.
Background Spatial gene expression assays can be used as a tool for verifying predicted regulatory interactions between genes and for predicting properties of missing components in a gene regulation network  [1, 2]. The largest potential of spatial gene product distribution datasets, is in verifying numerical models of regulatory interaction networks, which has been demonstrated for the embryonic development of fruit fly Drosophila melanogaster  [3]. Also the formation of digits in early mouse limbs has been replicated with mechanistic models with the help of gene expression patterns  [4]. To perform accurate simulations of such processes, the spatial gene expression patterns need to be digitally quantified and formatted to standardized profiles. A procedure for gene expression quantification has been described  [5]  and applied  [6]  for organisms with changing morphologies during embryonic development.
In these cases the subsequent stages of development can be qualitatively identified from the changing embryo morphology. These changes in morphology are caused by division and migration of cells, processes that are absent during the early cleavage cycles of flies. In the comparative gene expression database Kahi Kai, in situ RNA hybridization assays are collected for many marine invertebrates  [9]  and are classified according to the embryo morphologies. This database thereby allows for an analysis of spatial expression features for all gene entries. In this study, many genes from the Kahi Kai database are compared at various stages of development, based on their expression in different embryonic regions. First, the majority of in situ hybridization images are quantified and    [11, 18, 21].
) the quantified gene expression patterns are collected in a list of digital expression profiles. Stage-specific correlation analyses are performed on these spatial gene expression profiles to discover the embryo's major division in expression domains. Second, a subset of genes from the database is listed with a detailed description of the spatial expression in the stages for which data is available. This list provides an overview of the developmental stages with spatial hybridization images for each gene and allows a detailed description of expression properties beyond the general classifications. Progression of spatial expression is compared for subsequent available stages, and the main periods of gene expression dynamics are identified. A large set of gene expression patterns in the starlet sea anemone Nematostella vectensis is analyzed.
The determination of the secondary axis in N. vectensis is one aspect of gene expression that requires spatial localization. The database contains various genes that are expressed along this axis. The change in N. vectensis morphology during development is schematically displayed in Fig. 1. The nucleus in the egg is located at the future oral pole, which means that the primary (oral-aboral) axis is already determined before fertilization  [10, 11]. The determination of the secondary axis, which is defined by the location of the syphonoglyph, is unclear. The first structures that appear along this axis are the primary mesenteries, but differential gene expression is already observed during gastrulation  [12, 13]. Based on the early symmetry break in various gene Fig. 3  Gene expression quantification pipeline.
A digital morphology is overlaid with the gene expression image (a) and the points are manually dragged over the embryo's cell layer (b). After the cell layer is decomposed into segments (c) and the intensity is plotted as a function of cell layer position (d), the profile is manually edited to correct for artifacts (e) Fig. 2  Workflow overview. The information stored in the Kahi Kai gene expression database has been processed into convenient formats for two partly overlapping sets of genes. These processing methods and the methods used for additional analyses are described in the text. While this workflow may seem to converge to a single final result, all intermediate results can be explored for multiple purposes Fig. 4  Hybridization images for FoxB in the Kahi Kai database. If multiple images are available for a developmental stage, these are accessed with the blue arrow buttons Fig.
5  Expression summary for FoxB in the Kahi Kai database. The indicated expression domains are derived from the available in situ RNA hybridizations (some hybridization images are displayed in Fig. 4 ). Expression in the presumptive endoderm at the early gastrula is incorrect expression patterns and on early N. vectensis morphogenesis, a mechanism is proposed for secondary axis determination. Spatial gene expression patterns in early stages of development are necessary to study the determination and formation of the secondary axis. We analyzed spatial gene expression patterns in various stages of development in N. vectensis. Changes in these patterns are due to gene expression dynamics within stationary cells or due to migrating cells that retain their gene expression state. Fate mapping experiments can conclusively determine migratory behavior, but these data are not available for N.
Cluster analysis of quantified in situ hybridizations The genes listed in the Kahi Kai database for N. vectensis are screened for useful expression patterns. For one-dimensional quantification, suitable genes are genes that display cylindrical expression in broad domains up to the late planula stage. Other genes, such as those that are expressed on the syphonoglyph side only or in individual cells, require a two-dimensional or three-dimensional quantification method for a complete description. In situ hybridization images are imported into GenExp, a MATLAB interface designed to extract and quantify gene expression patterns  [5]. A continuous series of digital morphologies has been derived from a confocal microscopy study on N. vectensis gastrulation. A digital morphology is selected and overlaid with the hybridization image (Fig.
Overview and analysis of expression summaries A list of all genes in the Kahi Kai database with in situs available from the blastula to the late planula stage was retrieved with the built-in search tool. From this list, those genes were selected with images available in at least two different stages of development. The selected genes are listed in a database table, with descriptions of the expression patterns in available stages. The descriptions are derived from the Kahi Kai expression summary matrix, while correcting possible inconsistencies with the in situs (illustrated for the gene FoxB in Figs. 4  and 5  as an example). Expression in the endoderm wall and ectoderm wall is specified in more detail. If a gene expression pattern exhibits noncylindrical symmetry, this is briefly indicated.
All possible pairwise combinations of developmental stages are listed in a spreadsheet, and the expression pattern descriptions from the database table are inserted for subsequent available stages. All combinations with identical expression domains in both the first and the second stage are added up. The extent to which the expression pattern has changed is indicated in a separate column. The instances in which a pattern has remained within the same major region (minor change), shifted across major regions (major change) or vanished are catalogued for genes that start in a single major domain. For each major domain, all possible stages in which a pattern can display a minor change, display a major change or vanish are counted. The relative occurrences of these events in each stage are derived from their counts.