dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequence analysis The 16S rRNA gene clone sequences were reconstructed with Phrap (http://www. phrap. org/) and assigned to operational taxonomic units (OTUs) using DOTUR  (Schloss and Handelsman 2005). A 97% similarity threshold was used for OTU assignment  (Goebel and Stackebrandt 1994). For phylogenetic analysis, sequences were imported into the ARB database  (Ludwig et al. 2004 ). All sequences were imported into the ARB database and automatically aligned against the closest related 16S rRNA gene sequences defined by online BLAST results (http://www. ncbi. nlm. nih. gov/BLAST). The resulting alignments were manually checked and when necessary corrected. 16S rRNA gene sequence similarities were determined by using the distance matrix tool of the ARB program package. The Phylogenetic tree was generated by neighborjoining analyses  (Saitou and Nei 1987) , with Jukes and Cantor corrections, and a 50% invariance criterion for inclusion of individual nucleotide sequence positions in the treeing analyses.
Probe design and fluorescent in situ hybridization An oligonucleotide probe targeting the 16S rRNA gene sequence was used to identify WWE1 cells. The design of a probe targeting new clone sequences was accomplished using the PROBE_DESIGN tool from ARB  (Ludwig et al. 2004). A FISH probe was chosen by estimating its accessibility to target sites as described previously  (Behrens et al. 2003)  and was subsequently validated for specificity using the ProbeCheck database (http://131. 130. 66. 200/cgi-bin/ probecheck/probecheck. pl). The new probe was synthesized and labeled at the 5′end with fluorescein isothiocyanate (MWG, Ebersberg, Germany). Theoretical suitable hybridization conditions were estimated  (Nakatsu and Forney 1996). Hybridization conditions for a probe targeting WWE1 were further optimized with paraformaldehyde-fixed samples from 13 C-cellulose batch systems.
In situ detection of WWE1 candidate division members by FISH We developed a 16S rRNA oligonucleotide probe specifically targeting the WWE1 group (S-*WWE1-408-a-A-5′-G CTCCGAAAAGCTTCATCG-3′) based on the probe design tool of the ARB program package. A mixture of the new WWE1 specific probe (labeled with fluorescein isothiocyanate), and the bacterial probe EUB338 mix (labeled with monofunctional, hydrophilic sulfoindocyanine dyes Cy3 and Cy5) was applied for screening of the WWE1-related bacteria within the bacterial community of waste and 13 C-cellulose batch incubations. Because of the presence of autofluorescent particles in some samples, negative controls were always observed in parallel. Moreover, ambiguous fluorescent structures were bleached using the appropriate laser of the CLSM in order to confirm that the observed signals were due to the fluorescent probe.
Acknowledgments This study was supported by the  CMCU program  ( 07G081 ),  Irstea  and the  R2DS research program  of the  Region Ile de France. The experiments were performed in the  LABE (Laboratoire de recherche sur les Bioprocedes pour l'Environnement)  funded in the framework of the  CPER2007-2013 program. We gratefully acknowledge the contribution of all the HBAN team,  Irstea ANTONY , and we are particularly thankful to  Amal Hlioui  and  Ha€ ıtham Sghaier  for critical reading.
Funding Information Experiments were conducted on the LABE experimental platform in Irstea-HBAN Antony funded by DRRT in the framework of CPER2007- 2013 program. Rim Driss  stay in France was supported by  CMCU 07G 0801  project  from Service de cooperation et d'action culturelle, Minist ere des affaires etrang eres and by Irstea.
While many probes of homologous genes can be enriched for a GO signal, a limitation of this previous study is that this geneset-based approach is not designed to impute differential expression of individual mouse stromal genes. There is thus a need for improved species specificity of probes in newer arrays designed specifically for jointly studying stromal and cancer cell gene expression in xenograft models. In many tumor biology and pharmacology xenograft studies, the interplay and co-expression patterns of cancer and stromal cell genes need to be studied using the simultaneous analysis of both species in the same array experiment. When tissue expression is detected using separate mouse and human arrays, comparing mouse and human gene expression requires complex and artefact-inducing normalization of the two datasets.
Datasets and custom human-mouse whole genome arrays used in this study Microarray data from the two in vitro experiments (Table  1 ) are available in the GEO repository under accession number GSE23377 (initial in vitro biological experiment: GSE23054, version 1 GEO platform: GPL10714; in vitro validation experiment for CSH and stroma cell ratio estimation: GSE23364, version 2 GEO platform: GPL10749, Agilent Custom Array Order #023265). We used nine genomic datasets to compute human-mouse CSH (Additional file 1-Supp. Table  S1 ). The list of housekeeping mouse genes was previously published  [22, 23]  and the list of homologous mouse-human genes was downloaded from NCBI (ftp://ftp. ncbi. nih. gov/pub/ HomoloGene) on Feb, 2009 (Build 63). By design, these experiments generated Datasets S1, S2 and S3 that we provide for reuse by other groups at http://lussierlab.
org/ publications/HsMm_array H&M array design, annotation, and analysis (Figure  1 : panels A-C) As shown in Figure  1 , the experimental design consists first of an assemblage of the two sets of 44k probes based on Agilent's commercial human array and mouse array. Agilent also provided cross hybridizing estimates between mouse probes and human mRNAs and vice versa (7141 human and 6420 mouse probes; Datasets S2, S3), which are redesigned to correct for comprehensive CSH. We designed two custom arrays: the second and final one improved on the original design after biological validation. The H&M arrays contain all legacy probes from the commercial human and mouse arrays as well as the newly designed ones (Figure  1 , panel A). The second and final custom H&M array is identified as "H&M array version 2" (Agilent GEO platform: GPL10749; Agilent Custom Array Order #023265).
In an initial in vitro biological experiment, we cross-hybridize H&M array version 1 with mouse mRNAs, human mRNAs, and a combination of both human and mouse RNAs (Figure  1 , Step B1; Table  1 ). We also conduct a parallel and extensive BLASTn analysis of potential cross species hybridization of probes (Step B2). Taken together the biological and the computationally derived cross hybridizations serve as a model for CSH propensity of probes, and have an extensive optimization method (Step B3). As shown in panel B of Figure  1 , we conduct a second iteration of probe design (Step B4), and produce version 2 of the H&M array. A second and more comprehensive biological experiment that cross hybridizes six arrays (H&M array version 2) with mouse and human mRNAs is used for the final evaluation (Step B5; Table  1 ) and improved the annotation of the CSH of each probe (Datasets S2, S3).
We first obtain human and mouse universal reference RNAs from ArrayIt (Sunnyvale, CA), which were derived from a homogenate of all tissues from the organism, making them somewhat representative of any cell type. This universal RNA is amplified and labelled according to the Agilent Low RNA input Fluorescent Linear Amplification Kit protocol and according to Agilent's two-color quick amp labelling protocol. The two arrays are scanned with 5 µm resolution and 100% power gain at 600PMT for both Cy3 & Cy5 using the GenePix 4000B Axon Scanner. We quantify gene expression values using Agilent Feature Extraction Software and subtract the adjusted background for each probe from the mean expression value. Finally, we use an inclusion criterion to identify the probes that show some RNA binding where the minimum expression   intensity has to be larger than a value of "1" when exposed to the pooled human and mouse RNA in both arrays.
This threshold was set because when the expressed intensity was less than 1, probes vary widely across the two experimental repeats using the same quantity of pooled human and mouse RNAs. Step B2: BLASTn analysis of cross species hybridization in array probes The Basic Local Alignment Search Tool (BLAST) algorithm  [24]  is run with default parameters to theoretically predict the possibility of CSH for each probe by comparing human array probes with mouse RNAs, and mouse array probes with human RNAs. BLASTn was used because of its ability to find "fuzzy" alignments to related nucleotide sequences from other organisms. The hits are ranked by their BLASTn score of which the highest scoring hit is recorded, together with (i) the length of the alignments and (ii) the number of mismatches (Details are given in Table  2  of this manuscript and Figure D in the Suppl.
For H&M array version 1, using a dual channel experimental design, the background adjusted absolute CSH expression of probes (i. e. the raw, unmodified expression of human probes when exposed to mouse RNAs or the expression of mouse probes when exposed to human RNAs), was stratified and alternate GSs are produced accordingly. The optimal biological GS is determined using relative high F-scores  [17]. Since the single channel repeats of H&M Array version 2 allow for the use of a relative ratio of average expression of cross species hybridization over Legend: biological GS: biological gold standard; CSH: cross-species hybridization; Align: alignment; mis: mismatch; U: union. The Gold Standards used in the final design are annotated with an asterisks (*). These were selected by comparing the initial biological experimental data and BLAST predictions using H&M array version 1.
Additionally, probes were tagged for future design when they did not have any expression on the array and were predicted as cross-hybridising using BLASTn program (Table  3 : Model 1-1). Step B4: probe redesign (green boxes in Figure  1 ) I. Gene targets associated with cross species hybridizing probes were identified for redesign. Transcripts (RefSeq IDs) associated with each probe were identified from the Agilent microarray annotation file (Additional file 1-Supp. Table  S1 , resources 1-2). For those probes with a retired Refseq or Unigene ID, the batch BLASTn program  [24]  was used to retrieve the associated antisense Refseq or Unigene sequences. The transcripts targeted by these probes were downloaded from the NCBI GEO database via accession numbers GPL6840 and GPL7202. II. New probes for the identified gene targets were designed using the "GE Probe Design" program in the Agilent eArray (Agilent, Santa Clara, CA) software.
The eArray parameters, that we used, include probe length (60nt), probes per target (1), probe orientation (sense) and design options ("best probe methodology" and "design with 3' bias"). These probes were further verified computationally for cross-hybridization. III. Newly designed probes were evaluated by the eArray "Probe Check" program using the built-in transcriptome. The new candidate probes were retained if they did not hybridize within their species with other targets than the intended one nor across species with any other transcripts. Otherwise, these candidate probes were tagged as inappropriate and new probes were designed for the intended target iteratively until they met these criteria. IV. All probes of the commercial arrays were retained, while new probes were also added to the H&M array.
Additionally, the background-subtracted expression measurements of six arrays were normalized using VSN method  [25]  and were log2 transformed. Human and mouse probe sets were separately normalized to control for variation in the proportion of human and mouse RNA across xenograft replicates  [16]. For each probe, the mean signal intensity of the three biological replicates exposed to the RNAs of the same species was used as the expression value for that species. GS and BLAST parameter optimizations were performed as described in Step 4 and in our previous publication  [17] , and the propensity for CSH of each probe was annotated (Dataset S1). This extensive annotation of CSH was conducted by systematically extending the threshold of the biological GS in 10% increments from 0% to 100% for all probes.
Stromal cell to cancer cell mRNA ratio and its validation (Figure 1, step C-1) Evaluation of the stromal cell to cancer cell mRNA ratio, hereafter named as S/C ratio for simplicity, was independently carried in two steps. The samples of the in vitro biological experiments data for which the proportion of mouse to human RNA is designed and known (GSE23377) were used to validate the mouse to human RNA ratio metric. This metric then serves as a proxy for stroma to cancer cell RNA ratio in xenograft models consisting of human cancer cell tumors harvested from mice hosts.
Gene ontology enrichment of cross-species hybridizing probes (Figure 1, step C-2) and visualization After masking the top 5% of the most likely probes to exhibit CSH, KEGG/GO enrichment analysis was performed among the genes targeted by probes on the custom array using the conditional hypergeometric test in the Bioconductor GOStats package  [26]. For comparison, the same pathway/GO enrichment assessment was performed on the commercial CSH probes, for the human and mouse species separately. The resulting p-values were adjusted for multiple testing by FDR  [27]  and pathways/ GO terms with less than 500 gene members were counted  [28]. Hexagon plots were conducted using the R package hexbin with default parameters.
Stromal cells to cancer cell mRNA ratio (S/C ratio) validation Validations for our S/C ratio estimation was performed with an in vitro biological experiment, where the stromal cell proportions were controlled using known quantity of RNAs as shown in Table  1. Using the in vitro CSH experimental data, where two arrays containing both human and mouse probes were exposed to equal quantities of human and mouse RNAs (GSE23377), the ratios of 13 pairs of homologous housekeeping gene expression were larger than 10-fold when exposed to the intended RNA of one species only (Figure  4 , panels A,B), while was between 0 and 10 when exposed to an equal mix of human and mouse RNAs simultaneously (Figure  4 , panel C, Table  1 ). The higher expression of mouse SSPs in panel B indicated a better mouse RNA quality than human RNA.
Here, we provide a combined description of the interplay between the computational predictions of CSH of probes using BLASTn and observation of biological CSH that were utilized to develop an approach stratifying probes in terms of their propensities for cross-hybridization. We also implemented a rigorous rationale for the selection of a threshold needed to redesign the cross-hybridizing probes and offer the foundational datasets used for conducting analyses over a dual-species array: a comprehensive map of cross-hybridizations annotated with both sequence alignment and biological data intended to increase the accuracy and efficiency of measurements in tumor xenograft models. Additionally, we developed a novel method to estimate the mRNA ratio between mouse and human mRNA as a cellularity ratio, thus providing an alternative to conventional histological counts of stroma vs.
cancer cells in xenograft models. Of note, the H&M array could also be utilized in single species experiments, as long as the probes of that species are selected. This information is clearly available in the "species" column of Dataset S1. Further, for comparison of H&M array results against previously published dataset using single species commercial Agilent arrays, the H&M probes corresponding to the commercial platforms are annotated in the column "designer" of Dataset S1. One limitation of the current study is that grouping all of the stromal cell types into one category may be too strong of a simplification of the complex cellularity of the host tissue. For instance, changes in the presence of certain types of stromal cells have profound effects on the status of the tumor, and stromal cells are thought to genomically "co-evolve" along with cancer cells during tumor progression  [29].
Also, the xenograft model involves immune deficient mice, and therefore cannot be used to understand much about the stromal immune cells and associated pathways that participate in regulating the tumor in the xenograft models. However, xenograft models re-constituting bone marrow cells from syngeneic donors may alleviate this limitation in future studies. Further, with the advent of RNA-sequencing, the utility of the present study may reside in our supplemental datasets that allow to reanalyze the speciesspecific probes of commercial arrays for comparison with RNA-seq experiments or to inform assembly algorithms of RNA-seq in xenograft models. Another limitation is that we have not considered non-coding RNAs in this study.
Figure1Schematic diagram for the biological and computational design and annotation of the H&M microarray. The whole array design and analysis includes three stages, as described in the Materials and Methods Section. There were 3. 6k newly designed probes in step A and 7. 1k newly designed probes in step B. All probes on H&M (Agilent GEO platform GPL10749) were optimally and extensively annotated for cross-species hybridization as shown in Dataset S1. The Agilent pre-annotated cross-species hybridization probes from step A are given in Datasets S2-3, and the processes used to design the new probes in version 1 were previously published [17].
8, 10. 3 for mouse probes; P < 2. 2x10 -16 for both; Non-parametric Mann-Whitney U tests). Panels C and D show that these species-specific probes of the H&M array perform as well on average as those Agilent probes when exposed to the intended RNAs. Human-mouse gene cross-hybridization patterns we discovered are provided in Dataset S1 (http://lussierlab. org/publications/HsMm_array).
Therefore, our second approach (termed Top-tier here) searched PubMed with a much broader combination of words and then constrained the size of this set by limiting it to papers published in Science, Nature, Cell, and the Proceedings of the National Academy of Sciences (see "Notes" for details). Our third approach (termed CellProfiler citers here) was based on a set of papers curated by hand that cite our group's open-source software for high-throughput image analysis. We expected this group to be somewhat biased toward laboratories willing to use high-end informatics tools in their work. For all three approaches, we excluded book chapters, reviews, and comments, as well as papers in which presenting a method was the main focus (e. g. , development of an assay) as opposed to presenting a biological discovery.
, 2008)  has increased over 20-fold in the last 15 years  (Dame and Christian, 2006; Fulton, 2010). While structural uncertainty was rather seldom studied (e. g. Pinnegar et al. , 2005) , several methods are now available for this specific modelling software to address the issue of inherent uncertainties in the input data. Among these methods, some are implemented in the Ecopath software and used routinely while others are detailed in recent scientific publications  (Kavanagh et al. , 2004; Link, 2010; Niiranen et al. , 2012). However, their application has not yet become as an integral part of the modelling process. The more tests a model passes, the more confident modellers become in its predictions  (Rykiel, 1996). In the present paper, we propose an ordered suite of complementary analyses covering various aspects of data properties related to their use in static ecosystem models such as Ecopath  (Christensen and Walters, 2004; Christensen et al.
, 2008). We call these analyses the "DataReli" toolbox for "Data Reliability checking" toolbox. The analyses entering the DataReli toolbox were selected on three criteria: (i) their ease of implementation; (ii) their complementarity, i. e. they cover several integration levels (from single ecosystem components to whole ecosystem; and (iii) that they guarantee model long-term applications. The DataReli toolbox is thus conceptualized in the sense of fulfilling a list of basic prerequisites with regard to data reliability common to all ecosystem models and some more specific to the scope for which the model was designed. Three complementary analyses were chosen: (i) the pedigree index  (Pauly et al. , 2000) , which is designed to evaluate whether a model is based on extensive field sampling performed within the boundaries of the system during specific dates; (ii) the "PREBAL" or prebalancing analysis  (Link, 2010) , which assesses whether data are coherent to the system level by respecting some basic laws, rules and principles of ecosystem ecology; (iii) a sensitivity analysis that determines the robustness of commonly derived model outputs, namely Mixed Trophic Impacts (MTIs)  (Ulanowicz and Puccia, 1990) , to small variations in input data values  (Rochette et al.
, 2009). This sensitivity analysis is useful to strengthen identification of major connections (pairwise interactions between ecosystem components, and energy pathways) within a steady-state ecosystem. The present study aims at presenting the DataReli toolbox and ensuring its full transferability to all future potential users through a concrete application to an existing Ecopath model. Input data reliability is assessed for the model of the French continental shelf of the Bay of Biscay food web  (Lassalle et al. , 2011). The model under study was developed for studying the ecological roles played by top predators and small pelagics in the continental shelf food web of the Bay of Biscay  (Lassalle et al. , 2012)  and for ecosystem-based assessment of anthropogenic effects  (Lassalle et al. , 2014).
Results Application of the "DataReli" toolbox to the present case study indicates a model based on data of rather good quality, quantity and coherence at the ecosystem scale. The overall pedigree index for the Bay of Biscay continental shelf food-web model is 0. 60; biomass, diet composition and catches having higher entries on average than P/B and Q/B (Table  2 ). By comparison with other functional groups, cephalopods are noticeably described with data of globally low confidence and thus appear understudied in the Bay of Biscay continental shelf food web. PREBAL diagnostics applied to our case study show general coherent decompositions of parameters with increasing trophic levels (TLs) (Fig. 2 ). The ecological cohesiveness of the data is particularly demonstrated for vital rates, and total production and removals.
65)  (Morissette, 2007). As such, an overall pedigree index (τ) in the medium-high range as defined by  Morissette (2007) , i. e. between 0. 4 and 1, likely testifies to an ecosystem benefiting from a sufficient amount of data to allow revision of the model. The Bay of Biscay continental shelf food-web model falls into the highest range of this categorization. This high comparative pedigree value is even more meaningful when considering that in the present work the input data should have described a thousand km² of a system open to the Atlantic Ocean. (ii) For those models with τ above 0. 4, a closer look at the pedigree routine, i. e. values by parameters and compartments, and the PREBAL analysis would help to prioritize parameters needing improvement. We suggest investing extra effort particularly on parameters with low pedigree value that lead to noticeable departure from PREBAL diagnostics.