dataesr/scientific-paragraphs-categorization huggingface dataset:
Academic Social Networks Versus Open Access? A Comparison of Perceptions In the third part of this study we compare the perceptions of academic social networks and open access regarding four criteria: visibility, publication dissemination, data protection and data preservation. Figure  4  illustrates general results. For all areas, Open Access sees the more positive responses. Open Access repositories seem to fulfil a legacy role, keeping access to data secure and preserved for the future. This long-term position is their main strength. For publication dissemination, Open Access is at an advantage. But as far as visibility Breaking down these results by discipline, academic social networks already take the lead role on this issue in Life Sciences and Engineering Sciences, and are even with Open Access repositories in the Social Sciences (Figure  5 ).
Apart from this, Open Access remains seen as more efficient than academic social networks. The degree of conviction varies according to discipline: in Mathematics/ Computer Sciences Open Access unquestionably outperforms networks, but in other disciplines, this perception is more mixed or differs in relation to the different criteria. In the Arts & Humanities and Economics/Business/ Law, confidence in academic social networks for data protection and data preservation remains surprising given the lack of knowledge researchers in these domains have reported here about the policy of data usage for these networks. Using the data of this study, we can divide researchers' perceptions into two main groups. The first one includes the disciplines that rely on Open Access processes to disseminate and freely share scientific output making complementary use of academic social networks.
Pattern 1 corresponds to the 24 first spots that exhibit a minimum amount in the control sample and a constant increase with [ 3 H]thymidine incorporation until a plateau at 2 Gy. Pattern 2 corresponds to 2 spots displaying a V profile with a minimum amount at 0. 5 Gy and a maximum amount with the control sample and/or the highest doses of [ 3 H]thymidine. Pattern 3 corresponds to 8 spots displaying a minimum amount with the control sample and then a plateau with the different doses of [ 3 H]thymidine. These patterns were assigned according to the three first correlation groups proposed by the statistical analysis by the Progenesis Samespots software based on similarity in expression profiles. Most of the selected spots displayed a minimum of intensity with the control sample, whereas protein amounts increased upon [ 3 H]thymidine incorporation.
Indeed, only 2 spots (6%) were observed at maximum levels in the control sample (pattern 2). It is interesting to observe that all of the proteins implicated in the response to DNA damage or associated with oxidoreductase and/or antioxidant activity correspond to spots with lowest levels in the control sample and an increased expression in [ 3 H]thymidine-treated samples. Globally, the proteins identified by mass spectrometry and potentially involved in the response of CHO cells to [ 3 H]thymidine incorporation were associated with several biological processes. According to the "Gene Ontology" research tools of the Uniprot database (uniprot. org), 80% of the identified proteins were associated with metabolic processes, and 33% were involved in responses to stimulus (supplementary S2). More precisely, two proteins were described as implicated in the response to DNA damage (spot 12, Ubiquitin-conjugating enzyme E2 and spot 19, MICOS complex subunit Mic25).
4b ). Indeed, at this locus, [ 3 H]thymidine incorporation also stimulated mutagenesis A typical two-dimensional electrophoresis image of CHO cell nuclear proteins is presented using 100 μ g of soluble nuclear proteins. Proteins were visualized by Lava purple staining. Numbered spots were significantly increased or decreased in protein exacts from CHO cells upon [ 3 H]thymidine incorporation. Proteins were identified by mass spectrometry (Table  1 , supplementary data S1). Gene ontology of each protein was analyzed (Supplementary data S2), and the expression pattern of three representative proteins upon [ 3 H]thymidine incorporation is presented in Fig. 3. at doses between 0. 05 and 0. 5 Gy/nucleus. Then, mutagenesis decreased to reach a plateau for higher doses (from 1 to 10 Gy/nucleus). On the contrary, analysis of the mutagenesis of Na + /K + -ATPase membrane pump gene (Oua r ) after exposure to external radiation ( 60 Co γ -rays) at comparable dose rate (dose delivered in 20 h) or flash dose rate (dose delivered in 2 min) did not show any increase at low doses and a slight but significant increase for higher doses (Fig.
Analytical gels were performed with 100 μ g of protein and stained with Lava-purple (Serva) as recommended by the manufacturer and scanned to images with a Typhoon 9400 (GE). A preparative gel was performed for each condition with 250 μ g of protein and stained using a MS compatible silver staining protocol  31. Image analysis. Images from analytical gels were analyzed using the Samespots software v4. 0 (Non-linear Dynamics, UK). Gel replicates were grouped to create a global analysis with all conditions. Spots of each samples were compared between control and irradiated conditions. A multivariate statistical analysis was performed using the statistic mode of the Samespots software (Non-linear Dynamics, UK). Spots with significant differences (ANOVA t-test p < 0. 05) were first chosen. Then, only spots with a q-value < 0.
8-2. 1 kV, and a dry gas flow rate of 4 L/min at 140 °C. After an initial MS scan at 2 Hz, an information-dependent acquisition (IDA) mode was employed over the mass range of 300-1400 Th, for the five most intense precursors with an ion intensity above 2000 counts, a charge state of (+ 2) or (+ 3) and an active exclusion of 0. 15 min after 2 spectra. Collision energies were set to automatically adjust according to the charge state and mass of the precursor ions. Proteins were identified by MS/MS by information-dependent acquisition of fragmentation spectra of multiple charged peptides. Up to 5 data-dependent MS/MS spectra were acquired in positive ion mode. MS/MS raw data were analysed using Compass DataAnalysis software v. 4. 0. 275. 0 (Bruker Daltonik GmbH, Bremen, Germany) to generate the peak lists.
The Swissprot database (release 2015_03, 547 964 sequences) was queried locally using the Mascot search engine v. 2. 4. 0 (Matrix Science, http//www. matrixscience. com) and with the following parameters: Rodentia for the taxonomy, trypsin as enzyme, 1 missed cleavage allowed, carbamidomethylation of Cystein as fixed modification, oxidation of Methionine as variable modification. Mass tolerance was set to 10 ppm on full scans and 0. 05 Da for fragment ions. Proteins were validated once they contained at least two peptide with a p value < 0. 05. When identified, contaminants proteins (such as keratine) were removed from the identification list and only the two top hit remaining proteins with similar scores and emPAI were considered. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.
proteomexchange. org) via the PRIDE partner repository  32  with the dataset identifier < PXD003542>. Mutagenesis measurements. Cells were trypsinized, counted and divided into three fractions. The first fraction was used to measure the incorporation of labeled nucleotides. The second was used to measure the colony-forming efficiency, and the third was plated with 3 mM of ouabain (Sigma-Aldrich) or 0. 4 mM 8-azaadenine to measure the Na + /K + pump ATPase and APRT mutagenesis frequency, respectively  7, 33, 34. Measurement of labeled nucleotides incorporated into DNA. Cells were suspended and DNA was precipitated in ice-cold 10% trichloroacetic acid (TCA). Precipitated DNA was recovered on GF/C filters (Whatman), which were subsequently washed with 10% and 5% TCA. Incorporation of [ 3 H]-thymidine into DNA was determined by subjecting the filters to liquid scintillation counting.
Wild type aprt sequences from our cell line served as the control. Mutagenesis analysis was performed with Clustalw software. Oxidative stress probe. Immediately after tritium contamination, cells were washed in PBS three times, harvested and nucleofected with a 5-(and-6)-carboxy-2′ ,7′ -dichlorodihydrofluorescein diacetate probe (carboxy-H2DCFDA) provided by Invitrogen (Life Technology, Cergy Pontoise, France). Nucleofection was optimized from Amaxa protocols for CHO cell lines and performed with Amaxa Nucleofector II Device (Lonza, France). Nucleofected cells were then analyzed by flow cytometry (FACScalibur, Becton-Dickinson) to measure fluorescence. Statistical analyses were performed with FlowJo software (Tree Star, Inc. Ashland, OR, USA). Enzymatic activities. Contaminated and control cells were washed with PBS and trypsinized.
Acknowledgements This work was supported by  La Ligue Nationale Contre le Cancer ,  ANR  ( Agence Nationale de la Recherche ,  ANR-14-CE10-0010-02 ),  AFM-Téléthon  and  INCa (Institut National du Cancer ,  2011-1-RT-01 ,  2011-1-PLBIO-09, 2013-1-PLBIO-14 ).
Statistical Analyses Statistical analyses were performed by generalized linear mixed models (GLMM) in R (version 2. 15. 3). Prevalence data were analyzed by analysis of variance (ANOVA) χ 2 test on a logistic regression (glmer). Oocyst counts were analyzed by Wald Z-test on a zero-inflated negative binomial regression (glmmADMB). 16S data (qPCR, family proportions from pyrosequencing) were analyzed by ANOVA χ 2 test on a common linear regression (lmer). Survival data were analyzed using an ANOVA χ 2 test on a mixed effect Cox model (coxme), using replicates as random effects. Mixed model analyses decompose model effects into the contribution of a fixed component (the covariates in question, the treatments in this study) and a random component (the experiments in this study). The fixed effect estimates are the regression coefficients, and the odds ratios are their exponential value.
Acknowledgments We thank  Lara Sellés ,  Raymond Hien ,  Jean Bazié ,  Tamboula Souleymane ,  Fulgence Da , and  Baudoin Dabiré  for technical help;  Faye H. Rodgers  for critical reading of the manuscript;  Kathrin Witmer  for providing a P falciparum culture; all children and their parents for participating in this study; and the local authorities for their support. We also thank  Thomas Churcher  for useful discussions about transmission-blocking modeling.
In this work we used transcriptomics and proteomics to observe the adverse molecular effects of a mixture compared with the responses to the individual components. Immunocytochemistry was used as a physiological endpoint to assess the cytological changes induced by these substances on spermatogenesis using staining of the synaptonemal complexes (SCs) with an anti-SCP3 antibody. To achieve this aim we (i) designed omics methods to assess dose-and time-dependent responses, (ii) utilized a single bioinformatics tool, Ingenuity Pathways Analysis (IPA), to summarize the modulation of thousands of genes and hundres of proteins into hypotheses about specific cellular pathways of toxicity (PoT) towards these substances, (iii) observed the cytological changes of these specific cells after exposure to IPR and CBZ by immunostaining of the SCs, and (iv) analyzed the omics data sets transversely to identify potential biomarkers of testicular toxicity.
Microarray experiments Cell exposure, RNA extraction, RNA labeling and hybridization Two different pools of seminiferous tubules were exposed to two concentrations of CBZ, IPR or their mixture (50 nM and 500 nM) or to the complete medium with vehicle (control cells) for 7, 14, or 21 days. Total RNA was extracted using the RNeasy Mini Kit (Qiagen, Hilden, Germany). RNA preparations were quantified with the Nanodrop 1000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA) and their quality was assessed with the Agilent 2100 Bioanalyzer. RNA samples were amplified and labeled with the cyanine-3 fluorophore using a Low Input QuickAmp Labeling Kit (Agilent Technologies, Palo Alto, CA, USA). Hybridization was performed using Agilent Oligo Microarrays (Rat V3 4x44K). Fluorescence was scanned and signal data were extracted with Feature Extraction Software (Agilent Technologies, Palo Alto, CA, USA).
Design and statistical analysis of transcriptomic experiments In this experimental design, and for each fungicide (50 or 500 nM), six independent analyses were conducted versus each specific control for each time point. For example, for CBZ we analyzed the 50 nM CBZ-exposed cells versus control for 7 days, 14 days, and 21 days and 500 nM CBZ-exposed cells versus control for 7 days, 14 days, and 21 days. For each experimental point (i. e. , for a specific dose and time point), six raw fluorescence data files (three tests and three controls) were submitted to GeneSpring Software GX12 (Agilent Technologies, Palo Alto, CA, USA) using a widely used method for determining the significance change in gene expression  (Fisichella et al. , 2014; Ludwig et al. , 2011; Wright et al. , 2012). The fold change cutoff between control and exposed samples was set to 1.
Microscopic analysis A Zeiss Axioplan 2 Fluorescence Photomicroscope (Carl Zeiss, Oberkochen, Germany) was used to observe the spermatocyte base. These lists of altered genes and proteins were then processed to investigate their functional distribution, as defined by Gene Ontology. For each substance and time point, datasets and known canonical pathway associations were measured by IPA by using a ratio of the number of genes or proteins from a dataset that match to a specific pathway divided by the total number of genes/proteins that map to this canonical pathway. This ratio is expressed as a percentage. A Fisher's exact test was used to determine a p-value representing the significance of these associations. For each time point, protein datasets obtained for each substance were compared and five secreted proteins present in almost all tests at all time points were isolated as potential biomarkers.
Transcriptomics In a first step, gene expression changes depending on the tested substances, CBZ, IPR and CCK (a mixture -or cocktail -of IPR+CBZ), were evaluated in three biological replicates with commercial Agilent rat V3 4x44K microarrays. A specific cell control was run at each time point due to the differentiation of this organotypic cell culture during the 21 days of the experiment. The number of transcripts differentially expressed (up or down) by treated cultures compared with untreated cultures reflects the magnitude of the cellular disruption. Tab. S1 (http:// dx. doi. org/10. 14573/altex. 1601253s1) lists all the genes upand downregulated with each substance and at each time point. Figure  2  shows the number of differentially expressed transcripts detected in response to 50 and 500 nM CBZ, IPR and CCK.
The Orbitrap was continuously recalibrated using the lock-mass function on the ion m/z 445. 12003. The software Xcalibur™ 2. 2 and Chromeleon ® Xpress were used to control the LC-ESI-MS/MS instrumentation. Sequest HT was run by Proteome Discoverer™ 1. 4 to match the acquired MS/MS spectra, with a tolerance of 10 ppm/0. 02 Da, to a Rattus norvegicus protein database, with the sequences for human transferrin and bovine insulin. Sieve™ 2. 1 was used to derive the relative quantitation ratio between treated and control samples. Average ratios were calculated using variance weighting on unique peptides. Proteins with an average ratio ≥ 2 or ≤ 0. 5 and a p-value ≤ 0. 05 were selected for further computer analysis.
Biological analysis Lists of genes and proteins significantly induced or repressed after exposure to fungicides were uploaded into QIAGEN's Ingenuity ® Pathway Analysis software (IPA ® , QIAGEN Redwood City, http://www. qiagen. com/ingenuity) for biological analysis by comparison with the Ingenuity Knowledge Data-Fig. 2 : Time-and dose effect on the number of significantly differentially expressed genes after exposure to carbendazim, iprodione and a mixture (named cocktail) of both fungicides Ex vivo cell cultures of seminiferous microtubules from male Sprague-Dawley rats were exposed for 7, 14 and 21 days to two concentrations (50 nM and 500 nM) of A) CBZ, B) IPR and C) equimolar mixture of both substances (50 nM and 500 nM). Stacked bars represent the number of differentially expressed transcripts that were up-or downregulated after statistical analysis using Genespring GX12 software (Agilent) with p-value ≤ 0.
5 and p-value ≤ 0. 05). Relevant canonical pathways disturbed after exposures were revealed with the Ingenuity ® Pathway Analysis tool. Data sets obtained after cell exposure were compared quantitatively with the genes known to be part of the main canonical pathways of spermatogenesis. The contribution of each pathway, for each experimental condition, is represented by a percentage. We focused mainly on the altered biological processes that were specifically related to the testicular tissue, then gathered these into five groups, each including several characterized pathways. We paid special attention to the transcriptional change of meiotic genes. A list of 305 meiotic genes given by IPA was taken as the reference. We compared this list with each of our datasets established at 50 nM. As a result, 27, 79 and 69 meiotic genes were differentially expressed at all time points with CBZ, IPR and CCK, respectively.
77 and 0. 07 were observed at 50 and 500 nM, respectively. In a second step, and for each substance (CBZ, IPR and CCK) and time point (D7, D14 and D21), gene datasets were analyzed with Ingenuity Pathway Analysis for annotation, and the results are reported in Table  S2  (http://dx. doi. org/10. 14573/ altex. 1601253s2). All data sets were examined for their association with known canonical pathways, which are defined by a discrete number of genes. The quantification of this association was performed by calculation of multiple ratios, linking a specific dataset to several canonical pathways as described in the Methods section. In a third step, we focused on the altered biological functions and pathways that were specifically related to testicular tissue. These altered processes after exposure to CBZ, IPR, or CCK (50 nM) were gathered into five groups, representing specific characteristic dysfunctions: A) Altered pathways related to chemical toxicity (Acute phase signaling); B) Alteration of germ cell migration across the seminiferous epithelium (Impediment of germ cell migration, germ cell/ Sertoli cell junction signaling, Sertoli cell/Sertoli cell junction signaling); C) Endocrine disruption by interaction with steroidogenesis (Alteration of hormone steroidogenesis, cholesterol synthesis, LXR/RXR activation); D) Impairment of meiosis (Impairment of meiosis of male germ cells, arrest in pachytene of male germ cells); E) Compromising the future spermatozoa (Sperm motility, abnormal morphology of male germ cells, asthenozoospermia).
Proteomics Three cultures were treated with each fungicide (CBZ and IPR) or with their equimolar mixture (CCK) and compared with controls at each time point (D7, D14 and D21). The exoproteomic analysis of the culture media revealed that the apical chamber medium was richer in proteins than the basal medium  Ex vivo cell cultures of seminiferous microtubules from male Sprague-Dawley rats were exposed for 7, 14 and 21 days to 50 nM CBZ or IPR, or to an equimolar cocktail of both substances (50 nM each). The protein content of the apical medium was extracted, digested with trypsin, identified by nanoLC-MS/MS (analysis in duplicate) and compared using the SIEVE™ software (label-free semiquantification). Secreted proteins were found to be differentially expressed in the samples exposed to the toxicants compared with control samples cultured for the same number of days (ratio ≥ ±2, SIEVE™ p-value ≤ 0.
1: Key proteins differetially expressed after exposure Ex vivo cell cultures of seminiferous microtubules from male Sprague-Dawley rats were exposed for 7, 14 and 21 days to 50 nM carbendazim (CBZ) or iprodione (IPR) or to an equimolar cocktail of both substances (50 nM each). We isolated five secreted proteins present in almost all tests, at all time points. The expression fold changes of these proteins are indicated, versus untreated cells at the same time point, as calculated by comparison of the LC-MS peak intensities of the tryptic peptides of these proteins by Sieve software; Sieve p-value ≤ 0. 001 except for (*) p-value ≤ 0. 05. Standard deviations (SD) are reported next to each ratio. ND, undetected proteins. of germ cells and abnormalities provoked by these substances compared with control cells.
Carbendazim We applied microarray-based gene expression analysis, as well as proteomics of the extracellular compartment, to examine the toxicity of the two fungicides, CBZ and IPR, alone or as a cocktail (CCK), on this model. In addition, we examined cytogenetic modifications by counting the different populations the tested fungicides, we used a quantification method associating the main canonical pathways of spermatogenesis with each experimental condition. In the subsequent discussion we have exemplified the results with some genes and proteins that are representative of a given pathway because they are well documented, but beyond these, more genes and proteins demonstrate the existence of a specific pathway. The strength of the evidence is based precisely on the multiplicity of genes of a data set belonging to a canonical pathway and extracted via IPA (Tab.
LXR/RXR pathway disturbance was more marked with CBZ than with IPR and was also observed with CCK. The alteration of many genes involved in junctions between Sertoli cell-Sertoli cell and germ cell-Sertoli cell indicates a trend, namely membrane stiffening. A decrease of the plasticity of the junctions, which is essential for germ cell migration within the epithelium, may halt the germ cells at an intermediate stage of development. Interestingly, our proteomic datasets indicated that the protein, APOE, was either up-or downregulated with all treatments (CBZ, IPR and CCK), whatever the dose (50 or 500 nM) or exposure time point (D7, 14 and 21). APOE is known to facilitate the transfer of cholesterol/lipids between cells  (Olson et al. , 1994)  and we observed here that its homeostasis was compromised in the presence of the tested compounds.
Additionally, any spermatogenic cells that undergo apoptotic death before complete maturation into spermatozoa are also phagocytized by Sertoli cells  (Selva et al. , 2004). RXRregulated genes are required for the removal of lipids from Sertoli cells, such as the Abca multigene family, coding for the ATP-binding cassette transporter A. In transcriptomic datasets obtained with CBZ and CCK, we observed variations of the expression of Abca genes (Tab. S2, http://dx. doi. org/10. 14573/ altex. 1601253s2). These transporters, highly expressed in the testis, mediate lipid efflux from Sertoli cells. The selective elimination of ABCA1 in ABCA1 -/-knockout mice results in Sertoli cell lipid accumulation, decreased spermatogenesis, and reduced fertility  (Selva et al. , 2004). Testis germ cells are endowed with enzymatic scavenger systems to prevent lipoperoxidative damage.