dataesr/scientific-paragraphs-categorization huggingface dataset:
8 m/s and a weak equatorward undercurrent at 1200 m along the continental slope. 13 Using a streamwise frame of reference for their calculation, Beal et al. 13  find a mean volume transport of 84±2 Sv (1 Sv = 10 6 m 3 /s), and suggest that the Agulhas Current appears stronger in the austral summer months (December-February). A sustained ASCA array over a period greater than 5 years is needed to provide a robust seasonal cycle, with interannual variability needing a data set greater than 10 years, and decadal variability and climate trends needing 20+ years. Meander events are found to impact the Agulhas Current less than previously understood  12, 14, 15  with events occurring less often and having only a small impact on transport. Mesoscale meanders propagate downstream and are robustly linked to eddies from the Mozambique Channel and East Madagascar Current, which impact on the Agulhas Current in its northern reaches.
Crossroads: The Agulhas Current and its return path Current understanding of the co-variability of the southwestward flowing Agulhas Current and the eastward Agulhas Return Current, particularly the difference in fluxes between them, remains incomplete, with knowledge largely based on remote sensing or model outputs. 18 A dedicated monitoring line, critical to studying the long-term trend in the Agulhas Return Current, the nature of its warming, and its impact on local climate south of Africa, began in 2013. This transect, comprising full-depth conductivity, temperature and depth and expendable bathythermograph casts, follows directly under the altimetry track N198 and is currently in its fifth year of data acquisition. The transect was first initiated as a seismic oceanography study in 2012, north of the Agulhas Plateau, where the Agulhas Return Current meanders around a large anticyclonic eddy.
Given the relatively short data set (2013 to present), the Crossroads transect has already yielded interesting and useful results. The value, however, will lie in determining changes to the established baseline data, and investigating anomalies recorded. One limitation to the long-term study of fluxes and anomalies across this transect is that Crossroads is occupied once yearly on the return passage of the Marion Island Relief Voyage, thus providing only year-on-year changes and no seasonal data. Nevertheless, results from the GoodHope transect, discussed next, have shown the value of data acquired annually in helping to separate natural decadal variability from anthropogenic-induced changes in the region south of Africa. 21.
However, these currents are not yet resolved in state-of-the-art climate models, such as those used by the Intergovernmental Panel on Climate Change. Low resolution, lack of ocean measurements, and uncertainties in wind products make these results irresolute. 53 Again, in-situ observations of the real ocean are vital to constrain models and to give a true understanding of oceanic change around South Africa, bringing us full circle to the importance of the SAMOC-SA and ASCA programmes. Within the South African marine science community, proficiency has been developed with numerical model based coastal and regional oceanographic studies. Coupled with this is the growth of skilled users of satellite-derived marine data and products and algorithm development. It is essential that the standards of physical observational science are maintained at the same skilled level, to enhance our understanding of the Greater Agulhas Current System using all the tools available to us.
Different funding streams must be sought and matched with priority areas to achieve the basic scientific operations on which currently funded research relies, thus demonstrating the usefulness of the results achieved thus far. What is needed are clear and well-structured agreements (MoAs, MoUs and contracts) between the departments and various agencies, characterising the respective commitments and responsibilities to these large programmes as regards personnel, instrumentation, ship time, data processing and management, and the costs thereof. There are other significant challenges in sustaining these important observational systems for South Africa and for GOOS. Obtaining commitments to the necessary long-term funding is problematic, given the constraint of three-year government funding cycles.
Acknowledgements We acknowledge the continued support of the  Department of Science and Technology  and the  National Research Foundation of South Africa  through its  South African National Antarctic Programme and South African Environmental Observation Network (SAEON) , and the  Department of Environmental Affairs (DEA, Branch: Oceans and Coasts). Furthermore , we acknowledge the support of international partners to the  SAMOC-SA  project: the  French institutions Ifremer ,  Agence Nationale de la Recherche  ( ANR ; project  ANR-11-IS56-0004 ), the  European University Institute  for the  Sea (IUEM) , the  Coriolis and Euro-Argo data centers  and the European AtlantOS  H2020  Project with the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 633211  (AtlantOS), in particular to the GoodHope and SAMBA arrays, and to the  ASCA  project: The  Royal Netherlands Institute of Sea Research (NIOZ)  and  University of Miami (RSMAS).
Effect of fresh leaves/thalli on dinoflagellate growth Dinoflagellate cell densities were monitored at Days 0, 1, 3, 6, 8, and 10 by direct microscopic counts of cells. Maximum growth rates (μ max ; expressed in day -1 ) were calculated according to Guillard  [63]  from the slope of a linear regression over the entire exponential phase of growth by the least squares fit of a straight line to the data after logarithmic transformation: μ max = [Ln (N t )-Ln(N 0 )/(T t -T 0 )] where N 0 and N t are the cell densities (cells. mL -  ) at the beginning (T 0 ) and the end (T t ) of the exponential phase, respectively. The EC 50 (effective concentrations inducing a 50% reduction of dinoflagellates growth when compared to the control) were determined using curves that link the observed growth rates to the tested macrophyte weights (FW).
Effect of fresh leaves/thalli on dinoflagellate photosynthetic activity The efficiency of the photosynthetic apparatus of the four dinoflagellate species was assessed with a portable pulse amplitude modulated fluorometer AquaPen-C AP-C 100 device (Photon Systems Instruments, Czech Republic), measuring chlorophyll fluorescence parameters and by using the FluorPen 1. 0. 4. 2 software to access the data. The OJIP protocol (Chlorophyll Fluorescence Induction Kinetics,  [64] ) was applied after a 30-min dark-adaptation period before measurements. Photosynthetic activity was monitored at Days 1, 3, 6, and 10 and the ratio Fv/Fm, corresponding to the maximum quantum yield of Photosystem II (PSII), was used to characterize the physiological status of the microalgae, as it is classically done  [65].
Acknowledgments We thank  Abdessalem Shili  from the  Tunisian National Institute of Agronomy  for his help for macrophyte identification and  Vicky Diakou  from the  Technical Platform of Imagery MRI-UM-Biocampus DS Biologie Sante, Montpellier  for her help in epifluorescence microscopy observations. We also thank the four anonymous reviewers for their help in improving our manuscript.
This work was supported by the  LAGUNOTOX  project funded by  Fondation TOTAL. Thanks to LMI COSYS-Med  ( Laboratoire Mixte International Contaminants et Ecosystèmes Marins Sud Mediterraneens ) and to  IRD (Institut de Recherche pour le Developpement)  for funding  Hela Ben Gharbia 's Ph. D. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
| Detailed analysis of stem growth For this analysis, measures of stem length were carried out every 2 days (10 measures) on the control and simulated herbivory groups only. Ant-excluded plants were not included in this experiment, as frequent measurement of stems risked knocking off some of the numerous L. latipennis larvae feeding on those stems. To estimate the time lost in reaching the canopy after real or simulated herbivory, we compared the growth of intact and damaged stems using a repeatedmeasure ANOVA. We then used our data over the 20-day period to estimate by extrapolation the total time lost in reaching the canopy. This lost time includes three potential components: (1) the time required to activate lateral buds after destruction of the apical meristem, (2) the time required to complete a new round of establishment growth, and (3) the effect of herbivory on the rate of exploratory growth.
Larger stems grew more rapidly in length (Figure  5 ) and emerged from the F I G U R E 7 Modeling of growth to the canopy by stems of Dioscorea praehensilis with and without herbivory. Using the data from the 20-day experiment, we estimated by extrapolation the effect of herbivory on the total time lost in reaching the canopy. Assuming a canopy height of 15 m above ground, we took into account the sources of delay we measured: time required to activate lateral buds (4 days), time required to complete the new phase of establishment growth (8 days), and a lasting reduction of up to one-third of the maximal growth rate even 20 days later, during exploratory growth. The major uncertainty was the extent to which there was a long-lasting reduction of growth rates. Depending on the type of analysis conducted, the extension growth rates of cut plants had either attained a slope comparable to that for intact stems by the end of the experiment (Figure  6b ) or a slope that was still up to one-third lower than that for intact plants (Figure  6a ).
ACKNOWLEDGMENTS We thank the  Ministry of Research and Higher Education of the Republic of Cameroon  for permission to conduct this study. The study was supported by a  Foraging Peoples Fellowship  of the L. S. B. Leakey Foundation awarded to E. D. and by a grant from the program " Environnement, Vie, Sociétés " of the  CNRS (French National Centre of Scientific Research). We thank  Gaëlle Loutrel ,  Guillaume Gigot  and our assistants at Kouedjina for help with fieldwork. Wayne Dawson ,  Robert Marquis ,  Mark Rees ,  P. Barry Tomlinson ,  Truman Young , and two anonymous reviewers are thanked for their comments on early versions of the manuscript.
Data normalization Henceforth the term probe will be used to refer to a mRNA, lncRNA, snoRNA or miRNA probe. For mRNAs and other long non-coding RNAs, raw data were composed of 62,976 RNA expressions (probes). A first filter consisted in excluding non-expressed probes. To do so, a detection p-value, provided by Agilent which indicated how well the expressions of the gene were detected in a given sample, was used to filter out undetected probes. Probes, which were expressed in a certain proportion of the samples of interest, were kept for further analysis, leading to 28,244 probes (for the study of differentiation) and 22,935 probes (for the study of BPs on differentiation) in the final datasets. In addition, several normalization steps were applied, such as taking into account housekeeping genes or taking the log 2 of the measures.
The normalization was performed with the R package limma  [23]. For miRNAs, raw data were composed of a total of 62,344 miRNA probes which represented 2,027 miRNAs (30 probes per miRNA + normalization probes). After a preliminary between-array normalization, the first step of the normalization consisted in estimating a summary measure of the miRNA expression using a linear model to take into account the probe affinity over the 30 probes characterizing each one of the miRNA. Secondly, as for the mRNA data, a detection p-value was used to filter out non-expressed miRNA leading to consider 325 (differentiation) or 483 probes (BPs) as expressed. The normalization was performed with the R package AgiMicroRna  [24].
Biological analyses Lists of genes significantly induced or repressed after exposure to BPA were uploaded into Ingenuity Pathway Analysis Software (IPA, Ingenuity Systems, www. ingenuity. com) for biological analysis by comparison with the Ingenuity Knowledge Database. These lists of altered genes were then processed to investigate the functional distribution of the genes, as defined by Gene Ontology. Datasets and known canonical pathway associations were measured by IPA by using a ratio of the number of genes from a dataset that map a specific pathway divided by the total number of genes which map this canonical pathway. Fisher's exact test was used to determine a p-value representing the significance of these associations.
Results All microarray data results were deposited on Gene Expression Omnibus website, (http:// www. ncbi. nlm. nih. gov/geo/) under the accession number GSE98682 which includes both series of mRNA/lncRNA data (GPL16699) and miRNA data (GPL19730). The samples series providing both the raw and normalized data is accessible through the accession numbers from GSM2609729 to GSM2609851.
In addition, we represented the global expression profiles of the significantly differentially expressed miRNAs according to the three bisphenols in a heatmap (Fig  3 ). We observed, just like for mRNAs/lncRNAs, an overall homogeneity within the expression profiles, although we clearly see a separation in the clustering. As previously stated for the mRNAs/lncRNAs, BPF at 10 nM shows an overall reduced expression and the response to BPF at 10 μM is extremely similar to the response to BPA and S at 10nM. Analysis of biological functions. Similarities between the alterations of the profile were assessed using Ingenuity Pathway Analysis software. The mRNA differentially expressed in response to 10 nM and 10 μM exposure of all three bisphenols (BPs) were analyzed: top canonical associated pathways, upstream regulators as well as related diseases and disorders are displayed in Table  5.
Among the upstream regulators, beta-estradiol and ESR1 already known to be targeted by BPA were found as potential targets for all three BPs. In order to validate the microarray data by quantitative PCR, we first screened classical housekeeping gene expressions. POLR2A (Polymerase (RNA) II Subunit A) demonstrated no significant differential expression for the three bisphenols at both doses and was chosen as housekeeping gene (S4 Table ). Taqman qPCR analysis was performed using "best coverage" probes recommended by the manufacturer (S5 Table ). The direction and values of the expression fold-changes obtained by qPCR were consistent with the data from the microarray analysis for eight of the ten genes studied (S6 Table ). SET and SORD showed an up-regulation when analyzed by Taqman qPCR which is opposite to the microarray data.
In the microarray analysis, only one probe among three annotated for SET and SORD was differentially expressed therefore different transcripts were evaluated by microarray probes or by qPCR probes: this explains the observed discrepancies. Without SET and SORD probes, the correlation coefficient obtained by Spearman analysis was r = 0. 708 (p = 2. 56 x 10 -8 ) in expression between the data from the microarray analysis and from Taqman qPCR (S6 Table ). mir-4655-3p expression analyzed by Taqman was very low and was only detectable above the threshold of 35 cycles. We integrated data from the Comparative ToxicoGenomics Database (CTD, http://ctdbase. org/) which tells us if BPA has already been shown to modulate the expression of a given gene. CTD provides manually curated information about chemical-gene/protein interactions, coming from published microarray data.
Discussion We have intended here to make progress in toxicology analyses of common chemicals present in the human environment, by the use of human primary cells chronically exposed to these  For each bisphenol, the five most significantly enriched canonical pathways, top upstream regulators as well as diseases and disorders are displayed with the corresponding p-values. The enrichment is based on the comparison between the number of dysregulated gene involved in a given function in the dataset compared to the number expected by chance. The displayed range of p-values corresponds to the range of p-values encountered for the different annotations in this category. https://doi. org/10. 1371/journal. pone. 0179583. t005 chemicals, at a concentration similar to the range found in human fluids in many studies  [18] , with a hypothesis-free genomic approach analyzing both coding and non-coding RNA levels.
It is worth mentioning that some long intergenic non-coding RNAs and some small nucleolar RNAs were differentially expressed after the exposition to the bisphenols during differentiation. To our knowledge, there is few data reporting the induction of long intergenic noncoding RNAs or small nucleolar RNAs by any bisphenol. The long non-coding RNA HOTAIR was induced in breast cancer cells by BPA  [38]. Fifteen small nucleolar RNAs with C/D motif (SNORD) were shown to be reduced in cultures of primary prostate epithelial cells treated with BPA  [39]. SNORD are noncoding, small nucleolar RNAs known to regulate ribosomal RNA assembly and function, suggesting that bisphenol A and its substitutes may also act through epigenetic modifications, as already reported  [40]. Interestingly, two references were frequently found along comparison of our data with the Comparative ToxicoGenomics Database.
Acknowledgments We thank  David Hot  and the  Transcriptomics and Applied Genomics Group (Inserm U 1019 ,  CNRS UMR 8204 ,  Institut Pasteur de Lille, University of Lille ) for microarrays processing. The authors thank the  UMR 8199 LIGAN-PM Genomics platform (Lille, France)  which belongs to the ' Federation de Recherche' 3508 Labex EGID (European Genomics Institute for Diabetes ;  ANR-10-LABX-46 ) and was supported by the  ANR   Equipex 2010 session  ( ANR-10-EQPX-07-01 ; ' LIGAN-PM ') for support in global data processing and qPCR. The  LIGAN-PM Genomics platform (Lille, France)  is also supported by the  FEDER  and the  Region Nord-Pasde-Calais-Picardie.
This study was funded by the  Centre National de la Recherche Scientifique  (www. cnrs. fr), the  Ministère de l'Ecologie, du Developpement Durable et de l'Energie" (MEDDE)  according to the " Programme National de Recherche sur les Perturbateurs Endocriniens " ( 13-MRES-PNRPE-2-CVS-044 ; http://www. developpement-durable. gouv. fr), and the  European Research Council  (ERC-2011-ADG; https://erc. europa. eu). The authors thank the  UMR 8199 LIGAN-PM Genomics platform (Lille, France)  which belongs to the ' Federation de Recherche' 3508 Labex EGID (European Genomics Institute for Diabetes ;  ANR-10-LABX-46 ) and was supported by the  ANR   Equipex 2010 session  ( ANR-10-EQPX-07-01 ; ' LIGAN-PM ') for support in global data processing and qPCR. The  LIGAN-PM Genomics platform (Lille, France)  is also supported by the  FEDER  and the  Region Nord-Pas-de-Calais-Picardie.
In the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach.
In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling.
(2010). Authors suggested that additional hydrological processes and hydrogen pools could influence the signal (e. g. ,  Franz et al. , 2013a; Baatz et al. , 2014; Baroni and Oswald, 2015) , while others applied recalibration of semiphysical parameters to better fit individual site conditions (e. g. , Rivera  Villarreyes et al. , 2011; Lv et al. , 2014; Iwema et al. , 2015; Heidbüchel et al. , 2016). Despite the unambiguous improvements obtained by corrections and recalibration approaches, some features in many datasets could still not be explained using current knowledge and consequently seemed to be unrelated to hydrological processes. To address some of these knowledge gaps,  Franz et al. (2012b)  investigated soil hydrological processes with water transport simulations and found that wetting and drying cycles are non-uniquely represented by the CRNS signal.
As the understanding of cosmic-ray neutron physics in the environment has been more and more elaborated,  Köhli et al. (2015)  developed a dedicated computer model, URANOS, which helped to understand the spatial sensitivity of the neutron sensor. These authors revealed that the sensor is extraordinarily sensitive to the nearest few meters, rather than following a simple exponential decrease in sensitivity as was reported by  Zreda et al. (2008)  and  Desilets and Zreda (2013). This revision has since changed the way CRNS measurements are interpreted. Their findings extensively describe the footprint volume in which soil water content is measured, and can now be used to develop new weighting approaches and to revisit previous data analysis. The revised footprint and spatial sensitivity of the CRNS probe have since been confirmed by many observations.
Heidbüchel et al. (2016)  were the first to test the impact of the revised function on the performance of their calibration data. Schattan et al. (2017)  applied the revised weighting approach to average complex snow patterns in an alpine terrain. Encouraged by both of their promising results, the present study has hypothesized that this new theory could enable an improved performance of CRNS calibration and validation campaigns for a huge variety of sites and conditions. We further hypothesize that the initially published relation between neutrons and water equivalent  (Desilets et al. , 2010 ) might be widely applicable without the need to calibrate all of its parameters on site-specific conditions. Eventually, an overall improvement of the CRNS data could help to identify hydrological effects more accurately (such as precipitation, ponding, evapotranspiration, and infiltration processes).
The paper is structured as follows: firstly, we present the equally weighted, the conventional, and the revised formulations of the spatial sensitivity function (also called the weighting function). We then provide a procedure to generate a weighted average of point measurements that can be compared with the CRNS product. The assumptions and uncertainties of this approach are then discussed, followed by a short description of measures used to evaluate the calibration performance, and short descriptions of the studied sites. In the results section we present and discuss the sensor performance using the equal, conventional, and revised weighting approaches for calibration campaigns at two different sites, and for time series data at four sites.