dataesr/scientific-paragraphs-categorization huggingface dataset:
LIMITATIONS Despite the scientific interest raised by this meta-analysis, the methodology is not free of limitations. Both publication and language restriction bias may have inflated estimates of the effect. Therefore, care was taken to control for these sources of bias. Two of the databases used in the literature search (Kinpubs and Sport-Discus) included theses and dissertations, thereby allowing access to "gray literature. " Some other limitations specific to the topic of this meta-analysis probably limited the thoroughness of the analyses. For example, the efficiency of a particular recovery technique depends on the type of fatigue induced by previous exercise. Although we coded the characteristics of the training load, it was not possible to address its interaction with the magnitude of the effect, and it might constitute the cornerstone of the global efficiency of a particular recovery strategy.
Hairpin GGLT1 shows a specific decrease in RG-II L-galactose No significant differences were detected in the wall monosaccharide compositions of leaves from soil-grown EV and hpGGLT1 lines (Figure  2d , Data S1). This is not surprising since in primary cell walls D-galactose is far more abundant than L-galactose  (Baydoun and Fry, 1988). Moreover, D-galactose and L-galactose are not separated when the monosaccharide composition of the cell wall is determined by high-performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD). Rhamnogalacturonan-II is the only known L-galactosecontaining polysaccharide present in wild-type Arabidopsis cell walls, so we next determined whether the structure of RG-II differed in hpGGLT1 and EV plants. Material enriched in pectic polysaccharides, including RG-II, was obtained by extracting hpGGLT1 and EV leaf alcohol-insoluble residue (AIR) with ammonium oxalate, a calcium chelator.
Cell Integrity assays Live imaging of roots was performed as previously described  (Feng et al. , 2018). Five-day-old seedlings cultured on 19 MS, 1% (w/v) sucrose pH 5. 7 Gelrite media were transferred to media supplemented with 140 mM NaCl in a moisture chamber made from a 60 9 15-mm Petri dish (Fisher Scientific, AS4052). Brightfield images of the root tip area were captured every 15 min from a Leica DMI6000 inverted compound microscope with HC PL APO 109/0. 4 CS objective (Leica) and LAS software (Leica). Images were then stitched together using the ImageJ 'Pairwise.
Homology modeling of the bacterial EndoMS/NucS We constructed a model of the C. glutamicum EndoMS/NucS dimer with the modelling program Modeller (21) version 9. 18 by using the EndoMS-dsDNA structure carrying a mismatch (PDB code 5GKE) as the template. Five initial models were constructed and the best model was chosen using molpdf and the DOPE score and by evaluating the stereochemical quality with PROCHECK  (22). Molecular graphics and structures superposition were performed with the UCSF Chimera package  (23). Chimera was developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco (supported by NIGMS P41-GM103311).
The products generated by CglEndoMS with and without the Cgl␤-clamp were analyzed by denaturing PAGE. These experiments revealed that CglEndoMS cleaved the third phosphodiester bond on the 5 side of the mismatched base in both strands, to produce a cohesive end with a five-nucleotide long 5protrusion (Figure  4 ). The cleavage pattern by CglEndoMS was not different with or without Cgl␤-clamp (Figure  4B  and C), and was exactly the same as that observed in the presence of TkoEndoMS  (11). This observation is also fully compatible with our structural models of the CglEndoMS dimer using the TkoEndoMS-dsDNA structure (PDB code 5GKE), indicating the excellent superimposition of the catalytic and substrate binding sites (Figure  4D  and E ).
ACKNOWLEDGEMENTS We thank  Takeshi Yamagami  and  Namiko Imai  for technical assistance. We also thank  Christine Houssin  and  Célia De Sousa-d'Auria  ( I2BC ,  Univ. Paris-Sud, France)  for their expertise in C. glutamicum manipulation. We also thank  Julia Gross  for help with DNA sequencing experiments and  Ursula Liebl  for helpful comments on the manuscript.
In the European Multi Lake Survey (EMLS), lakes across the continent were sampled once in a snapshot approach, for physical, chemical and biological parameters, during the summer of 2015. Standardized and commonly practiced field protocols along with centralized laboratory analyses for all parameters other than microscopy were undertaken for all samples, avoiding inconsistencies between data in this large dataset. In addition, we addressed our research questions minimizing confounding effects of seasonality, by sampling all lakes during the locally two warmest weeks of summer based on at least 10-year air temperature records. In this study, we investigated how the distribution of toxin concentration and toxin quota were defined by environmental parameters. We hypothesize that, in an unusually warm summer-like 2015-was in parts of Europe  [42] -temperature, either through direct (surface temperature, epilimnetic temperature) or indirect (water stability expressed as maximum buoyancy frequency) effects, strongly influences the distribution of toxin concentrations and toxin quota.
Discussion Our study shows that MCs were, by far, the most abundant cyanotoxins across the European lakes in our dataset, being detected at greater frequency than either CYN or ATX (Table  1 ). However, it is important to note that we analysed only the intracellular toxin content on filter samples, which might have resulted in an underestimation of CYN or ATX concentrations, as they can be largely extracellular  [36]. We found that among the microcystins, MC-LR was only the third most abundant microcystin variant, after MC-YR and MC-dmLR (Table  1 ). MC-dmRR was the least common toxin in the EMLS lakes, but showed the highest concentrations (up to 14 µg/L). Furthermore, CYN was detected less frequently, but, in several cases, it was the only toxin detected that could indicate that CYN producers might have a potential to exclude the producers of other toxin variants (Figure  1 ).
Samples for microscopy were taken, but each participating laboratory counted these locally, using different quality microscopes and varying levels of taxonomic expertise. Given a recent discussion  [46]  on problems with the trustworthiness, even at the genus level, of a long-term phytoplankton dataset in which all samples were counted by a small team of experts, supervised by the same person over the years, we could not trustfully use microscopic counts from so many different labs in our study. Moreover, in recent studies, there is a strong tendency to focus on key functional traits as the focal point of phytoplankton ecology (e. g. ,  [47] ). Cyanobacterial key traits, rather than taxonomic relatedness are also the basis for successful management of cyanobacterial blooms  [48]. For the purpose of this study, we examined toxins as functional traits and aimed to study how much we can understand about the spatial drivers of toxin abundance by just focusing on the traits themselves.
Toxin diversity and richness showed a significant increase with latitude, which means that northern areas exhibited a higher toxin diversity (Figure  3 , Table  4 ). In a parallel study based on the same lake dataset, Mantzouki et al. (in preparation) showed that, during summer 2015, a significantly higher algal-and specifically cyanobacterial-biomass was found in the Boreal climatic zone, compared to lakes in Continental and Mediterranean climate. The higher cyanobacterial biomass was potentially explained by the heat wave that occurred mainly in northern European regions (NOAA online data). The majority of the Boreal EMLS lakes (75%) were sampled during a two week-period where temperature anomalies exceeded the local long-term average summer temperature by +5 • C, compared to a much smaller temperature anomaly in the Mediterranean lakes (+1.
This leads to the selection of metalimnetic species with a very specific set of functional traits, like well-controlled buoyancy regulation and elevated phycoerythrin content  [62]. Under such conditions where a single cyanobacterium monopolizes the resources, we may expect that the low cyanobacterial diversity leads to a low toxin diversity (Table  3 ). On the other hand, in lakes with less stringent environmental conditions, like the more shallow and eutrophic lakes in the EMLS dataset, the scope for co-existence of several less specialised cyanobacterial species is enhanced, hence a more diverse toxin community might be established. In the EMLS dataset, high MC concentrations did not exclude production of the other two toxin classes (neurotoxins, cytotoxins), but rather increased the probability of CYN and ATX occurrence together with MCs, resulting in an increase in the TDI (Table  4 ).
Sampling Survey The European Multi Lake Survey (EMLS) was organized by 26 European countries during summer 2015, with each participating group using their own financial means to conduct their sampling. Since the EMLS was a voluntary effort, individual countries contributed samples from lakes that they routinely sampled, and these were typically lakes with a history of eutrophication. A total of 369 lakes were sampled using standardized protocols for sampling, processing and preserving. Sampling took place during the two warmest weeks of the summer, which was specific for each region. Data collectors identified the correct sampling period using long-term air temperature data spanning at least 10 years. The sampling location was either the historically sampled location, or the centre of the lake if the lake was not routinely sampled.
A temperature profile, with a minimum required resolution of 0. 5 m sampling intervals, served to define the sampling depth. An integrated water column sample, which will henceforth be referred to as epilimnetic sample, was taken from 0. 5 m below the surface until the bottom of the thermocline. This was defined as the point where there was a ≥1 • C temperature change per meter lake depth. If the lake was shallow, then the entire water column was sampled until 0. 5 m above the lake bottom. All data collectors constructed a simple device using a stoppered hose of the correct length in order to acquire the epilimnetic sample. The hosepipe was lowered with the bottom end open in the water, at a depth of just under the end bottom of the thermocline. When the hosepipe was vertical and the water level was visible at the surface layer of the hosepipe, then the stopper was inserted to create vacuum pressure.
The filters were stored in -20 • C and in the dark until shipping. For analyses of total nutrients, unfiltered water subsamples of 50 mL were stored in -20 • C until shipping. All samples were shipped frozen using dry ice in Styrofoam boxes. All participating countries took part in a one-week training school to discuss and practice all field procedures. All samples were shipped to and stored at the University of Wageningen (Netherlands) until further analysis. Each of the nutrients, pigments and toxins analyses were done in one dedicated laboratory, by one operator on one machine, to minimize analytical errors and maximize integration of the datasets. Specifically, the nutrients, MCs and NOD analyses were done at the University of Wageningen; the pigment analysis at the University of Amsterdam; and the CYN and ATX analysis at the German Environment Agency.
We interpolated all the temperature profiles at a 0. 5 m resolution to standardize the data, as most of the profiles were obtained at a higher resolution than the required minimum interval of 0. 5 m. From the interpolated profiles, we calculated the epilimnetic temperature as the average temperature from surface until the bottom of the thermocline. The surface temperature value corresponded to the surface temperature. We calculated maximum buoyancy frequency (BuoyFreq) as a metric of stratification strength  [75]. In the rLakeAnalyzer package  [76]  in R 3. 3. 3. statistical software (R Core Team, Vienna, Austria), temperature profiles were used to estimate profiles of buoyancy frequency (N 2 ). N 2 is defined as the Brunt-Väisälä equation: N 2 = -(g/ 0 ) × (δ (z) /δ (z) ), where g is the gravitational acceleration, 0 is the density at each depth, and δ (Z) /δ z is the density gradient.
We used a total of 137 out of the 369 sampled lakes in order to test our hypotheses. Selection of a smaller subset was justified since, in some samples, several environmental variables were missing either due to shipping issues or due to deviations from preservation protocols that could affect the integrity of the dataset. Additionally, in order to build the ordination approaches described further down, lakes that had zero concentrations in all toxin variants needed to be excluded to build a meaningful similarity matrix of toxins. Hence, it was necessary to sacrifice a big number of lakes to build a concise dataset where all parameters could be used to select the right model and test the toxin distribution in the EMLS. Samples that were below the limit of detection (LOD), i. e. , a toxin signal was detected qualitatively, but it was too weak to quantify, and was assigned a very small value of half the limit of quantification (LOQ), enabling their inclusion in the analysis.
The toxins MC-LF and NOD-which were found only in two lakes-and MC-LY and MC-LW-which were absent from all lakes-were removed from the analysis following the approach of  [79]  for the most rare species in a dataset. Any statistical results included in this paper correspond to the subset of the 137 EMLS lakes. Supplementary Material provides a table with information on the total number of lakes where toxin variants were (a) not present (no toxin signal); (b) present; and (c) missing, for the 369 EMLS lakes (Table  S1 ). All response variables and environmental parameters, along with the Toxin Diversity Index (TDI) and toxin Richness of the 137-lake subset are provided in the Supplementary Material (Table  S2 ).
Acknowledgments: The authors acknowledge  COST Action ES 1105  " CYANOCOST-Cyanobacterial  blooms and toxins in water resources: Occurrence impacts and management" and  COST Action ES 1201  " NETLAKE-Networking Lake Observatories in Europe"  for contributing to this study through networking and knowledge sharing with European experts in the field. Evanthia Mantzouki  was supported by a grant from the  Swiss State Secretariat for Education, Research and Innovation (SERI)  to  Bas Ibelings  and by supplementary funding from the  University of Geneva. We thank  Clare Ahnlund ,  Ena Suarez  and  Irene Gallego  for helping out with the Swiss survey. We thank  Wendy Beekman  and  Els J. Faassen  for the nutrient and toxin analysis.
Winslow, L. A. ; Read, J. S. ; Woolway, R. I. ; Brentrup, J. A. ; Leach, T. H. ; Zwart, J. A. rLakeAnalyzer: Lake Physics Tools. Zenodo. Available online: https://zenodo. org/record/1003169#. Ws82F9NubEY (accessed on 6 October 2017).
Prenatal diagnosis, care and management are involved in mortality and morbidity of every country. A high prevalence is estimated in Africa. We use bibliometrics and mapping tools to explore the area studies and countries involved in scientific research on prenatal diagnosis, care and management in Africa. We used two databases: Web of Science and Pubmed. We extracted sets of data as publication years, organizations, funding agencies, countries from Web of Science core collection database and Medical Subject Headings from Pubmed database. We mapped the data using VOSviewer. We performed keyword analysis. We accessed 463 articles published between 1956 and 2015 in Web of Science Core collection Database and 3372 from Pubmed database. The majority of which were after 2004. The main countries involved in research on prenatal field in Africa were the USA, the United Kingdom, France and South Africa.
Methods We used previously described methods  [5] [6] [7]. Briefly, we accessed through two databases: the Science Citation Index-Expanded (SCIE) database Core collection, from the Web of Science (WOS) platform Thomson Reuters and Pubmed database. Concerning the WOS database, in the advanced search from WOS, we obtained the articles using this formula: TS = (Prenatal and Africa) for the period 1956-2015. We verified each record to ensure its relevance. And we verified the author's affiliations. There were no restrictions regarding the document types. Then, we performed the "analysis results" function of WOS. We extracted: countries, funding agencies, organizations, publication years and Web of Science categories. In order to analyze the Web of Science categories, we exported the date into a file "analyze.
txt". This file can be read by the program wc10. exe. It generated map-files for VOS viewer  [8] [9] [10]. These analyses were to compare with the search TS = Prenatal in WOS database, not restricted to Africa. We added search from Pubmed. We obtained publications using this formula: (Prenatal AND Africa) AND ("1950"[Date -Publication]: "2015/12/31"[Date -Publication]). We extracted publication years. We extracted data with MEDLINE file. We then analyzed Medical Subject Headings (MeSH) and we generated VOS-viewer diagram as described  [8] [9] [10]. Finally, we performed several keyword researches to get the main relevant topics  [5].
Results Using WOS core collection database, we obtained 463 records. More of the records were after 2004 (Figure  1 ). The main research funding agencies were Bill and Melinda Gates Foundation, the NIH and the University of Cape Town (South Africa). The four most represented institutions were the University of Cape-Town (South Africa) (12%), The University of Witwatersrand (South Africa) (7%), the University of California (USA) (6%) and Harvard University (USA) (6%). The main countries involved were the United States of America (42. 7%) and South Africa (27. 6%). We presented the countries involved according to the percentage of publications found in WOS database (Figure  2 ). We used VOSviewer to map the Web of Science Categories (Figure  3 ). The most important categories were obstetrics gynecology and public environmental health.
We compare it to the map of the search without restraining to Africa (Figure  4 ). The most important category was obstetrics and gynecology. We then compared the data from WOS database with another database: Pubmed. We extracted the Medical Subject Headings (MeSH) from the search "Prenatal and Africa" between 1950 and 2015. 3372 articles were found. We mapped with VOSviewer the MeSH (Figure  5 ). Finally, keyword analysis revealed two main diseases: fetal alcohol syndrome and HIV.
Discussion Most articles were published after 2004 according to the analysis of two databases Pubmed and WOS. This is in accordance with indexation and publication rates of previous studies  [5] [6] [7]. On the one hand, the increase of publications after 2006 is concordant with the general increase. On the other hand, this increase is concordant with new international policies on healthcare in Africa  [1] [2] [3]. South Africa is the main African country involved in prenatal diagnosis, care and management. This is a confirmation of previous general studies on the leading role of South Africa in the continent  [5]. In Western Africa, Nigeria and Cameroon are involved in research on prenatal care as well as Kenya and Tanzania in Eastern Africa (Figure  2 ). Nevertheless, the involvement of these countries is less important than South Africa.
n. or intradermally (i. d. ) for three times at 2 weeks apart with 10 μg of TCTA1T or 2 μg of CT. The sera were harvested at 2 weeks after the last immunization. The 96well ELISA plates were precoated with CTA1 (1 μg/ml) or CT (2 μg/ml) and CTA1-or CT-specific Ab titers were determined by ELISA as described above. 2. 13. Statistical Analysis. Statistical tests were subjected to one-way analysis of variance (ANOVA) using SPSS software (SPSS Inc. , Chicago, IL). A P value of less than 0. 05 was considered significant.
Meanwhile, the levels of OVA-specific serum IgG1 and IgG2a titers elicited by CTA1 were 4-fold lower than those induced by TCTA1T (data not shown). In addition, we observed that IgG1 was the predominant isotype in the sera of mice that had received OVA with TCTA1T, TmCTA1T, or CT (Figure  2(a) ). Various aspects of mucosal immunity serve critical functions in the defense against respiratory pathogens; one of which is the production and secretion of mucosal IgAs  [3]. To examine whether adjuvantation of OVA with TCTA1T also enhances the mucosal IgA responses, OVA-specific mucosal IgA titers were examined in saliva, nasal wash, BAL, and lung tissues of the immunized mice. While i. n. delivery of OVA alone failed to induce OVA-specific IgAs, significant levels of OVA-specific sIgAs were observed in all external secretions of mice that received OVA immunization with TCTA1T or CT.
Figure5: In vitro and in vivo toxicities of TCTA1T. (a) 20 μg of TCTA1T and TmCTA1T or 1 μg of CT in 10 μl of PBS was injected into the hind paw. The thickness of the footpad was measured after 24 hrs. (b) Mice were anesthetized with ketamine, and abdomen was opened to make a loop in the small intestine. Then, 10 μg of TCTA1T and TmCTA1T, or 2 μg of CT, in 100 μl of PBS were injected into the loop. The ratio of weight to length was determined after 6 hrs and the data expressed as milligrams per centimeter (mg/cm) of fluid accumulation. (c) BHK21 cells were seeded at 1 × 10 6 cells/well in a 6-well plate. The cells were incubated with 10 μg of TCTA1T and TmCTA1T, or 1 μg of CT for 3 hrs. The concentration of cAMP in the supernatant was measured by ELISA according to the manufacturer's instructions.
Acknowledgements. This research is supported by the  Netherlands Organization for Scientific Research  ( NWO-grant 821. 01. 007 ). The lidar data were generously provided by  INEGI, Mexico. We acknowledge  Philippe Martinez ,  Jacques Giraudeau  and  Pierre Carbonel  for the XRF core scan measurements, and we would like to thank  Peter Douglas ,  Pete Akers  and  Gerald Haug  for providing their data. We thank  Konrad Hughen  for valuable suggestions for updating the age-depth model for Cariaco's sediment core  1002D. We thank  Ton Markus  for improving the figures and  Mark Brenner  and an anonymous reviewer for their valuable comments for improving the paper. Edited by: Alessio Rovere Reviewed by:  Mark Brenner  and one anonymous referee.
Data availability. The relative beach ridge elevation record, spanning the Pre-Classic period, as well as the complete PC-1, amorphous silica, charred plant fragments and flood layer record of lake Tuspan core C (Fig. 4a), are available at the PANGAEA website (Nooren et al. , 2018). Clim. Past, 14, 1253Past, 14,  -1273Past, 14,  , 2018     www. clim-past. net/14/1253/2018/.
The survey was delivered electronically (via SurveyMonkey). The GKHA questionnaire was divided into 2 sections that addressed the core areas of countries and regional capacity for kidney care delivery. A detailed description of all survey sections has been reported elsewhere. 22, 46 his study focused on the "Health Workforce for Nephrology" module of the GKHA survey. It consisted of 9 items subdivided into 2 components: existing manpower capacity and nephrology training capacity. Survey respondents were asked about the absolute numbers of nephrologists and nephrology trainees in their country. Density was calculated by measuring the median of absolute numbers of nephrologists and/or nephrology trainees reported by at least 3 key representatives of each country and then converting it to PMP to estimate numbers relative to the population size.