dataesr/scientific-paragraphs-categorization huggingface dataset:
com/aaltool) atlas. 39  Only 90 cerebral regions were considered, because the cerebellum is often affected by stronger artifacts and is not always fully covered in the FOV. 38  T1-weighted data were registered to the B 0 volume of the DTI dataset and to the Montreal Neurological Institute space template using the FMRIB Linear Image Registration Tool (FLIRT; http://www. fmrib. ox. ac. uk/fsl/fslwiki/FLIRT). 40, 41  This step allowed the application of the AAL atlas in the DTI space to produce the 90 nodes of the network. The registration and segmentation results were visually inspected for errors. The resultant ROIs were dilated by 1 voxel. To define the connections between the ROIs, we considered only streamlines with at least 5 points, and we used the average FA value within voxels along streamlines connecting the ROIs as a proxy for the connectivity strength.
Statistical Analysis Adolescent Structural Connectivity Analysis. Our main assumption was that aberrant connectivity underlies attentional problems. To test whether significant associations between connectivity and attention exists, we performed t tests using attention as the grouping variable. We did not assume that the connections associated with this contrast of interest form a connected component. To control for the massive number of multiple comparisons, we applied a standard link-based controlling procedure, the false discovery rate (FDR). We used the implementation in Matlab by Zalesky et al  42  with default parameters (a significance value of. 05 and 1000 permutations). In this context, the FDR is referred to as a link-based controlling procedure because each link is treated independently for the sake of the family-wise error rate control.
The rest of the statistical analyses were performed using SPSS Statistics software (Version 25; IBM, Armonk, New York). Neonatal predictors of attentional problems in adolescence were tested using the Kruskal-Wallis test for continuous variables and the Fisher exact test for categoric variables. Neonatal predictors of abnormal structural connectivity associated with attentional problems in teens were investigated using a linear regression model with abnormal connectivity as the dependent variable and neonatal clinical and imaging metrics and the age at the adolescent MR imaging visit as independent variables. Additionally, the number of rejected diffusion volumes affected by motion was compared between the groups of adolescents with normal and abnormal attention using the independent-samples t test.
ACKNOWLEDGMENTS We would like to thank all of the study participants and their parents who made this work possible. We also thank  Drs Bridget Johnson  and  Elizabeth Rogers  for their help with the psychological assessments of the study participants. Finally, we thank  Laurel Haeusslein ,  Veronica de Santiago ,  Shrey Goel , and  Kristyn Casalino  for the study coordination. Disclosures:  Olga Tymofiyeva -RELATED: Grant:  National Institutes of Health , Comments:  National Institutes of Health  grants  P01NS082330 ,  R01HD072074 , and  R21AT009173 *; Support for Travel to Meetings for the Study or Other Purposes:  National Institutes of Health , Comments:  National Institutes of Health  grants  P01NS082330 ,  R01HD072074 , and  R21AT009173 *. Dawn Gano -RELATED: Grant:  National Institutes of Health *; UNRELATED: Grants/Grants Pending: PERF*.
Under ongoing climate change and increasing anthropogenic activity, which continuously challenge ecosystem resilience, an in-depth understanding of ecological processes is urgently needed. Lakes, as providers of numerous ecosystem services, face multiple stressors that threaten their functioning. Harmful cyanobacterial blooms are a persistent problem resulting from nutrient pollution and climate-change induced stressors, like poor transparency, increased water temperature and enhanced stratification. Consistency in data collection and analysis methods is necessary to achieve fully comparable datasets and for statistical validity, avoiding issues linked to disparate data sources. The European Multi Lake Survey (EMLS) in summer 2015 was an initiative among scientists from 27 countries to collect and analyse lake physical, chemical and biological variables in a fully standardized manner.
In EMLS, standardized sampling procedures were strictly followed to ensure data homogeneity and eliminate site or operator related observation effects. Finally, lake samples for nutrients, algal pigments and toxins were analysed in dedicated laboratories by one person on one machine, minimizing variation in analytical errors. Apart from providing a solid research dataset on which several analyses are being conducted, the EMLS helped to enhance the standards for limnological data collection and stimulate international collaboration. A subset of the EMLS toxin dataset has already been used in a recent publication to show how the distribution in cyanobacterial toxins and toxin quota was determined by both direct and indirect effects of temperature  23. Here we additionally present the data for all lakes, including those without toxins for the full set of data.
Organization To make EMLS a robust survey we bridged two European COST Actions 24 , CyanoCOST  25, 26  (Cyanobacterial blooms and toxins in water resources: occurrence impacts and management) and NETLAKE  27  (Networking Lake Observatories in Europe), planting the idea and promoting the benefits of an extended collaboration amongst researchers from all over Europe. This research was expanded to many other scientists not directly involved in these COST Actions. The EMLS protocols required that each group of data providers collected and handled the samples following the same standards. Therefore, the steps outlined below had to be followed by all participant groups to ensure standardized sampling, sample processing and analyses, resulting in a homogenous dataset. Any deviations from these protocols were recorded in the metadata spreadsheets, and these data were handled with care after contacting the data collectors.
Data acquisition Date and location (in situ). The field methods for the EMLS were designed to be completed within one field day for each lake, but typically the sampling itself could be completed within two hours. Remote, poorly accessible lakes required more time to reach and field crews had to plan accordingly. To optimize time investment, lakes in close proximity were covered in one sampling trip. Each sampling group was responsible to organize and prepare the sampling material and equipment for their sampling campaign. In several cases, sampling groups of different areas or even countries collaborated and shared material such as instruments and boats. Each EMLS data collector team had to identify the right sampling period, defined as the warmest twoweek period in summer, based on long-term air temperature data in each region, covering the last 10 or more years.
This predefined time-period served to minimize confounding effects of seasonality. Each lake was sampled within this time window (Date; Table  1 ). The sampling location for each lake was defined as the central point of the lake. If a particular lake had been previously sampled at a specific location for long-term monitoring, the sample location from the long-term monitoring was used instead of the lake centre. For lakes with more than one relatively isolated basin, individual basins were sampled separately when possible, and indicated as such in the dataset (e. g. TR_BEY_I and TR_BEY_II). The latitude and longitude of the sampling location were recorded with a GPS device and provided in decimal degrees according to the WGS84 coordinate system. If cyanobacterial surface blooms (defined as the presence of a visible surface scum) were present close to where the team entered the lake or in close proximity to the sampling point, a second sampling location was considered.
A scoop surface sample, using a small sealable container like a Falcon tube, of the cyanobacterial scum was acquired along with the water column sample. The location of the scum sample was noted. Data collectors also provided -when availablemaximum depth, mean depth and altitude (Table  1 ) of the lake. Temperature and Secchi depth (in situ). Temperature profiles were measured with available probes, such as various CTDs or a Fluoroprobe (BBE Moldaenke). If no profiling instrument was available, water samples were taken at 0. 5 m intervals from the lake surface to the bottom of the thermocline (top of the hypolimnion) and water temperature was measured using hand-held thermometers directly after sampling. Here, the thermocline range was defined as the depth interval at which the rate of temperature decreased at least 1 °C per metre.
The bottom of the thermocline (where temperature no longer decreased by 1 °C per meter), defined the sampling depth in the case of stratified lakes. For lakes where a thermocline was not observed (mostly shallow lakes), the temperature profile was measured until the lake bottom. In this case, the sampling depth was determined at 0. 5 m above the lake bottom. From the temperature profiles, we obtained surface and epilimnetic temperature (Table  1 ) as the average temperature from surface until the bottom of the thermocline. The temperature profiles were also used during data analysis, to calculate the location where the thermocline lies even between two temperature measurement depths, which corresponds to where the thermocline is the most stable (point of maximum buoyancy frequency). To calculate this thermocline depth (ThermoclineDepth_m, Table  1 ) we used the command thermo.
depth from the R package rLakeAnalyzer  28. The Secchi depth (Table  1 ) was also recorded using a Secchi disk to the nearest 0. 05 m. Integrated water sample (in situ). All data collectors constructed a simple device, known as the "Anaconda", using a stoppered hose of the correct length in order to acquire the epilimnetic sample. The hosepipe was lowered with the bottom end open into the water column until the right depth (see above). 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 hydrostatic pressure. The bottom end of the hosepipe was pulled-up with a rope to the surface to collect the sample in a bucket. The diameter of the hosepipe was appropriate to sample the required water volume (about 5-10 L for hypertrophic and eutrophic, 15-30 L for mesotrophic and oligotrophic lakes) for the analyses, in an acceptable number of runs.
The first three sampling runs served the purpose of rinsing the hosepipe, the sampling bucket and the plastic rod. The subsequent runs were the water sample taken for analysis. The water sample in the bucket was mixed adequately before being divided into different bottles for further processing prior to analysis. All samples were shipped frozen using dry ice in Styrofoam boxes. Shipping and storage of the EMLS samples was centralized at the University of Wageningen (The Netherlands). There, samples were sorted and sent to the dedicated laboratories for 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, microcystins and nodularin analyses were done at the University of Wageningen, the pigment analysis at the University of Amsterdam and the cylindrospermopsin and anatoxin analysis at the German Environment Agency.
202 μg/L for chlorophyll-b, 0. 115 μg/L for zeaxanthin, 0. 177 μg/L for diadinoxanthin, 0. 224 μg/L for fucoxanthin, 0. 098 μg/L for diatoxanthin, 0. 077 μg/L for alloxanthin, 0. 284 μg/L for peridinin, 0. 093 μg/L for chlorophyll-c2, 0. 104 μg/L for echinenone, 0. 089 μg/L for lutein and 0. 250 μg/L for violaxanthin. In the cases where no pigment signal was detected, the respective pigment was considered absent and noted as 0 μg/L in the dataset. If the calculated pigment concentration in the dataset is above the limit of detection (qualitatively detected signal) but below the quantification limit (too small to quantify), we suggest the assignment of a very small value of half the detection limit to enable the inclusion of these samples in statistical analyses (if applicable). Alternatively, other statistical approaches that account for data censoring can be followed based on the research question and the statistical analysis followed (for suggestions see  33  ).
Cyanotoxin analysis (laboratory). For toxin analyses, a volume of 50-250 mL for hypertrophic and eutrophic lakes and 500-1000 mL for mesotrophic to oligotrophic lakes, was filtered through 47 mm Glass fibre filters (GF/C or GF/F or similar) using a filtration device. Filters were stored at -20 °C until shipping. In the laboratory, frozen filters were transferred to 8 mL glass tubes and freeze-dried (Alpha 1-2 LD, Martin Christ Gefriertrocknungsanlagen GmbH, Osterode am Harz, Germany). The freeze-dried filters were used for the Liquid Chromatography with tandem Mass Spectrometry detection (LC-MS/MS) analysis of microcystins, nodularin, cylindrospermopsin and anatoxin as described below. In the cases where no toxin signal was detected, the respective toxin was considered absent and noted as 0 μg/L in the dataset.
Wtcc4S5ubRZ The GeoNode open source platform version 2. 0 has been used for sharing the EMLS datasets among the partners. GeoNode is a web-based application that facilitates the visualization, download, sharing, and collaborative use of geospatial data through web services. GeoNode can be easily obtained at http:// geonode. org/ as it is freely available under a GNU General Public License. QGIS 2. 18 Las Palmas was used for creating, managing and uploading the ESRI shapefile layers into the GeoNode platform.
Data Records The final dataset includes all lake, environmental, nutrient, pigment and toxin data in one data table. The description of each feature in the table can be found in Table  1. The data table is made freely available as a static copy, through direct download from the online Environmental Database Initiative (EDI) and it is provided under the name "EMLSdata_10Aug_afterRev_dateformated. csv" (Data Citation 1). The data table is also available at the GeoNode platform (http://gleon. grid. unep. ch/), where it can be downloaded through the provided web services that secure accessibility to data by using interoperable standards as provided by the Open Geospatial Consortium (OGC). The OGC-compliant web services available are 1. the Web Map Service (WMS 1. 1. 1) and 2. the Web Map Tile Service (WMTS 1.
0. 0) for accessing the maps; 3. the Web Feature Service (WFS 1. 1. 0) for accessing vector data and 4. Catalogue Service for the Web (CSW 2. 0. 2) to access the metadata. These interoperable web service endpoints enable the user to easily access and/or integrate these datasets in their desktop, web-based client, or own workflows. The user can find and download several features of the data table as vector layers under the tab "Layers". In the interactive tab "Maps", the user can visualise and download datasets of combined "Layers", or create their own maps using the available layers. The vector layers are provided in several formats such as: ESRI shapefile, Geography Markup Language (GML) and Keyhole Markup Language (KML), JPEG, pdf etc. Both the database and the GeoNode platform can be easily updated with the expected data from DNA and flowcytometry analysis (to follow).
Location data All latitude and longitude records for each lake were verified by checking visually the provided locations on google maps. Lakes were marked as verified under the following conditions: (1) the location on google maps, using either satellite or map view, was found for a lake which matched the provided name, (2) the location on google maps was located close to a water body (approximately 1 km or closer) with a matching name, (3) the location was in or beside a lake which had no name or a different name, but the name provided matched with names of regions in the area (i. e. this represents cases where lakes were named according to their closest city, or region), or (4) the provided location was near an unnamed lake in the correct country where there were no other lakes nearby. All other cases were considered unverified and the data collectors were contacted to provide the right location (Latitude and longitude in decimal degrees; WGS84).
Acknowledgements 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  University of Geneva. We thank  Wendy Beekman  for the nutrient analysis and the  University of Wageningen  for covering the costs of this analysis from the personal funding of dr. Miquel Lürling. We thank  Pieter Slot  for assisting with the pigment analysis and the  University of Amsterdam  for covering the costs of the analysis through funding from the group of  Prof.
Jef Huisman  and dr. Petra Visser  (IBED). We would like to thank the  Environmental Data Initiative  for covering the cost of archiving the EMLS dataset. We would like to thank the  Leibniz Institute of Freshwater Ecology and Inland Fisheries, Dept. of Experimental Limnology  and the  Aquatic Microbial Ecology Group  for logistic and technical support of  J. Fonvielle  and H. -P. Grossart; and the  Leibniz Association  for financial support. The collection of data for Lough Erne and  Lough Neagh  were funded by the  Department of Agriculture, Environment and Rural Affairs, Northern Ireland.
Methodology/Principal findings 4 final reaction primer mixes were obtained by using a combination of Principal Component Analysis of the full DENV genome sequences, and LAMP primer design based on sequence alignments using the LAVA software. These mixes contained 14 (DENV1), 12 (DENV2), 8 (DENV3) and 3 (DENV4) LAMP primer sets. The assays were evaluated with an External Quality Assessment panel from Quality Control for Molecular Diagnostics. The assays were serotype-specific and did not cross-detect with other flaviviruses. The limits of detection, with 95% probability, were 22 (DENV1), 542 (DENV2), 197 (DENV3) and 641 (DENV4) RNA molecules, and 100% reproducibility in the assays was obtained with up to 10 2 (DENV1) and 10 3 RNA molecules (DENV2, DENV3 and DENV4). Validation of the DENV2 assay with blood samples from Tanzania resulted in 23 samples detected by RT-LAMP, demonstrating that the assay is 100% specific and 95.
Analysis of the reactions was conducted using LightCycler software version 4. 1. 1. 21 (Roche). The Institut Pasteur in Dakar performed a qRT-PCR  [43] , using the ABI7500 Fast Realtime PCR System (Applied Biosystems, Foster City, CA). An RT-PCR assay, which simultaneously detects the 4 DENV serotypes, followed by a nested PCR, that specifically detects each DENV serotype, were used  [20].
LAMP primer design A two-step approach was used. First, all available sequences of DENV1 to 4 were downloaded from the NCBI database. Searches were limited to the samples collected between 2004 and 2014. All sequences were then aligned (for each serotype) using GramAlign v3. 0  [44] , and diversity was assessed using the glPCA module of R/adegenet v1. 4. 1  [45]. Finally, based on the Principal Component Analysis (PCA) and phylogenetic tree (Neighbor-Joining tree using the R/ape 3. 2 package), the sequences were manually split into different clusters in order to maximise the region of sequence identity. LAMP DNA signatures for each cluster (and all combinations to minimise the number of primer sets) were designed using a modified version [https:// github. com/pseudogene/lava-dna] of LAVA  [39]  applying the loose parameters set for DENV1-3 and the standard parameter set for DENV4.
Full scripts and methods are available on GitHub at https://github. com/pseudogene/lamp-denv. All the designed sets of primers were first checked for primer dimerisation with the Visua-lOMP version 7. 8. 42. 0 (DNA Software, Ann Arbor, MI). In addition, primer combinations for each of the DENV assays were tested for primer dimerisation by comparing amplification signals in positive and negative controls.
Cross-specificity and cross-detection tests Cross-specificity tests for the four RT-LAMP assays were carried out at the Institut Pasteur (Paris) using the QuantStudio 12K Flex Real-Time PCR System, and results were analysed with the software QuantStudio 12K Flex v1. 2. 2. (Applied Biosystems, Carlsbad, CA). Each of the RT-LAMP assays was tested using 1 μL RNA extracted from the DENV strains described in Table  1. Cross detection of other flaviviruses, ZIKV, Yellow fever virus (YFV), West Nile virus (WNV) and Ntaya virus (NTAV), was analysed using the Genie II (Optigene) at the University of Stirling. The RT-LAMP assays were also tested against several DNA pathogens (Salmonella Typhi, S. Paratyphi, Streptococcus pneumoniae and Plasmodium falciparum). DNA samples were provided by MAST Diagnostica GmbH.
LAMP primer design and evaluation In total 1,145, 477, 376 and 58 genomic sequences were retrieved from the NCBI database for DENV1, DENV2, DENV3 and DENV4, respectively. Each serotype dataset was split into When combining the amplicon primer sets for each RT-LAMP assay, amplification was not observed when using published standard LAMP primer concentrations for each primer set: 0. 2 μM F3, 0. 2 μM B3, 1. 6 μM FIP, 1. 6 μM BIP, 0. 8 μM FLOOP and 0. 8 μM BLOOP. To determine the concentration window of the complicated primer mix, a 2-fold dilution series of the above primer mix was used. Amplification yielding the best possible detection without amplification in the NTC was achieved at a dilution of 1:4 (50 nM F3, 50 nM B3, 400 nM FIP, 400 nM BIP, 200 nM FLOOP and 200 nM BLOOP, Fig  2C).
However, its use for routine screening in dengue epidemics is questioned in terms of clinical necessity  [52]. For molecular RNA detection, nested PCR  [20]  and real time PCR-assays  [21] [22] [23] [24] [25] [26]  with high specificity and sensitivity are being used but need expensive and sophisticated thermocyclers and experienced staff. In recent years, isothermal amplification assays have been described, such as RT-LAMP  [8, 30, [32] [33] [34] [35] [36] [37] [38]  and RT-RPA  [53, 54]. These assays require less expensive equipment and can be delivered in dried pellet format, making handling easier and amenable to poor infrastructure settings. Worldwide monitoring and the use of Next Generation Sequencing methods have increased the number of complete DENV genomes sequenced and deposited in GenBank to 2,988 (as of June 2016).
It is virtually impossible to use this amount of sequence information to manually align and design amplicons for molecular detection methods. There have been several attempts to create algorithms to derive signature sequences for PCR techniques from sequence datasets or alignments  [55, 56]. LAMP amplicons are inherently more challenging to design as they require a minimum of 4 and a maximum of 6 signature sequences. LAVA software was developed to facilitate the determination of signature sequences for LAMP primer design using a set of aligned sequences  [39]. The original and modified version of LAVA take into consideration the limitations observed with other primer-design programs (LAMP DESIGNER [http://www. optigene. co. uk/lamp-designer/] and PRIMER EXPLORER [https:// primerexplorer. jp/e/], such as preventing the use of extensive alignments or sequences longer than 2,000 nt.