dataesr/scientific-paragraphs-categorization huggingface dataset:
U) groups after 9 months of chronic radionuclide ingestion through drinking water (40 mg/L)   (Smith et al. 2006). Peak picking was performed with the 'cent-Wave' method ('peakwidth' parameter reduced to 3-15 s to fit to UPLC performance, and 'snthresh' to 5 to detect more peaks), retention time correction with the obiwarp method ('profStep' reduced to 0. 1 m/z as recommended for QTOF mass spectrometers), peak grouping with 'bw' and 'mzwidth' parameters reduced to 5 and 0. 025, respectively, and gap filling with the default parameters. To check for and ensure data quality, three successive filtering steps were applied to the pre-processed data using an in-house script ran on R, based on the signal/noise ratio of each MS feature in the pool samples (cutoff set at 5 between features matching both in biological and blank samples), the coefficient of variation of the intensity of the variables in the pool samples (cutoff set at 20 %) and the coefficient of the autocorrelation of the variables in a given 'pcgroup' (cutoff set at 0.
Statistical analyses The most discriminatory metabolites were sought in the dataset based on partial least-square discriminant analysis (PLS-DA) using the variable importance in projection (VIP) procedure. All multivariate statistical analyses were performed with SIMCA-P? 12. 0 software (Umetrics, Umea ˚, Sweden). For each discriminatory compound, a receiver operating characteristic (ROC) curve as well as the area under this ROC curve (AUC) were computed with the R package pROC. A 95 % confidence interval was assessed for sensitivity with 2000 bootstrap replicates. A boxplot by group was calculated for each compound selected.
Metabolite identification The 40 most discriminatory metabolites were searched in the MZedDB, a web browser screening several databases simultaneously, for tentative annotation using the accurate measured mass (accuracy \15 ppm) (http://maltese. dbs. aber. ac. uk:8888/hrmet/index. html)  (Draper et al. 2009 ) and the Bru ¨ker Data Analysis molecular formula engine, using isotopic patterns. The KEGG compound ID of any hits was recorded, and all recorded IDs were inserted into the KEGG Mapper (http://www. genome. jp/kegg/tool/map_ pathway2. html) for tentative pathway identification. For the compounds involved in the first four proposed pathways, a standard molecule was purchased, when available, and subjected to MS/MS analysis. We compared spectral data obtained with standard molecules and experimental samples to one another and searched for matches in the Metlin (http://metlin.
Urinary metabolites responsible for discriminating between control and contaminated rats Of the top 40 discriminatory metabolites in urine samples (Table  2 ), 36 were tentatively annotated with the MZedDB database browser, and 28 had a corresponding KEGG ID. Fourteen of these 28 were mapped in pathways according to the KEGG Mapper search: 8 in the tryptophan metabolism pathway, 7 in global metabolic pathways, and 2 in the nicotinate and nicotinamide metabolism pathway. Eight of these 14 molecules were available from suppliers and purchased for further analytical confirmation. N1-methylnicotinamide (also called trigonellinamide), creatine and 4,6-dihydroxyquinoline were proposed to be [M?H]1? forms of the molecules, so the target masses for MS/MS analyses were the same for the standards and the U50 urine sample, which had the highest intensities for these metabolites.
057, is present in U50 at a high intensity (and also in MassBank database at a much lower intensity). The commensurate increase of creatine peak height at 31 s by spiking the urine sample with various amount of an authentic standard also brought a supplementary chromatographic confirmation. There was no spectrum match for 4,6-dihydroxyquinoline in either Metlin, MassBank or HMDB, due to the lack of MS/MS data in these databases for this particular compound. Nevertheless two quite superimposable fragment spectra were obtained for the standard and U50 samples at 138. 6 s retention time (supplemental Fig. 3 ). The main fragments obtained at 20 eV collision energy were the precursor ion (m/z 162. 05) and a fragment at m/z 144. 05 (loss of a water molecule). Spiking the urine sample with the authentic standard also brought a chromatographic confirmation.
2. 3. 1 (supplemental Fig. 5 ). Thus that this converting enzyme can be a target for nat. U biological effect. In addition, as pointed out above uranium interferes with the cytochrome P450, Vitamin D, cholesterol, and xenobiotic metabolisms: all these pathways use the enzyme cofactor nicotinamide adenine dinucleotide phosphate (NADP). Further targeted searches on this pathway would probably be especially useful to decrypt the underlying mechanisms by which uranium acts on these metabolisms. We also identified creatine, 4,6-dihydroxyquinoline, and 5-hydroxyindoleacetic acid as highly discriminatory features (ranked 6th, 11th, and 32nd in the PLS-DA model, respectively). Based on these identified metabolites and 10 tentatively annotated ones (based on database searching), chronic low-dose nat.
Acknowledgments This study was part of the  ENVIRHOM research program  supported by the  Institut de Radioprotection et de SureteNucleaire (IRSN). We wish to thank  F. Voyer  and  T. Loiseau  for expert animal care and  J. -M. Bertho  for reviewing the manuscript. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
All optical properties directly measured (backscatter, extinction, and particle linear depolarization ratio) are stored in the EARLINET database available at www. earlinet. org. A specific relational database providing the volcanic mask over Europe, realized ad hoc for this specific event, has been developed and is available on request at www. earlinet. org. During the first days after the eruption, volcanic particles were detected over Central Europe within a wide range of altitudes, from the upper troposphere down to the local planetary boundary layer (PBL). After 19 April 2010, volcanic particles were detected over southern and south-eastern Europe. During the first half of May (5-15 May), material emitted by the Eyjafjallajökull volcano was detected over Spain and Portugal and then over the Mediterranean and the Balkans.
EARLINET and its observations during the volcanic event EARLINET, established in 2000, is the first coordinated aerosol lidar network whose key remit is the provision of a comprehensive, quantitative, and statistically significant database on the spatial and temporal aerosol distribution on a continental scale  (Bösenberg, et al. , 2001). At present, the network includes 27 stations distributed over Europe (see Table  1 ). More information about EARLINET can be found at www. earlinet. org. Lidar observations within the network are performed on a regular schedule since May 2000, allowing for the collection of long-term data sets for climatological studies  (Matthias et al. , 2004a). In addition to the routine measurements, further observations are devoted to the monitoring of special events such as Saharan dust outbreaks  (Ansmann et al.
Efforts to guarantee and improve the quality of data derived from observations in EAR-LINET are continuous  (Freudenthaler et al. , 2010). All measured profiles are stored in a standardized data format in a centralized database which allows for easy access to the complete data set for further scientific studies. Presently, the EARLINET database represents the largest collection of ground-based data of the vertical aerosol distribution on a continental scale. For the occurrence of special atmospheric events an alerting system has been implemented in EARLINET. In the case of the volcanic eruption of Eyjafjallajökull, an alert was sent to the whole network on 15 April 2010. The alert was based on information from different sources: (i) the Icelandic Meteorological Office Bulletin for the daily status of the volcanic event, (ii) the ash dispersion models provided by the volcanic ash advisory center (VAAC, http://www.
metoffice. gov. uk/aviation/vaac/vaacuk vag. html), (iii) the European air pollution dispersion (EURAD) model (http://www. eurad. uni-koeln. de/index e. html), and (iv) available satellite data (as those provided by MODIS, SEVIRI, OMI, CALIPSO). On 15 April the eruption plume reached continental Europe, causing closure of the airspace over large parts of Northern Europe. On the same day at 10:00 UTC an alert from CNR-IMAA, Potenza, was sent to all EARLINET stations informing them about a large amount of ash being transported towards the northwest of Europe. Almost all EAR-LINET stations promptly started continuous measurements, whenever weather conditions allowed it. Despite the fact that EARLINET stations are not truly operational, the lidar systems were run almost continuously, weather permitting, coordinated by daily alerts.
More than 5000 h of measurements were performed day and night during the period 15 April-26 May 2010. Thus, the evolution of the volcanic cloud was monitored, providing, for the first time, a four-dimensional distribution of the volcanic cloud over Europe for the entire event. Even though the EAR-LINET observations were initially driven by scientific interest, it became obvious after a few days that the information obtained from the coordinated effort of the network would be very useful for European aviation authorities. A page with the quicklook data of the lidar range-corrected signals was made available on the EARLINET website in order to provide near-real-time qualitative information about the presence and altitudes of the volcanic cloud over Europe. The quicklooks were accompanied with a measurement report (updated daily) to explain the significance of the data at the time.
The aerosol mask methodology provides snapshots of distinguishable aerosol type distributions over Europe with one hour temporal and high vertical resolution and unprecedented sensitivity to aerosol presence. The mask was generated with a temporal resolution of 1 h in order to be able to compare the dataset directly with results from models (e. g. , ECMWF) which typically provide data with a one-hour time resolution. The volcanic mask is provided with the best possible effective vertical resolution, as determined individually by each group within EARLINET, typically between 60 and 180 m. Only backscatter data with a relative statistical error lower than 50 % are considered in order to gain a reliable aerosol mask. The aerosol mask methodology is described in detail by  Mona et al. (2012). The most relevant points are therefore only summarized here in brief.
The first step is the layer identification through the first derivative of the particle backscatter profiles. Tests performed on several EARLINET station data identified 30 % as a reasonable statistical error limit for the application of the derivative method. At altitudes where the derivative method is not applicable, because the signal-to-noise ratio is too low, layers are identified as those regions where the scattering ratio (i. e. the total-to-molecular backscatter ratio) is higher than a pre-defined threshold chosen as the value for typical aerosol background conditions plus 15 %. The second step in the procedure is to perform a rigorous cloud screening on the data and to assign an aerosol type to each identified layer. Cloud screening is performed manually by each station (low clouds) and in a centralized way (cirrus clouds).
The particle layer identification and typing is performed for each station on individual backscatter profiles. A consistency check is carried out on the temporal evolution of the resulting layering for each station. A further check is performed by taking advantage of the geographical distribution of EARLINET stations. In particular, stations located at relatively short distances (below 500 km) from each other can be considered representative for a specific region, giving us the opportunity to also study local phenomena. When a doubtful atmospheric scenario is observed in a specific region, multiwavelength Raman lidar data from at least one close station, including lidar ratio, Ångström exponent, and particle linear depolarization ratio, are used as additional information supporting aerosol typing.
Results: 4-D distribution of volcanic aerosol over Europe The methodology described in Sect. 3 was applied to all EARLINET data provided individually by each station with the required 1 h temporal averaging. In this way, information about the aerosol layering and types within the whole network was gathered. Geometrical properties of the volcanic cloud over Europe are presented with high resolution (typically 60-180 m) in terms of the base and top of the volcanic layer. Once the top and the base of a layer are identified, the center of mass of the aerosol layer can be also estimated from lidar profiles. The center of mass gives us information about  the altitude where the most relevant part of the aerosol load is located. In absence of wind, the temporal evolution of the center of mass of the aerosol layer could give insight about the dynamics of the whole layer.
Stations are listed in order of decreasing latitude, i. e. from North (top) to South (bottom) for each cluster. Further data from other stations (Andoya, Cork, Garmisch-Partenkirchen, Neuchatel, Payerne, Sofia, Barcelona, and Madrid) are not shown in the figures, but are available in the EARLINET database. Starting from 15 April, 00:00 UTC, the following quantities are provided hourly: center of mass of the identified volcanic layer (multiple layers are regarded as a unique extended layer), base and top of the identified volcanic layer. These quantities are shown as black dots, blue lines, and cyan lines, respectively. Scenarios where a layer with volcanic aerosol mixed with another kind of aerosol are noted as well. For these cases the center of mass of the mixed layer is indicated. Mixes with dust and continental aerosol are highlighted by orange and dark yellow symbols, respectively.
Multi-wavelength Raman lidar data from Granada were also used to retrieve microphysical properties during the night from 7 to 8 May. Particle effective radius ranged from 0. 30 ± 0. 11 µm to 0. 55 ± 0. 13 µm in the volcanic plume along this night. This study indicated that the volcanic plume over this station was mainly composed by sulfate and sulphuric acid droplets  (Navas-Guzmán et al. , 2013). In the Eastern Mediterranean cluster, Bucharest (bu), Thessaloniki (th), and Athens (at), generally less volcanic aerosol occurrences were recorded (see Fig. 8 ). The typical height of the center of mass of the volcanic layer has been observed at about 2. 5 km over Athens and Thessaloniki and at a higher altitude (around 4. 6 km) over Bucharest. These values observed over Greece were lower in comparison to those observed over Central Europe and in good agreement with data from Southern Italy.
Integrated backscatter at 532 nm is reported because the dataset of this optical property is the most abundant. For each cluster the mean  integrated backscatter evaluated inside the volcanic aerosol layer identified and discussed above and averaged inside the cluster is reported for each hour of observation. As a reference, the IB as measured at a representative site inside each cluster is reported too. Three main periods (15-26 April, 5-13 May and 17-20 May) are observed by the network and are characterized by: different vertical distributions (discussed above), different amounts of volcanic particles, specific horizontal path, and different modification and dispersion processes over Europe. During the first phase (15-26 April), the volcanic cloud moved from the CE cluster down to the CM and then to EM.
This is probably related to differences in the time of reported model/observations, but could also be the effect of missing observations due to bad weather conditions and/or to EURAD model uncertainties at the boundary. A detailed and quantitative comparison between models and observations would require a devoted study with a strong contribution by the modelers. It is therefore beyond the scope of this paper and could be the object of further investigations. For these and other studies, the database of EARLINET observations of volcanic cloud over Europe will be of great value. In particular, geometrical data for the layers related to the volcanic cloud have been collected and are publicly available on request in a relational database. More details about the Eyjafjallajökull 2010 EARLINET relational database can be found in the data user manual available on the EARLINET website.
This database, specifically set up for this event, contains details about layers identified as volcanic (ash, sulfates, and modified volcanic particles) and mixed layers involving the presence of volcanic aerosol (e. g. , volcanic-dust and volcanic-locally mixed layers). For each layer, base, top, and center of mass altitudes are reported together with aerosol backscatter mean and standard deviation values, integrated backscatter, and mean relative aerosol backscatter statistical errors evaluated within the layer. Each layer is linked to the corresponding aerosol backscatter profile available in the EARLINET database. The following information is provided for each aerosol backscatter profile: station name and coordinates, time, minimum and maximum height investigated within each profile, first altitude above the fixed threshold in the relative error, PBL height, and the indication of whether there was an intrusion into the PBL.
The name of the corresponding original file (netCDF) contained in the public EAR-LINET database (www. earlinet. org) is also reported for further exploitation. The Eyjafjallajökull 2010 EARLINET relational database is available on request through the related link on the EARLINET website. Quantitative optical data, specifically aerosol backscatter and extinction coefficient profiles and particle linear depolarization ratio at different wavelengths, are available in the EARLINET database (www. earlinet. org) in the volcanic category. From these data, it is possible to derive lidar ratio (extinction-to-backscatter ratio) and Ångström exponent data. This kind of spectral extensive and intensive optical data are useful for aerosol typing and for the retrieval of microphysical properties  (Müller et al.
The results about the 4-D distribution of the volcanic cloud are reported in a specific relational database available on request through the EARLINET website. Quantitative optical data, specifically aerosol backscatter and extinction coefficient profiles, linear depolarization ratio at different wavelengths, and derived lidar ratio and Ångström exponent profiles, are available through the EAR-LINET database (www. earlinet. org) in the volcanic category. Typical optical data observed for this particular event have been also reported in this publication. EARLINET data collected for this event, including the specific relational database related to the geometrical properties of the volcanic cloud,.
Acknowledgements. The financial support for EARLINET by the  European Union  under grant  RICA 025991  in the  Sixth Framework Programme  is gratefully acknowledged. Since 2011 EARLINET has been integrated in the  ACTRIS Research Infrastructure Project  supported by the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 262254. The authors acknowledge the  ESA  financial support under the  ES-RIN  contract  21769/08/I-OL  and  22202/09/I-EC. Additional financial support has been received from the  Andalusia Regional Government  through project  P10-RNM-6299  and from the  Spanish Ministry of Science and Technology  through projects  CGL2010-18782 , and  CSD2007-00067.
When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance.
We experiment all our kernel frameworks over the multidomain proteins from Genolevures Yeast database  [27]. The performance of our combined spectral kernels with modified symmetry are compared to other state-of-the-art combined cluster kernel methods. The experimental outcomes also demonstrate the superiority of introducing modified-symmetry over kernel space with spectral clustering to detect remote homologs more accurately even for multi-domain and promiscuous domain proteins. Moreover statistical and quantitative performance evaluations with five validity measures to demonstrate the significance of our proposed approaches are also performed. We also study the comparative results over our chosen dataset provided by the already-existing string  [28]  and mismatch  [22]  kernels with our proposed kernels.
Data The Genolevures database explores nine complete genomes (Candida glabrata, Eremothecium gossipii, Kluyveromyces Lactis, Yarrowlo lipotytica, Zygosaccharomyces rouxii, Saccharomyces kluyveri, Kluyveromyces thermotolerans, Debaryomyces hansenii, Saccharomyces cerevisiae)  [31] ,  [27]  from the class of Hemiascomycete yeasts. The non-redundant protein-family database was generated by progressively taking protein-coding gene-sequences following the family structures of Genolevures Release 3 candidate 3 data (2008-09-24)  [32] [33]. We use 323 sequences as unlabelled data from 23 Multiple choice families GL3M: Ã which are complicated families like polyproteins and repeat-domains. Therefore proper homology detection among them is suitable for our remote homology experiments. We use the Genolevures Release-3 candidate-3  [32]  family structure as the true-clusters for ROC analysis.
Evaluation framework Several frameworks have been implemented for demonstatating the performance of twelve different kernels proposed in this article. The PSI{BLAST  [8, 34]  iterations with composition based statistics  [38]  are performed on a Cluster with 62 Opteron nodes [2:60 GHz, 322:4 GFLOPs] using MPIBlast and the command-line program blastpgp. We implement OrthoMCL version 2. 0  [19]  for our experiments. All the kernels are generated in Matlab v7. 10 (R2010a) 64-bit. The normalized spectrum kernel with subsequence/string length = 4 settings in the Kernel-based Machine Learning Lab (kernlab) package  [39]  in R  [40]  from CRAN is used. This is utilized for spectral clustering  [29]  over all our local alignment and combined kernel matrices. The spectral clustering results of all methods are evaluated using the receiver operating characteristic (ROC) score, commonly called Area Under ROC Curve (AUC) and the ROC-50, which is the ROC score or AUC computed only up to the first 50 false positives.
For the ROC  [41]  analysis of the kernel matrices, ROCR packages  [42]  have been used. Finally the CRAN statistical package R  [40]  with RCommander library  [43]  have been used for Wilcoxon signed rank test. The modified symmetry based clustering approach using MPICH has been implemented. We utilize the existing string kernel of LIBSVM  [28]  software for comparing its results with our kernel clustering results. We also experiment over our chosen dataset with the pre-existing spectrum mismatch  [22]  kernels on SVM. To verify the performances over a large dataset, we execute all our proposed as-well-as those already-existing kernels over the chosen 54 target families from SCOP version 1. 59  [22]  from literature as mentioned in Data section. We also utilize the linear kernel with SVM of SPIDER  [44]  framework in MATLAB to obtain the comparative results.