dataesr/scientific-paragraphs-categorization huggingface dataset:
2009 ) of these genes containing functional PPREs. Thus, the DGB and IDGB groups consisted of 12 and 24 genes, respectively-with the large majority of genes belonging to the NGI group. The non-PPARa transcription factors that regulate genes in groups (  2 ) and (  3 ) were identified from the TRANSFAC (Biobase Corporation, Beverly, MA) database of confirmed transcription factor-DNA interactions assembled from other genome-wide ChIP studies. Only those transcription factors known to be expressed in the liver were included. All of these regulatory interactions were assembled to obtain the topology of the ''latent'' PPARa transcriptional regulatory network (Fig. 38 ). A small number of transcriptional ''hubs'' (highly connected transcription factors in the network)-ERa, HNF4a, STAT1, ETS1 and Sp1regulate the majority of differentially expressed genes that did not show evidence of PPARa binding.
2003; Ledwith et al. 1996; Mounho and Thrall 1999; Pauley et al. 2002; Rokos and Ledwith 1997; Teruel et al. 2003). Protein-protein interaction databases reveal that a subset of these kinases (ERK1/2, SRC and p38) can also activate the majority of hub transcription factors identified in the network. In summary, the current state of knowledge of the PPARa toxicity pathway is described, as well as an outline of an approach to map out this pathway by combining highthroughput data sets from human primary hepatocytes and human hepatoma cell lines. Currently there is no published genome-wide survey of PPARa binding in primary human cells. Other issues that could be clarified by surveying the PPARa pathway in human hepatocytes include:.
2008). With a 47. 9 % positive predictive value and 100 % negative predictive value, genetic pretesting is currently in place for abacavir patients as to identify those who should be treated with other medications. This has reduced the incidence of hypersensitivity with the drug from 2. 7 to 0 %  (Russmann et al. 2010). The association of iDILI with specific HLA type thus indicating a T-cell-mediated etiology has been successfully identified in a range of other drugs too (Table  9 ). A paper from 2011 stated that the World Health Organisation (WHO) adverse Drug Reaction database had recorded almost 1,500 cases of DILI due to flucloxacillin  (Lee and Lewis 2011). It is a beta-lactamase resistant antibiotic and well known to cause iDILI. Usage increased between 1991 and 2006 despite a warning in 1992 from the UK medicine control agency about the associated DILI  (Li et al.
As an example, the work of  Afshari et al. (2011)  gives a comprehensive overview on the history, development and recent issues. Although the relationships between changes in gene expression and adverse effects are not fully understood at this time, there is a belief that changes in the gene expression pattern may prove to be useful in predicting a specific toxic outcome. This would allow the extrapolation of toxic effects from known model compounds to unknown compounds by comparison of their expression profiles  (Hamadeh et al. 2002a, b; Zidek et al. 2007; Ellinger-Ziegelbauer et al. 2004; Hrach et al. 2011). The development of specific databases based on expression profiles of reference compounds and the use of ever improving statistical methods to discriminate compounds on the basis of their gene expression profiles will result in highly informative gene clusters which can be used to predict the class membership of new unknown compounds  (Hamadeh et al.
This is of particular importance when compoundspecific gene expression changes after treatment are assessed. Sandwich-cultured rat hepatocytes were treated with well-known hepatotoxic and non-hepatotoxic model compounds in a proof-of-concept study. Gene expression data were collected and used as a training set to build a classification model, using four different clustering algorithms. Ranking of genes according to their contribution to the classification and misclassification rates were calculated, and a classifier composed of 724 genes was found that could discriminate between the two compound groups (Fig. 44 )-and therefore, in principal, predict human drug toxicity. Furthermore, the model was challenged with another, unknown, model compound and proved its discriminative capability  (Hrach et al.
Utilizing in vitro models will make it possible to build large databases with time-matched gene expression data of cells treated with many reference compounds and drug candidates. Furthermore, the insecurity of extrapolating the results between species may be overcome by the possibility to conduct these experiments with human cells, as well as the potential to explain the species differences which are too often seen in preclinical animal studies. 10. 6. 1. 4  Hepatocytes and the influence of culture conditions on cellular signaling The applicability of results from in vitro studies to in vivo situations is directly dependent on the degree of similarity between the in vitro experimental condition and the in vivo environment. Differing conditions would render data non-reproducible in vivo and result in controversies on mechanisms of disease and potential therapeutic targets as mentioned earlier regarding lipid peroxidation in APAP hepatotoxicity.
Acknowledgments We gratefully acknowledge financial support by the  BMBF-VIRTUAL-LIVER network ; the  BMBF- projects,  DEEP , '' innovations in drug development' ',  LUNGSYS I, II , and the  PE-MIALF-study ; the  FP6 program  projects,  Liintop ,  PREDICTOMICS  and carcinoGENOMICS, the  FP-7 EU SEURAT-1  projects,  NOTOX ,  DETECTIVE ,  ESNATS ,  Body-on-a-Chip  and  PASSPORT ; the  BMBF program  '' Alternatives to animal testing ''; the  ANR- projects,  Sine2Arti  and  PHYSCANCER ; the  National Research Foundation of Korea  Grant funded by the  Korean Government (MEST)  ( NRF-2011-0009592 ); the  Institut d'encouragement de la Recherche Scientifique et de l'Innovation de Bruxelles ;  ISRIB, Innoviris (Brustem project) ; the  Belgian Science Policy (BELSPO)  project:  HEPRO ; the  Geconcerteerde Onderzoek Acties (GOA project) ; the  Fonds Wetenschappelijk Onderzoek Vlaanderen (FWO)  and  Research Council VUB ; the  National Institutes of Health  Grants  AA12916  and  DK070195 ; the  Medical Faculty of the University of Regensburg ( ReForM-Program ); the  German Ministry of Education and Research (BMBF) within the Human Tissue and Cell Research (HTCR) Foundation ; the  Excellence Initiative of the German Federal and State Governments (GSC-4) ;  Spemann Graduate School of Biology and Medicine (SGBM)  and  EXC-294 ,  Centre for Biological Signaling Studies (BIOSS)  and  KSF  by the  GRK 1104-From Cells to Organs: Molecular Mechanisms of Organogenesis  from the  Deutsche Forschungsgemeinschaft (DFG)  and the  Swiss National Science  grant  31003A_124652.
5 % hepatocellular, 20. 6 % mixed, 2. 9 % cholestatic HLA-DRB1*0101 Hepatotoxic Hepatitis black box HLA A*3303 Black box but not Cholestatic for DILI CYP2C9*2/*3, Black box for GI Excessive CRM formation and reduced UGT2B7*2, and CV only biliary excretion, metabolic and possible ABCC2 C-24 T immune mediated IL10 AA 627 AC, Mainly hepatocellular or mixed, immune- IL4 TT 590 CT mediated cytokine enhancement HLA-DRB1*0701, General hepatotoxicity, link to HLA DQB1*0201, suggests immune mediated DQA1*0102 CYP2E1 c1/c1 Hepatitis, build up of CRM Slow NAT-2 Hepatotoxicity Hepatitis, build up of CRM acetylators black box GSTM1 null, Reduced CRM detoxification, lack of ROS MnSOD T/C or clearance C/C Reported cases in WHO database 2010, no. with acute liver failure 20761, 3471 4261, 1470 8670, 1385 29178, 2051 180 cases in first 3 years 8582, 2487 Use NRTI Antibiotic Antibiotic Direct thrombin inhibitor Tyrosine kinase inhibitor COX2 inhibitor NRTI Anti-platelet NSAID NSAID Anti-TB Anti-TB Anti-TB Anti-TB Drug Abacavir Coamoxiclav Flucloxacillin Ximelagatran Lapatinib Lumiracoxib Nevirapine Ticlopidine Diclofenac Diclofenac Rifampicin, isoniazid, pyrazinamide Isoniazid, Rifampin, pyrazinamide, ethambutol combination Isoniazid General anti-TB.
This paper presents a extented Kalman filter based on a dynamic model of a commercial lithium ion battery pack in automotive applications, and experimental data are collected using the Noao. This vehicle is an electric track with range extender, which has been developed and produced by the association Pôle de Performance de Nevers Magny-Cours (PPNMC). This model has been developed with MATLAB/Simulink to investigate the output characteristics of lithium-ion batteries. It incorporates I-V performance of the battery, battery capacity fading, temperature effect on battery performance, and the battery temperature rise. This estimation technique is used in order to estimate some parameters, which cannot be measured directly by physical sensors such as SOC and SOH and to compensate for uncertainties in the model parameters and the measurements.
We present a summary of biomass data for 11 plankton functional types (PFTs) plus phytoplankton pigment data, compiled as part of the MARine Ecosystem biomass DATa (MAREDAT) initiative. The goal of the MAREDAT initiative is to provide, in due course, global gridded data products with coverage of all planktic components of the global ocean ecosystem. This special issue is the first step towards achieving this. The PFTs presented here include picophytoplankton, diazotrophs, coccolithophores, Phaeocystis, diatoms, picoheterotrophs, microzooplankton, foraminifers, mesozooplankton, pteropods and macrozooplankton. All variables have been gridded onto a World Ocean Atlas (WOA) grid (1 • × 1 • × 33 vertical levels × monthly climatologies). The results show that abundance is much better constrained than their carbon content/elemental composition, and coastal seas and other high productivity regions have much better coverage than the much larger volumes where biomass is relatively low.
, 2005). This MARine Ecosystem biomass DATa (MARE-DAT) special issue in Earth System Science Data is the result, with 11 papers on 11 PFTs and 1 paper on HPLC-based (high-performance liquid chromatography) phytoplankton pigments (http://www. earth-syst-sci-data. net/special issue7. html). There are six papers relating to autotrophic groups: picophytoplankton  (Buitenhuis et al. , 2012a) , diazotrophs  (Luo et al. , 2012) , coccolithophores  (O'Brien et al. , 2013) , Phaeocystis  (Vogt et al. , 2012) , diatoms  (Leblanc et al. , 2012) , and HPLC-based phytoplankton pigments  (Peloquin et al. , 2013). There are six papers relating to heterotrophic groups: picoheterotrophs (Bacteria and Archaea,  Buitenhuis et al. , 2012b) , microzooplankton (here, we briefly reiterate and correct the microzooplankton biomass database that was recently published by  Buitenhuis et al.
, 2010) , planktic foraminifers  (Schiebel and Movellan, 2012) , mesozooplankton  (Moriarty et al. , 2013) , pteropods  (Bednaršek et al. , 2012) , and macrozooplankton  (Moriarty et al. , 2013). By this collaborative effort we are able to provide global databases for 9 out of the 10 PFTs that were proposed by Le  Quéré et al. (2005). The missing PFT is mixed phytoplankton, which is mostly made up of autotrophic dinoflagellates and nanophytoplankton other than coccolithophores and Phaeocystis. Nanophytoplankton are a taxonomically diverse group of algae, including prymnesiophytes, chlorophytes, and cryptophytes, which are not consistently treated as a distinct group in the literature. Where the term nanophytoplankton is used in the literature it often includes members of PFTs that have been included in the data products in this special issue.
Therefore, we excluded this PFT from the current collection of data, even though nanophytoplankton represent a significant part of the phytoplankton biomass. Demarcation issues between nanophytoplankton and the other PFTs will need to be resolved before we can complete the abundance based atlas of phytoplankton biomass. Chemotaxonomic interpretation of the HPLC-based phytoplankton pigment database described in this special issue  (Peloquin et al. , 2013)  offers one pathway toward resolving mixed phytoplankton biomass distribution. However, the most reliable way to prevent double counting and achieve a consistent dataset would be to measure the biomass and cell size of all distinct phytoplankton groups in the same samples in transects that cross all ocean basins. A similar demarcation issue occurs for the zooplankton.
, 2012) , which include both shelled aragonite-producing species and naked species. These data should be valuable in complementing research into phytoplankton calcifiers, i. e. coccolithophores, and in addressing the biogeochemical cycling of calcium carbonate, and thus alkalinity and atmospheric CO 2. The diazotroph dataset contains both biomass estimates and nitrogen fixation rate data, which are useful to evaluate the ecological roles of diazotrophs or to quantify marine nitrogen fixation  (Luo et al. , 2013). Since 1994, the World Ocean Atlas (WOA) has been synthesising interpolated global gridded climatological datasets of physical and chemical parameters  (temperature, oxygen, nutrients, etc. ). Initially, these datasets were annual averages, but increasingly they cover seasonal variations on a monthly basis in the surface ocean.
WOA provides datasets that achieve data coverage across all grid points by interpolation. These data products are used extensively by global ocean modellers for initialisation and/or evaluation (e. g. Doney et al. , 2009) , in combination with biogeochemical datasets such as dissolved inorganic carbon, alkalinity, pCO 2 , and DMS, which have been synthesised through initiatives like MAREDAT (e. g. the CARINA special issue in ESSD http: //www. earth-syst-sci-data. net/special issue2. html). The goal of MAREDAT is to provide, in due course, similar data products and coverage for all planktic components of the global ocean ecosystem, and this special issue is the first step towards achieving this. For ease of use we have gridded the biological variables onto the same grid as used for the WOA (1 • × 1 • × 33 vertical levels × monthly climatologies).
Because of the large seasonal variability in biological components over most of the ocean, we have chosen to provide monthly files from the start. At this point, there is not yet Furthermore, we provide a database of microzooplankton distribution that was recently published by  Buitenhuis et al. (2010). Some errors in the previous version  (C. Stock, personal communication, 2011)  have been corrected (n = 682), and we also took this opportunity to grid this dataset in the same way as the others (doi:10. 1594/PANGAEA. 779970).
Quality control by Chauvenet's criterion In all contributing PFT papers, Chauvenet's criterion has been used to exclude very high values from the gridded databases  (Glover et al. , 2011). While all data had already been quality controlled by the contributing researchers, there was still a risk of over-representation of high values from (1) studies that specifically targeted the occurrence/bloom of the relevant PFT, (2) high productivity coastal sites or (3) errors in the reported units. Chauvenet's criterion assumes that the data has a normal distribution and rejects data on the presumption that if a set of n measurements were carried out twice, outliers would be excluded at 1/(2n) probability of occurrence, thus preventing any bias between the outlier rejection in the two sets of measurements.
The critical value occurs at p = 1 -1/2n. None of the datasets had a normal distribution, therefore Chauvenet's criterion was performed on log-transformed data. Log-transformation of biomass values meant the exclusion of zero values. Zero and very low values for biomass are a true reflection of the ecology of the oceans. Zero abundance or biomass is often treated as a lack of measurement (i. e. represented by a blank entry rather than a zero concentration), even though it supplies useful information about the absence of an organism or PFT. This means that zero values are usually under-represented, especially in the deep sea. Therefore, a one-sided Chauvenet's criterion was applied to identify only the high value outliers. For the smallest dataset (foraminifers, n = 1057 non-zero observations) the critical value was 3.
Gridding In order to arrive at a consistent collection of gridded data products for all abundance based PFTs, they were all gridded with the same program, and include the number of observations, average abundance, average biomass, median abundance, median biomass, standard deviation of abundance, and standard deviation of biomass, both for the total datasets and for the non-zero observations. In some datasets, some of this information was excluded for methodological reasons: there is no abundance for mesozooplankton, and the databases for picoheterotrophs, diatoms and mesozooplankton contain no zero observations. All PFT and pigment data were gridded on a 1 • × 1 • horizontal grid, with grid box centres from 179. 5 • W to 179. 5 • E and 89. 5 • S to 89. 5 • N. The vertical axis also follows the WOA spacing, centred on the 33 depths: 0, 10,  20, 30, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1750, 2000, 2500, 3000, 3500, 4000, 4500, 5000 , and 5500 m.
Microzooplankton biomass database The microzooplankton biomass database contains 4044 georeferenced data points. The gridded database contains 2029 data points (Fig. 1 , Table  1 ). Data from the Northern Hemisphere makes up 64 % of the database, data from the top 225 m makes up 96 % of the database, and data from the spring and summer months makes up 63 % of the data. The average biomass is 7. 0 ± 15. 3 µg C L -1 with a median   2010 ), the corrected database contains both lower and higher values. The mean of the whole dataset has decreased by almost a factor 3, while the median has decreased by 10 %.
Comparison of PFT biomass concentrations The number of data points that are available for each PFT differs by two orders of magnitude (Table  1 ). The larger databases have been built on earlier data synthesis efforts  (O'Brien et al. , 2002; Vaulot et al. , unpublished data; Gosselin et al. , unpublished data). We intend to maintain this group effort and extend the databases in future. By making both the raw and gridded databases publicly available, we also hope to encourage other researchers to publish extended versions. The horizontal distribution of each PFT at selected depths is presented in the contributing papers. Here, we compare the global average vertical profiles of the 11 PFTs and phytoplankton pigments (Fig. 2 ) and the zonal average sections (depth versus latitude) of the 11 PFTs in the top 200 m (Fig.
Autotrophic biomass The highest phytoplankton biomass recorded in our datasets is found for Phaeocystis (Fig. 2a ). As can be seen in figure 7 of  Vogt et al. (2012) , there is considerable sampling bias towards coastal waters, where dense Phaeocystis blooms can be a regular occurrence, and under which conditions a mix of colonial and single cells are often found. Under non-bloom conditions, Phaeocystis is mostly found as single cells, which cannot be distinguished from other nanophytoplankton with standard microscopic protocols, and therefore such measurements of background numbers of single Phaeocystis cells are almost absent from the database. The next most abundant phytoplankton PFTs are picophytoplankton and diatoms (Fig. 2a ), which is consistent with accepted wisdom about the importance of these two groups.
Comparison of autotrophic biomass with pigment distributions Recent syntheses suggest that virtually no pigment can be unequivocally assigned to quantify one marine algal type or species, since most of these pigments are shared across multiple phytoplankton taxa  (Higgins et al. , 2011). For this reason, statistical methods employing multiple pigment to chlorophyll ratios are required to adequately resolve the algal community composition (e. g. Mackey et al. , 1996; Van den Meersche et al. , 2008). Nonetheless, a preliminary analysis of the basin-scale distribution of a few key diagnostic pigments from the global phytoplankton pigment database  (Peloquin et al. , 2013)  permits a rough comparison with some of the autotrophic biomass distributions from the abundance databases. For example, zeaxanthin, roughly indicative of the presence of cyanobacteria, exhibited a maximum concentration around the equatorial region, and is an order of magnitude lower poleward of 50 • (figures 8l and 9l in  Peloquin et al.
, 2013). However, Fig. 3e  indicates much less meridional variability in diatom abundance, which further highlights the need for incorporating the complexity of multiple pigment ratios when assessing phytoplankton distributions. In order to also compare the depth profiles of biomass from the abundance and pigment databases, we first use pigment concentrations to calculate the contribution of different size classes of the phytoplankton to the overall chlorophyll concentration using the conversion factors of  Uitz et al. (2006) , and then convert chlorophyll to biomass using the C : Chl ratios of the PlankTOM5. 3 model  (Buitenhuis et al. , 2013) , which uses photosynthetic parameters synthesised by  Geider et al. (1997). Because the C : Chl ratio of diatoms is about a third of the other two phytoplankton groups in the model, we use one profile of C : Chl ratios as a function of depth for diatoms and another profile for all other phytoplankton.
The resulting pigment based biomass concentrations for diatoms and prokaryotes agree quite well with the abundance based biomass concentrations (Fig. 2a, c ). The pigment based biomass for nanophytoplankton is close to the abundance based biomass of coccolithophores plus picoeukaryotes. This is rather surprising given the taxonomic diversity of larger eukaryotes that have not been included in the biomass from abundance, and the exclusion of Phaeocystis biomass. The pigment based biomass of dinoflagellates is estimated at roughly a third of the diatom biomass. A thorough chemotaxonomic analysis of the global pigment database will reveal patterns in phytoplankton community structure on finer scales, and potentially contribute missing information on autotrophic dinoflagellates and nanophytoplankton.
The biomass of planktic foraminifers is low, 0. 04 µg C L -1 at 10 m depth, and decreases to less than 0. 01 below 300 m. At present, the database of foraminifers is limited to the Northern Hemisphere (Fig. 3i ). The numbers of observations drop dramatically below 1000 m for the zooplankton. Picoheterotrophs are the only PFT for which there are still around 100 observations at each level down to 3000 m. It is difficult to make definite statements about the ecology of the deep sea with so little information, but the data suggest that there are nonnegligible concentrations of zooplankton in the deep sea: up to 6 µg C L -1 macrozooplankton in the mesopelagic and 0. 19 µg C L -1 microzooplankton below 2750 m, compared to 0. 36 µg L -1 picoheterotrophs. These biomass concentrations are low relative to the surface biomass, but the volume of the deep sea is much larger, suggesting that zooplankton could make a substantial contribution to global ocean biogeochemical cycles in the deep sea as well.
The conversion factors are probably the main source of uncertainty in all the datasets, while the importance of lack of coverage or of sampling bias appear to vary between datasets, e. g. for Phaeocystis there is a substantial sampling bias towards the coast, as noted above, which appears to explain the high horizontally averaged profile (Fig. 2a ). For bacteria it was shown that a potential bias www. earth-syst-sci-data. net/5/227/2013/ Earth Syst. Sci. Data, 5, 227-239, 2013  towards the coast (in this case due to an observed change in the conversion factor rather than over-representation of sampling) was unimportant  (Buitenhuis et al. , 2012b). We compare our phytoplankton biomass concentrations to the WOA 2005 total chlorophyll measurements. Without conversion to carbon, this equates to 18 to 30 Tg Chl.