dataesr/scientific-paragraphs-categorization huggingface dataset:
Site U1397 Background and objectives IODP Site U1397 (proposed Site CARI-10B; 14°54. 41′N, 61°25. 35′W; 2482 mbsl) is located west of Martinique (Fig. F11 ; Table  T1 ). The bathymetric survey for Site U1397 revealed a region with a topographic high bound by large canyons. Site survey seismic data indicated that this site could, in the upper part, penetrate regular (unperturbed) sedimentary reflectors, whereas in the lower part some chaotic reflectors are visible. The drill site is located on the topographic high to try to avoid perturbations. Site U1397 is located very close to Core CAR-MAR 4 (7 m long) taken during the Caraval cruise in 2002. The CAR-MAR 4 core provides a sedimentary record that extends to ~32 ka, as determined by δ 18 O chronostratigraphy. The sedimentation rate (including tephra) is ~20 cm/k.
Paleomagnetism Generally, the retrieval of a magnetostratigraphic record for this site was as complicated as the retrieval of a biostratigraphic record because of the nature of the cored material and recovery. Using the detailed core description logs, only data measured on identifiable hemipelagic sediment layers were used for interpretation. These layers were highly discontinuous at Site U1397, particularly below ~55 mbsf. Shallower than ~55 mbsf, inclination values obtained on discrete samples show scattered but positive inclination, clustering around the expected GAD inclination. Declination shows little variation, suggesting that all sediment and tephra to a depth ~55 mbsf was deposited in the Brunhes Chron (<780 ka). Below this depth the record becomes increasingly discontinuous, making confident interpretation of ages based solely on paleomagnetic data difficult.
Structural/functional relatedness -lessons from protein engineering Proteins with conserved sequence motifs and/or structural features are often grouped into protein families, which can provide important information about the possible biochemical role of a given protein  (Buljan & Bateman, 2009; Ganfornina & Sanchez, 1999; Moore et al. , 2008; Thornton et al. , 1999). As of November 2011  (Punta et al. , 2012) , there were nearly 16 million unique protein sequences in public databases that could be grouped into $13 500 protein families based on sequence and structure. The relatively limited number of homologous protein families highlights the constraints that evolution, protein structure and folding, and biological function place on amino acid sequences. The relatedness and diversity of proteins in food can help guide a HOSU evaluation.
Analysis of sequence and structure databases addresses a fundamental safety question: does the introduced protein share any sequence or structural similarity with proteins known to pose possible toxicological hazards? These databases range from repositories of protein sequences derived from translations of nucleic acid sequences to curated databases that include additional information about proteins that have been validated for expression and/or function (Table  2 ). Because high sequence and structural similarity often correlates with a conserved biological role (e. g. Mills et al. , 2004) , computer algorithms to evaluate phylogenetic relationships of proteins are useful in safety evaluations. Sequence comparisons help define superfamilies of proteins that catalyze similar chemical reactions on different molecules or families of homologous (and orthologous) proteins.
Recently, population threshold doses for elicitation of allergic reactions to peanut allergens were proposed based on the review of clinical records for 286 peanut-allergic patients in France  (Taylor et al. , 2010). The threshold dose was low for the most sensitive population (approximately 2-4 mg/person) and considerably higher for those whose allergic reactions were less severe. Thus, it may be possible in the future to establish population elicitation thresholds for at least some allergens in food. It is unlikely that the sensitization thresholds will be known but it is hypothesized that they are higher than the elicitation thresholds. With respect to toxicity, the final proposed decision tree omitted proteins because the databases used to derive TTC values do not include data on proteins  (Kroes et al.
CP4. TTC levels were estimated for acute and chronic exposures based on available toxicology data for a variety of different proteins, many of which are enzymes used in food processing. TTC levels were determined following adjustments to correct for purity of enzymes in fermentation batches tested in animals and applying a 100-fold safety margin below the no-observed-adverse-effect levels (NOAELs) in animal toxicology studies. For acute exposure, the TTC was calculated to be 17. 9 mg/kg BW, or 1074 mg/person/day (assuming a 60-kg BW). For chronic exposure, the TTC was 2. 49 mg/kg BW, or 149 mg/person/day  (Hammond & Cockburn, 2008). Comparison of these threshold levels to actual dietary exposure levels assumes a ''worst-case'' scenario in which there is no loss of the introduced protein when the GM crop is processed into food.
approximately 100 times what humans might consume (based on the European and North American maize consumption Dietary Exposure Evaluation Model [DEEM] UK and DEEM US databases, Exponent, Inc;WHO GEMS, 2006), and no treatment-related adverse effects were reported. These studies have been reviewed by the EFSA and results have been published on their website for the following event combinations:MON 810 Â MON 863 (Cry1Ab Â Cry3Bb1) (EFSA, 2005a) MON 863 Â NK603 (Cry3Bb1 Â CP4 EPSPS) (EFSA, 2005b) MON 810 Â MON 863 Â NK603 (Cry1Ab Â Cry3Bb1 Â CP4 EPSPS) (EFSA, 2005c) DAS 1507 (Cry1F Â PAT) (EFSA, 2005d) DAS 59122-7 (Cry34Ab1/Cry35Ab1 Â PAT) (EFSA, 2007a) DAS 15Ø71 Â DAS-59122 (Cry1F Â Cry34Ab1/Cry35Ab1 Â PAT) (EFSA, 2009b).
Acknowledgements The authors and Task Force members thank the following individuals for participating in the review process and for providing many constructive comments and suggestions:  Bruce Chassy ,  University of Illinois, Urbana-Champaign; Andrew Cockburn, University of Newcastle and Toxico-Logical Consulting Ltd. ;  Johanna Dwyer ,  Tufts-New England Medical Center ;  Karin Hoffman-Sommergruber ,  Medical University of Vienna ;  Ib Knudsen ,  Danish Institute for Food and Veterinary Research ;  Richard Lane ,  PepsiCo ;  Drew Lichtenstein ,  Novus International ;  Mark Martens , FORMAC Pharmaceuticals N. V. ;  William Price ,  US Food and Drug Administration Center for Veterinary Medicine  (retired);  Joseph Scimeca ,  Cargill ;  Dae Yeul Son ,  Department of Herbal Foodceutical Science, Daegu Haany University, Daegu, Korea ;  John Thomas, University of Texas and Indiana School of Medicine ;  Rick Tinsworth , Exponent;  Hai Bin Xu ,  Chinese Center for Disease Control and Prevention ; and  Hiroshi Yoshikura ,  Japanese Ministry of Health Labour and Welfare.
Acknowledgements. The experimental part of this work was partially supported by Chapman Chair funds, kindly supplied by the late Norbert Untersteiner during F. Domine's stay at the  Geophysical Institute, University of Alaska Fairbanks. F. Domine is very indebted to  W. R. Simpson  for organizing his stay at  UAF , and thanks  M. Sturm  for the use of the permeameter, as well as  A. -S. Taillandier  for assistance with some of the measurements. B. Hauer  very kindly proposed the LARS site for the experimental part of this study and provided assistance throughout the field work. We are grateful to numerous CNRM-GAME colleagues, in particular  J. -M. Willemet , the SURFEX support team for their help in maintaining and using the model,  F. Flin  and  N. Calonne  for discussions related to snow permeability estimates from snow microstructure variables, and  S.
Back Close Full specifically on understanding the atmospheric natural variability at the annual and interannual time scale  (de Angelis et al. , 2003; Correia et al. , 2003; Vimeux et al. , 2008). However, logistical constraints and harsh weather conditions prevailing over the high altitude cold glaciers of the Andean Cordillera make it often difficult to get the full set of glaciological and meteorological data required by the understanding of processes leading to the building of Andean ice archives. Thus, improving the dating of the cores by developing reliable dating tools remains a key challenge. Particular attention must be paid to sublimation processes and snow remobilization by wind which is suspected to play a prominent role at sites influenced by major cyclonic activity or seasonal convection.
We assessed the percentage errors and the mean angular errors. These direction estimates are expected to be accurate when participants have memorized a well-developed, map-like representation of the environment  [10]. This task measures survey knowledge. Real wayfinding task: This task consisted of reproducing the learned path in the real environment; this task measures the spatial transfer of participants. During this restitution task, position and time data were acquired using a Magellan™ GPS Cros-sOver, and a video was recorded using an AIPTEK™ DV8900 camera mounted on a bicycle helmet worn by the participant. Direction errors were calculated and expressed in percentages. When a participant made a mistake, s/he was stopped and invited to turn in the right direction. This wayfinding task is based on the use of landmarks, as well as route and survey knowledge  [1] [2]  [3] , and may be considered as a naturalistic assessment of navigational abilities based.
For the Allocentric Sketch mapping task, the ANOVA did not reveal any significant effects for the Input Devices or Rotation factors (p>0. 800; p>0. 300; two-way interaction, p>0. 100). The performances did not reveal any differences among the four VR learning conditions. Concerning the Allocentric starting point estimation task, the only significant Input Devices effect [F(1,68)=4,38; p<0. 05; η²=0. 06] revealed by ANOVA was that the Joystick condition resulted in poorer performances than the Treadmill condition. No other effects were significant (Rotation, p> 0. 200; two-way interaction, p>0. 800). For the Wayfinding task (transfer task), two data (speed restitution and percentage of direction errors) were collected. For the speed restitution, the ANOVA results revealed a significant effect for Rotation [F(1,68)=4,22; p<0.
Methods GLobal epidemic and mobility model. We use the GLobal Epidemic and Mobility (GLEaM) model  24, [33] [34] [35] [36] [37] [38]  , which is based on a high definition geographically structured metapopulation approach  39, 40. The model is composed of three layers. The first one, the population layer, integrates distinct census areas for a total of 3,362 subpopulations in 220 countries around the world. For each subpopulation, the number of individuals is obtained from ''Gridded Population of the World,'' a project by Columbia University that provides population estimates worldwide for cells of 15 3 15 minutes of arc  41. The second layer of the model contains information about human mobility flows across the census areas. We consider both commuting flows, which is collected from various sources  37  , and airline traffic worldwide, which is provided by commercial databases  42, 43.
Acknowledgements We are grateful to the  IATA  and  OAG  for making the airline commercial flight database available to develop GLEaM. This work was partially funded by the  DTRA -1-0910039  award to A. V. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the  Defense Threat Reduction Agency  or the  U. S. Government.
, 2011). The recognition and lysis of target cells by NK cells pass through sensing "missing self " due to the lack of MHC class I molecules and through interaction of activating receptors with specific ligands. How does the interaction between monocytes or macrophages and NK cells trigger immune response? Both cell to cell contact and soluble mediators contribute to the activation of NK cells. A new understanding of NK cell responses in health and in infectious diseases is emerging where macrophages play a crucial intermediary role. Here we summarize recent data regarding the molecular interactions that regulate the monocyte/macrophagedependent activation of NK cells.
NKp46-DNAM-1 INTERACTIONS In human, the susceptibility of macrophages to be lysed by autologous NK cells can also be mediated by the activating receptors NKp46 and DNAM-1. Thus, blocking of NKp46 and DNAM-1 decreases NK cell cytotoxicity against cytomegalovirusinfected macrophages and LPS-stimulated macrophages derived from PBMC. NKp46 is also involved in the lysis of M. tuberculosisinfected alveolar macrophages  (Vankayalapati et al. , 2005; Bellora et al. , 2010; Romo et al. , 2011). In the case of resting microglial cells, only NKp46 engagement is implicated in the killing  (Lunemann et al. , 2008). Furthermore, the coculture of NK cells and PBMC-derived macrophages increases the expression of NK activating receptors like NKp30, NKp44, and NKp46  (Nedvetzki et al. , 2007). In regard of the different data reported, it seems that NKp46 participates mainly to NK cell cytolytic activity whereas 2B4 is rather involved in the induction of IFNγ production.
2012 ). In the present study, CD-1 fetuses and neonates were exposed to low doses of BPA [0. 025, 0. 25, or 25 μg BPA/kg body weight (bw)/day] administered to their mothers from the end of gestation day (GD) 8 through day 16 of lactation. Female offspring were examined for reproductive outcomes and found to have decreased fertility and fecundity  (Cabaton et al. 2011) , whereas the male offspring were examined for changes in global metabolism by assessing 1 H NMR profiling and analyzing the data using multi variate statistics. Male offspring were examined on post natal day (PND) 2 (whole body) and on PND21. For the latter group, samples included the liver (the main metabolizing organ), serum (circulating metabolites), and the brain, given the fact that recent studies have suggested that peri natal exposure to low doses of BPA could have persistent effects on brain structure, function, and behavior in rodents  (Richter et al.
1 H NMR analyses. All 1 H NMR spectra were obtained on a Bruker DRX-600-Avance NMR spectrometer (Bruker, Wissembourg, France) operating at 600. 13 MHz for 1 H resonance frequency using an inverse detection 5 mm 1 H-13 C-15 N cryoprobe attached to a cryoplatform (the preamplifier cooling unit). The 1 H NMR spectra were acquired at 300K using the Carr-Purcell-Meiboom-Gill (CPMG) spin-echo pulse sequence with presaturation, with a total spin-echo delay (2nτ) of 100 msec to attenuate broad signals from proteins and lipoproteins. A total of 128 transients volume 121 | number 5 | May 2013 • Environmental Health Perspectives were collected into 32,000 data points using a spectral width of 12 ppm, a relaxation delay of 2. 5 sec, and an acquisition time of 2. 28 sec. Prior to Fourier transformation, we applied an exponential line-broadening function of 0.
3 Hz to the free induction decay. To confirm the chemical structure of metabolites of interest, we performed two dimensional (2D) 1 H-1 H COSY (correlation spectroscopy) and 2D 1 H-13 C-HSQC (heteronuclear single quantum coherence spectroscopy) NMR experiments on selected samples. Data reduction and multivariate statistical analyses. All NMR spectra were phased and baseline corrected, then data was reduced using AMIX (version 3. 8; Bruker Analytik, Rheinstetten, Germany) to integrate 0. 01 ppm-wide regions corresponding to the δ 10. 0-0. 5 ppm region. The δ 5. 1-4. 5 ppm region, which includes water resonance, was excluded. A total of 791-861 NMR buckets were included in the data matrices. To account for differences in sample amount, each integrated region was normalized to the total spectral area.
Background: While a large body of work exists on comparing and benchmarking descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 amino acid descriptor sets have been benchmarked with respect to their ability of establishing bioactivity models. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI, BLOSUM, a novel protein descriptor set (termed ProtFP (4 variants)), and in addition we created and benchmarked three pairs of descriptor combinations. Prediction performance was evaluated in seven structure-activity benchmarks which comprise Angiotensin Converting Enzyme (ACE) dipeptidic inhibitor data, and three proteochemometric data sets, namely (1) GPCR ligands modeled against a GPCR panel, (2) enzyme inhibitors (NNRTIs) with associated bioactivities against a set of HIV enzyme mutants, and (3) enzyme inhibitors (PIs) with associated bioactivities on a large set of HIV enzyme mutants.
Summary of the benchmarking performed In the current work all descriptor sets are used on four different data sets by constructing structure-bioactivity models and comparing their performance (see Table  2  for details). The datasets are firstly a previously published set of 58 dipeptides that have an inhibitory effect on the angiotensin-converting enzyme (ACE)  [29] ; secondly, a set of 32 GPCRs and approximately 100 active and 100 inactive compounds per receptor obtained from ChEMBL-16  [30] ; a set of 451 non-nucleoside reverse transcriptase inhibitors (NNRTIs) tested for activity against 14 HIV mutants (used in a previous publication where the protein descriptor set was kept constant throughout the study)  [28] ; and finally a set of 9 clinically approved protease inhibitors (PIs) tested for activity against a panel of 1060 HIV mutants (used in a previous publication where the protein descriptor was kept constant)  [7].
70-30 validation on ACE inhibitors The first benchmark performed on the dataset of dipeptides inhibiting ACE was a 70-30 validation experiment where a random 70% of the data set was used for training and 30% for testing. The results of this validation on the test set are shown in Figure  1. The figure shows that all descriptor sets capture the bioactivity space of the peptides reasonably well, represented by RMSE values on the test set of below 0. 700 log units. The best performing descriptor sets are Z-scales (Binned) (RMSE is 0. 430 log units and the R 0 2 is 0. 794), and ProtFP (PCA3) with Z-Scales (Binned) (RMSE 0. 431 log units and R 0 2 0. 806), followed by Z-Scales (5) (RMSE 0. 439 log units and R 0 2 0. 790) and MS-WHIM (RMSE 0. 442 log units and R 0 2 0. 787). The worst performing descriptor set is ProtFP (Feature) (RMSE 0.
627 log units and R 0 2 0. 566). Given that the ProtFP (Feature) descriptor set merely encodes for presence or absence of features (amino acids) and that dipeptides are modeled (hence only two features per datapoint) a slightly lowered performance was expected. The BLOSUM, ProtFP (PCA5) and ProtFP (PCA8) (RMSE 0. 496 -0. 502 log units and R 0 2 0. 732 -0. 726) descriptor sets are performing better than ProtFP (Feature), but are still lagging compared to the above descriptor sets. This was not expected as these descriptor sets provide a continuous value description like the others. The numerical values for the RMSE, Q 2 and R 0 2 of the model fit are included as Additional file 1: Table  S1 , which also includes The datasets were selected to obtain a diverse collection of sets amenable to PCM modeling.
The well-performing descriptor sets ProtFP (PCA3), Z-scales (3) and MS-WHIM also display a clustering similar to Z-scales (Binned). The PCA shows the highly active peptides to cluster together and the lesser actives are separated from these actives. Hence, overall investigating the 'Neighbourhood Behavior' in the descriptor spaces considered rationalizes why descriptor sets perform better or worse on this dataset. It can be seen that Z-scales (Binned), and ProtFP (PCA3) combined with Z-Scales (Binned) performed the best on this dataset, followed by Z-scales  (5). The ProtFP (Feature) descriptor set showed worst performance in this case.
Furthermore, the descriptor sets on the protein side now describe a smaller part of the entire data set as we also have the presence of chemical descriptors, which are held constant in the different models. The best performance has been obtained in this case by the Z-scales (3) and Z-Scales (3) combined with Z-Scales (Avg) (MCC 0. 432 and sensitivity 0. 786), followed by Z-Scales (Binned), FASGAI, and ProtFP (Feature) combined with Z-Scales (3), (MCC 0. 431 and sensitivity 0. 786). On the other hand, T-Scales, and ST-Scales (MCC 0. 428 and sensitivity of 0. 783) followed by ProtFP (Feature) (MCC 0. 412 and sensitivity of 0. 771) perform the worst. Another interesting observation is that all descriptor sets performed the best on the Muscarinic Acetylcholine receptor (ACM) 4 receptor and the worst on the histamine H3 receptor (followed by the H4 receptor), irrespective of the protein descriptor set selected (Additional file 1: Figure  S4 ; although absolute differences in performance could be observed).
LOSO validation GPCRs In order to benchmark the extrapolation capabilities of the descriptor set a Leave-One-Sequence-Out experiment was performed on the GPCR dataset, the results of which are shown in Figure  4. The overall performance is worse compared to the 70-30 benchmark (MCC values between 0. 367 and 0. 400 and sensitivity between 0. 669 and 0. 695). This is to be expected as leaving out a GPCR at the time leaves out a much larger and congeneric part of the data set compared to leaving out a randomized fraction. However there are still some differences that can be observed between the descriptor sets, with the best performance now being delivered by Z-Scales (3) and Z-Scales (Avg) (MCC 0. 400 and sensitivity 0. 695), followed by the ProtFP (PCA3) and Z-Scales (Binned) (MCC 0. 400 and sensitivity 0.
Analysis of GPCR target space From the PCA analysis of target space we can rationalize the poor performance on the histamine receptors (Figure  5 , and Additional file 1: Figures S6, S7, S8). In the PCA of all GPCR targets used in this dataset, and employing the different descriptor sets, the histamine receptors are not clustering. The distance between the receptors is rather large, in particular when comparing these distances with the distances between the other receptor families. In literature it has been shown that the chemical space for the ligands of the H1, H3, and H4 receptors actually overlaps (where the similarity between H3 and H4 is higher than with H1)  [33, 34]. This is only partially reproduced in  our target similarity. It is therefore likely that the models are unable to reliably extrapolate for this receptor based on the H1 receptor.
70-30 validation on NNRTIs While the above GPCR ligand dataset was based on rather diverse ligands, the NNRTI dataset employed in this study covers a more neatly defined area of both chemical (ligand) space as well as biological (target) space and hence we also included the set. Moreover, this set has been very difficult to model with QSAR approaches (leaving out the target information, see methods for further details) and is ) Slightly more variance is seen compared to the GPCR experiments. In this case BLOSUM performs the worst among all descriptor sets considered, while ProtFP (Feature) performs the best. hence a very good example of cases where PCM can add value  [28]. The first step to evaluate descriptor set performance on this set was again a 70-30 validation experiment to assess the ability of the different descriptor sets to capture the ligand-target interaction space, the results of which are shown in Figure  6.
It is striking that ProtFP (Feature) performs significantly better on this data set than the other two sets. On the NNRTI set, ProtFP (Feature) ranks 1st in the 70-30 validation and 4th in the LOSO validation, in the ACE inhibitor set it ranks 16th and also in the GPCR set the descriptor ranks 16th. A PCA analysis was again performed to connect these observations of descriptor set performance to the similarity of the sequences and the way the descriptor sets characterize the space.