dataesr/scientific-paragraphs-categorization huggingface dataset:
Health Data Newborn health data are available from the first birth certificate registered by the Maternal and Child Care department of Paris (named PMI: Protection Maternelleet Infantile). This certificate is completed by parents and the health professional before exit of the maternity, within the 8 days following birth, then sent to the PMI local unit. Several mother and newborn characteristics are available from this certificate including birth date, gestational age and postal address of residency of the mother at the time when the certificate was completed, three crucial data needed to run the present package. All the postal addresses were geocoded at the level of residential census block. For the purpose of this article, we illustrate our package on the births that occurred in the city of Paris between 1 st January 2011 and 31 st December 2011.
To do so, the mtsdi package available on CRAN (https://cran. r-project. org/web/packages/mtsdi/index. html), can be used: the imputation obtained takes account for temporal dimensions, correlations between measurements in different monitoring stations, and the log-normality of the data (or normality if initial data distribution is already Gaussian)  [25]. Trimester Exposure function calculates individual exposure by pregnancy trimester. It also requires two data sets: a data. frame with daily air pollution concentrations by census block (which can be the one obtained using the Reconstitution function), and a data. frame with births information. In the latter, there must be no missing values for the variables used by the function, i. e. the identification number, birth date, gestational age, and the census block where the mother lived.
Acknowledgements This paper was supported by the following grant(s):  Fondation de France   201300040943 2013-2016  to  Séverine Deguen. This work is supported by  Fondation de France  (grant  N° 201300040943 2013-2016 ) and the  EHESP School of Public Health. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
4  that shows the dependence of N Pu , N Om and N Os on the particle surface-to-volume atom ratio, S/V. The S/V values have been calculated using Crystal Maker software to establish the minimum number of Pu or O atoms required for uniform coating of spherical PuO 2 nanoparticle. In the literature, short Pu-O bonds at about 2. 2 Å were attributed to terminal Pu-OH moieties and/or to the μ 3  -O atoms that reside on the edge of the particle and bonded to three Pu(IV) ions, medium bonds at about 2. 34 Å to bulk plutonium dioxide, and longer bonds at ~2. 7-3. 0 Å to coordinated water molecules  9, 28. Consequently, the plot in Fig. 4  confirms the relatively higher contribution of hydrolysed Pu(IV) moieties for smaller PuO 2 NPs. Taking into account that the isoelectric point of intrinsic Pu(IV) colloids is at pH = 8.
The isotherm data were fitted using the Brunauer-Emmet-Teller (BET) model to calculate the surface area values. The scanning electron microscopy (SEM) images were obtained with a Quanta 200 ESEM FEG (ThO 2 ) and a Zeiss Supra 55 (PuO 2 ) devices. High-resolution transmission electron microscopy (HRTEM) measurements were performed at the Joint Research Centre of the Institute for Transuranium Elements, Karlsruhe, Germany (JRC-ITU) using a TecnaiG2 (FEI TM ) 200 kV microscope equipped with a field emission gun, modified during its assembly to enable the examination of radioactive samples. XAFS measurements were performed at the European Synchrotron Radiation Facility (ESRF) at the Rossendorf Beamline (BM20-ROBL) using a Si(111) double crystal monochromator. BM20 is equipped with a collimating and focusing mirror, and the beam spot size is reduced by horizontal and vertical slits to 3 × 1 mm.
Spectra were recorded in transmission mode with 3 argon-filled ionization chambers giving I 0 , I 1 and I 2. The energy was calibrated in situ by measuring after the sample (I 1 /I 2 ) the first derivative XANES spectrum of a Zr foil defined at 17988 eV. Experimental spectra were recorded at Pu L III edge and the ionization energy (E 0 ) was preliminary defined at the maximum of the white line at 18068 eV and varied as free fit parameter. The energy range investigated for EXAFS structure determination was chosen depending on Pu content in the sample from 2 Å -1 to 14-17 Å -1. EXAFS data analysis was performed with Athena and Arthemis software from the IFEFFIT package  34. Further efforts were made to optimize the atomic background function to reduce the amplitude between 0 and 1. 2 Å on the Fourier transformed magnitude (FT).
Acknowledgements This work was supported by  French CEA/DEN/PAREC  research program and by  European TALISMAN research program  (Grant No. TALI-C05-17 ). The authors thank  L. Berardo  for XRD,  G. Jouan ,  R. Podor  and  J. Lautru  for SEM,  X. Le Goff  for TEM analysis,  L. Guerin  and  L. Chareyre  for alpha spectroscopy,  C. Rey  and  E. Chomis  for BET and  M. Guigue ,  J. Maurin  and  J. Vermeulen  for technical support. Parts of this work and DKS were supported the Director,  Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences (CSGB) Heavy Element Chemistry program of the U. S. Department of Energy (DOE)  at the  Lawrence Berkeley National Laboratory (LBNL)  under Contract No. DE-AC02-05CH11231. MES Beamline 11. 0. 2  at the  Advacned Light Source (ALS)  is supported by the Director,  Office of Science, Office of Basic Energy Sciences, Division of CSGB   Condensed Phase and Interfacial Molecular Sciences program  of the U.
After exposure to these compounds, the medium with compounds was removed, cells were washed with PBS (phosphate buffer saline) (pH 7. 2) and fixed with 80% ethanol (for 20 min at room temperature). After air-drying, the cells were stained with 1 µg/mL DAPI in the dark. The morphology of cells was observed at 1000× magnification under a fluorescent microscope (Nikon Eclipse Ci H600L, Tokyo, Japan), connected to a digital camera (Nikon Digital Sight DS-U3, Tokyo, Japan), and analysed using NIS-elements BR 3. 0 imaging software (Nikon, Tokyo, Japan).
Statistical Analysis Comet assay data were analysed using two-way analysis of variance (ANOVA), while a particular mode of interaction × time was used to compare the effects induced by the chemicals at this mode of interaction. Differences between samples with normal distribution were evaluated by Student's t-test. Both Student's t-test and ANOVA were performed using OriginPro 6. 1 software (Northampton, MA, USA). Significant differences were accepted at p < 0. 05. The results were presented as mean ± standard error of the mean (S. E. M. ) (for the comet assays), and ± standard deviation (SD) (for LDH).
03% DMA or TMA. In our previous study  [10] , by using Giemsa/May-Grünwald staining we demonstrated that the treatment of LMH cells with these compounds produced inter alia chromatin condensation and fragmentation. The IC 50 doses of test odorous compounds changed nuclear morphology and induced apoptosis in LMH cells, but the data requires confirmation by other tests. Genotoxic activity of ammonia was documented by several researchers. In this paper, using the comet assay we found that, depending on the concentration, ammonia induced extensive DNA damage in chicken cells. In contrast, Yadav and Kaushik  [18]  found that ammonia induced micronuclei formation in Swiss albino mice, and inhibited DNA repair in mouse fibroblasts. One study examined the genotoxic effect of ammonia in humans  [18].
Acknowledgments: The study was funded by the  Centre for Research and Development, Poland  (Grant title " Innovative deodorising biopreparation for poultry farmhouses "; grant number  PBS2/B8/14/2014 ). Author Contributions: Adriana Nowak conceived, designed and performed the experiments, analysed the data and wrote the paper;  Katarzyna Matusiak  and  Sebastian Borowski  corrected the manuscript;  Tadeusz Bakuła  and  Remigiusz Gałęcki  analysed the data;  Beata Gutarowska  initiated the research concept and corrected the manuscript.
Results are combined from at least two experiments. (e) A sandwich ELISA was performed to assess detection of 50 viral strains from the EQAPOL viral subtype diversity panel. The ELISA was in a sandwich format with a mAb capture antibody (CP-HIV-1/2) and 59H10 detection nanobody. Error bars show standard deviation of the mean from at least 6 wells across 2 or 3 independent experiments. (f) (i-v) Plots showing the relative wavelength shift against time measured by biolayer interferometry of sensors coated with p24 to seven different concentrations of each analyte for (i) 59H10; (ii) 48G11 (raw data shown instead of relative binding as raw data showed no binding; therefore, data could not be normalized as other analytes); (iii) 37E7 nanobody; (iv) CP-HIV-1/2; (v) NIH-3537. (g) Summary plot to compare the binding affinities of 59H10 (blue circles), CP-HIV-1/2 (red squares), NIH-3537 (green diamonds), and 37E7 (orange triangles).
5) × 10 -3 1. 7 (1. 5-1. 9) × 10 -4 2. 0 (1. 0-2. 9) × 10 a Each biophysical parameter is shown with the corresponding 95% confidence interval indicated in brackets. The K D was determined from fits of the experimental data, shown in Figure  1f , using the Langmuir Adsorption isotherm (1:1). The k on and k off were determined from the observed reaction rate, k obs (Figure  S3 ). All assays were carried out using HIV-1 subtype B p24 (AG6054). mAb = monoclonal antibody. to p24 A204 and L205, the closest of these contacts to the 59H10 R50 and p24 E213 salt bridge. The HIV-1 capsid protein p24 exists in different oligomeric forms including dimers, pentamers, and hexamers. 43 In principle, the nanobody 59H10 can potentially bind all forms (PDB code 5HGN),  44  (PDB code 5I4T),  45  (PDB code 2M8L)  46  ) due to the exposure of the binding epitope.
Interaction with p24 present in the mature capsid  47  would not be expected because the nanobody binding epitope is partly occluded in the polymeric form. × 10 -7 2. 2 (1. 5-2. 5) × 10 -8 1. 6 (1. 1-2. 0) × 10 -9 k on (M -1 s -1 To validate the p24 epitope for 59H10, recombinant p24 with a truncation after L189 (cleaving helix 9) was used in an indirect ELISA, and the results were compared to a full p24. A range of conventional mAbs was used that had previously had their epitopes mapped, in addition to polyclonal antibodies. Figure  S4  shows that while antibodies mapped to bind between helices 1 and 8 showed equivalent binding to full or truncated p24, 59H10 showed no binding to the truncated form of p24. This provides strongly supportive functional data for the epitope of p24 shown by the crystal data in Figure  2.
40 To create starting structures for relaxation, the input for nanobody atomic coordinates was derived from structure homology using Swissprot. 48 To obtain equilibrated and stable nanobody geometries, molecular dynamics simulations were run for 100 ns using an NVT ensemble at 333 K. Figure  3a  shows the crystal structure of PDB entry 2XA3, for nanobody D7, shown here for comparison to the p24 binding nanobodies. The mode of interaction for D7 binding is thought to be via the long CDR3 loop. The long CDR3 is found in another gp120-binding nanobody, A12 (PDB 3RJQ), and is thought to mediate a favorable inserted configuration into the pocket of gp120. 40 Its absence in the p24-binding nanobodies suggests that they could bind by a completely different mechanism. Comparisons of structure and sequence are shown in the view of superimposed 59H10, 48G11, and 37E7 and colored by sequence homology in Figure  3b  and structural homology in Figure  3c.
Also shown in Figure  5a  is p24 P207, which moves away from the hydrogen bonds found with 59H10 R50 in the crystalline form when in simulation in solution; hence, no occupancy is found. The WXR motif (59H10 amino acids 36-38) is highly conserved, with the central X amino acid most commonly F, R, or a minority V. A sequence logo representing the relative distribution of amino acids found at this site is shown in Figure  S8. An unusual "W" at position 37, giving WWR, was found in only two other unique sequences out of almost 800 available from the NCBI (GenBank AJ237346 and AF442913  49, 50  ). This W37 was predicted both by analysis of the crystal structure and from molecular dynamics simulations to positively contribute to binding by formation of a hydrogen bond and to create an alignment of hydrophobic patches on 59H10 and p24, eliminated by F93D of 48G11.
We report an X-ray crystal structure, which shows the binding epitope and paratope regions on the p24 antigen 59H10 nanobody, respectively. Igonet et al. 53  previously reported a nanobody that binds a different epitope region on p24 (PDB code 2XV6); however, the epitope reported and examined here would be accessible in many oligomeric forms of the p24 antigen. This is very advantageous and has particularly exciting repercussions in the design of a diagnostic or therapeutic tool. We have brought together functional experimental data, a crystal structure, and molecular dynamics simulations to elucidate the structural basis of this novel, high affinity nanobody. Further, the atomistic detail from the crystal structure of 59H10 provides a template with which to compare nanobodies with differing affinities using molecular dynamics simulations.
continued (c) 37E7 are shown in order of overall positivity (37E7 has the highest total positive charge). Note the changes in amino acids at CDR2 and CDR3 create an electronegativity gradient over the entirety of the nanobodies from 48G11 (least electropositive), through 59H10 to 37E7 (most electropositive). Circled in yellow is the binding interface in 59H10 (b). (d) Electrostatic potentials and skeleton structures of full p24 (subtype B, based on PDB code 1E6J). The region circled in yellow highlights the binding interface for 59H10. The skeleton structure of p24 is shown below for comparison. (e) Electrostatic potentials of full p24 and skeleton structure of 59H10, showing 59H10 binding to p24. There is charge complementarity upon complex formation between the positive (blue region on the 59H10) and the negative (red region on the p24).
5 h at 37 °C. Crystallization, Data Collection, and Structure Determination. Crystal screening was performed at the High Throughput Crystallization Laboratory (HTX lab, Grenoble) in 96-well sitting drop vapor diffusion plates (Greiner). Following optimization, crystals used for diffraction studies were grown at 20 °C in hanging drop vapor diffusion plates and obtained by streak seeding. The p24-59H10 complex was crystallized in 100 mM Tris base, pH 8. 0, 20%, and polyethylene glycol (PEG) 6000 by mixing 1 μL of the reservoir and 1 μL of protein at a concentration of 8 mg/mL. The crystals were cryoprotected in the crystallization buffer containing 25% ethylene glycol and flash cooled in liquid N 2 before data collection. Data were collected on beamline ID30-B at the ESRF at a wavelength of 0.
976251 Å. Data were processed with the program XDS. 57  The crystals belong to space group P1211 (Table  3 ). The structure was solved by molecular replacement using the program Phaser 58 using the PDB 2GOL (for p24) and 4LDO (for the nanobody 59H10) as input models. The model was rebuilt using COOT  59  and refined using REFMAC  60  and Phenix. 61 Statistics for data reduction and structure refinement are presented in Table  3. The models were evaluated by using COOT and Phenix validation tools. Two copies of the p24-59H10 complex are present in the asymmetric unit. Only the C-terminal domain of p24 is present in the crystal and chains A and C contain residues 149 to 221. For the nanobody 59H10 and chains B and D, residues are numbered according to Kabat. The structure was refined against 2.
Analyte binding proceeded for at least 60 min. Normalization was carried out using an in situ probe functionalized with p24 and dipped in sample solution without added analyte, to account for nonspecific reactions occurring on the surface. At high concentrations of analyte, the signal appears to plateau and reach equilibrium within the time frame of the experiment. However, at low analyte concentrations, the signal does not reach equilibrium within this time frame. Therefore, the equilibrium signal was determined by fitting the raw data for each concentration to the exponential equation: y = A[1exp(-k obs t)], where y is the binding in nm, A represents the equilibrium signal, k obs is the observed rate constant, and t is time. The equilibrium signal (A) as a function of the concentration was then fitted to a Langmuir isotherm y = B[c/K D + c], where K D represents the binding affinity of the protein to p24, B is the saturation signal when all the available sites are occupied, and c is analyte concentration.
* S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acsinfecdis. 6b00189. Supplementary methods; binding data subtype D strains; biolayer interferometry binding data; processing of biolayer interferometry fractional saturation analysis; binding to a truncated p24 by 59H10, mAbs and pAbs; superimposed crystal 59H10/p24 and simulated 59H10/ p24; RMSD plots; energetic contribution scatter plots; sequence logo; affinity constants calculated from ELISA data; binding energy contributions (PDF).
■ ACKNOWLEDGMENTS This work was funded by grants i-sense  EPSRC IRC Early Warning Sensing Systems in Infectious Disease  ( EP/K031953/ 1 ) to E. R. G. , J. C. B. , V. T. , R. A. W. , and  R. A. M. (www. i-sense. org. uk);  EPSRC Grand Challenge in Healthcare Diagnostics :  Multimarker Nanosensors  for HIV ( EP/G062064/1 ) to B. L. J. W. and R. A. M. ; NIHR II-LA-1111-20004 Next generation mobile HIV diagnostics with wireless connectivity to E. R. G. , J. C. B. , V. T. , R. A. W. and R. A. M. ;  Medical Research Council  Grant  G0801176  to L. E. M. , L. A. G. , and R. A. W. ;  Royal Society Wolfson Research Merit Award  to R. A. M. W. W. is a member of the  Institut Universitaire de France  and acknowledges the platforms of the Grenoble Instruct Center (ISBG;  UMS 3518 CNRS-CEA-UJF-EMBL ) supported by the  French Infrastructure for Integrated Structural Biology Initiative FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology (PSB).
2002; Xie et al. 2002; Hastenrath 2002; Krishnamurthy and Kirtman 2003; Annamalai et al. 2003) , particularly after 1976  (Annamalai et al. 2005) , with El Niño tending to excite a positive IOD and therefore yielding weakened Pacific and Indian Ocean WCs. ENSO and IOD, however, can sometimes operate independently (e. g. ,  Yamagata et al. 2003; Sun et al. 2015) , during which times the Indian and Pacific Ocean WCs do not necessarily covary. On multidecadal and centennial time scales, trend analyses of the Indian and Pacific Ocean WCs have been carried out, using surface observations and reanalysis data to infer changes in the WC strength. Yet no consensus has been reached. Observational studies provide contrasting results, depending on the datasets and techniques used in the analyses. Over the Pacific Ocean, several observational studies have argued for weakened zonal sea level pressure (SLP) and sea surface temperature (SST) gradients and a slowdown of the surface easterlies, and therefore a weaker WC since the midnineteenth century, during the twentieth century and since 1950  (Vecchi and Soden 2007; Deser et al.
g. ,  Kitoh et al. 2013). Standalone atmospheric general circulation models simulated inconsistent changes in the WC strength depending on which SST product was used to force the models: Using HadISST of  Rayner et al. (2006)  produced a strengthened Indo-Pacific WC for the twentieth century and since 1950, using the Extended Reconstructed SST of  Smith and Reynolds (2004)  generated a neutral response, and using Hadley Centre SST, version 3 (HadSST3), of  Kennedy et al. (2011a,b)  produced a weakened WC (see  Tokinaga et al. 2012a; Meng et al. 2012). The lack of consensus in both observational and modeling studies may arise from many factors, such as temporal heterogeneity of the observational datasets, differences in analysis methods, and systematic errors in models. These inconsistencies are detrimental to detecting centennial and multidecadal trends because the trend signals are much weaker than the amplitudes of decadal and interdecadal variability over the Indo-Pacific basin (not shown).
b. Present research The goal of this research is to explore the variability of the Indian and Pacific Ocean WCs on time scales of one to a few decades, which we refer to collectively as ''decadal variability'' hereafter, and to understand the causes. Our period of interest is from the 1960s to the present, when more reliable ocean-atmosphere datasets are available. First, we document the variability of the Indian and Pacific Ocean WCs together with the warmpool convection, and explore their covariability by analyzing available satellite data, in situ observations, and reanalysis products. Here, we focus on examining the surface branches of the WCs, as in the existing studies that address their centennial and multidecadal trends (see above). Then, we investigate the association of their decadal variability with climate modes and other climate indices, using a novel approach of the Bayesian dynamic linear model (DLM; e.
Data and approach a. Datasets for detecting decadal variability of Indo-Pacific WCs and warm-pool convection To document the variability of warm-pool convection, we analyze the monthly cloudiness from 28 3 28 data from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) available for 1800-2014 (e. g. ,  Woodruff et al. 2011; Freeman et al. 2017 ) and monthly precipitation over land from the University of East Anglia Climatic Research Unit (CRU) 0. 58 3 0. 58 data for 1901-2012  (Jones and Harris 2013). To discern whether the cloudiness or precipitation over land can better represent the warm-pool convection for our period of interest from the 1960s onward, we also analyze the satellite-derived 2. 58 3 2. 58 monthly outgoing longwave radiation (OLR)-a proxy for tropical deep convection-from 1974 to 2013  (Liebmann and Smith 1996) , and 2.
1996)  for 1948-2013, the Japanese 55-year Reanalysis (JRA-55;  Kobayashi et al. 2015)  for 1958-2013, and the operational European Centre for Medium-Range Weather Forecasts (ECMWF) ocean reanalysis system (ORAS3) available for 1959-2009, which uses ERA-40 winds before 2002 and numerical weather prediction operational analyses thereafter  (Balmaseda et al. 2008). As discussed in section 1, the reanalysis winds show large uncertainty in detecting multidecadal trend since the 1960s. On the other hand, the observation-based WASWind have many missing values over the warmpool region (see  Han et al. 2014b ). The monthly surface winds from Cross-Calibrated Multiplatform (CCMP) satellite observations  (Atlas et al. 2011; Wentz et al. 2015) , version 2. 0 (V2. 0), since 1987 are also analyzed. This multiple-dataset approach aims to identify signals robust to cross-dataset differences and thus increase the confidence in our analysis.
b. Datasets for climate modes and other climate indices To understand the causes for decadal variations of the Indian and Pacific Ocean WCs and their covariability, we examine their relationships with decadal variability of ENSO and IOD, the two dominant climate modes over the Indo-Pacific basin on interannual time scale. For simplicity, we will refer to decadal variability of ENSO (IOD) simply as ''decadal ENSO (IOD)''. Note that decadal ENSO, represented by 8-yr low-passfiltered Niño-3. 4 index and multivariate ENSO index (MEI), is highly correlated with the interdecadal Pacific oscillation (IPO; e. g. ,  Power et al. 1999; Folland et al. 2002; Meehl and Hu 2006)  and the Pacific decadal oscillation (PDO; e. g. ,  Mantua et al. 1997; Minobe 1997; Zhang et al. 1997) , with the Niño-3. 4-IPO and IPO-PDO correlations being about 0.