dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS The authors thank  Be´ne´dicte Michel  and  Ivan Matic  for insightful discussions. They thank  France-Audrey Peltier  for the V. cholerae over-expression library and the  Institut Pasteur Microbiology 2012 Master  students for the genetic screening and identification of the mfd mutant in neomycin and tetracycline. They thank  Genevie`ve Garriss  for the helpful reading of the manuscript. FUNDING  Institut Pasteur , the  Centre National de la Recherche Scientifique  [ CNRS-UMR3525 ]; the  European Union Seventh Framework Programme  [ FP7-HEALTH-2011single -stage] 'Evolution and Transfer of Antibiotic Resistance' (EvoTAR) ; and the  French Government 's  Investissement d'Avenir program Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  [ ANR-10-LABX-62-IBEID ].
, 2011). The data include latitude and longitude of the tangent point, pressure, temperature, water vapor pressure, refractivity and altitude of the perigee above sea level. The data in NetCDF format are available from the CDACC (COSMIC Data Analysis and Archive Center) website hosted by the University Corporation for Atmospheric Research (UCAR). The COSMIC data used in this study span from 2006 through 2011, whereas the analysis was restricted to solstice months, i. e. , austral summer (DJF) and boreal summer (JJA) at low latitudes between 25 • S and 25 • N. The interpolated profiles are indexed with respect to the tangent point lati-tude and longitude at 17 km altitude, the mean altitude of the tropical cold point tropopause (CPT). The diurnal variation of temperature is examined by looking at its anomaly compared to the daily mean profile within the 10-35 km altitude range.
Acknowledgements. The COSMIC RO data used in this study are those available from the  Data Analysis and Archive Center (CDAAC)  of the  University Corporation for Atmospheric Research  (http://cdaac-www. cosmic. ucar. edu/cdaac/). The work was supported by the  French Agence Nationale de la Recherche (ANR) Tro- Pico  project (http://www. univ-reims. fr/site/autre/tro-pico/)and partly by  EU   FP7   ARISE  project (no. 284387 ) and Russian MK-2912. 2013. 5 grant. We thank  Geraint Vaughan  for valuable suggestions, which helped considerably improve the manuscript. Edited by:  F. Fierli The publication of this article is financed by  CNRS-INSU.
Grazing-incidence X-ray Diffraction: The grazing-incidence X-ray diffraction (GIXD) measurements were done at beam line 11-3 at the Stanford Synchrotron Radiation Laboratory. The measurements were done with a beam energy of 12. 735 keV and an incidence angle of 0. 12°. The samples were enclosed in a He environment to limit beam damage and reduce air scattering. The data was collected using a MAR345 image plate. The measurement was calibrated using a LaB 6 crystal standard.
Acknowledgements The authors would like to acknowledge  Scott E. Watkins  ( CSIRO Materials Science and Engineering, VIC 3169, Victoria, Australia ) for PESA measurements. This work was in part carried out under the  EPSRC  Project  EP/F056710/1 ,  EP/G037515/1 ,  EC FP7 ONE-P 245 Project 212311  and  DPI Grant 678 , with support from the  Centre for Plastic Electronics at Imperial College London  and the  National Research Fund of Luxembourg. Portions of this research were carried out at the  Stanford Synchrotron Radiation Lightsource , a Directorate of  SLAC National Accelerator Laboratory  and an  Office of Science User Facility  operated for the  U. S. Department of Energy Office of Science by Stanford University.
A final extension step was carried out for 5 min at 72°C. The amplified products were detected by electrophoresis as above. Negative control was included in each nested PCR analysis to monitor contamination and false-positive amplification. To rule out amplicon carry-over, nucleotide-free water negative control was used throughout the steps of the protocol. The sequence-derived data reported herein were authentified as negative controls introduced in every PCR experiment remained negative, excluding the possibility of cross-contamination during the experiments. The amplified products for 16S rDNA, IGS and FLA were directly sequenced by Eurofins (Ebersberg, Germany).
Sequence alignment and phylogenetic inferences All sequences obtained were aligned using ClustalW (v. 1. 8. 1 in BioEdit v. 7. 0. 5. 3. ) [50]. Maximum Likelihood (PhyML) tree reconstruction was conducted from the 16S rRNA aligned sequences (457 nucleotides) of ticks. For each Borrelia of soft tick, the FlaB gene (FLA, 269 nucleotides) and Intergenic Spacer (IGS, 510 nucleotides) sequences were concatenated for the phylogenetic analyses (PhyML). For each dataset, we used Modeltest 3. 4  [51]  to select the appropriate model of molecular evolution. The best-fitting ML model was HKY85  [52]  for 16S rRNA sequences and GTR (General Time Reversible + Gamma distribution) for IGS-FLA concatened sequences. The highest-likelihood DNA and corresponding bootstrap support values were obtained by PhyML (freely available at http:// mobyle.
pasteur. fr/cgi-bin/portal) using NNI (Nearest Neighbor Interchange) branch swapping and 100 bootstrap replicates  [53]. Molecular distances with standard error estimate were conducted using the Kimura 2-parameter model among Ornithodoros 16S sequences and among Borrelia concatened IGS-FLA sequences as used in other phylogenetical pathogens/vectors studies  [54, 55]. In the final dataset, the analysis involved 165 different sequences grouping 821 16s RNA tick sequences and 105 different sequences grouping 216 IGS-FLA Borrelia sequences. Evolutionary analyses were conducted in MEGA 5  [56]  and all positions containing gaps and missing data were eliminated. Phylogenetic analyses of Ornithodoros moubata 16S rRNA (GenBank accession number AB073679), O. porcinus (GenBank acc. no. AB105451), O.
turicata (GenBank acc. no. L34327), and O. parkeri (GenBank acc. no. EU009925) were treated as outgroups. Borrelia crocidurae (GenBank acc. no. GU350723 and NC017808), B. duttonii (GenBank acc. no. DQ000279 and DQ346833), B. hispanica (GenBank acc. no. GU350718 and GU357614), B. merionesi (GenBank acc. no. JX257047 and JX257050) and B. recurrentis (GenBank acc. no. DQ000277 and DQ346814) for the IGS and FLA sequences were concatened for the phylogenetic analyses of spirochetes. In the goal of molecular diagnostic of Ornithodoros species of North and West African countries, we determined Single Nucleotide Polymorphism (SNP) based on 16S rRNA sequences using parsimony-informative site.
Diagnostic SNPs of Ornithodoros ticks To allow a rapid molecular identification of Ornithodoros species, we highlighted 25 single nucleotide polymorphisms among the 9 species in 16S rRNA sequence dataset (Table  6 ). We detected at least one diagnostic SNP for each species, i. e. at a given position of the 16S sequence a nucleotide type is present in all sequences of a given species and absent in other species. According to species, the total number of diagnostic SNPs that can be used for taxonomic purpose of soft tick of the genus Ornithodoros varied from 1 to 8 (Table  6 ).
Acknowledgments We thank  Jean-Louis Camicas  ( IRD, Montpellier ) for assistance in tick determination and  Stephen C. Barker  ( University of Queensland, Brisbane ) for useful comments on a first draft of the manuscript. We thank all those in  Dakar, Bamako, Ouagadougou, Niamey, NDjamena, Yaoundé, Cotonou, Lomé, Lamto, Monrovia, Kindia, Bissau, Nouakchott, Kénitra, Casablanca, Algiers, Annaba, Tunis  and Montpellier who provided assistance for field surveys, laboratory work or data analysis.
• O. occidentalis Trape, Diatta, Durand & Renaud, sp. nov. Diatta, Bouattour, Durand, Renaud & Trape, sp. nov. Holotype: IRSNB IG. 32. 280/004/1; ZooBank: urn:lsid:zoobank. org:act:DC0E7B5B-FBC3-4A7B- A223-77E0F2CFF2F5; GenBank: KC311536, KC311537. Holotype: IRSNB IG. 32. 280/002/1; ZooBank: urn:lsid:zoobank. org:act:DCC9185B-5FA8-494C- BF45-985A60478DC7; GenBank: KC311546. Holotype: urn:lsid:zoobank. org:act: IRSNB IG. 32. 280/005/1; ZooBank: • O. merionesi Trape, Diatta, Belghyti, Sarih, Durand & Renaud, sp. nov. 43B277DD-7488-4277-8EC6-7AA9653882D0; KC311531, KC311532. GenBank: Holotype: urn:lsid:zoobank. org:act: IRSNB 8B64EB2B-0288-4488-9E39-94A2E3367FF5; IG. 32. 280/001/1; ZooBank: GenBank: • O. rupestris Trape, Bitam, Renaud & Durand, sp. nov. KC311538. Holotype: urn:lsid:zoobank. org:act:B76003D9-IRSNB IG.
32. 280/003/1; C807-4194-97C7-1FE7CFA3398D; GenBank: KC311545. ZooBank: The four other species identified were: O. erraticus, GenBank: KC311539, KC311540, KC311541, neotype: IRSNB 32. 280/006; O. marocanus, GenBank: KC11533, KC311537, KC311535; neotype: IRSNB 32. 280/007; O. sonrai, GenBank: KC311525, KC311526, KC311527, KC311528, KC311529, • O. costalis • O. kairouanensis Trape, Diatta, Bouattour, Durand & Renaud, sp. nov.
In this paper, we critique the methods commonly used in evaluation studies and propose a number of new ideas. The plan of the paper is as follows. Section 2 describes some data from the CMIP5 archive that are used throughout the paper for illustration. Section 3 discusses the choice of performance measure. Section 4 focuses on ensemble predictions and explores how the method of evaluation should depend on how the ensemble is interpreted. In Section 5 the role of timescales in evaluation is explored. Our conclusions are summarized in Section 6.
In such cases, the adjusted ensemble CRPS will still be unfair. In the case of independent and identically distributed ensemble members that we considered above, however, the convergence holds and the adjusted ensemble CRPS is fair. If there is only one ensemble member (m = 1) then the measures listed above typically cannot be adjusted to be fair  (Ferro, 2007; Ferro et al. , 2008). Ideally, the predictor would tell us how to interpret and, therefore, use the ensemble-as its belief distribution, as a specific collection of functionals of this distribution, or as a random sample from this distribution. In practice, this information is often unavailable. For example, no such information is provided with the CMIP5 data archive. In that case, the best we can do is to choose a way to interpret the ensemble and evaluate accordingly.
Acknowledgements This work was part of the  EQUIP  project (http://www. equip. leeds. ac. uk) funded by  NERC  Directed Grant  NE/H003509/1. The authors thank  Leon Hermanson ,  Doug Smith  and  Holger Pohlmann  for useful discussion,  Helen Hanlon  for assistance with obtaining data, and two anonymous reviewers for comments that helped us to improve the presentation of our ideas.
However, empirical examples of the relationships between host biodiversity and parasite transmission are still relatively rare, in part because suitable datasets that may allow comparisons are deficient  [4]. Further complexities are encountered when the distribution range of a pathogen covers a large number of host communities and climatic zones. Such a wide-extent distribution-range consists of a nested hierarchy of transmission systems that can be interconnected in space and time via dispersion  [5]. At these scales, datasets with high resolution and precision, describing hosts, disease distribution and environmental factors (such as climate and land cover) are most often heterogeneous. Human alveolar echinococcocosis (AE) is a highly pathogenic parasitic disease caused by the larval stage of the cestode Echinococcus.
Environmental and climate data. Monthly and yearly average rainfall and temperature for China were extracted from the 0. 5 degree  [23]  gridded dataset of the FAO Global maps (http://www. fao. org/geonetwork). Altitude maps were derived from the Global Land One-km Base Elevation (GLOBE) Project  [24]. Using the same grid and buffers as above we computed the mean, median, minimum and maximum altitude. Information on land cover was obtained from the Global Land Cover 2000 project  [25]. It was based on Spot Vegetation data in the period 1. 1 to 31. 12. 2000 and consisted in 24 land-cover classes mapped at a.
Data handling and models. Land cover was estimated as the proportion of pixels belonging to a Global Land Cover 2000 category in question within a circular buffer centered at a given pixel. Elevation, yearly rainfall and yearly average temperature, were extracted from a 0. 1 degree gridded dataset of the FAO Global maps, and assigned the median pixel values within the buffer. Such metrics vary with respect not only to landscape composition but also to the buffer radius R. Therefore radii of 10 km, 20 km, 50 km and 100 km were considered. This was repeated for every pixel in a 50632 grid with a 20620 km resolution. To avoid resolution differences between human data collected in each village and environmental data available for every pixel of the 20620 km resolution grid, human data were assigned to the nearest pixel centroid.
Selected GAMMs were further subjected to Bayesian analysis using the software BayesX  [30, 31]  which enabled the inclusion of a spatial random effect to account for otherwise unexplained between-pixel spatial autocorrelation. The spatial random effect was modeled as a Markov random field  [32, 33]  on the 20 km resolution grid. Non-linearity was modeled using a degree 3 P-splines on 20 equidistant knots and a second order random walk was used to penalize against over fitting,  [34, 35]. Non-informative inverse gamma priors were assumed for variance components of the P-spline and random effects with shape and scale hyper-parameters initialized at 0. 001). The parameter space of the Bayesian GAMMs was sampled using Markov Chain Monte Carlo (MCMC) techniques. For each model, MCMC was run for 500,000 iterations, discarding an initial burn-in period of 50,000 iterations and sub-sampling every 400th sample thereafter.
Acknowledgments The authors thank  Dr. Chen Xingwang ,  Professors Qiu Dongchuan ,  Wang Qian ,  Yang Wen  and  Xiao Ning  ( Institute of Parasitic Diseases, Sichuan Centers for Disease Control and Prevention ) and  Professor Mark Danson  ( University of Salford ) for their participation and dedication throughout our projects in Sichuan. We also thank countless local people who have regularly provided invaluable support and aid during our field expeditions.
This asymmetry in the cause-effect relationships can be used to predict change. Additionally, the statistical estimation of time lags can shed light on cause-and-effect relationships in temporal data. Second, time is uni-dimensional, whereas space has three dimensions. In this respect, strictly temporal patterns are simpler to analyse than spatial patterns. In fact, spatial patterns are often collapsed into fewer dimensions, such as transects along latitudinal, topographic and habitat gradients  [15]. However, time and space are frequently confounded, as in historical fisheries records that cover time periods when the fleet was focusing on different areas, or palaeo records that cover different spatial locations as well as periods of time. Every time series is embedded in a spatial context, just as every spatial dataset is embedded in a temporal context.
These patterns would not be expected to occur on unbounded temporal series. For bounded time series, directionality means that the effect of a starting boundary is different from that of an ending boundary. The starting point is an important part of the successional pattern that follows  [17]. Moreover, when studying temporal change relative to an arbitrary starting point, sensitivity of the conclusions to the chosen baseline needs to be considered, and potential effects of a shifting baseline should be recognized  [18]. Temporal and spatial datasets also share some qualities. The concept of grain  [11, 19]  is equally applicable to spatial and temporal data. For temporal data, grain size is the degree of time averaging within each data point, which is akin to spatial averaging where biodiversity is quantified within an area, rather than at a single point in space.
In practice, the study of autocorrelation in space and time typically differs in three ways. First, temporal data are typically collected at constant time intervals, allowing easy calculation of lags between points, whereas spatial variables are often recorded at irregular locations distributed continuously in space, requiring the use of techniques such as binning distances to estimate variograms. Second, for historical reasons, variograms (based on variance) are typically used for spatial autocorrelation, while correlograms or autocorrelation function (ACF) plots based on correlation are used for time. Third, the uni-dimensionality of time series, in combination with the prevalence of cyclic changes, means that spectral analysis (see below and figure  2 ) is often done in time but rarely in space.
Most statistical methods that are used to forecast future trajectories of biodiversity implicitly assume that the mechanisms driving historical and recent trends continue into the future, albeit with new levels for some covariates. However, drastic changes often involve pressures upon ecosystems, generating novel systems  [80]  that function differently, as the pool of functional traits changes  [81] , and combinations of environmental variables arise that have no contemporary analogues  [82]. In many cases, palaeo-climates encompass a greater range of projected conditions and may provide important clues for expected biotic responses  [83]. These issues create challenges for predicting biodiversity trends that require us to understand the mechanisms driving diversity and call for placing greater statistical weight on datasets that better represent the anticipated change.
Conclusions Availability of long-term, large-scale, high-resolution data is the single most important factor limiting progress in understanding temporal patterns in biodiversity. Given the difficulty of obtaining data from the past, we reiterate the appeal to preserve data and associated metadata in publicly accessible archives  [84]. Public databases of biodiversity records are providing unprecedented insight into large-scale, long-term patterns (e. g. Paleobiology database-http://paleodb. org, Global Biodiversity Information Facility-http://data. gbif. org/). Establishing standards for meta-information should ensure that future scientists can not only access but also take full advantage of the data we are now collecting  [85]. The challenges that arise from dealing with historical data (see the electronic supplementary material) should help signal pitfalls to avoid when making contemporary data available.
Figure2. Tools for assessing temporal autocorrelation. These data examine changes in species richness of a small rodent community over 26 years at the long-term research site in Portal, Arizona run by James Brown, Morgan Ernest and others [50, 51]  at control un-manipulated sites. All analysis done in R v. 2. 12. 2 [52] ; the code is included as electronic supplementary material. The data are monthly or yearly and detrended (via the difference operator) or trend-retained, as described in the titles. (a) Autocorrelation function (ACF) analysis on monthly data, with the expected decay of correlation (y-axis) with increasing time lags (x-axis). (c) The same dataset after removing the trend via differencing, highlighting 5-month cycle (these patterns can also be seen in the trend-retained data but less obviously).
Experimental Crystal data  Data collection: APEX2  (Bruker, 2004 ); cell refinement: SAINT  (Bruker, 2004) ; data reduction: SAINT; program(s) used to solve structure: SIR2002 (Burla et al. , 2005); program(s) used to refine structure: SHELXL97 (Sheldrick, 2008); molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg & Berndt, 2001) ; software used to prepare material for publication: WinGX  (Farrugia, 2012)  and CRYSCAL (local program). The authors are grateful to all the personnel of the research team 'Synthese de molecules aobjectif therapeutique' of the PHYSYNOR Laboratory, UniversiteConstantine, Algeria, for their assistance. Thanks are due to the MESRS (Ministere de l'Enseignement Superieur et de la Recherche Scientifique, Algerie) for financial support.
Computing details Data collection: APEX2  (Bruker, 2004 ); cell refinement: SAINT  (Bruker, 2004) ; data reduction: SAINT  (Bruker, 2004 );   (Farrugia, 2012)  and DIAMOND  (Brandenburg & Berndt, 2001) ; software used to prepare material for publication: WinGX  (Farrugia, 2012)  and CRYSCAL (local program).
Thus, if Bik ubiquitylation process is turned off in the model, only the formation term remains in equation 1 which is now the same for parental and transformed cells, with different initial conditions. This equation can be solved analytically: ½Bik(t)~Bik 0 zk fBik t ð3Þ where Bik 0 stands for Bik initial concentration taken equal to Bik steady state concentration in parental and transformed fibroblasts. We did not observe any significant apoptosis either in normal or Src-transformed cells in the first six hours of staurosporine treatment (data not shown). In non-transformed cells, setting t = 360 min in equation 3 gave ½Bik(360)~2:2Bik 0 which meant that Bik concentration would only double in six hours if this hypothesis was right. This was tested by measuring Bik protein level during staurosporine exposure in parental cells.
Estimating parameters of the mitochondrial apoptosis model Parameters of this model of mitochondrial apoptosis were estimated by fitting experimental data in parental and Src-transformed cells from  [5]  and integrating biological results from literature. First, we assessed quantitative molar values of considered Bcl-2 family proteins in non-apoptotic conditions as follows. We set Bax total concentration Bax tot in Src-transformed cells to 100 nM according to  [46]  in which the authors stated that this was a physiological level in tumor cells. This value is also in agreement with concentration ranges found in the literature  [33, 34, 36, 47, 48]. Then, in  [46] , they found that anti-apoptotic total concentration had to be 6 times higher than that of Bax in order to prevent apoptosis. Therefore, we set Bcl2 tot ~600nM in Src-transformed cells.
Confocal microscopy For the analysis of Bik relocalization to mitochondria upon staurosporine exposure, cells were pre-incubated with Mitotracker-red (1:10,000) for 20 min before a 20 min washing in fresh medium. Then, cells were fixed by addition of paraformaldehyde (4%). Mitotracker-red background was removed by a 10 min incubation in acetone at 220uC prior further processing according to standard procedures. Endogenous Bik was detected by the specific anti-Bik-BH3 antibody  (1:200). The secondary antibody (Molecular Probe) was labeled with FITC. Nuclei were stained with Hoechst-33342 dye (1:50,000) in the mounting medium. Images were acquired by confocal microscopy on a Axiovert 100 M, LSM510 (Zeiss) using a Plan Apochromat 63x/ 1. 4 Oil DIC objective. Colocalization of Bik fluorescence and mitotracker staining was quantified using ImageJ software in 30 single cells treated with staurosporine for 2 h.
Mathematical modeling and parameter estimation Matlab ode15s solver was used to solve the systems of ordinary differential equations. Parameter estimation for Bik kinetics model consisted of a least-square approach in which minimization tasks were performed by the CMAES algorithm (  [63] , Text S1). Parameter estimation for the mitochondrial apoptosis model involved the design of a cost function which was also minimized by the CMAES algorithm. This cost function was the sum of a Least square term accounting for the fitting of experimental data of Figure  6  and a term for the constraints described in the Results section. This term was equal to zero when all constraints were satisfied and to a thousand times the number of unsatisfied constraints otherwise  [64].