dataesr/scientific-paragraphs-categorization huggingface dataset:
The model is expected to underestimate the spread of C. burnetii as we ignore beef herds in the study region, which can transmit C. burnetii to dairy cattle herds by airborne transmission, and also as we consider cattle trade within the concerned department only. Indeed, according to the analysis of a larger database over the period 2005-2009, 22% of all the concerned transactions of cows involving dairy herds located in the Finistère department corresponded to purchases from outside the department (personal communication: B. L. Dutta). However, no epidemiological information was available for these herds. Similarly, the impact of small ruminant flocks was also neglected as very few such flocks are present in the region. Accuracy of the model could be further improved if epidemiological data about beef herds and other livestock flocks in and around the region were available.
Acknowledgements This work was carried out with the financial support of the  French Research Agency (ANR) ,  Program Investments  for the  Future ; project  ANR-10-BINF-07  (MIHMES), by the  European Union  through the  European fund for the regional development (FEDER)  of the Pays-de-la-Loire, and by  Oniris. Data for the cattle trade was provided by the  Groupements de Défense Sanitaire de Bretagne, France. We thank the reviewers for their careful reading and their helpful comments. Received: 13 July 2015 Accepted: 4 February 2016.
In the LTQ-Orbitrap Velos (Thermo Fischer Scientific), full-scan MS spectra were acquired at a target value of 1E6 with a resolution of 60,000. The 10 most intense ions were isolated for fragmentation in the linear ion trap, with a dynamic exclusion of 20 s. Peptides were fragmented after filling the ion trap at a target value of 1E4 ion counts. The MS/MS spectra were searched with the MaxQuant software (version 1. 4. 0. 3)  (39, 40)  against The Arabidopsis Information Resource (TAIR10_pep_ 20101214 containing 27,416 protein-coding genes) database, with a precursor mass tolerance set at 10 ppm for the first search (used for nonlinear mass recalibration) and at 4. 5 ppm for the main search. Methionine oxidation was searched as fixed modification, whereas variable modifications were set for pyroglutamate formation of aminoterminal glutamine and acetylation of the protein N terminus.
Mass tolerance on peptide precursor ions was fixed at 10 ppm and on fragment ions at 0. 5 Da. The peptide charge was set to 2ϩ,3ϩ. The instrument was put on electrospray ionization-TRAP. Endoproteinase-LysC was the selected protease, with one missed cleavage allowed; cleavage was accepted as well when lysine was followed by proline. Only peptides were withheld that ranked first and scored above the 99% confidence threshold score. 13 C 3 -propionate and 12 C 3 -propionate were used as heavy and light labels, respectively, with specificity for lysines and peptide N termini. The feature "matching between runs" was activated. The false discovery rate (FDR) for peptide and protein was set to 1% and the minimum peptide length was set to 7. All mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the PRIDE accession PXD003879.
The results can be accessed through MS-Viewer (41) on the Protein Prospector website (http://prospector2. ucsf. edu/prospector/cgi-bin/msform. cgi?formϭ msviewer) with the Search Key: ls5iutdkzh. Statistical Analysis of the Shotgun Proteomics Data-We first applied a stringent filter to the data set, keeping only protein for which at least two valid values were available from the four biological repeats for every condition tested. The remaining 1,968 proteins were analyzed by fitting a linear mixed model of the following form: Y ijkl ϭ ϩ ␤ i ϩ g j ϩ m k ϩ t l ϩ gm jk ϩ gt jl ϩ mt kl ϩ gmt jkl ϩ ijkl (Eq. 1) partitioning the variation in protein abundances (Y ijkl ) into fixed genotype effects (WT and max2 represented by g i ), treatment effects (mock and strigolactone represented by m k ), time effects (0 h, 9 h and 24 h for mock, 9 h and 24 h for strigolactone represented by t l ) and all interaction effects, and random block effects, referring to the biological replicates.
The column temperature was maintained at 40 °C and the autosampler temperature at 10 °C. A flow rate of 350 l/min was applied during the gradient elution starting at time 0 min 5% (B), 30 min 50% (B), and 33 min 100% (B). The eluant was directed to the mass spectrometer equipped with an electrospray ionization source and lock spray interface for accurate mass measurements. MS source parameters were: capillary voltage 2. 5 kV, sampling cone 37 V, extraction cone 3. 5 V, source temperature 120 °C, desolvation temperature 400 °C, cone gas flow 50 L/h, desolvation gas 550 L/h. The collision energy for trap and transfer cells was set at 4 V and 3 V, respectively. For data acquisition, the dynamic range enhancement mode was activated. Full-scan data were recorded in negative centroid V-mode with a mass range between m/z 100 -1000 and a scan speed of 0.
2 s/scan by means of the Masslynx software  (Waters). Leu-enkephalin (400 pg/l solubilized in water/acetonitrile (1:1, v/v) acidified with 0. 1% (v/v) formic acid) was used for the lock mass calibration by scanning every 10 s with a scan time of 0. 5 s; three scans were averaged. For MS/MS purposes, the same settings were applied, except that the trap collision energy was ramped from 10 V to 45 V. For the LC-MS data processing, the Progenesis QI software v 2. 0 (Nonlinear Dynamics, Durham, NC) was used to align all chromatograms and to analyze statistically the ArcSinh-transformed compound intensities (normalized to dry weight) through principal component analysis and analysis of variance (p value threshold ϭ 0. 01). Descriptive statistics were calculated by EZinfo extension (v 3. 0) (Umetrics, San José, CA) on Pareto-scaled compound intensities.
RNA Extraction and Quantitative (q)RT-PCR-Roots from WT and max2-1 plants were harvested and snap-frozen in liquid nitrogen 24 h post treatment (hpt). Cell walls were disrupted by 3-mm metal beads in 2-ml tubes (Eppendorf, Hamburg, Germany) with a mixer mill 400 (Retsch, Haan, Germany) for 2 min at 20 Hz. RNA was extracted and purified with the RNeasy mini kit (Qiagen, Hilden, Germany). Genomic DNA was removed by DNase treatment and the samples were purified by ammonium acetate (2. 5 M final concentration) precipitation. Concentrations were measured with a ND-1000 Spectrophotometer (Thermo Fisher Scientific Nanodrop). The iScript cDNA synthesis kit (Bio-Rad) was used to reverse transcribe RNA. qRT-PCR primers were designed with the Quant Prime website software. SYBR Green detection was used during qRT-PCR run on a Light Cycler 480 (Roche Diagnostics).
Rac-GR24 treatment of WT roots resulted in the accumulation of compounds stained mainly orange and blue, corresponding to quercetin and kaempferol derivates, respectively (Fig. 6A ). Furthermore, this rac-GR24triggered flavonol accumulation was abolished in the max2-1 mutant background (Fig. 6A ), confirming the UHPLC-MS data that revealed an increase in flavonol production upon rac-GR24 treatment in roots. The applied rac-GR24 consisted of two enantiomers, GR24 5DS (GR24ϩ) and GR24 ent-5DS (GR24-), thought to mimic naturally occurring strigolactones and potentially karrikins or other unknown compounds, respectively  (18). Next, the specificity of the observed flavonoid response to one of the two enantiomers was evaluated. Flavonols accumulated after treatment with both 1 M GR24ϩ or 1 M GR24-in roots of 5-day-old plants (Fig.
back feathers). We calculated the intraclass correlation coefficient across all three metrics; variables were scaled prior to analysis. We also estimated correlations between variables to avoid collinearity of variables in the AIC analysis. Predictors of reproductive decision (dependent variable) included back feather CORT (in picograms per millimetre; n = 52), tail feather CORT (in picograms per millimetre; n = 54), faecal CORT (in nanograms per gram; n = 32) and body condition index (scaled mass index according to Peig and Greene, 2010, using a principal components analysis of tarsus and head/bill length for structural size, n = 48). We also included the year of capture and individual identity as random effects. We used a general linear mixed model (glmm) from the lme4 package in R, and selected the top models using an Akaike Information Criterion (AICc) approach (using AICcmodavg in R).
Conservation management implications In this study, we evaluated the possibility of four different glucocorticoid measures serving to identify the likelihood of reproductive deferral in harlequin ducks, incorporating both current measures (plasma CORT and FGMs) and historical measures (back and tail feather CORT metabolites). Our study has important implications for how and when we can use CORT physiology as a biomarker of reproductive success. First, our data point to a combination of current and carryover effects influencing reproductive deferral in this population. Conservation management strategies typically mediate/ restore conditions at the breeding grounds for species at risk. Inclusion of carry-over effects, however, opens up new habitats for consideration in species management. In the present study, higher CORT metabolites in back feathers indicate that conditions at the wintering grounds during the prenuptial moult might be suboptimal for female harlequins, and mediation of that habitat might be beneficial for reproductive output.
Acknowledgements This work was made possible by all of the volunteers who helped with trapping, telemetry and analysis:  Dan Savage ,  John Ashley ,  Cyndi Smith ,  Barbara Summer ,  Jim Rogers ,  Barry Hansen ,  Art Woods ,  McKay Breuner ,  Heather Jameson ,  Lindy Key ,  Karen Chickering ,  Gary Moses ,  Laura Holmquist ,  Gerard Byrd ,  Peter Brumm ,  Dustin Allen ,  Tim Fawell ,  Becca Marshall  and  Glacier National Park Wildlife Biologist John Waller  and  Technicians Alaina Strehlow ,  Courtney Raukar ,  Clay Miller  and  Mary Ann Donovan. Thank you to  Colette Trouvé  at the  Centre d'Etudes Biologiques de Chizé  staff for assistance with the feather assays.
Acknowledgements We thank  Laurence Ramon  for her fine illustration, the curators of P and NOU for the loan of specimens and the curator of  MPU  for permission to work in the collections, SMC/DIMENC for authorization to use "cadastre minier". Thanks to  Frédéric Rigault  for help with locating his collection,  Jérémy Girardi  ( NOU ) for scanning specimens, and  Grégoire Flament  (P) for assistance with MacKee's collections. Thanks to  Pete Lowry  for help in translating, and comments on an earlier version of this manuscript. Collecting permit was provided by the  North Province of New Caledonia. Thanks to an anonymous reviewer,  Gordon McPherson  and  Marco Duretto  for useful comments.
An alternative approach is the use of dynamical downscaling which offers a self-consistent approach that captures fine-scale topographic features and coastal boundaries by using regional climate models (RCMs) with a fine resolution (∼10-50 km) nested in the GCM  (Paeth et al. , 2011; Glotter et al. , 2014). The use of dynamical downscaling in long-range climate projections has recently increased with the growth of computing resources and large simulations databases of downscaled climate outputs are available for intercomparison and impacts assessment  (Glotter et al. , 2014). For instance the international Coordinated Regional Climate Downscaling Experiment Africa (CORDEX Africa) simulations are now publicly available and used in the literature, including a downscaled subset of GCMs simulations with different RCMs  (Diallo et al.
, 2013). To overcome this issue, climate impact studies generally require some level of climate data bias correction. The simplest correction method is the delta method used by  Müller et al. (2010)  or  Sultan et al. (2013)  which consists to add a computed mean annual anomaly between future and current simulated climates of a given GCM to a current observation-based dataset. Promising results are obtained by  Oettli et al. (2011)  when applying a more complex bias correction technique  (Michelangeli et al. , 2009)  to climate model outputs. In particular the authors showed that means and standard deviations of simulated yields of sorghum in Senegal are much more realistic with bias corrected climate variables than those using raw climate models outputs. Another important issue which has already been discussed in Section Climate Change Scenarios is the large plausible range of future climate changes at the regional scale of West Africa.
Although there are some robust features in climate change scenarios in the region (see Section Climate Change Scenarios), there is a wide spread in current climate model projections of regional rainfall changes over West Africa, especially with respect to summertime rainfall totals  (Druyan, 2011)  which are crucial for yields of staple food crops in West Africa  (Berg et al. , 2010; Guan et al. , 2015). Up to now, using the largest number of GCMs from the CMIP5 ensemble of around 36 GCMs remains the best way to represent the range of climate futures in impact assessment. Knox et al. (2012)  showed that increasing the number of climate models used to force crop models reduces the median range and outliers about the mean change in future yields. Important biases or underestimation of uncertainties can be expected from climate impact assessments based on subsets of CMIP datasets, and similarly from downscaled or bias-corrected datasets (like CORDEX) which are based on a restricted subset of GCMs.
, 2011; Knox et al. , 2012). Metaanalyses that combine and compare results from numerous studies are widely used in epidemiology and medicine and can be a useful way of summarizing the range of projected outcomes in the literature and assessing consensus. The meta-analysis conducted by  Challinor et al. (2014)  used a data set of more than 1700 published simulations to evaluate yield impacts of climate change and adaptation which is the largest pool of data from diverse modeling studies ever used for a global synthesis of this kind  (Rötter, 2014). The meta-analyses published by  Knox et al. (2012)  and  Roudier et al. (2011)  are based on a smaller data set (1144 and 347 published simulations respectively) but concern specific regions: Asia and Africa in database compiled by  Knox et al. (2012)  and only West Africa in the database compiled by  Roudier et al.
, 2015; SARRA-H and APSIM in Sultan et al. , 2014)  using the same forcing climate datasets. They all found a general agreement in the sign of the crop yield response to climate change scenarios while the amplitude of the impact varied strongly across models and simulated crops. Regional Differences Important regional differences have been found in the response of crop yield to climate change. Roudier et al. (2011)  found that cropped areas in the Soudano-Sahelian zone are likely to be more affected by climate change than those located in the Guinean zone. This difference can be explained by the projections of future climate in Africa which show a greater warming over continental Africa (particularly in the Sahel and Sahara) while the temperatures of the Guinean zone, which are influenced by the Atlantic Ocean, are expected to increase more slowly.
Most adaptation options illustrated in this review are implemented in process-based crop models to adjust cropping systems (change in varieties, sowing dates and density, irrigation, fertilizer management) to future climate. Results of the cited studies are crop and region specific and no clear conclusions can be made regarding the most effective adaptation options. Although substantial progress has been made in the assessment of the effect of climate change on crop yield and potential for adaptation in West Africa, large gaps still exist. Important processes like the effect of heat stress or ozone are missing in crop models  (Ewert et al. , 2015) , most effort on model development and intercomparison are biased toward major crops in temperate regions and the African region generally suffers from a lack of sufficient data for accurate calibration and validation of crop models  (Lobell and Burke, 2010).
ACKNOWLEDGMENTS This work has been funded by the  NERC/DFID   Future Climate For Africa programme  under the  AMMA-2050  project, grant number  NE/M020126/1. MG was also supported by the  LABEX (Laboratoire d'Excellence) L-IPSL  (LABEX of the IPSL), which was funded by the  French Agence Nationale de la Recherche  under  Programme Investissements d'Avenir  Grant  ANR-10-LABX-18-01.
In Table  1 , we demonstrate how to tailor data collection efforts to system-specific constraints by listing ideal methods along with more easily collected proxies. Researchers then use independently collected variables from monitoring efforts to test outcomes and fit uncertain relationships. Sensitivity analyses identify the most important parameters to collect, ensuring that resources go toward producing the greatest gains in accuracy. Based on these analyses, researchers can collect improved or new parameter estimates and revise the model through successive iterations of the approach. Crucially, results from multiple independent models should be combined because ensemble forecasts often prove more accurate  (9, 59). Researchers also need to articulate clearly how uncertainty in parameter estimates and model choice propagates at each modelling step.
Groups would select species representing a broad range of regional trait diversity and build initial models with available data to estimate parameter sensitivity. To address immediate extinction threats, regional working groups might also characterize the climate change risk for threatened species on The International Union for Conservation of Nature Red List. Groups should then develop plans to refine sensitive parameters through targeted funding opportunities and citizen science. Collected biological information must be accessible, quality-checked, standardized, and maintained in databases such as Encyclopedia of Life's TraitBank (traits) and Global Biodiversity Information Facility (species' occurrences). The IPCC's development of climate change predictions provides a template for how to achieve comparable progress in biodiversity projections.
ACKNOWLEDGMENTS This study was supported by the  FRB  ( IMMINV  project), the  ERA-Net Net-Biome , and the  Agence Nationale de la Recherche  ( FRAG&BINV  Project) to BF, SG, and  CE , the  Conseil Régional de Bourgogne (BF and SG) , the  Conseil Régional de Guyane  ( BF, SG, CE, TL ), and the  Conseil Régional de la Martinique (BF, SG, CE). Bird sampling has been carried out with permits from several organizations: DEAL Guadeloupe, Guyane and  Martinique ,  Forestry Department in Commonwealth of Dominica and Forestry Department in Saint Lucia. We would like to thank  Arlington James ,  Lyndon John ,  Georges Tayalay ,  Eric Marcon ,  Christian Chabrier ,  Cyrille Barnerias , and  Julien Mailles  for their invaluable advice and logistic assistance, and  Gabriele Sorci ,  Stéphane Cornet , and  Michael Cherry  for comments and suggestions on early drafts of this manuscript.
Total general 618 Sequences of both strands were aligned using Clustal-W  [24]  provided in BioEdit version 7. 2. 0  [25] , and corrected in case of any discrepancy by analysing the corresponding chromatograms. Sequences were trimmed to 235-319 bp and the search for the most similar sequences was done using Blast software (http://blast. ncbi. nlm. nih. gov/ Blast. cgi) in GenBank. The aim was to identify the genus and species of blood meal sources, based on the degree of sequence homology. Homology of 90 % or more allows the genus and species of the blood meal source to be identified and below 90 %, only the genus.
Results Blood meal sources of wild T. infestans The intestinal contents of 618 wild T. infestans captured in Andean Dry Forests, Prepuna and Gran Chaco eco regions were processed. Positive PCR (meaning that DNA from a vertebrate was present) was obtained for 371 samples, and the blood meal sources were identified for 144 samples (23. 3 %) (Table  1 ). Multi-banding HDA patterns characteristic of multiple blood meal sources were observed for 32 other samples (8. To identify the species corresponding to the 18 different HDA patterns and their reproducibility, 59 PCR products were sequenced. After inference in GenBank with the Blast procedure, very high identity (98-100 %) with sequences deposited in GenBank was generally obtained, allowing identification of blood meal sources at the species level (Table  2 ).
The sequences corresponding to identical HDA patterns always had very high identity between them (98-100 %), and they best aligned with the same species in GenBank. However, sequences corresponding to different HDA patterns could also have sequences that best aligned with the same species in GenBank. This was detected for three species: (i) Octodontomys gliroides had two different HDA patterns (P1 and P6) whose corresponding sequences best aligned with different GenBank sequences of O. gliroides (Table  2 ). The similarity between these sequences was 99 %; (ii) Galea musteloides had four different HDA patterns (P2, P7, P13 and P16) whose sequences best aligned with four different G. musteloides sequences deposited in GenBank. The similarity between the sequences of the patterns P16 and P7 was 96 and 99 % between P2 and P13, but between the two groups the similarity reached only 78 %; (iii) Graomys domorum had three different HDA patterns (P8, P9 and P11) whose sequences were best aligned with one GenBank sequence of G.
The blood meal sources identified generally corresponded to animals we observed occupying the capture sites. However, we must also emphasize that wild animals (O. gliroides, O. cuniculus and G. musteloides) were also identified as blood sources for the triatomines collected in the peridomiciles of Quillacollo and Thago Thago. These blood meal sources were identified mainly in 3 rd to 5 th nymphal instars (72. 7 %) and therefore less likely to spread into the wild and return to the peridomicile. Consequently, in this specific case, these data suggest the movement of wild animals to the peridomicile. Similarly, both phenomena possibly occur at the same time. These animals circulating among wild and domestic habitats play a particular epidemiological role because they link the sylvatic cycle of the parasite with a domestic one.
Acknowledgments We are particularly grateful to the direction of the  INLASA (Instituto de Laboratorios de Salud, La Paz Bolivia ),  Dr. José Raphael Gutiérrez  and  Dr. Walter Agreda  to have hosted this work in the Department of Medical Entomology directed by  Dr. Tamara Chavez. We also thank  Natalie Wickenkamp  for collaborating with the English review. This study received financial supports from the  ANR (Agence Nationale de la Recherche, France) , and the  IRD (Institut de Recherche pour le Développement, France).
ACKNOWLEDGEMENTS We express our sincere gratitude to  Dr. Bimal Kumar Nirmal , Deputy Director, Directorate of  Livestock Services (DLS) ,  Hariharbhawan Lalitpur , Nepal and  Mr. Hari Bahadur Rana  (PhD candidate),  Department of Veterinary Microbiology and Parasitology, Tribhuvan University (TU), Nepal  for their technical help and for guiding the shape of this manuscript.