dataesr/scientific-paragraphs-categorization huggingface dataset:
The plot centre is marked and hidden to make sure that the personnel in charge of the harvesting does not behave differently within the plot than in the rest of the cut block. Plots are revisited after harvesting to determine the status (living or cut) of each tree that was previously identified during the pre-harvest measurement. The results of the survey are then compared with the standards associated with the treatment that was planned for this particular stand. If the standards are not met, the private company has to correct for the problem whenever possible or pay a fine otherwise. The data were recovered from these control surveys for a total of 59,794 plots, which covered the 1988-2005 period. Most plots were established after 1997 and many different treatments were controlled. From this point on, this article will focus on two major treatments: selection cutting and commercial thinning.
Commercial thinning is mostly carried out in coniferous or mixed stands. According to MNR standards, this treatment is to be done in stands whose site index is equal to or higher than 15 m at 50 years of age  (MRNF, 2004). Stand basal area must be between 23 and 33 m 2 ha -1 before harvesting, while residual basal area should not be lower than 16 m 2 ha -1. Removal should be between 28 and 35% of the initial basal area. Thinning is from below since the ratio between the mean quadratic diameter after and before the treatment should be equal to or greater than 1. 05. The dataset that was used to model the three treatments, i. e. the 1997-2004 selection cutting, the post-2004 selection cutting, and the commercial thinning, encompassed 45 567 plots. A summary of these data is provided in Table  1.
ARTEMIS growth simulator ARTEMIS is a distance-independent growth simulator that is currently used in the province of Quebec for forest management purposes. The simulator was largely described in  Fortin and Langevin (2010, 2012). Its architecture is simple, with only four dynamic modules that are all based on 10-year growth intervals: a diameter growth module, a mortality module, a recruitment module and a recruit dbh module. Tree heights and volumes are predicted using a height-diameter relationship model  (Fortin et al. , 2009a ) and a volume model  (Fortin et al. , 2007). The four dynamic modules of the simulator were fitted to the data of the provincial permanent-plot network. At the end of a particular growth step, the simulator provides a list of trees with the species, dbh, height, volume and status for each one of them.
Roughly, any standardised residual larger than 2 or smaller than -2 might indicate a lack of fit. These standardised residuals are particularly useful to identify unaccounted for trends in the models. Once the fit of the models was found to be satisfactory, a 10-fold cross-validation was also performed. The plots were split into 10 groups. The models were then fit 10 times, each time omitting a group. At each run, the model predictions were produced for the observations of the omitted group. After the 10 iterations, each observation of the dataset has a prediction as if it had been generated from an independent dataset. It is worth mentioning that this process also made it possible to detect over-parameterisation, which actually resulted in a lack of convergence when one of the 10 groups was omitted.
Once the cross-validation proved to be satisfactory, the final models were fit with the whole dataset. The Hosmer-Lemeshow test  (Hosmer and Lemeshow, 2000)  is a popular method for assessing the goodness of fit for logistic models. The test consists in ordering the observations by their predicted values. The observations are then divided into 10 even groups. Like the binomial residuals above, the predicted values are averaged within each group. If o k and p k are the sum of observed values and the average predicted probability in group k, respectively, the Hosmer-Lemeshow statistic is calculated as. This statistic follows a Chi-square distribution with 8 degrees of freedom. Large values for X 2 HL indicate a lack of fit. This test was performed on predictions that resulted from the cross-validation.
Results The Hosmer-Lemeshow statistics and the area under the curve computed from the cross-validation are shown in Table  3. All three models exhibited Hosmer-Lemeshow statistics with probabilities Pr > χ 2 HL smaller than 0. 01. However, when plotting the average predicted probabilities against the average proportion of events for each group, no major departures could be found. The AUC value ranged from 0. 684 to 0. 734, with the maximum and minimum values observed for the 1997-2004 selection cutting. These AUC values indicated an average accuracy. In other words, the models were better than randomness but could not be considered as highly accurate. The parameter estimates resulting from the final fit using the whole dataset are reported in Tables 4, 5 and 6. To illustrate the different trends, predicted probabilities were generated for the three models and the most abundant species in each treatment.
Discussion Predicting future forest conditions in managed stands is challenging because it involves the coupling of single-tree growth simulations and harvest predictions. Whenever the harvesting is partial, i. e. , not clearcut, the selection of the trees to be harvested in growth forecasts has to be predicted in some way. In this study, three statistical harvest models, which stood for two selection cuttings and a commercial thinning treatment, respectively, were fitted to be compatible with the ARTEMIS growth simulator. Two reasons motivated the use of statistical models over harvest algorithms. First, a large dataset was available to f it these harvest models. Second, the statistical approach provides more information about the model uncertainty. Because the model relies on a parametric distribution, the different sources of variability are known and inferences can be made, for example, on the errors in the parameter estimates.
, 2008a,b). According to the standards for this treatment, the residual stands should have a higher proportion of vigorous stems than before selection cutting. Consequently, after harvesting, it could be reasonably expected that the probabilities of mortality would decrease, whereas the diameter increments would increase. In its current form, the growth simulator cannot account for any change in the proportion of vigorous trees. Because the mortality module in ARTEMIS does not include tree vigour, the trees keep dying at the same rate after harvesting, all other things being considered. Knowing that the probabilities of mortality for non-vigorous trees are about six times those of vigorous trees for sugar maple  (Fortin et al. , 2008a) , changing the proportion of vigorous stems in a sugar maple-dominated stand or stratum cannot be taken lightly in this case.
This remains a major limitation of this coupling between harvest models and ARTEMIS. Of course, the reader can rightfully wonder why tree vigour has not been taken into account in the simulator. In fact, modelling the evolution of tree vigour requires permanent-plot data in which this variable was measured. Tree vigour has only been assessed since the early 2000s in the provincial permanent plot network that served to fit the component of ARTEMIS. We can expect ARTEMIS to include a vigour and a quality module in the next few years since the plots will be revisited, but for the time being, these features cannot be accounted for. Such harvest models actually aim at representing the mathematical expectation of stochastic phenomenon, namely tree harvesting. As such, their purpose is not to guide the harvest in the field.
In spite of all the explanatory variables that were included in the models, their accuracy was not as high as expected. In fact, the tree marking is often based on their vigour and quality. Unfortunately, it was impossible to take these features into account in our harvest models. Whether these variables had been available or not in the dataset was of lesser importance, knowing that the AR-TEMIS growth simulator did not consider them and that the harvest models had to be used with this simulator. The monitoring of quality and vigour, as currently assessed in the province of Quebec (see  Boulet, 2007) , will eventually provide the data for fitting vigour and quality modules in ARTEMIS and for including these variables in our harvest models. In the meantime, growth forecasts in broadleaved stands under unevenaged management might underestimate stand growth.
Acknowledgements Daniel Pin ,  Gordon Weber  and  Gyna Gagnon  from  Le Bureau du Forestier en Chef du Québec  provided the database and support for this study. Special thanks are due to  Catherine Collet  ( LERFoB-INRA Nancy ) and  Jean-Pierre Saucier  ( DRF-MRNQ ) for their comments on a preliminary version of this paper. The author is grateful to the  Direction de la recherche forestière du Ministère des Ressources naturelles du Québec who  provided the funding for the implementation of the harvesting models in the CAPSIS platform. The UMR 1092 LERFoB is supported by the  French National Research Agency through the Laboratory of Excellence ARBRE  ( ANR-12-LABXARBRE-01 ).
Case definitions and data collection The case definitions used for leptospirosis surveillance in New Caledonia have been the same since 1995 and were described previously  [18]. Briefly, based on a clinical suspicion, cases were considered as confirmed if Leptospira is cultured, or if its genome is evidenced by PCR or real time PCR from blood, urine or cerebrospinal fluid, or if a seroconversion (from nil to at least a 400 titer) or a significant sero-ascension (at least a fourfold raise in titers) is observed in paired sera using the MAT. The case was considered as probable leptospirosis if a single MAT titer $800 is observed for at least one pathogenic serogroup in the serum of a patient with a clinical suspicion. Anonymous data used in this study were extracted without patient identification from the Institut Pasteur laboratory database and originated from routine diagnostic activities as part of public health surveillance.
Multivariate analysis To estimate the total contribution of climate and meteorological variation to the burden of leptospirosis, we next performed multivariate analysis. Because there was a high degree of collinearity among the potential climate and meteorological variables  [34] , we reduced the number of variables in the model by first performing principal components analysis (PROC PRINCOMP in SAS v9. 3)  [35]. The first 5 principal components accounted for 81. 3% of the variation in the climate and meteorological variables (Supporting Table  S1 ). We then used the BMA package in R  [36, 37]  to perform BIC-based model averaging  [38]. The variables for model averaging included the first five principal components  [35] , sine and cosine terms with a 12 month period, and 1-month, and 12-month autoregressive terms (log-transformed).
For the cumulative sum variables, all ranges from the previous 0 to 9 months were tested. Only the lag or cumulative sum with the lowest BIC score for each variable is presented here. A difference in the BIC scores of greater than 2 is considered to be important. All of the models include sine and cosine terms with a 12-month period. For the full table of results, see Supporting Table  S3. doi:10. 1371/journal. pntd. 0002798. t001 variables in the multivariate model dampens the association between El Nin ˜o and leptospirosis. The ''forecasting'' analysis was performed after SST anomaly was identified as being important in the full dataset. Additional years of data will be required to confirm the usefulness of this model in forecasting epidemics in New Caledonia. Predicting outbreaks of leptospirosis is of prime importance for public health decision makers.
Acknowledgments This study largely relies on the biological database of the  Institut Pasteur Medical Biology Laboratory , under the responsibility of  Dr Berlioz-Arthaud  (until Oct. 2007),  Dr Guigon  (Nov. 2007-Apr. 2009) and  Dr Gourinat  (since May 2009). The authors acknowledge the decisive commitment of the staff during outbreaks. New Caledonian meteorological data was kindly provided by  Meteo France. This work is part of the deliverables of the working group ''Predict'' of  GLEAN (Global Leptospirosis Environmental Action Network ), which main purpose is to help countries in developing and implementing policies and tools for leptospirosis outbreak prediction. Thanks to  Joe Lewnard  ( Yale ) for helpful comments on the analyses.
This point frequently poses a problem in numerous modelling approaches  (Nearing et al. , 2005). Nonetheless, the assumptions are retained here for simplicity and tractability. Tab. 2. Water infiltration capacities (in mm. h -1 ) in Seine-Maritime, according to various studies, and minimal values chosen for the flash flood assessment in this study. To account for rainfall variability, we assess the susceptibility of basins playing with different rainfall intensities. Two choices were possible: either we implement rains according to the frequency analysis methods for extremes, i. e. the SHYREG database  (Renard et al. , 2013) , or we consider project-rainfall scenarios for all basins. The first choice was not appropriate for this study for two reasons: (1) the statistic calculation of rains presenting small probability and large return periods introduce high uncertainties; (2) earlier research  (Douvinet et al.
Acknowledgments The authors are grateful to the  Service Central d'Hydrométéorologie en Appui à la Prévision des Inondations (SCHAPI)  for funding the  RuRan  project over the period 2011-2013 within which the study could be carried out. They also want to express their gratitude to the  French Research Ministry and CNRS  for funding the  SYMBAD  project (2004)(2005)(2006)(2007), within which RUICELLS was developed and calibrated on a few basins. In addition, thanks.
Data analyses In total, 15 of 16 arrays passed the appropriate quality control criteria as previously proposed  [21]. The data was GC-RMA normalized (Log2). In order to improve statistical power by excluding inappropriate probe-sets  [22] , the data was filtered by the MAS5 algorithm. Those probe-sets expressed in less than 50% per experimental group were skipped. Further, those probe-sets which revealed a small SD (SD <0. 20) were excluded from further data processing, because the corresponding transcripts were not likely to show altered mRNA abundances. Finally, probe-sets which showed a mean less than 2. 5 were skipped. In order to evaluate relative changes of mRNA abundances, a variance analyses was performed (SAS Institute, Cary, NC, USA), including individual and combined effects represented by stress level, sex, slaughter batch, mixing group, and stress level*sex (V ijkl = μ + stress level i + sex j + slaughter batch k + mixing group l + (stress level*sex) ij + error ijkl ).
In order to account for multiple testing p-values were converted to a set of q-values  [23]. The level of significance was set at p ≤0. 05 and q ≤0. 25. The raw data has been deposited in a MIAME compliant database, the National Center for Biotechnology Information Gene Expression Omnibus (www. ncbi. nlm. nih. gov/geo) (accession number: [GSE44992]).
Pathway analyses The probe-sets were annotated by EnsEMBL Susscrofa 9  [24]. In order to unravel putative pathways associated with altered mRNA abundances in porcine PBMC cells, gene lists obtained from the microarray analyses were evaluated with 'Ingenuity Pathway Analysis' (IPA release winter 2012, Ingenuity Systems, Redwood City, CA, USA). The significance of association between dataset and pathway analysis was calculated according to the Benjamini-Hochberg correction implemented in IPA (p ≤0. 05).
Unaltered transcriptional patterns due to stress level The analysis revealed 706 probe-sets with an altered mRNA abundance at p ≤0. 05 that correspond to q-values ranging between 0. 24 and 0. 36. Indeed, only 2 probe-sets (Ssc. 13877. 1. A1_at and Ssc. 3445. 1. S1_at, annotated as Trip11 and ARL4C, respectively) appeared to be altered in HS samples compared to LS samples at q <0. 25. However, in order to get a first hint about transcriptional differences due to stress response, we uploaded this particular gene list (p ≤0. 05; q ≤0. 36) to IPA. HS and LS samples showed deviations within numerous immune pathways (HS > LS), including TREM1 signalling, dendritic cell maturation, Toll-like receptor signalling, and IL-6 signalling. Thus, these processes might represent transcriptional alterations which were only pronounced in a particular subgroup of the dataset.
Funding This work was part-funded by the 6th  Research Framework Programme  of the  European Union  as part of the  SABRE  project (cutting-edge genomics for sustainable animal breeding) and the  Federal Ministry of Education and Research (BMBF)  as part of the  PHENOMICS  project (support code:  0315536F ). FBN,  PIC  and  INRA  provided their own matched funding. SRUC matched funding was provided by the  Rural and Environment Science and Analytical Services (RESAS) Division of the Scottish Government.
83E-02 Gene symbol CD14 LY96 MYD88 TLR2 TLR4 Fold Change +3. 89 +1. 43 +1. 85 +1. 82 +3. 71 TREM 1 signaling HSF > LSF 3. 22E-05 Gene symbol CASP1 ICAM1 IL1B JAK2 MYD88 TLR2 TLR4 TREM1 TYROBP Fold Change +1. 70 +1. 56 +2. 35 +1. 69 +1. 85 +1. 82 +3. 71 +2. 80 +1. 92 P-value: significance of association between dataset and IPA according to Benjamini-Hochberg multiple testing correction; Details regarding involved genes were displayed in Additional file 1.
Identity testing Paired tumor-normal DNA samples were genotyped using the Coriell Identity Mapping kit (Coriell). Genotyping fragments were size separated on an ABI-3730xl DNA analyzer (Applied Biosystems). Alleles were scored using GeneMapper software. Primer design, PCR amplification, nucleotide sequencing and variant calling M13-tailed primer pairs (Additional file 2) were designed to target 577 of 591 exons that encode the catalytic domains of the 86 protein tyrosine kinases (Additional file 3), using previously published methods  [26]. Sequence constraints precluded the design of primers for 14 of 591 exons. Primers were also designed to target the exons that encode the exonuclease domain (exons 3 to 13) of POLE (polymerase (DNA directed), epsilon, catalytic subunit) and are available on request.
PCR amplification conditions are available upon request. Bidirectional Sanger sequencing of PCR products and subsequent nucleotide variant calling were performed as previously described  [27]. Variant positions were cross-referenced to the dbSNP (Build 129) database to annotate and exclude known germline variants. To determine whether novel variants were somatic mutations or germline variants, the appropriate tumor DNA and matched normal DNA were re-amplified in an independent PCR followed by sequence analysis of the variant position. Primers used in the secondary screen of TNK2 and DDR1 are provided in Additional file 4.
Quantitative real-time PCR Predesigned primers targeting TNK2 (VPH103-1002824A), DDR1 (VPH106-0859748A) and B2M (beta-2-microglobulin) (VPH115-0515670A) were purchased from SABiosciences (Qiagen). Reactions were assembled to contain either Taqman control genomic DNA (Applied Biosystems) or 2 ng of tumor genomic DNA, 2 μl of primers (diluted 1:4), 3. 5 μl SYBR Green Rox qPCR mastermix (Qiagen), to a final 10 μl reaction volume. qPCR was preformed on a ABI 7900 HT Fast Real-Time PCR System (Applied Biosystems) with the following cycle conditions: 50°C for 2 min, 95°C for 10 min, and 40 cycles of 95°C for 15 sec and 60°C for 1 min. A standard curve was generated with Taqman control genomic DNA, to permit a determination of the absolute quantitation using SDS 2. 4 software (Applied Biosystems).
Acknowledgements We thank  Niraj Travedi  for advice on statistical analyses. Funded in part by the  Intramural Program of the National Human Genome Research Institute, National Institutes of Health  (DWB, TGW, and JCM), by  NIH   R01CA140323  and the  Ovarian Cancer Research Fund (AKG) ,  The Avon Foundation (DCS) ,  National Institute of Health  ( R01CA112021 , DCS), The  Department of Defense Breast Cancer Research Program  ( W81XWH-04-1-0606 , DCS), and the  NCI SPORE  in breast cancer at  Massachusetts General Hospital (DCS).
G1709A p. R570Q Low Affects function Probably damaging T117 c Endometrioid (G1) c. 2216delA p. N740Ifs*10 - - - Transcript accession numbers: TNK2 (Ensembl ID ENST00000392400), DDR1 (Ensembl ID ENST00000454612). Protein accession numbers: TNK2 (CCDS33928), DDR1 (CCDS4690). G: Grade. [19]e no. T3 is also known as OM-1323, T15 is also known as OM-1529. bPOLE-mutated. cMSI-positive tumors, as reported previously[19]. *Denotes the position of a new stop codon introduced by the corresponding frameshift (fs) mutation.
The specificity of the immunostaining was assessed by omitting the primary antibodies, which resulted in the absence of signal. Immunofluorescent staining was analyzed by using a motorized Zeiss Axioplan 2 microscope equipped with a Hamamatsu C4742-95 digital camera (Hamamatsu Photonics France SARL, Massy, France) (switching between FITC and Texas Red filter sets) driven by MetaMorph â 5. 4 software. In each rat, image acquisition and fluorescent signal quantification were done using seven different sections, each taken at a given rostrocaudal plane within the MDH (from 0 to À2160 lm at 360 lm intervals). To reduce the variability in staining between sections, the same area of the MDH was taken in consideration for quantification of the fluorescent signal. Measurement of PKCc fluorescence was taken from the layer IIi as it contained the majority of interneurons positive for this marker.
The data are collected from seven sections in each rat. The final data are presented as a mean value of the total number of each rat group. Brainstem sections were categorized according to their approximate rostrocaudal location from the MDH subnucleus interpolaris junction. The pERK1/2-positive cells were counted under a 209 objective according to their location in the different laminae of the MDH from sections costained for PKCc, a cellular marker that highlights the inner lamina II  (Polgar et al. 1999). The delineation of the MDH was based on the Paxinos and Watson atlas. The data are expressed as the sum of the total number of labeled cells counted from all the sections analyzed in each animal. Tyrosine hydroxylase (TH) immunolabeling was performed using anti-TH primary antibody (Millipore, Molsheim, France), as described above.
Quantification of the impact of the 6-OHDA lesion on the SNc was investigated as described previously  (Pailleet al. 2007; Zengin-Toktas et al. 2013 ) using frozen coronal sections (40 lm) under a 209 objective. For each rat, TH-positive cells were counted at different rostrocaudal levels (every 0. 12 mm; to À4. 64 until À6. 2 relative to bregma) using Image J software (ImageJ v1. 41, National Institute of Health, Bethesda, MD). Split-cell counting errors were corrected using Abercrombie's formula, where N = n [t/(t + d)] (N: total number of cells; n: number of cells counted; t: section thickness; and d: cell diameter). The total number of cells in the SNc was calculated using Konigsmark's formula, where Nt = Ns (St/Ss) (Nt total number of cells; Ns number of cells counted; St total number of sections through the SNc; Ss number of sections in which cells were counted  (Pailleet al.
2007 ). Image analysis was completed using ImageJ software. The point picker plugin available in ImageJ freeware can be used to mark cells with a colored cross by clicking with the mouse. The number of cells counted is displayed in a text box on completion. For each group of animals, the data were the total number of cells in the section expressed as the percentage of the number of cell bodies measured for the shams (mean AE SD). Anatomical and behavioral analyses were performed by an experimenter blind to the treatments.