dataesr/scientific-paragraphs-categorization huggingface dataset:
Regional Monitoring of Decrease in Forest Vitality The four different probability classes identified allowed us to create a clear-cut probability mask at the same spatial resolution as a MODIS image. The interpretation and use of these four classes depended on the objectives of the analysis that we ourselves set up: (1) For the quantification of clear-cut areas, the medium and high clear-cut probability classes (class M and class VH) are of particular interest for users. By combining these two classes, the majority of large area clear-cuts are detected. However, the comparison with clear-cut reference data revealed an underestimation of approximately 16% of the total clear-cut area. The error of omission is mainly related to small clear-cut areas which do not generate a strong enough breakpoint to be included in class M and class VH.
In this study, two criteria (the proportion of pixels considered cut, and the impact of the proportion of intra-pixel clear-cut on vitality trends) were taken into account to establish reference data at the same resolution as MODIS images. Finally: (4) how should the accuracy of a clear-cut detection model be evaluated? To define different thresholds, it is possible to use binary data for each threshold to calculate a confusion matrix and errors of omission and commission. These indicators of accuracy identify the proportion of error for each threshold. To validate their probabilistic model, some authors only take the threshold with the best trade-off between errors of omission and commission into account  [50]. In our case, the omission and commission rates were calculated for each threshold to help select the most appropriate threshold for the end user.
2005 (Larson et al. , 2007;; Groves 2008). Breeding programs for S. cebifrons in zoos have been set up to maintain the species at least in captivity. Here, we combine pedigree information, genotype data, and next-generation sequencing data to perform in silico management of two pig populations with distinctly different histories: a commercially maintained population of the Pietrain breed of S. scrofa; and a captive zoo population of the critically endangered S. cebifrons species. By comparing the decay of heterozygosity over 10 generations, we assess which of three management strategies maintains the most diversity. These strategies are based on (1) genealogical coancestry; (2) molecular marker-by-marker coancestry; or (3) shared regions of the genome. As S. cebifrons is known to have undergone recent bottlenecks that have led to establishing the conservation programs for this species in captivity, we infer the demography of Sus cebifrons to determine the effect of population-specific demography on the management outcome.
These results emphasize the use of resequencing data not only to estimate coancestries, but also to better understand the distribution of detrimental variation, which is crucial to decide which management strategy to use. Most breeding programs in zoos are still based on pedigrees  (Fienieg and Galbusera 2013). Sequencing costs are decreasing rapidly and therefore NGS provides an increasingly affordable means to measure genetic variation and potentially fitness at the individual and population levels  (Ouberg et al. 2010). This is especially the case if a (closely related) reference genome already exists, where it might be worthwhile to sequence all available founders in management strategies of small zoo populations, in order to get insight into the genetic variation and the inbreeding load.
Methods We used resequencing data from seven S. cebifrons individuals, genotypes from 46, and sequence data from 11 individuals of the Pietrain breed of S. scrofa. Sus cebifrons is a critically endangered pig species that is endemic to the Philippine islands and can be found currently on only two islands. Its estimated population size in the wild is low: 200-500 (Negros Island) and 500-1000 (Panay Island). We used pedigree data from two studbook-keeping zoos: Rotterdam Zoo, founded with six individuals from Negros; and San Diego Zoo, founded with seven individuals from Panay. All individuals were sequenced to ∼10× depth of coverage with the Illumina paired-end sequencing technology (Illumina). The 47 Pietrain individuals were genotyped on the Illumina PorcineSNP60 BeadChip (the 11 resequenced individuals are a subset of this 47).
Phylogeny A pairwise distance matrix (1-IBS) was constructed using the -cluster option in PLINK v1. 07  (Purcell et al. 2007 ) for both matrices, and a neighbor-joining tree with random input order was created in PHYLIP  (Felsenstein 2005). The tree was visualized using FigTree (http://tree. bio. ed. ac. uk/software/figtree/).
Ne estimation We estimated past and recent effective population size in the S. cebifrons data using two methods independently. First, we used the method of  MacLeod et al. (2013)  to filter the heterozygous sites in our VCF files for false positives. Briefly, this method does not remove actual false positives; but based on the false positive rate, it randomly removes a number of heterozygous sites in a particular window along the genome. The false positive rate was estimated based on the rate of heterozygotes on the non-pseudo-autosomal regions of the X chromosome for males. Then we recoded the distribution of heterozygous sites to PSMC-FASTA files and conducted the demographic analysis with a hidden Markov model approach as implemented in PSMC (Li and Durbin 2011), using T max = 20 and n = 64 (4 + 50 × 1 + 4 + 6).
The generation time was set at 5, mutation rate 10 -8 , which is identical to that used for human and cattle resequencing data  (MacLeod et al. 2014). False negative rate was estimated from the distribution of depth of coverage per site, ranging from 15% to 45%. Confidence intervals for each sample were estimated based on bootstrapping  (Li and Durbin 2011). To be able to compare the inferred demography from the PSMC method, we used the iterative approach described by MacLeod et al. (  2013 ) to infer past effective population size for the male S. cebifrons from Panay. The false positive rate was estimated as above, and the false negative rate was based on the proportion of sites in the genome, where the average coverage was between 0. 5 and 2 times the average coverage. Based on these inferences, the scaled mutation rate was 4.
Signatures of selection We performed an iHS test  (Voight et al. 2006)  as implemented in the R package rehh  (Gautier and Vitalis 2012)  in all 46 Pietrain pigs that were genotyped on the Illumina PorcineSNP60 BeadChip. After filtering for missing data and MAF, a total number of 51,165 markers were included. Ancestral states of alleles were determined based on the genotypes from four outgroup Suids from Island South-East Asia (ISEA) obtained from  Groenen et al. (2012)  and  Frantz et al. (2013). The significance of iHS was calculated by the ihh2ihs option.
Acknowledgments This work was financially supported by the  European Research Council  under the European Community's  Seventh Framework Programme  (FP7/2007-2013)/ ERC  Grant agreement number  249894. M. A. R. deC. and F. A. were funded by  LabEx  grant  ANR-10-LABX-0003-BCDiv  from the  French government , managed by  Agence Nationale de la Recherche  under " Investissements d'avenir" program   ANR-11-IDEX-0004-02. Additional funding for M. B. to realize this collaboration was provided by the  European Science Foundation  exchange grant Advances in Farm Animal Genomic Resources (GENOMIC-RESOURCES) no. 4579  to visit the M. A. R. d. C. and F. A. group. We thank  Simon Boitard ,  Flora Jay , and  Iona MacLeod  for valuable discussions on the methodology; and  Willem Schaftenaar ,  Aryanna Herscheid ,  Janno Weerman , and  Harald Schmidt  for collaboration.
Study Design. This was a single-center, randomized, controlled, open-label, two-parallel group study in healthy male low water consumers who smoke (ClinicalTrials. gov no. NCT01583387). The study was conducted at Centre CAP (Montpellier, France) between February 21 and July 4, 2011. The primary objectives of this study were to assess the effect of increased water intake on the mutagenicity of smokers' urine and subsequent formation of 4-ABP and BPDE-DNA adducts in exfoliated bladder cells in 24-hour urine.
Study Conduct. Eligible subjects were randomized 1 : 1 to the test or control group. The randomization list was generated using SAS version 9. 2 (SAS Institute, Cary, NC), and subjects were assigned to groups sequentially. Subjects assigned to the test group had to consume three 500 mL bottles daily of natural mineral water (Evian, Danone, Paris, France) in addition to their usual water intake for 50 consecutive days. Bottles were opened at the beginning of every meal (breakfast, lunch, and dinner) and had to be fully consumed before the next meal. Subjects in the control group continued their normal fluid consumption. At screening, investigators recorded physical characteristics, demographics, smoking behavior, alcohol consumption behavior, medical history, and concomitant medication. In addition, smoking habits were assessed at randomization and study end using the Fagerström Test for Nicotine Dependence  [15].
Statistical Analysis. Statistical analysis was performed using SAS version 9. 2. Safety, demographics, and baseline characteristics were assessed in the full analysis set, which included all subjects randomized. Primary and secondary endpoints were assessed in all subjects completing the study according to protocol as well as in all subjects randomized and for whom data were available. For continuous outcomes, the main model was a generalized linear model on the raw change of the value with the study group as fixed factor, value at baseline, and number of cigarettes consumption at baseline as covariate. All adduct measures were logtransformed to provide normally distributed data. Before being log-transformed, the measures equal to 0 were replaced by 0. 1 (limit of detection divided by two).
The minimum distance of a track to a primary vertex, the impact parameter (IP), is measured with a resolution of (15 + 29/p T ) µm, where p T is the component of the momentum transverse to the beam, in GeV. Different types of charged hadrons are distinguished using information from two ring-imaging Cherenkov detectors  [19]. Photon, electron and hadron candidates are identified by a calorimeter system consisting of scintillating-pad and preshower detectors, an electromagnetic calorimeter and a hadronic calorimeter. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers  [20]. The trigger  [21]  consists of a hardware stage, based on information from the calorimeter and muon systems, followed by a software stage, in which all tracks with p T > 500 (300) MeV are reconstructed for data collected in 2011 (2012).
Dalitz plot fit The Laura++  [46]  package is used to perform the Dalitz plot fit, with the two trigger subsamples fitted simultaneously using the Jfit method  [47]. The two subsamples have separate signal and background yields, efficiency maps and background SDP distributions, but all parameters of the signal model are common. The likelihood function that is used is L = Nc i k N k P k m 2 i (D + π -), m 2 i (D + K -) , (19) where the index i runs over N c candidates, while k distinguishes the signal and background components with N k the yield in each component. The probability density function for signal events, P sig , is given by Eq. (  16 ) where the |A (m 2 (D + π -), m 2 (D + K -)) | 2 terms are multiplied by the efficiency function described in Sec. 4. 2. The mass resolution is approximately 2.
Several other parameters are also determined from the fit as described below. In Dalitz plot fits it is common for the minimisation procedure to find local minima of the likelihood function. To find the global minimum, the fit is performed many times using randomised starting values for the complex coefficients. In addition to the global minimum of the likelihood, corresponding to the results reported below, several additional minima are found. Two of these have negative log-likelihood (NLL) values close to that of global minimum. The main differences between secondary minima and the global minimum are the interference patterns in the Dπ S-and P-waves, as shown in App. A. The shape parameters, defined in Eq. (  15 ), for the nonresonant components are determined from the fit to data to be 0.
Several cross-checks are performed to confirm the stability of the results. The data sample is divided into two parts depending on the charge of the B candidate, the polarity of the magnet and the year of data taking. Selection effects are also checked by varying the requirement on the neural network output variable and the PID criteria applied to the bachelor kaon. A fit is performed for each of the subsamples individually and each is seen to be consistent with the default fit results, although in some cases one of the secondary minima described in App. A becomes the preferred solution. To cross-check the amplitude model, the fit is repeated many times with an extra resonance with fixed mass, width and spin included in the model. All possible mass and width values, and spin up to 3, were considered.
Although the physiological cost of refereeing has been already studied in the literature, especially in soccer umpires, it remains unknown whether referees spontaneously adapt their energy intake during game days. Six national soccer referees completed 24-hour dietary recalls (assisted by the SU. VI. MAX copybook) during a control day (CON) and a day with a game (GAME). The stress level and hunger feelings were assessed using visual analogue scales. Total energy intake, energy derived from macronutrients and energy intake at each meal were analyzed using the Bilnuts nutrition software. Total daily energy intake was not significantly different between conditions (CON: 2270 ± 535 vs. GAME: 2782 ± 293). Energy derived from fat and protein was not different between conditions but the participants ingested more calories derived from carbohydrates during the GAME day (45.
Measures Energy intake. The participants were asked to complete 24 hours dietary recall during both CON and GAME days. They were asked to indicate all the consumed calories and the ingestion time. In order to facilitate the completion of the recall and to improve its accuracy, the SU. VI. MAX copybook was provided to each participant. Based on pictures that represent a large variety of food depending on their volume, the SU. VI. MAX allows a better estimation of the caloric intake. This method had been developed and previously used as accurate in different populations  (Hercberg et al. , 2004). The energy intake and food choices were qualitatively and quantitatively assessed using the Bilnuts software (Bilnut 4. 0 SCDA Nutrisoft software, France). Hunger and stress feelings. Hunger feeling was assessed before each meal using a Visual Analogue Scale on which the participants had to indicate their sensation from 0 to 10.
Statistical analyses Data are expressed as mean ± standard deviations. Since the sample size was limited the data were not normally distributed (after verification by means of a Smirnov-Kolmogorof normality test) and non-parametric tests were used. The total daily energy intake, energyderived from Fat, Protein and CHO were compared between CON and GAME days using the Wilcoxon signed rank test for a paired t-test. The energy intake, at each meal was compared using the Friedman non-parametric procedure © Editorial Committee of Journal of Human Kinetics (condition x meal). Area Under the Curves (AUC) were calculated for stress and hunger scores and the Friedman test was performed (meal x condition). The level of significance was set at p<0. 05. The z statistical value has also been obtained and effect sizes calculated using Hedge's calculation that had been developed for small sample size using paired analyses with effect size = 0.
5 cm. The calibration sources -60 Co, 152 Eu, 133 Ba, 166 Ho and 56 Co -were placed at the target position. The data was acquired using an external nonsegmented EUROBALL Ge detector as a reference. The output of the preamplifiers of each AGATA detector was digitized by a flash ADC. In this measurement the EU-ROBALL detector was connected to one of the AGATA digitizers. Therefore, it followed the same digitization procedure. The offline data treatment requires several corrections to provide reliable energy and position informaa e-mail: N. Lalovic@gsi. de tion for the final tracking stage. In the following, the main stages of the data processing are addressed.
First Results The analysis was initially performed on the data from the run with 60 Co. First estimate of efficiency and P/T at 1. 173 MeV refers to AGATA as a calorimeter, summing up energies recorded by the central contacts of all crystals. The second scenario measures efficiency on the basis of energy from the central contact of only a single crystal. Preliminary γ-ray spectra of 60 Co in these two cases are shown in Fig. 1. Furthermore, AGATA was treated as a tracking array, utilising the energy information from reconstructed γ-ray interactions. The calorimetric approach results in an efficiency of 3. 3 % and P/T of 32 %, whereas the obtained tracking efficiency yields 2. 5 % and P/T of 34 %. The latter results were obtained with standard values of tracking parameters, which still require optimization in the course of the further analysis.
0 eV. Element peak intensities were corrected by Scofield factors  (Scofield 1976 ). The spectra were fitted using the Casa XPS v. 2. 3. 13 Software (Casa Software Ltd. , UK) and applying a Gaussian/Lorentzian ratio, G/L equal to 70/30. In addition, the error in the peak fitting process was estimated at 5% or less with respect to the integration of the raw data.
In Vivo Dox Quantification The integrated density (IntDen) from the red channel (obtained after setting a threshold) representing released doxorubicin at 42°C into mice injected with either RGD-Dox-TSL or Dox-TSL was quantified from 13 positions from each group, which were obtained from three mice. The data are presented as an average of IntDen of all the positions of each mouse. The data was analyzed by Image J software. When DiD quantified, 3-6 positions were used per mouse obtained from three mice from each group. The data are presented as an average of IntDen of all the positions of each mouse.
In Vivo Dox Release upon HTand Dox Uptake by Tumor and Endothelial Cells In order to understand whether RGD-TSL release Dox in vivo upon HT treatment and to follow Dox distribution in the tumor, intravital microscopy was performed. Circulation of TSL and RGD-TSL for 5 h in the blood stream did not cause any premature release of Dox (data not shown). However, when HT at 42°C was applied, immediate Dox release (red) was observed from TSL (Fig. 7a ) and RGD-TSL (Fig  7b ). Dox from both formulations was released first intravascularly from the circulating DiD-labelled liposomes (purple), after which it was gradually taken up by endothelial cells and tumor cells surrounding the blood vessels (Fig. 7d  and supporting information, video 1). The Dox uptake in the both treatment groups increased in time and was maximal after 1 h of HT, when also the lumen of the blood vessels was cleared from Dox.
(44). There was no difference in cytotoxicity of Dox-TSL and Dox-RGD-TSL to any of the cell lines at NT conditions. A HT trigger could not further increase the cytotoxicity of Dox-RGD-TSL in B16F10 and HUVEC but only in B16Bl6. This is likely due to the nature of the assay in which cytotoxicity is measured 72 h after nanoparticle incubation and heat treatment. During the remaining 72 h also non-heat triggered RGD-TSL will release their Dox contents intracellularly due to nanoparticle processing, causing cellular cytotoxicity. The 5 h targeting phase did not show any premature Dox release from neither RGD-TSL or TSL (data not shown), which is in accordance with other targeted TSL  (42). During this phase, RGD-TSL bound to tumor endothelial cells already 20 min after liposome injection and were observed bound up to 24 h.
ACKNOWLEDGMENTS AND DISCLOSURES We thank  Dr. Marcel Verheijen  and  Monja Kaiser ,  Philips Research Eindhoven , NL for TEM cryo imaging,  Sandra van den Bosch  and  Prof. Holger Grull ,  Philips Research Eindhoven , NL for assistance in pharmacokinetics and biodistribution studies,  Thomas Soulie  and  Debby Schipper ,  Erasmus MC, Laboratory Experimental Surgical Oncology, Rotterdam  for support in some in vitro experiments, and  Dr. Peter Burgers ,  Erasmus MC, Rotterdam  for MALDI analysis. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
The tRNA multigene family usually comprises 20 amino acid accepting groups (also called amino acid isotypes), each containing one or more tRNA members that recognize different codons commonly referred as tRNA isoacceptors  (Sprinzl and Vassilenko 2005; Goodenbour and Pan 2006). To function as substrate in protein biosynthesis, each tRNA molecule attaches to a single amino acid upon selectively base-pairing its three-base anticodon to a complementary three-base codon sequence of mRNA. tRNA genes can be computationally predicted using several software (e. g. , tRNAscan-SE, ARAGON), enabling the full identification of tRNAs (with their anticodon loop) within any genome  (Lowe and Eddy 1997; Laslett and Canback 2004; Chan et al. 2011 ). tRNA databases have been constructed from draft and complete genomes as well as metagenomic sequences  (Chan and Lowe 2009; Lee et al.
Genome Data Sets The complete genomes of 2,765 prokaryotic (2,600 bacterial and 165 archaeal) organisms available in December 2013 were downloaded from National Center for Biotechnology Information (NCBI) ftp site (ftp:/ftp. ncbi. nih. gov/genomes/ Bacteria/) and constituted the primary data source. Closely related organisms were not excluded as evolutionary differences (presence/absence of the tRNA gene array, loss/gain of a tRNA gene within an array) in species belonging to the same genus could be observed. The A. ferrivorans CF27 genome (accession number CCCS020000000) was retrieved from the MicroScope website (https://www. genoscope. cns. fr/agc/ microscope/)  (Vallenet et al. 2013)  under the AcidithioScope project. tRNA Gene Prediction and Identification of tRNA Array Units The search for tRNA genes was carried out using the tRNAscan-SE 1.
The tRNA isoacceptor was designated by its anticodon. In-house Perl scripts were then used to locate tRNA array units within the overall prokaryotic genomes. For that purpose, we first identified regions of 5, 10, 15, 20, 25 and 30 kb along the genome (incremental path of 1 kb) that contain at least  5, 10, 15, 20, 25, or   S1 , Supplementary Material online). Based on 1) a previous study  (Bermudez-Santana et al. 2010)  in eukaryotes in which two adjacent tRNA genes were considered as "clustered" if located less than 1 kb apart (in other words, a tRNA gene density higher than two tRNA genes per kb), and 2) that array seeds of 5 kb-size are all included within array seeds of 10 kb-size (see gray cells in supplementary table  S1 , Supplementary Material online), the 10 kb-length was chosen as the size for the tRNA array seeds with a minimal tRNA gene density set to 2, in order to focus on array units with high number of tRNAs.