dataesr/scientific-paragraphs-categorization huggingface dataset:
of each isolate precedes the isolate name. Nucleotide positions correspond to sites from P209 (Genbank EF065175). Dots represent nucleotide site identical to reference strain (either P209 for TcI or CANIII cl1 for TcIV). Dashes represent missing nucleotides. *The following 17 US TcI sequences were identical: Human isolates (TC CC, CA R), domestic dogs (USA Dog Y), Virginia opossums (USA Opossum, Opossum 1970, 92101601P cl2, FH4, 93070103P cl2, FL Opo 3, FL Opo 15), armadillos (Armadillo 1973, USA Armadillo), triatomine bugs (Florida C1F8, Florida), and raccoons (GA Rac 143, 93053103R cl3, FL Rac 13). { The following 26 US TcIV sequences were identical: Raccoons (STC 10R cl3, FL Rac 9, 92122102R, 93071502R cl2, 93040701R cl1, 93072805R cl3, FL Rac 15, FL Rac 46, FL Rac 5, FL Rac 30, GA Rac 134, GA Rac 69, GA Rac 107, Maryland Rac, STC 35R), domestic dogs (Samantha Dog, Caesar Dog, Dog Theis, OK Dog, Smokey), ring-tailed lemurs (Clarence, Meg, Nilda), striped skunk (GA Sk 1), triatomine bug (T sang5 cl1), and armadillo (GA Arm 20).
N Amino acid change VRM I RL T RR Genotype of each isolate precedes the isolate name. Nucleotide positions correspond to sites from SilvioX10 cl1 (Genbank AY540739). Dots represent nucleotide site identical to reference strain (either Silvio X10 cl1 for TcI or CANIII cl1 for TcIV). *The following 17 US TcI sequences were identical: Human isolates (TC CC, CA R, TC California), domestic dogs (USA Dog Y), Virginia opossums (92101601P cl2, 93041401P cl2, FH4, FL Opo 2, FL Opo 3, FL Opo 15, Opossum 1970), armadillos (Armadillo 1973, USA Armadillo), triatomine bugs (Florida C1F8, Florida), and raccoons (93053103R cl3, FL Rac 13). { The following 23 US TcIV sequences were identical: Raccoons (FL Rac 9, 92122102R, 93071502R cl2, 93040701R cl1, 93072805R cl3, FL Rac 15, FL Rac 46, FL Rac 5, GA Rac 134, GA Rac 143, GA Rac 69, Maryland Rac, STC 35R), domestic dogs (Samantha Dog, Caesar Dog, Dog Theis, OK Dog, Smokey), ring-tailed lemurs (Clarence, Meg), rhesus macaque (Texas Theis), striped skunk (GA Sk 1), and armadillo (GA Arm 20).
So, a calculation method was developed and tested on numerous isothermal ternary systems with large demixing zone. The semi-empirical method consists in using the experimental results for the different tie-lines. A database was constructed from experimental data taken from the National Institute of Standards Technology NIST  [1]. The determination of each critical point was conducted in two steps: the first one is based on a generalization of the straight diameter method and the second, on the exploitation of the modulus of the experimental tie-lines. This contribution presents the original method of calculation derived from the rectilinear diameter law and the analysis of results shows its limits and its relevance.
There is another major dataset available on the terrestrial carbon cycle in the form of continuous measurements of fluxes at very small scales (e. g. ,  Foken and Wichura, 1996; Aubinet et al. , 2000; Baldocchi, 2003; Rebmann et al. , 2005; Reichstein et al. , 2005; Papale et al. , 2006; Lasslop et al. , 2010; Kuppel et al. , 2012; and references therein). Recently,  Williams et al. (2009)  reviewed the use of the international flux measurement network FLUXNET data  (Baldocchi, 2003)  for improving land surface models. These have afforded much information on processes affecting the terrestrial carbon-cycle (e. g. ,  Ciais et al. , 2005; Piao et al. , 2008). They have been used in various assimilation efforts (e. g. ,  Wang et al. , 2001; Knorr and Kattge, 2005; Medvigy et al. , 2009). They have also been tested in a simplified assimilation system  (Kaminski et al.
Transport model and CO 2 concentrations For the tracer transport, we use the precomputed transport Jacobians of the TM3 model  (Heimann and Körner, 2003)  For CCDAS, we use monthly mean atmospheric CO 2 concentration data from 68 sites from the GLOBALVIEW database [GLOBALVIEW-CO 2 , 2004) and some additional CO 2 measurement sites for which the TM3 Jacobians are available. The uncertainties in these data include those from models (BETHY and transport) and measurement errors and range from 0. 50 ppm to about 5. 0 ppm (see Table  A2  in the Supplement), as described in  Koffi et al. (2012).
(2010). These data come from three large data bases: The NOAA Earth System Laboratory (ESRL) archive, the CarboEurope IP project, and the World Data Centre for Greenhouse Gases (WD-CGG) of the World Meteorological Organization (WMO) Global Atmospheric Watch Programme. The three databases include both in situ measurements made by automated quasicontinuous analyzers and air samples collected in flasks and later analyzed at central facilities. The data treatments are fully discussed in  Chevallier et al. (2010). Data collected from up to 104 stations are considered (see Fig. 2  for locations of the stations). The errors in the LMDz model are included in the observational error following  Tarantola (2005). The treatment of these errors follows that of  Chevallier et al. (2010). Values range from 0.
Acknowledgements. We acknowledge the financial support of the  European Commission  through the  IMECC Integrated Infrastructure Initiative  ( I3 ) project under the  6th Framework Program  (contract number  026188 ). P. Rayner is in receipt of an  ARC Professorial Fellowship  ( DP1096309 ). We thank the anonymous reviewers of this manuscript for their constructive comments. Edited by: C. McNeil The publication of this article is financed by  CNRS-INSU.
T-box conservation analysis The T-box Stem I alignment was assembled as follows. We sought T-boxes in the 5 0 upstream sequences of bacterial genes retrieved from NCBI bacterial genomes using the RNAMotif program (6) and a T-box descriptor (Supplementary Text S2) comprising the basis of Stem I and the rho-independent terminator at the 3 0 end of the T-box. No sequence constraint was applied to the apical loop. RNAMotif hits were filtered to retain only those located upstream of aminoacyl-tRNA synthetase genes, which provided a collection of 131 T-boxes. The Muscle program  (7)  was initially used to refine the alignment; however, it generated disorders within the helical part of the alignment. Starting from the RNAMotif output, a manual alignment of the apex region of Stem I was performed instead, ensuring that it would fit the secondary structure model and locations of the AGNNA and UGNNA motifs (Figure  3A ).
(C) Sequence conservation encompassing the two T-loops of the structures in A, B and D: 1) L1 stalk (23S rRNA), 2) J11/12-J12/11 module of RNAse P and 3) apex of Stem I in T-box leaders. Sequence logos (highest value = 2 bits) were produced using the Weblogo interface (8) while using the following sequence alignments: 23S rRNA: primary alignments of 592 sequences from three phylogenetic domains, from the Comparative RNA Web site (13) ; bacterial RNAse P, class A: 340 sequences from the Ribonuclease P database (14); and T-box Stem I: alignment generated from 131 T-box sequences ('Materials and Methods' section). (D) B. subtilis tyrS T-box leader: apex of Stem I. Secondary structure (solid lines only) and base numbering according to (2).
We propose a generic framework to handle missing weak classifiers at testing stage in a boosted cascade. The main contribution is a probabilistic formulation of the cascade structure that considers the uncertainty introduced by missing weak classifiers. This new formulation involves two problems: (1) the approximation of posterior probabilities on each level and (2) the computation of thresholds on these probabilities to make a decision. Both problems are studied, and several solutions are proposed and evaluated. The method is then applied to two popular computer vision applications: detecting occluded faces and detecting faces in a pose different than the one learned. Experimental results are provided using conventional databases to evaluate the proposed strategies related to basic ones.
Occluded face detection In this section, we evaluate the performance of McCascade coupled with the principle of cascading with evidence in a specific application: detecting faces with top occlusions (like sunglasses) or bottom occlusions (like a scarf ). We only consider these two types of occlusions for two reasons. The first is that we are working in a video surveillance context in which these two types of occlusions are often encountered. The second reason is that a public database with these two types of occlusion is available: the AR database.
Evaluation on the AR database The AR database  [21]    For all scenarios, FROC curves are computed. To create the FROC curve of a cascaded detector, several threshold values are tested for the last level which results in corresponding points of detection rate and number of false-positives. To get more points (points with a higher detection rate and a higher number of false-positives), the last level must be removed, and then different thresholds for the new last level are tested. This procedure continues until enough points are collected. When several cascades are associated (e. g. , in the system 'C + C A + C B + evidence'), creating a FROC curve is not straightforward because each cascade has its own thresholds. To alleviate this problem, we use the idea proposed by Viola and Jones in  [22].
Multiview face detection In this part of the experiments, the boosted McCascade algorithm has been applied to another specific application: detecting faces in different poses using an upright face detector. The FERET database  [24]  was used to evaluate the system. We test our method on faces turned 22. 5°, 45°and 67. 5°. For each angle, all the subwindow positions are first adjusted using the procedure described in Section 5.
Ellipsoid parameters To modify the subwindow positions, parameters w, a, b, and c must be fixed. Parameter w corresponds to the size of the training images which is 24 in our case. To fix ellipsoid parameters a, b, and c, we do an exhaustive search and keep the parameters, giving the best results on validation sets from the FERET database. Two validation sets were created: one for the angle 22. 5°and one for 45°. For each angle, we keep half of the images to fix the ellipsoid parameters. The other half is used to evaluate the complete system. For each parameter value (a i , b i , c i ), we apply the following methodology: the area under ROC curve is computed which gives auc 22. 5   i (auc is a criterion to compare ROC curves: the higher it is, the better the ROC curve). Using C 45 , we also get auc 45 i.
Modification of subwindow positions Here, the use of an ellipsoid to modify subwindow positions is evaluated. Three detectors are built: • C 22. 5 is a detector of faces that turned 22. 5°, • C 45 is a detector of faces that turned 45°, and • C 67. 5 is a detector of faces that turned 67. 5°. Each one is built from C by modifying subwindow positions. Subwindows that disappear are handled by the naive approach. These detectors are then applied to images from the FERET database. The results are available in Figures  23  and 24. In each curve, the upright face detector C is noted 'Cascade'. Detectors C 22. 5 , C 45 and C 67. 5 are noted 'MaCascade' (for cascade with multiview adaptation). On faces turned 22. 5°, the improvement is slight because the appearance of such faces is still close to the appearance of upright faces.
The first part of the experiments related to McCascade performance (Sections 6. 2 and 6. 3) are done with an upright face cascaded detector of three levels with 5, 10, and 25 weak classifiers, respectively. Positive examples come from the labeled upright faces in the wild database [17], and negative samples were generated from 1,310 images containing no face. A total of 4,000 positive examples and 8,000 negative examples are used to train each cascade level. The second part of the experiments related to applications (Sections 6. 4, 6. 5, and 6. 6) are done with an upright face detector of nine levels. This detector is noted C. Each level was trained with 5,000 positive examples and 5,000 negative examples. Each level was designed so that a detection rate of at least d min = 0. 998 and a false-positive rate of at most f max = 0.
21 , h 22 , h 23 , h 24 , h 27 , h 29 } and {h 22 , h 23 , h 25 , h 26 , h 27 , h 28 }. Given the sets of the three levels, there are 2 × 2 × 2 = 8 possible configurations to test resulting in eight ROC curves. Means and standard deviations are then computed to produce the final ROC curve. For each configuration, thresholds are first computed and the resulting classifier is applied to the test database. This test process is repeated for each cost function associated to each posterior probability computation strategy: P boost , P knn , and P comb. For the last two strategies, we fix the number of neighbors k at 3. All the ROC curves are available in Figure.
This step can last a few seconds if a public database exists. It can also last a few days if images must be manually gathered. 2. Generate false-positives. At the beginning of each level, the negative samples are generated by applying the current classifier to the set of the background images. This step can last a few seconds to a few minutes. 3. Train a cascade level. At each boosting iteration, several weak classifiers are learned (one for each subwindow), and the best one is kept. The number of iteration depends on the classification performance that must be reached. This step can last a few minutes to a few hours. http://jivp. eurasipjournals. com/content/2013/1/55.
Study population We collected data from consecutive patients who consulted at the Departments of Ophthalmology at Strasbourg University Hospital (France) and Quindı ´o University Health Center (Armenia, Colombia) between August 2008 and August 2010. Both departments were tertiary-level centers able to perform anterior chamber paracentesis. For both patient populations, a complete ocular examination was conducted, including best-corrected Snellen visual acuity, slit-lamp biomicroscopy, tonometry, and indirect ophthalmoscopy. The clinical diagnosis of OT was based on criteria previously described by G. Holland  [6, 18]. Screened patients with clinically suspected OT and seropositive for anti-Toxoplasma immunoglobulin G (IgG) antibodies were subsequently submitted to biological investigations to assess the local presence of Toxoplasma DNA and/or the intraocular antibody synthesis  [19]  to confirm OT.
Cytokine-Chemokine Profile measurement in aqueous humor The Bio-Plex Human 27-Plex Cytokine Panel assay (Bio-Rad, Marne-la-Coquette, France) was used according to the manufacurer's recommendations to measure cytokine and chemokine levels in aqueous humor. The assay plate layout consisted in a standard series in duplicate (1 to 32 000 pg/mL), four blank wells and 20 mL duplicates of AqH samples, diluted to 50 mL with BioPlex Human serum diluent  [23]. A set of Toxoplasma seropositive cataract patients were used as control, 9 Colombian and 10 French. Data were analyzed with Bio-Plex Manager TM software V1. 1.
Statistical analysis Mann-Whitney test followed by Dunn's Multiple Comparison test was applied for comparison of clinical and laboratory characteristics for French and Colombian patients with confirmed active ocular toxoplasmosis (P values,0. 05 were considered statistically significant; Stata software, College Station (Tx) USA). Fisher's exact test was used to compare diagnostic performances of IB and PCR as well as the serotype prevalence. Wilcoxon matched-pairs signed rank test was performed to compare IB patterns. Mann-Whitney test was used to compare intraocular parasite loads (P values,0. 05 were considered statistically significant. Kruskal-Wallis test followed by Dunn's Multiple Comparison test were applied for comparison of cytokine and chemokine levels in aqueous humor between control and OT populations in both countries (P values,0.
This work was supported by  Colciencias , Grant  111345921861 , the  Ecos Nord Program , the  Intramural Research Program  of the  National Institutes of Health  and  NIAID  and the  Fondation Nationale pour la Recherche , Grant Retinal Physiopathology  DPR20121125433. We would like to thank the  Universidad del Quindı ´o, Hopitaux Universitaires de Strasbourg  for the PHRC grant  2007-3964 , and  Universitede Strasbourg  for additional financial support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
To perform immunohistochemistry studies, deparaffined and rehydrated sections were first extensively rinsed in PBS (pH 7. 5). Then, endogenous peroxidase activity and nonspecific binding were blocked with peroxidase block buffer (DakoCytomation) and 1% bovine serum albumin, respectively. Sections were incubated overnight at 4°C with a monoclonal antibody anti-lysozyme (Thermo Scientific, PA1-40308) and anti-ATG4B (Sigma, A2981). ATG4B signal was analyzed using the ImageJ software program (version 1. 46k). Human tissues were obtained as formalin-fixed, paraffinembedded tissue sections from the archives of the tumor bank of Hospital Universitario Central de Asturias. They remained anonymous according to guidelines approved by the Hospital's Research Ethics Board. Immunoblotting. Colon tissue was immediately frozen in liquid nitrogen after extraction and homogenized in a 20 mM Tris buffer pH 7.
Control animals were given water only. Weight was monitored daily and rectal bleeding was measured and scored as previously reported. 58 After treatment, blood was collected by submandibular bleeding and mice were sacrificed by CO 2 inhalation. The colon was removed and its length was measured. To evaluate the effect of antimicrobial agents on experimental colitis, WT and atg4b -/- mice were treated with ampicillin (1 g/L; Sigma A9518-5G), vancomycin (0. 5 g/L; Sigma 861987-1G), neomycin sulfate (1 g/L; differentially expressed genes was generated for each comparison between genotypes and between control and DSS-treated mice. Array data were clustered using Cluster 3. 0 software and the heat maps were created using TreeView 1. 1. 5 software. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus 62 and are accessible through GEO Series accession number GSE 36056 (www.
ncbi. nlm. nih. gov/ geo/query/acc. cgi?acc=GSE36056). 2-D DiGE analysis. Colon tissue from WT and atg4b -/-mice was homogenized in lysis buffer (7 M urea, 2 M thiourea, 4% CHAPS, 30 mM TRIS-HCl pH 8. 5). A total of 50 μg of protein of each sample were covalently labeled with 400 pmol of a specific fluorophore (CyDye, GE Healthcare, 25-8010-65): Cy3 (WT), Cy5 (atg4b -/-) and Cy2 (pooled 1:1 WT and atg4b -/-). Labeled proteins were mixed and set to 450 μL with rehydration buffer (8 M urea, 4% CHAPS, 13 mM DTT, 1% IEF buffer). Samples were applied to IPG strips (24 cm, nonlinear pH gradient 3-11; GE Healthcare, 17-6003-74), and isoelectric focusing (IEF) was performed on an IPGphor Unit (GE Healthcare) for 26 h at a gradient voltage in the dark at 18°C. Once the IEF step was finished, strips were equilibrated for 15 min in SES-DTT buffer (6 M urea, 30% glycerol, 2% SDS, 75 mM TRIS-HCl pH 6.
8, 0. 5% DTT and bromophenol blue), and for another 15 min in SES-IA (6 M urea, 30% glycerol, 2% SDS, 75 mM TRIS-HCl pH 6. 8, 4. 5% iodoacetamide and bromophenol blue). Proteins were then resolved in 13% SDS-PAGE gels using a Hoefer S600 instrument (Ettan DALTsix, GE Healthcare). After electrophoresis, cyanine dye-labeled proteins were visualized directly by scanning using a Typhoon™ 9400 imager (GE Healthcare). Cy2 images were scanned using a 488 nm laser and an emission filter of 520 nm band pass (BP) 40. Cy3 images were scanned using a 532 nm laser and a 580 nm BP30 emission filter. Cy5 images were scanned using a 633 nm laser and a 670 nm BP30 emission filter. All gels were scanned at 200 μm resolution, analyzed with Progenesis SameSpots software and stained with SYPRO Ruby (Molecular Probes).
Tryptic digestion and MALDI-ToF analysis. Differential spots were manually excised over a transilluminator. Gel pieces were washed with 25 mM ammonium bicarbonate/acetonitrile (70:30), dried for 15 min at 90°C, and incubated with 12 ng/ μL trypsin (Promega) in 25 mM ammonium bicarbonate. The digestion was allowed to proceed for 1 h at 60°C. Peptides were purified with ZipTip C18 (Millipore, ZTC18S960) and eluted with 1 μL of CHCA (α-cyano-4-hydroxycinnamic acid, Sigma, C2020) to be placed onto MALDI-ToF's plate. Once dried, peptides were analyzed by mass spectrometry on a time-of-flight mass spectrometer equipped with a nitrogen laser source (Voyager-DE STR, Applied Biosystems). Data from 200 laser shots were collected to produce a mass spectrum. Data explorer version 4. 0. 0. 0 software (Applied Biosystems) was used to analyze the spectra.
Purity of samples and efficiency of extractions were verified by spectrophotometry (NanoDrop) and using an Agilent 2100 Bioanalyzer. Double-stranded cDNA synthesis and in vitro transcription to generate cRNA was performed using a commercial kit of Affymetrix (Sense Target Labeling kit). The biotin-labeled cRNA was purified, fragmented and hybridized to a Affymetrix GeneChip ® Mouse Gene 1. 0 ST Arrays according to the manufacturer's protocol. Washing and scanning were performed with a Fluidics Station 450 and GeneChip Scanner 7G (Affymetrix), and quality control of microarray data was performed using Affymetrix Expression Console™ following standard Affymetrix Exon Array protocols. After scanning, raw data were processed with the RMAExpress program (http:// RMAExpress. bmbolstad. com), using default settings.
The normalized array data were log 2 -transformed and averaged. A list of (FP7-Microenvimet). The Instituto Universitario de Oncología is supported by Obra Social Cajastur-Asturias and Acción Transversal del Cáncer-RTICC, Spain. S. C. was supported by a fellowship from CONACYT; C. L. -O. is an Investigator of the Botin Foundation.
Analyzed organisms and their genome sequences The genomes of the following SHTs and of the respective symbionts were examined: Strigomonas oncopelti TCC290E (accession number AUXK00000000), S. culicis TCC012E (AUXH00000000), S. galati TCC219 (AUXN00000000), Angomonas deanei TCC036E (AUXM00000000), and A. desouzai TCC079E (AUXL00000000)  [23]. Their corresponding symbionts are referred to as: ''Candidatus Kinetoplastibacterium oncopeltii'', ''Ca. K. blastocrithidii'', ''Ca. K. galatii'', ''Ca. K. crithidii'', and ''Ca. K. desouzaii''  [13]. The endosymbiont genomes were finished to a closed circle as previously described  [24]. The genomes of two RTs were also analyzed: Herpetomonas muscarum TCC001E (AUXJ00000000) and Crithidia acanthocephali TCC037E (AUXI00000000)  [23].
Gene discovery and annotation Initially, the trypanosomatid genes were discovered and mapped to metabolic pathways using ASGARD  [40] , using as reference the UniRef100  [41]  and KEGG  [39]  databases. The identified segments of DNA were then extracted from the genomes and manually curated for completion and proper location of start and stop codons by using the GBrowse genome browser  [42]. Putative sequence functions were confirmed by domain searches against the NCBI's CDD (conserved domain database)  [43]. For each enzyme characterized in this work, corresponding putative orthologous genes from all domains of life were collected from the public databases by BLAST search (E-value cutoff of 1e-10, maximum of 10,000 matches accepted) against the full NCBI NR protein database, collecting sequences from taxonomic groups as widespread as possible and keeping one from each species (or genus, if the tree was too large) for subsequent phylogenetic analysis.