dataesr/scientific-paragraphs-categorization huggingface dataset:
Covariate inclusion As expected, there was a clear correlation between weight and height, so we decided to test weight and BMI for significance in addition to age. According to the plots, weight, BMI, and age all appeared to correlate with rupatadine clearance (data not shown). Therefore sex and height were not further evaluated in NONMEM. BMI was tested for significance in NONMEM, but no clear improvement of the model was detected respect to the base model (OFV = -114 vs -112, P>0. 05). Although an allometric scaling model was tested, the function that best described the weight-CL/F relationship was a linear model (equation below). CLi ¼ y 1 þ y 2 Ã ðWeight=38:5Þ The incorporation of weight into the rupatadine clearance model decreased the value of the OF in 3 points (P = 0. 08), generally improved the estimation errors with respect to the base model and explained 11.
Respondents (P-set) In Q-studies, selection of the respondents is purposive as in qualitative techniques. With respect to the size of the P set, Brown suggested that 40-60 respondents are more than likely adequate  [33, 38, 42]. Because of possible differences in viewpoints between PLHW and HHP, we planned to recruit 40 respondents from each group to perform data analysis at the population level. Respondent recruitment and questionnaire administration occurred between June and July 2015 in five French HIV services with a history of HIV clinical trial participation. Some, but not all, were involved in HIV cure research, (Hopital Bicêtre, Le Kremlin Bicêtre; CHU de Bordeaux, Bordeaux; CHU Sainte Marguerite, Marseille; CHU Hotel-Dieu, Nantes; Hopital Foch, Suresnes). PLWH were recruited the day of their follow-up consultation and were provided details about the study by the clinical research technician of each unit and through an information letter.
Analysis and interpretations The Q-sorts were analysed using a dedicated software package PQMethod 2. 35 (http:// schmolck. userweb. mwn. de/qmethod/downpqwin. htm). The first step in Q-factor analysis (here a principal component factor analysis followed by a varimax rotation) is to calculate a correlation matrix, which presents the degree of similarity between individuals' different Qsorts. The objective of the factor analysis was to identify a limited number of similar ways in which respondents completed their Q-sorts. For each viewpoint (factor) obtained, an idealized Q-sort was calculated: Q-sorts with higher loadings were given more weight in the idealized Q-sort since they better exemplified the viewpoint  [36, 42]. This idealised Q-sort represented the fictive Q-sort of a person correlated at 100 percent with that viewpoint.
Acknowledgments We thank all the participants, PLWH and clinical research technicians from the participating infectious disease units  Hopital Bicêtre, Le Kremlin Bicêtre ;  CHU de Bordeaux, Bordeaux ;  CHU Sainte Marguerite, Marseille ;  CHU Hotel-Dieu, Nantes; Hopital Foch, Suresnes  and PLWH from the  French NGO AIDES. We also acknowledge the members of the  APSEC Scientific committee  for their advice:  F. Barre-Sinoussi ,  J. Barbot ,  A. Che  ´ret,  E. Choucair ,  P. Delobel ,  V. Dore ,  S. Fainzang ,  C. Gasiglia ,  C. Gauzente ,  J. Ghosn ,  C. Goujard ,  C. Herve  ´,  O. Lambotte ,  J. -D. Lelièvre ,  L. Meyer ,  M. Preau ,  A. L. Ross ,  C. Rouzioux ,  B. Spire ,  M. Suzan-Monti. Finally, we also thank  Jude Sweeney  for the English revision and editing of our manuscript and the three anonymous reviewers for their helpful comments.
Visual localization is widely used in autonomous navigation system and Advanced Driver Assistance Systems (ADAS). However, visual-based localization in seasonal changing situations is one of the most challenging topics in computer vision and the intelligent vehicle community. The difficulty of this task is related to the strong appearance changes that occur in scenes due to weather or season changes. In this paper, a place recognition based visual localization method is proposed, which realizes the localization by identifying previously visited places using the sequence matching method. It operates by matching query image sequences to an image database acquired previously (video acquired during traveling period). In this method, in order to improve matching accuracy, multi-feature is constructed by combining a global GIST descriptor and local binary feature CSLBP (Center-symmetric local binary patterns) to represent image sequence.
Introduction In many applications, it is crucial that a robot or vehicle localizes itself within the world especially for autonomous navigation and driving. Since visual sensors are widely used in the robotic community, visual localization based on place recognition plays a key role in visual Simultaneous Localization and Mappings (vSLAM) systems  [1, 2]. In this context, identifying previously visited locations in a long-term period under environment changes is a big challenge  [3, 4]. There have been lots of visual localization methods by regarding place recognition as a problem of matching a sensed image against a previously acquired image database in which locations are represented by images. One can cite the FAB-MAP algorithm  [5] , which employs a Bag-of-Words (BOW) image retrieval technique and a Bayesian frame-work  [6]  that achieves robust image matching.
In  [26] , a method LRACI (Location Recognition algorithm for Automatic Check-In applications) was introduced. Experimental results have shown a location recognition accuracy of about 90%, opening the door to real LRACI employments. Nowadays, one of the most fashionable and challenging topics in life-long visual topological localization is to recognize previously visited places along different seasons. The method presented in  [3]  approaches visual topological localization over seasonal conditions by using SURF descriptors  [27, 28]. Another very recent proposal focused on the prediction of changes on places based on superpixel vocabularies  [29] , which has been tested using 3000 km dataset across all four seasons.
Proposed Visual Localization Approach In order to make the place recognition more robust, the proposed method uses CSLBP and GIST features to describe the place. Then, visual localization across seasons is realized based on sequence matching. As illustrated in Figures  1  and 2 , there are four main components in our approach: sequence feature extraction, sequence matching, matching validation and the last is visual localization based on the matching result. To provide detail, a set of GPS tagged training images is firstly acquired. After image preprocessing, CSLBP and GIST features are extracted independently from the images of the training database and then concatenated together to form multi-feature CSLBP++GIST. Then, multi-feature CSLBP++GIST obtained from images of a sequence are concatenated (++) to form the final sequence feature (F) representing the sequence (Section 3.
1). Here, the sequence consists of consecutive images and each sequence is independent. A current place (represented by a testing sequence) is then recognized through sequence matching step based on Chi-square distance (Section 3. 2). In the step of sequence matching, for each testing sequence, the sequence candidate from the training database that provides the minimum distance can be considered as the most similar one to the testing sequence. In fact, the two best sequence matching candidates are conserved for further verifying the final matching result. Effectively, the best matching candidate will be validated through a distance ratio SS (Section 3. 3), computed from the two minimum scores (the first minimum distance divided by the second minimum distance). If the ratio SS is lower than or equal to a threshold Th, the first best sequence candidate (with the lower distance) is confirmed and regarded as positive matching.
+ + f m-2 + + f m-1 + + f m , (6) where i, i + 1, • • • , m are indexes of the consecutive images in the sequence, and L length = mi + 1 is length of the sequence. The feature dimension of each sequence is 528 × L length , and 528 is the sum of 16 (dimensions of CSLBP feature) and 512 (dimensions of GIST feature). In our work, the original image database is simply divided into sequences with the same length, and each sequence is composed of consecutive images. Since training and testing routes are traveled at a similar speed, the same sequence length is used for the training and testing sequences. Therefore, each sequence can be represented using sequence feature (F) with the same dimension.
Image Sequence Matching To perform sequence matching, similarities between sequence features are evaluated through chi-squared distance. The chi-squared distance is a nonlinear metric that can be calculated easily. Suppose the numbers of training and testing images are N train and N test , respectively, and sequence length is L length. Therefore, the training sequence number is M = N train /L length and the testing sequence number is N = N test /L length. Given a testing sequence Q test i (composed of the last L length consecutive testing images), it will be compared with each training sequence Q train j (j = 1, 2, • • • , M) from the training database. Since the sequence lengths are same, the sequence matching can be conveniently conducted using the chi-squared distance. The similarity value between the two sequences Q test i and Q train j is measured using the chi-squared distance D ij , computed as follows: D i,j = χ 2 (F test i , F train j ) = ∑ k ((F test i ) k -(F train j ) k ) 2 |(F test i ) k + (F train j ) k | , ( 7 ) where F test i is the feature vector of the current testing sequence Q test i , and F train j is the feature vector of a training sequence Q train j (from the training dataset).
Dataset and Ground-Truth The dataset used in our work is an open dataset called Nordland (https://nrkbeta. no/2013/01/15/ nordlandsbanen-minute-by-minute-season-by-season/). It is composed of footage videos of a 728 km long train ride between two cities in north Norway  [8]. The complete 10 h journey has been recorded in four seasons. Thus, the dataset can be considered as a single 728 km long loop that is traversed four times. As illustrated in Figure  4 , there is an immense variation in the appearance of the landscape, ranging from green vegetation in spring and summer to colored foliage in autumn and complete snow-cover in winter over fresh. In addition to the seasonal changes, different local weather conditions like sunshine, overcast skies, rain and snowfall are experienced on the long trip.
Feature Combination Analysis In a first set of experiments, we evaluate how well feature combinations perform for place recognition and also compare the results with those obtained by the state-of-the-art SeqSLAM method. For the evaluation of SeqSLAM, the source code provided by OpenSeqSLAM  [8]  is used. For the SeqSLAM parameters setting, the image size is 32 × 32, V min = 0. 9, V max = 1. 1, V step = 0. 02, and the other parameters use the default as reported in  [8]. The experiments were conducted using the videos presenting an extreme situation in terms of appearance changes (spring vs. winter). The length of each sequence is 200 images. As shown in Figure  5 , the proposed method using CSLBP performs relatively well at a high precision level, while GIST outperforms the SeqSLAM method.
Conclusions In this paper, a feature combination based sequence matching method is proposed to perform robust localization even under substantial seasonal changes. After feature extraction, chi-square distance is used to measure similarity between a testing sequence and the training sequences of a training database. A distance ratio is then calculated before applying a thresholding procedure to validate the good matching candidates. Thanks to precision-recall based evaluation, experimental results showed that the proposed sequence matching method is more robust and effective for long-term visual localization in challenging environments. The proposed method takes advantage of local and global image information, which can reduce the aliasing problem. Sequence length analysis demonstrated that sequences as long as 200 frames could provide viable recognition results.
Among ST398 strains, the spa type t011 was largely predominant (44/53, 83. 0%) followed by t108 (1/53, 1. 9%), t1255 (3/53, 5. 7%), t899 (2/53, 3. 8%), t1451 (1/53, 1. 9%) and t2346 (2/53, 3. 8%) (Table  3 ). The rep-PCR technique delineated 38 different clusters (using a similarity index of 98. 38% as determined by the Diversilab software) with 66 isolates being grouped in ≥2-isolate clusters and 19 isolates corresponding to singletons (Fig. 1a  and Fig. 2a ). There was a good agreement between MLST and rep-PCR with the latter method being much more discriminative, especially within the ST398 cluster (Fig. 1b  and Fig. 2b ). Then, it was possible to distinguish some specific lineages related to certain geographical regions. In addition, rep-PCR had the advantage to dissect the genetic relatedness of ST398 clones since these isolates are not typeable by pulsed-filed gel electrophoresis (PFGE) using SmaI  [11].
The graphs were obtained using the using R software and ggplot2 package was confirmed by the detection of both mecA and mecC genes, as previously described  [5, 25, 26]. Antimicrobial susceptibility testing was performed using the agar diffusion method, as recommended by the Antibiogram Committee of the French Society for Microbiology (www. sfm-microbiologie. org/). The following antibiotics were tested: oxacillin, kanamycin, tobramycin, gentamicin, erythromycin, clindamycin, pristinamycin, ciprofloxacin, vancomycin, teicoplanin, linezolid, tetracycline, minocycline, rifampin, cotrimoxazole, chloramphenicol and fusidic acid. A double-disc diffusion test (D-test) was used to detect the inducible MLS B resistance phenotype.
PCR and molecular typing Genes conferring resistance to MLS [erm(A), erm(B), erm(C), and msr(A)], aminoglycosides [aph(3′)-IIIa, ant(4′)-Ia, and aac(6′)-aph(2″)] and tetracyclines [tet(M) and tet(K)] were screened by PCR, as previously described  [27] [28] [29]. Mechanisms of fluoroquinolone resistance were studied by sequencing QRDRs of gyrA and parC genes  [30]  and by determining MICs of ciprofloxacin with or without reserpin (10 μg/ml). Both pvl and tst genes coding for Panton-Valentine leukocidin (PVL) and toxic shock staphylococcal toxin (TSST-1), respectively, were screened by PCR as previously described  [31] [32] [33]. For molecular typing, four different techniques were used for all the strains. The MLST was performed as previously described  [34]  using the MLST database (http://saureus.
mlst. net/). The spa typing was carried out as previously described  [35]  using the Ridom StaphType software (Ridom GmbH, Würzburg, Germany). The typing of staphylococcal cassette chromosome mec element (SCCmec) was performed according to mec and ccr complexes previously defined  [36]. Genetic relatedness was determined by rep-PCR using the semi-automated Diversilab system (bioMérieux, Marcy l'étoile, France)  [14, 37].
Simultaneously because of the possible incongruence between gene trees, the focus is also shifting from gene tree to species tree inference using methods, that take into account incomplete lineage sorting (ILS), the most common cause of locus incongruence  (Edwards 2009). We followed the suggestion of  Carstens et al. (2013)  that unites the two mentioned tasks into one analysis, i. e. , species delimitation and species tree estimation. Firstly, species are delimited from a set of individuals by several species delimitation methods, possibly based on different approaches (based on trees, genetic distances, haplowebs, etc. ). Some methods or some loci may be more prone to over  delimitation (i. e. , the method splits the dataset into more potential species compared with other methods or loci) of species than others, so it is recommended to compare more methods with as much loci as possible  (Carstens et al.
Hence, new primers specific for section Restricti (fRPB2ResF100, fRPB2ResR950, Table  2 ) were designed based on the alignment of available sequences obtained with the  Liu et al. (1999)  primer set. Quality control (hairpin, self-dimer or hetero-dimer formation, melting temperature mismatch) was performed in OligoAnalyzer v. 3. 1 (available online http://eu. idtdna. com/calc/analyzer). Standard and touchdown PCR protocols were described previously  (Hubka et al. 2014 , Hubka et al. 2016). PCR product purification followed R eblov a et al. (2016). Automated sequencing was performed at Macrogen Sequencing Service (Amsterdam, The Netherlands) using both terminal primers. Sequences were deposited into GenBank with accession numbers shown in Table  1. All alignments are available from the Dryad Digital Repository: https://doi.
Phylogenetic analysis Sequences were inspected in FinchTV (available online http:// www. geospiza. com/Products/finchtv. shtml) and assembled in Bioedit v. 7. 2. 5  (Hall 1999). Alignments were performed using the G-INS-i strategy, as implemented in MAFFT v. 7  (Katoh & Standley 2013). The benA alignment contained 431 characters with 227 variable and 217 parsimony informative sites, CaM 652 characters with 288 variable and 268 parsimony informative sites, RPB2 819 characters with 272 variable and 249 parsimony informative sites, ID 1 123 characters with 231 variable and 175 parsimony informative sites. The concatenated alignment contained 3 025 characters, with 1 018 variable and 909 parsimony informative sites. Phylogenetic trees based on the concatenated dataset were inferred with both Maximum likelihood (ML) and Bayesian inference (BI) analysis.
Partitioning scheme and substitution models for analyses were selected using PartitionFinder v. 1. 1. 1 (Lanfear et al. 2012)  with settings allowing introns, exons and codon positions to be independent datasets. Proposed partitioning schemes for each dataset are listed in Table  3. Hamigera avellanea NRRL 1938 was used as outgroup. The ML trees were constructed with IQ-TREE v. 1. 4. 4  (Nguyen et al. 2015)  with branch support values obtained from 1 000 bootstrap replicates. Bayesian posterior probabilities (PP) were calculated using MrBayes v. 3. 2. 6  (Ronquist et al. 2012). Optimal partitioning scheme and substitution models were selected using PartitionFinder v. 1. 1. 1 as described above. The analyses ran for 5 × 10 6 generations, two parallel runs with four chains each were used, every 1 000 th tree was retained, and the first 25 % of trees were discarded as burn-in.
Species delimitation and species tree inference Nucleotide substitution models for each locus were determined using jModeltest v. 2. 1. 7 (Posada 2008 ) based on the Bayesian information criterion (BIC) and are listed in Table  4. To assign individuals into species, several species delimitation methods were employed. In all cases the alignment was split into two parts. The first part contained the A. restrictus, A. conicus and A. vitricola clades, the second part the A. penicillioides clade. The Bayesian version of the general mixed yule-coalescent model (bgmyc) was performed in R v. 3. 3. 1 (R Core Team 2015) with the bGMYC package  (Reid & Carstens 2012). The general mixed yule-coalescent method (GMYC) was performed in R v. 3. 3. 1 using the splits package  (Fujisawa & Barraclough 2013).
Single-locus ultrametric trees created in BEAST v. 2. 4. 2  (Bouckaert et al. 2014)  were used as an input for both methods. Chain length for each tree was 1 × 10 7 generations with 25 % burn-in. The highest credibility tree was used for the GMYC method and 100 trees sampled throughout the analysis were used for the bGMYC method. These trees were obtained by equal sampling of all the trees from the analysis after discarding the first 60 % of trees. The analysis according Poisson tree processes (PTP) model was performed on species delimitation server  (Zhang et al. 2013 ). The method does not require an ultrametric tree, so the single-locus input trees were calculated using ML analysis in IQ-TREE web server  (Trifinopoulos et al. 2016). Species delimitation using the Automatic barcode gap discovery (ABGD) was performed on ABGD web  (Puillandre et al.
Comparison with 454 sequence data Reference sequences generated in this study were compared to 454-pyrosequences obtained from house dust collected during a world-wide survey  (Amend et al. 2010). Information with regards to dust collection and metagenomic analyses methods, readers are refered to  Amend et al. (2010). For our comparisons, 454sequences belonging to sect. Restricti were harvested by firstly doing a BLAST search of ITS barcodes from sect. Restricti against the main 454 database and retaining all sequences with at least 90 % similarity. This dataset was aligned in MAFFT v. 7 using the G-INS-i algorithm and subsequent neighbour-joining tree calculated in MEGA v. 7  (Kumar et al. 2016). This tree was used to remove all sequences that do not belong in sect. Restricti. The dataset was subsequently re-aligned and neighbour-joining tree calculated using Hamigera avellanea as outgroup.
Micromorphological characters (length and width of conidia, width of stipes and vesicles and length of phialides) were measured from at least five isolates of each species (when available). Slides were prepared from both the colony centre and margins. Each character was recorded at least forty times for each character and isolate. Linear discriminant analysis was performed with measured data in R 3. 3. 1 (R Core Team 2015) with packages MASS  (Venables & Ripley 2002)  and ggplot2  (Wickham 2009). The isolates were assigned to groups based on the results of molecular phylogenetic analyses (see above). Scanning electron microscopy (SEM) was performed using a JEOL-6380 LV microscope (JEOL Ltd. Tokyo, Japan) as described previously  (Hubka et al. 2013). In brief, plugs from colonies (5 × 5 mm) grown 14 d on M40Y containing conidiophores, and ascomata in the case of A.
Physiology At least five isolates from each species, when available, were selected for determining species growth rates in an osmotic gradient. Each strain was cultivated at 25 °C on MEA with six different concentrations of NaCl ranging from 0 to 25 %. After 14 d, colony sizes were recorded and growth curves for each species calculated using local regression (LOESS) in R v. 3. 3. 1. (R Core Team 2015) using the ggplot2 package  (Wickham 2009).
Phylogeny of subgenus Aspergillus Restricti, but its exact position remains unclear. It is apparent from the radial representation of the tree (Fig. 2 ), that there are large genetic distances between the different clades of sect. Restricti, but these gaps may represent only hidden variability that has not been discovered during our study due to insufficient sampling or the use of inappropriate isolation media. The retaining of the current classification scheme of subg. Aspergilllus with two sections seems currently the best solution until more data on species diversity in sect. Restricti are collected. Additionally, sects. Restricti and Aspergillus are well supported by phenotypic data (see discussion).
All four loci have sufficient variability for reliable species identification and can be used as DNA barcodes. ID region has the lowest discriminative power but it is still sufficient for differentiation of all species. The locus with the highest ratio of variable positions to the sequence length was benA. The species validation analysis results are listed in Table  5. All species were supported by the posterior probability 1. 00 based on the analysis in BP&P v. 3. 1  (Yang & Rannala 2010)  under all three scenarios simulated by different prior distributions of parameters w (ancestral population size) and τ 0 (root age). The gsi calculations and significance testing performed using the genealogical sorting index software also confirmed that all delimited species can be considered separate evolutionary lineages.