dataesr/scientific-paragraphs-categorization huggingface dataset:
2017; Rodrigues et al. 2011; Tam et al. 2014). This underlines the need of expanding spectral databases to reach good identification performances. Regarding the results obtained in the present study, MALDI-TOF MS spectra analysis could be as powerful as the polyphasic approach commonly used to identify Aspergillus species of this section which may include morphological, physiological, molecular and/or extrolite data analysis  (Frisvad et al. , 2019 , Carvajal-Campos et al, 2018). These results are very promising even though they are based on a relatively low number of isolates and spectra and that only 16 species among the 33 currently recognized species were included in the database for validation. The next step would be to integrate more species and isolates to the database as it can influence identification performances  (Normand et al.
Database construction and validation To go further in the assessment of the MALDI-TOF MS discriminative power at the intraspecific level, a database was constructed with 252 spectra, representing 42 strains, with 14, 15 and 13 isolates belonging to populations 1, 2 and 3, respectively. Cross-validation results are shown in Table  3. Overall, the results of cross-validation showed that 94. 1% of all spectra (235 out of 252 spectra) were correctly assigned to their corresponding genetic population. All spectra derived from population 2 and 3 isolates were either correctly assigned to the right population  (96. 67-98. 72 % of all spectra)  or not identified at all (3. 33-1. 28 % of all spectra corresponding to 3 and 1 spectra, respectively). These non-identified spectra were derived from strain F41-4 from population 2 and strain F34-1 from population 3.
Finally, the six incorrectly assigned spectra belonged to strain F77-1 for which all spectra were incorrectly assigned to population 2. This result could be explained by the fact that this isolate had MS spectra which were more similar to those of several population 2 isolates (Figure  2B ). For external validation, 126 spectra corresponding to 21 independent isolates including 7 isolates from each genetic population, were used to challenge the database. The results are shown in Table  4. 95. 24 % of all spectra (110 out of 126 spectra) were correctly assigned to their corresponding population. Spectra acquired from external isolates from populations 1 and 2 were all correctly assigned, with the exception of one spectrum from the strain IHEM3196 and F53 from population 1 and two spectra from strain LCP03969 from population 2.
Hence, it could explain why their assignment to the right population was problematic. Noteworthy, several strains from populations 1 and 2 that were chosen for external validation also shared common alleles with isolates from other populations  (Gillot et al. 2015). Indeed, strains IHEM3196 and F53, that were assigned to population 1 by  Gillot et al. (2015) , also shared 1 common allele with isolates from population 2, while strains UBOCC-A-111178, DSMZ1999 and UBOCC-A-111170, assigned to population 2 by  Gillot et al. (2015)  shared 2 common alleles with population 2 and for the two other alleles, presented alleles that were different from those of other isolates from population 3. Nevertheless, spectra of these isolates were correctly assigned after external validation suggesting that their MS peak profiles were much more closely related to that of the isolates which were used to build the database.
As indicated in the 'Materials and methods' section, for database construction, peak lists of spectra from each population were binned and a predictive model was established. In order to understand what were the main differences in the MS spectra of the three P. roqueforti populations, we investigated which bins (or mass-intervals) had a high impact (positive or negative) for population assignment. The two bins that had the higher and lower impact on population assignment are shown in Table  5. All these bins had masses ranging from 3000 to 7716. 1 Da. In comparison,  Hettick et al. (2008)  found differences between spectra of Aspergillus flavus isolates in a mass range from 7000 to 10000 Da, but in contrast to the present study in which the ASC algorithm was used, such comparison was performed on whole spectra based on peak lists.
Then, after comparison with theoretical masses of proteins predicted from available sequenced genomes of P. roqueforti, it may be possible to identify which are the proteins (and their functions) that permit to discriminate one population from another. However, such comparison can lead to the identification of several different proteins for one single peak because different proteins can share the same m/z value  (Spinali et al. 2015). Altogether, these results suggest that there is a very good agreement between genetic data and MS peak profiles and that MALDI-TOF MS could be used as a rapid tool for typing of P. roqueforti isolates using a dataset of well characterized isolates at the intraspecific level. Indeed, MALDI-TOF MS was able to highlight intra-specific polymorphism and yielded similar results to those obtained from genetic analysis based on the study of three microsatellites markers of Penicillium roqueforti isolates.
In addition, it was even possible to assign external isolates to the right populations by constructing a spectral database. However, two strains which possessed allelic profiles found in other genetic populations, could not be classified accurately with the approach used in the present study. Despite a very good agreement between genetic data and MS profiles, it must be noted that differentiation between the three populations is not made on the same markers in each analysis. Indeed, genetic analysis is based on microsatellite markers, short and repeated DNA sequences  (Mathimaran et al. 2008) , while MALDI-TOF MS detects predominantly ribosomal proteins and other proteins, that are constantly expressed and highly abundant  (Santos et al. 2010). In conclusion, the results obtained in this study, together with those previously published by  Quero et al.
(2018)  confirm that MALDI-TOF MS can be a powerful tool to differentiate and identify food-related filamentous fungi not only at the species level but can also be applied to differentiate species complex and cryptic species as well populations from a same species. This method could be of great interest for the management of mycological safety and quality of foods. Indeed, correct species identification of moulds is of high importance and the possibility of going beyond species identification could also be a valuable asset for source tracking of fungi in the food chain. (B) Multidimensional scaling (MDS) of the spectra dataset obtained after MALDI-TOF MS analysis of P. roqueforti isolates based on 6 replicates per strain (3 biological replicates and 2 technical replicates), after calibration of the spectra.
Subjects who had ceased smoking for more than 6 months were considered to be ex-smokers. The severity of COPD was determined according to the spirometric classification (GOLD 1: FEV 1 ≥ 80% predicted, GOLD 2: 50% ≤ FEV 1 < 80% predicted, GOLD 3: 30% ≤ FEV 1 < 50% predicted, GOLD 4: FEV 1 < 30% predicted)  [16]. Frequent exacerbations were defined as at least 2 exacerbations in the past 12 months  [16, 17]. All subjects provided their written informed consent to the study (Research and Innovation in Chronic Inflammatory Respiratory Diseases-RINNOPARI, NCT02924818). All subjects underwent fiberoptic bronchoscopy with bronchial brushings, biopsies and/or bronchoalveolar lavage (BAL) under routine clinical conditions according to international guidelines  [18].
Immunofluorescence staining Samples were rehydrated with PBS and blocked with 10% PBS-BSA for 30 min at room temperature before incubation with the following primary antibodies overnight at 4 °C in 3% PBS-BSA: rabbit anti-Arl13b (17711-1-ap, ProteinTech, 1:200); mouse anti-Muc5ac (NBP2-15196, Novus Biologicals, 1:100); mouse anti-Acetylated-α-tubulin (T6793, Sigma Aldrich, 1:1000); goat anti-p63 (AF1916, R&D systems, 1:100); rabbit anti-pancytokeratin (E-AB-33599; Clinisciences, 1:100); mouse anti-vimentin (M0725; Dako, 1:100); rabbit anti-Gli1 (HPA065172, Sigma Aldrich, 1 μg/mL); rabbit anti-Gli2 (HPA074275, Sigma Aldrich, 0, 4 μg/mL); goat anti-Shh (AF464, R&D systems, 1:100); rabbit anti-Ptch1 (E-AB-10571, Clinisciences, 1:100); mouse anti-Hhip (WH0064399M1, Sigma-Aldrich, 1:100). Samples were washed with PBS and incubated with the appropriate secondary antibodies in PBS-BSA 3% for 1 h at room temperature.
Statistical analysis Data are expressed as mean ± standard deviation (SEM). All continuous variables are represented with dot-plot and median. Associations between features were studied using Chi-square or Fisher's exact test, as appropriate. A non-parametric Mann-Whitney test was used to analyse differences between experimental conditions and linear regression was performed with the Spearman process. Multiple groups were analysed using the Kruskal-Wallis procedure followed by the Conover and Iman test. To evaluate differences in expression levels between candidate genes, we used the Mann-Whitney U test for independent samples of the normalized log2-transformed microarray expression values. In all exploratory analyses, results with two-sided p-value ≤0. 05 were considered significant. XLSTAT software (Addinsoft Company, Paris, France) was used to analyse and format data.
Shh ligand is deficient in COPD lung tissues To validate our results in an independent cohort, we then analysed a transcriptomic dataset obtained by RNA sequencing of lung tissue in COPD and non-COPD subjects (GSE47460). We confirmed a significant reduction in SHH gene expression in the COPD group compared to the non-COPD group (3. 666 vs 3. 883, log2 relative.
Altogether, our results suggest dysregulation of the SHH pathway either at the level of the production of the ligand or affecting the activating/inhibiting receptor binding balance in COPD. This result was confirmed using the lung transcriptome dataset from of an independent COPD cohort. Although significant, the minor differences in gene expression levels observed may be due to the sample collection technique, involving extraction of mRNA from both epithelium and subepithelial compartments with enriched HH players, such as fibroblasts  [36]. Our results are consistent with our previous observations showing that prevention of ligand-induced HH activation resulted in epithelial remodelling with an increased number of basal cells and decreased ciliogenesis  [15] , therefore mimicking the remodelling features of COPD observed in endobronchial samples in our study.
Study design and population This prospective, exposed/non-exposed, observational study was carried out in the department of visceral, urological, and pediatric plastic surgery in a university hospital in western France. All children hospitalized for a surgical procedure were eligible for inclusion. Inclusion was continued until at least 20 undernourished children with full data available had been included (between July 2015 and March 2016). Children with anthropometric measures missing upon admission and/or at discharge were excluded. In our institution, a resident in pediatrics is specifically dedicated to nutritional status assessment in surgical wards. The resident's role was, half day on work days, to perform screening and nutrition therapy of all hospitalized children. This resident in pediatrics junior physician is a ''go-between'' and interacts with surgeons, anesthetists, and pediatricians specialized in nutrition.
Calculated variables All data were gathered from the hospital's computerized medical records. e-Pinut expert software (www. epinut. fr) was used for the calculation of height and Z-WFH. Length of stay (LOS) was expressed in days. Preoperative nil per os (NPO) duration (h) was the difference between the last oral feeding and the starting time of the surgical procedure. Postoperative NPO was the difference between the end of surgery and the time of recovery of any type of oral intake. Surgical procedure duration was the time between surgical incision and closure. The amount of infused sodium during the surgical procedure was calculated from medical records and expressed in mEq/kg.
Ethics The Internet data and e-Pinut expert software (www. epinut. fr) were approved by the advisory committee on the processing of research information in the field of health (CCTIRS) on 23/03/2016. The National Commission for Data Protection and Freedoms (CNIL) issued a declaration of conformity, no. 2034866, on 13/02/2017. Written information was delivered to parents, giving them the opportunity to decline participation in this non-interventional study. The local ethics committee approved the study.
Statistical analysis Statistical analysis was performed using JMP 1 12. 2. 0 software (SAS Institute Inc. , Cary, North Carolina, USA). Continuous variables are expressed as mean AE standard deviation. The primary outcome for the two groups was tested using a t test. Univariate associations between the main outcome, Z-WFH change, and some variables of interest (age, Z-WFH upon admission, LOS, pre-and post-operative NPO duration, duration of the surgical procedure) were tested using Spearman's correlation coefficient. The effect of qualitative variables (group, type of surgery, level of emergency of the surgical procedure, ASA score, enteral/parenteral nutrition implementation) on the Z-WFH was analyzed through a t test or Anova for more than two categories. A multivariate logistic regression was used to test continuous variables (age, duration of hospitalization, amount of sodium perfused during surgery) and categorical variables (gender, presence of undernutrition) associated with positive weight gain.
Study population characteristics A total of 161 children of 278 eligible participants were included in the analysis (Fig. 1 ). We compared the characteristics of the included children (n = 161) with those of the excluded children using the anthropometric data upon admission if they were available (n = 78). Excluded children did not differ in Z-WFH and sex ratio but they were older (8. 6 AE 0. 6 vs. 5. 5 AE 0. 4 years, P < 0. 0001). Surgical procedures were digestive (61%), urological (22%), plastic (12%), or other (5%). Surgical procedures were performed within 6 h and 24 h, respectively, in 8% and 47% of cases. Elective surgery represented 45% of surgical procedures. ASA scores were 1 (64%), 2 (25%), 3 (11%), and only one child had an ASA score of 4. The characteristics of the children are presented in Table  1.
NMR data were recorded on a [ 15 N-13 C] LpoP sample and the backbone and side chain 1 H-15 N-13 C resonances were assigned using conventional tridimensional experiments. The assigned spectra revealed that residues Asp 154 to Ser 258 form a globular domain consisting of a succession of six helices (as determined by chemical shift index analysis--Figure  S1 )  (Berjanskii and Wishart, 2005). 1 H-15 N nuclear Overhauser effect (NOE) relaxation measurements displayed positive NOE values (>0. 6) for residues 154-258, in agreement with the presence of a globular and stable domain (Figure  1A ). With the exception of the short stretch from residues 147 to 153 with intermediate NOE values (0. 15-0. 4), the rest of the residues produced low or negative NOE values, which indicated fast motion (Figure  1B ).
, 2004; Zeytuni and Zarivach, 2012). Numerous inter-helical hydrophobic contacts favored by the high percentage of Leu (12%) and Ala (22%) stabilize the core structure (Figure  S2A ). LpoP has a strik-ing structural similarity to protein domains formed by repetition of TPR domains (prosite PS50293 family). In particular, LpoP has a structure similar to the TPR domain of CpoB (PDB: 2XEV;  Krachler et al. , 2010)  (root-mean-square deviation [RMSD] = 1. 2 A ˚over main chain atoms from 59 residues and 2. 5 A ˚over all 100 residues, in comparison with an RMSD of 0. 3 A ˚over all 104 residues among the 8 LpoP molecules in the asymmetric unit) despite a limited sequence identity of 18% (Figure  2C ) and varied electrostatic surface potential (Figure  S2B ). There was no significant structural conservation observed between LpoP and either of the activators LpoA  (Vijayalakshmi et al.
Superimposition of [ 1 H, 15 N] BEST-TROSY spectra (Figure  4B ) of LpoP in the presence or absence of PA UB2H readily identified perturbed LpoP amide resonances, and the progressive shifts observed for the different concentration suggests a rapid exchange regime between the two domains, in agreement with the moderate affinity constant measured for this complex. Chemical shift perturbation was determined for each amino acid resonance and reported in a graph (Figure  S5 ). The maximal chemical shift perturbations were mapped on the LpoP structure (Figure  4C ) and found to concentrate mainly in the region connecting the extremities of helix 2 and helix 3. As shown in Fig-  ure 4D , this region also contains the most conserved amino acid residues as obtained by alignment of 150 sequences using ConSurf software.
We see the use of TPR motifs in both the negative regulator CpoB, as well as in the LpoA (D) Sequence conservation scores were calculated using the ConSurf webserver  (Landau et al. , 2005)  and displayed on the surface representation of LpoP using the same orientation as (C). Scores range from 1 (not conserved, cyan) to 9 (highly conserved, magenta). activator of E. coli PBP1A. Interestingly, in the case of LpoA, these TPR repeats are seen to be involved in the extension of Lpo across the periplasm, instead of a direct interaction module as seen in LpoP and CpoB. Regardless, it is remarkable how activators and regulators of class A PBPs evolved from similar domain pieces in these distinct lineages. Beyond the purely structural differences between the LpoP and LpoB systems, the different effects of CpoB on the two activators is of interest.
02% Tween) Buffer. Tips loaded with UB2H have been inserted into different LpoP concentrations (0 to 6mM) in HBS-T Buffer at 23 C. Kinetics have been recorded with 1200s for association and 1000sec for dissociation phases, experiments have been reproduced twice with 10mM HCl pulses (3x) used for regeneration between cycles. Responses at equilibrium of the two series (end of the association phase) have been simultaneously fitted with the equation Req = Rmax3conc Kd3conc by ForteBio data analysis software. X-ray Crystallography and Structure Determination P. aeruginosa LpoP protein was crystallized at 20 C by sitting drop vapour diffusion using 0. 2 mL protein solution (30 mg/mL purified protein in 20 mM HEPES, pH 8. 0, 150 mM NaCl) and 1 mL of mother liquor (0. 1 HEPES pH 7. 5, 2. 5 M AmSO 4 , 1% PEG 400, 2.
5% 1,2butanediol). X-ray diffraction data of LpoP was collected on Advanced Light Source beamline 5. 0. 2. Diffraction data have been processed using XDS  (Kabsch, 2010)  and the structure was solved by molecular replacement using an ensemble of truncated Rosetta  (Leaver-Fay et al. , 2011)  refined homology models in AMPLE  (Bibby et al. , 2012)  based on a model from SWISS-MODEL  (Waterhouse et al. , 2018)  and PDBID:5XW7  (Nojima et al. , 2017). The structure was auto-built using Buccaneer  (Cowtan, 2006)  and subsequently refined using Phenix  (Adams et al. , 2010)  and Coot  (Emsley and Cowtan, 2004). See Table  S1  for data collection and refinement statistics.
ACKNOWLEDGMENTS Research described in this paper was performed using beamline 502 of the Advanced Light Source, which is a DOE Office of Science User Facility under contract no. DE-AC02-05CH11231. This work was funded by operating grants to N. C. J. S. from JPIAMR-CIHR and the Howard Hughes International Senior Scholar program. We also acknowledge infrastructure funding from the Canadian Foundation of Innovation and British Columbia Knowledge Development Fund. N. A. C. holds an NSERC PGS D Award and N. C. J. S. is a Tier I Canada Research Chair in Antibiotic Discovery. This work used the platforms of the Grenoble Instruct-ERIC center (ISBG; UMS 3518 CNRS-CEA-UGA-EMBL) within the Grenoble Partnership for Structural Biology (PSB), supported by FRISBI (ANR-10-INBS-05-02) and GRAL, financed within the University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS (ANR-17-EURE-0003).
C. (USNM). Representatives of as many species as possible are also deposited in the USNM. All other specimens are deposited in the Montana Entomology Collection, Montana State University, Bozeman (MTEC), unless noted otherwise. Label data for primary types of new species are cited verbatim in quotation marks with lines separated by a slash ("/"), labels separated by a semicolon (";"), and annotations in square brackets ("[]"). Label data for other material is presented in a standardized format, utilizing locality names for major sites presented in Table  1. Nearly all material derived from the Centre Hills Biodiversity Assessment from  [2001] [2002] [2003] [2004] [2005]   (Young 2008 ) and material collected by the author in June 2017. In addition, several large collections (California Academy of Sciences, San Francisco; Canadian National Collection of Insects, Ottawa; Natural History Museum, London; National Museum of Natural History, Smithsonian Institution, Washington, D.
Acknowledgements This project was funded and facilitated by the  Montserrat National Trust ,  Durrell Wildlife Conservation Trust  ( John Fa  and  Richard Young ),  UK Overseas Territories Conservation Forum  (special thanks to  Mike Pienkowski  and  Catherine Wensink ), the  Royal Society for the Protection of Birds (Geoff Hilton) , the  Montserrat Forestry and Environment Division of the Montserrat Department of Agriculture (Gerard Gray) , and  Montana State University. Special thanks to Michael and  Donna Ivie  ( Montana State University ) and  Frank Etzler  ( Clemson University ) for facilitating the 2017 trip to Montserrat -you are first-rate companions. Numerous collectors are thanked, including  Lloyd Martin ,  James Boatswain ,  John Martin ,  James Daley ,  Lloyd Aymer ,  Philemon Murrain ,  Calvin Fenton ,  Katie Marske ,  Kelvin Guerrero ,  Ian Foley ,  Vincent Martinson ,  Patrick Hughley ,  Levi Lehfeldt ,  Robert Semplet ,  Anne Krakower , and  Bridget Beatty.
Statistical Methods Statistical analysis was performed using IBM SPSS version 22 comparing data means (±SE) with one way-ANOVA, and Duncan's multiple range tests were calculated for the significant data at P < 0. 05. Measurements were done in four biological replications/treatment (each replication consisted of a pool of two to three individual measures/sample). Pairwise metabolite effect correlations were calculated by Pearson's correlation test using the R program.
To over this large uncertainty, a common genetic algorithm (GA) was designed to find optimal global solution of temperature fitting by limiting these unknown parameters changing in a predetermined range  (Reeves and Rowe, 2002). In the GA, the crossover fraction is set to be 0. 9 while the migration fraction is 0. 2. To obtain accurate solution and save calculation time, we set the population size to be 8000 and the generations to be 20. Usually, after 15 generations of iteration, the optimal solution can be found. All the calculations were performed with MATLAB R2014b software. For each deep borehole, the fitting experiments were trialed five times to avoid random error of GA. Then, the average value in the five fitting experiments was used as the GHF from bedrock into ice at selected site and the uncertainty ranges came from the difference between the maximum/minimum and the average GHF values.
Acknowledgments This work was supported by grant No. 41327804  from the  National Natural Science Foundation of China  and the  Program for Jilin University Science and Technology Innovative Research Team (Fundamental Research Funds for the Central Universities , Project No. 2017TD-24 ). We are grateful to  H. Ueda  (retired from  USA CRREL ) for providing original Byrd borehole temperature-log data. We also thank  D. Dahl-Jensen  ( Centre for Ice and Climate, University of Copenhagen, Denmark ) for fruitful discussion and useful comments.