dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2014). This indicates that both phages appear to efficiently hijack the host transcription machinery to complete their infectious cycle. The transcriptional strategy of PAK_P4 is broadly similar to PAK_P3 PAK_P4 temporally regulates its transcription over the course of infection, as previously found for PAK_P3  (Chevallereau et al. , 2016). Most of the gene features encoding unique hypothetical proteins (that is, with low or no homology to any other amino acid sequence in databases) are expressed during the early stage of infection (3. 5 min). This 'early region' encompasses gene features encoding gp080 through gp124, while the 'late region' encompasses genes encoding gp001 to gp034. These gene features, which are massively expressed 13 min post infection, primarily encode the predicted phage structural proteins (Supplementary Table  2 ; Figure  3 ).
analyses delineating evolutionary relationships between cellular lineages, the lack of universal genes has hampered such analyses in viruses. In addition, the diversity of phage sequences is such that most genomes contain numerous ORFs displaying no homology to any other sequences in databanks. Therefore, reconstruction of viral evolution cannot be based solely on genomic sequences and requires further information such as elucidation of mechanistic processes  (Forterre et al. , 2014). In this context, we have assessed whether two phages (PAK_P3 and PAK_P4), with divergent proteomic contents and infecting the same host strain, conserved the infectious strategy of their common ancestor during their evolution. First, we observed that both phages share similar infection parameters although PAK_P4 displays a smaller burst size and a longer latent period than PAK_P3.
Acknowledgements This research was supported by the  Geconcerteerde Onderzoeks Actie grant  ' Phage Biosystems ' from the  KU Leuven. (http://www. kuleuven. be/onderzoek/kernprojecten/goa. htm). BB has a PhD scholarship within the framework of an  Onderzoeks Toelage grant  of the  KU Leuven. AC was supported by a  PhD fellowship  from the  Ministère de l'Enseignement Supérieur et de la Recherche  (ED  N°516 B3MI/ ED N°157 BioSPC Paris Diderot Université ). RL and LD are members of the  « PhageBiotics research community » , supported by the  FWO Vlaanderen. We deeply thank  Damien Mornico  ( Hub Bioinformatique et Biostatistique, Institut Pasteur -C3BI, USR 3756 IP CNRS ) for his invaluable help to sucessfully update the annotations of strain PAK.
Field-scale emissions estimates Overall there is very good agreement between the emission calculations from both downwind concentration datasets. The average emission rate calculated by bLS-R for the S3 measurements (QS3) is 0. 29 µg m -2 s -1 , while the QS1 average is 0. 27 µg m -2 s -1. The modelled emission of NH 3 is low (generally below 0. 2 µg m -2 s -1 ) during the first 24 h, as the measured concentration gradient across the field was less than 1 µg m -3. As the cattle were introduced to the field on the first morning (18 May) it likely took some time for NH 3 to "build up" from the hydrolysis of excreted urea before significant emissions occurred. Downwind concentrations (CS1 and CS3) peaked during the next day (19 May), with peak emissions occurring at midday when there was a 5-6 µg m -3 horizontal concentration gradient ( C) measured between the upwind and downwind receptors.
Field-scale emissions estimates The average net emission rate (Q) from the grazed field estimated using the S1 measurements was 0. 27 µg m -2 s -1 , while much lower emissions were estimated from the S3 measurements (0. 12 µg m -2 s -1 ). Both estimates show a generally diurnal trend of peak emissions during the afternoon, similar to the trend observed during Period 1. However, there are gaps in QS1 and QS3 overnight due to data filtering as u * drops below the defined threshold (0. 1 m s -1 ). Peak emissions occurred on 22 May when the maximum concentration difference between upwind and downwind receptors was measured. Grazing of the field ended and the cattle left the field at 15:00 GMT on 23 May. After this point a generally decreasing trend in emissions is derived from the decreasing concentrations measured by S1 and S3.
2 10. 4 Grass sward: crude protein 168 168 EF (g NH 3 cow -1 day -1 ) Q 5. 7 6. 2 content (g kg DM -1 ) Q dep 6. 5 7. 2 a N excretion calculations are given as the herd total for each measurement period. b Q is the net emission rate derived without including deposition in the bLS-R simulation and Q dep is the gross bLS-R emission estimate including dry deposition, with an R c offset of 130 s m -1. EFs are derived from the S3 flux estimates due to better data coverage during both measurement periods, and Period 2 fluxes are derived from Scenario 2 estimates. riod 2 fluxes may for these reasons be considered to be more representative of the total emissions from grazing, where emissions are estimated to be 6 and 7 g NH 3 cow -1 day -1 and 9 and 10 % excreted urine-N emitted as NH 3 for the Q and Q dep scenarios respectively.
Acknowledgements. This study was conducted as part of the  French BtEP project (Emissions gazeuses au Bâtiment , sTockage, Epandage et Pâturage des systèmes bovins laitiers), convention no. 1360C0032 , with funding provided by  ADEME (Agence de l'environnement et de la maîtrise de l'énergie). We are thankful to the  Région Bretagne regional council  for PhD grant co-funding under the ARED scheme. We wish to thank  David Sidaner ,  Jacques Lassalas  and all of the staff at the  INRA-Méjusseaume dairy experimental farm. We wish to thank  Andi Móring  for assistance during the measurement campaign. We wish to thank.
ACS Chemical Biology Articles murine macrophages in a previously described and wellestablished assay was tested. 16, 17 Experimental data from the macrophage assay are shown in Figure  6. In summary, compounds 1, 22, 23, and 25 exhibit significant boosting of ethionamide in the macrophage assay. In addition to an ethionamide boosting effect, compounds 1, 22, and 25 have an intrinsic bactericidal activity against M. tuberculosis at concentrations higher than 1 μM. The most potent derivatives of compounds 1 and 2, inhibitors 14 and 28, were also tested in the macrophage assay and were shown to exhibit EC 50 = 40 and 50 nM, respectively, in the presence of 1/10 of the MIC of ethionamide. Nevertheless, compounds 14 and 28 also exhibited a significant intrinsic bactericidal effect in the absence of ethionamide with IC 50 ≈ 1 μM.
NotesThe authors declare no competing financial interest. NMR spectra related to this publication are also available at the University of Cambridge data repository (https://www. repository. cam. ac. uk/handle/1810/254768). Protein X-ray crystallography structures of compounds 1, 3-5, and 7-10 bound to M. tuberculosis EthR are available via the RCSB Protein Data Bank via PDB codes 5F1J, 5IOY, 5IOZ, 5J3L, 5IPA, 5J1R, 5J1U, and 5IP6, respectively. ■ACKNOWLEDGMENTSWe would like to thank A. Coyne for help in the preparation of this manuscript. P. O. N. would like to thank the EPSRC for providing Ph. D. funding. We also thank the Bill and Melinda Gates Foundation and the EU FP7MM4TB Grant No. 260872, the ERC-STG INTRACELLTB Grant No. 260901, the Agence Nationale de la Recherche (ANR-10-EQPX-04-01), the Feder.
NotesThe authors declare no competing financial interest. NMR spectra related to this publication are also available at the University of Cambridge data repository (https://www. repository. cam. ac. uk/handle/1810/254768). Protein X-ray crystallography structures of compounds 1, 3-5, and 7-10 bound to M. tuberculosis EthR are available via the RCSB Protein Data Bank via PDB codes 5F1J, 5IOY, 5IOZ, 5J3L, 5IPA, 5J1R, 5J1U, and 5IP6, respectively. ■ACKNOWLEDGMENTSWe would like to thank A. Coyne for help in the preparation of this manuscript. P. O. N. would like to thank the EPSRC for providing Ph. D. funding. We also thank the Bill and Melinda Gates Foundation and the EU FP7MM4TB Grant No. 260872, the ERC-STG INTRACELLTB Grant No. 260901, the Agence Nationale de la Recherche (ANR-10-EQPX-04-01), the Feder Corresponding Author ORCID Chris Abell: 0000-0001-9174-1987 *E-mail: ca26@cam.
CT scans were segmented using Mimics software (Materialise, Leuven, Belgium) and replicas were built from plastic (Objet VeroGray RGD850; Stratsys, Ltd. , Eden Prairie, MN, USA) using a PolyJet 3D printer (Objet Eden 350V High Resolution 3D Printer; Stratsys, Ltd. , Eden Prairie, MN, USA). Segmented 3D airways based on both the MR and CT images are presented in Figure  1. Geometric parameters for each of the 15 replicas were obtained using MeshLab (Visual Computing Laboratory, Istituto di Scienza e Tecnologie dell'Informazione, Italy) and ParaView (Kitware, Clifton Park, NY, USA). These parameters are shown in Table  1.
The sleep breathing pattern was tested using both pulse mode and sleep mode in order to comprehensively evaluate potential device performance. The volume of the chamber of the test lung was recorded during simulated breathing at a sampling frequency of 512 Hz using the ASL 5000 software. Oxygen concentrations were measured using a GA-200 CO 2 and O 2 Gas Analyzer (iWorx, Dover, NH, USA), and were corrected for delay and time constant, which was done by approximating the concentration as a first order system and following methods outlined in Langer et al. 26  We assumed that the step response of the gas analyzer could be well approximated by a first order exponential function. The time constant for this exponential function, which is defined as the time required for the response of the system to reach 63% of its final value as the result of a step increase, was measured to be 227 ms in preliminary experiments by rapidly switching supply to the gas analyzer via a three-way stopcock between two different gas mixtures of known concentration (in this case, air and 100% oxygen).
Measurement of pulse characteristics A 1130 series O 2 Conserver Testing System (Hans Rudolph Inc, Shawnee, KS, USA) was used to obtain oxygen pulse volumes, durations, and delays for both the 2. 0 and 6. 0 settings of the POC using the breathing frequency of each breathing pattern shown in Table  3. SimplyGo was connected to the testing system using a standard 2. 1 mm single lumen cannula. Data were recorded as the average of 10 successive pulses for each breathing pattern/setting combination. Variance around reported averages was very small. The oxygen concentration of the pulse itself was measured to be 94% across all breathing pattern/setting combinations.
Acknowledgments The authors would like to thank  Cole Christianson  for his work in measuring airway replica pressure drops,  Kelvin Duong  for his help in analyzing raw data,  Eric Bracke  for contributions to building the CT replicas, and  Dr Joëlle Texereau  for reviewing and providing helpful comments on an earlier version of this manuscript. This work was funded by  Air Liquide  and by  Alberta Economic Development and Trade  and benefited from an equipment grant from the  Canadian Natural Sciences and Engineering Research Council.
Background: Several Matrix-Assisted Laser Desorption/Ionization Time-of-Flight mass spectrometry protocols, which differ in identification criteria, have been developed for mold and dermatophyte identification. Currently, the most widely used approach is Bruker technology, although no consensus concerning the log(score) threshold has been established. Furthermore, it remains unknown how far increasing the number of spots to compare results might improve identification performance. In this study, we used in-house and Bruker reference databases as well as a panel of 422 isolates belonging to 126 species to test various thresholds. Ten distinct identification algorithms requiring one to four spots were tested. Results: Our findings indicate that optimal results were obtained by applying a decisional algorithm in which only the highest score of four spots was taken into account with a 1.
7 log(score) threshold. Testing the entire panel enabled identification of 87. 41% (in-house database) and 35. 15% (Bruker database) of isolates, with a positive predictive value (PPV) of 1 at the genus level for both databases as well as 0. 89 PPV (in-house database) and 0. 72 PPV (Bruker database) at the species level. Applying the same rules to the isolates for which the species were represented by at least three strains in the database enabled identification of 92. 1% (in-house database) and 46. 6% (Bruker database) of isolates, with 1 PPV at the genus level for both databases as well as 0. 95 PPV (in-house database) and 0. 93 PPV (Bruker database) at the species level. Conclusions: Depositing four spots per extract and lowering the threshold to 1. 7, a threshold which is notably lower than that recommended for bacterial identification, decreased the number of unidentified specimens without altering the reliability of the accepted results.
7 indicates "unreliable identification". However, these score thresholds have been designed for bacteria identification and are not necessarily appropriate for fungi (especially molds and dermatophytes) while using the manufacturer's reference database  [4, 7, 17, 32, 33]. The majority of publications dealing with MALDI-TOF MS routine identification of filamentous fungi have used a single score threshold, usually ranging from 1. 7 to 2. 0, depending on the study  [3, 4, 6, 8, 17]. However, a study by Shultness et al. [8]  has compared the 1. 7 and 2. 0 score thresholds for MALDI-TOF MS identification after a liquid culture growth step in which no assessment was performed to justify these thresholds. Additionally, while the manufacturer typically recommends a single spot for bacterial identification, some authors have advocated the use of several spots of the same sample to identify molds and dermatophytes  [2, 21].
According to these authors, the concordance between identification results can be tested using replicates. Theoretically, this may improve the reliability of identification results, although no study has thoroughly evaluated the optimal number of spots and log(score) threshold for mold identification. Altogether, the use of various thresholds and validation criteria for mold and dermatophyte identification complicate the interpretation of results. The present study assessed 10 distinct identification algorithms combining several thresholds for one to four spots per sample. Because the Bruker reference database is not comprehensive enough to enable proper identification of many mold species,  [4, 8]  identification algorithms were assessed using an extensive in-house database (5044 references belonging to 619 species and 165 genera) and the Bruker commercial database (V3.
Reference databases The in-house reference database was constructed using a Microflex LT system (Bruker Daltonics, Bremen, Germany) in coordination with the BCCM/IHEM culture collection and the Mycology Laboratory of the AP-HM with the protocol described by Normand et al. [6]  (several strains per species, four subcultures per strain and 10 spots per subculture, for a total of four references per strain). Details of the MS in-house database are listed in Additional file 1: Table  S1.
Assessment panel The assessment panel consisted of filamentous fungus strains that were identified via both DNA sequencing and MALDI-TOF MS. The strains were either selected at the Mycology Laboratory of Marseille (n = 224) or the BCCM/IHEM in Brussels (n = 198). Of the 224 strains from Marseille, 177 were included because the identification at the species level had to be either clarified or confirmed at the time of the study, while the remaining 47 were randomly selected among frequently identified species in the clinical laboratory. The 198 Belgian strains either originated from the BCCM/IHEM collection or were clinical strains from a previous study  [34]. The 422-strain panel included 126 different species belonging to 38 genera, of which 280 strains were represented three or more times in the MALDI-TOF MS reference database (191 strains for the commercial Bruker database), 130 strains were represented once or twice (76 strains for the commercial Bruker database), and 12 strains were not represented in the reference database (155 strains for the commercial Bruker database) (Tables  1  and 2 ).
After a 10-min centrifugation step at 13,000 rpm, the pellet was resuspended in 10 μL of formic acid (Sigma-Aldrich, Lyon, France). After 5 min of incubation, 10 μL of acetonitrile (Prolabo BDH) was added. The suspension was then centrifuged at 13,000 rpm for 2 min, and four spots of 1 μL of protein extract per isolate were deposited. Last, the samples were covered with 1 μL of HCCA (α-cyano-4-hydroxycinnamic acid) matrix (Sigma-Aldrich, Lyon, France). MS acquisition was performed either in Marseille or Brussels using a Microflex LT system. All raw spectra were then centralized in Marseille and compared to the two reference databases. For all 1688 tested spectra (four spots for each strain) and each database, three possible scores were collected: the highest score corresponding to a true MS identification result at the species level if available, the highest score corresponding to a true identification at the.
Identification algorithms The following 10 distinct algorithms were used to identify the 422 isolates included in the panel. A: Only one spot (i. e. , the first of the four spots) is taken into account. Fig. 1  Highest log(score) value distribution of the 1688 tested spectra. a considering the first accurate species identification (dark line), the first accurate genus identification (gray line), and the first false genus identification (dotted line) using the in-house reference database. b considering the first accurate species identification (dark line), the first accurate genus identification (gray line), and the first false genus identification (dotted line) using the Bruker reference database of the other three identifications (at either the species or genus level). -D 4 : To be categorized as a concordant identification, the four identifications corresponding to the four spots must be identical (at either the species or genus level).
Based on the individual spectrum results, 10 identification algorithms (or combinations of parameters) were tested per strain. For each spectrum, only the first identification (either true or false at the species and genus level) and the corresponding scores were collected. Each of the 10 algorithms was then tested using the identification thresholds 1. 5, 1. 6, 1. 7, 1. 8, 1. 9, 2. 0, 2. 1, 2. 2 and 2. 3 for the entire panel consisting of 422 strains. The same process was applied for a reduced panel of 280 isolates that were represented at least three times in the in-house reference database and for another reduced panel of 191 isolates that were represented at least three times in the commercial database by Bruker.
Statistical analyses An initial identification assessment was carried out by categorizing the first identification for each spot into three classes: (1) concordant at the species level with the DNA-based identification, (2) concordant at the genus level but not at the species level with the DNA-based identification, or (3) discordant at the genus level with the DNA-based identification. We then determined the best rules for accepting identification at the genus and species levels. For each threshold, algorithm, panel and database, we calculated the percentage of submitted strains that fulfilled the criteria for identification as well as the positive predictive value (PPV) of the identification result at the genus and species levels.
MALDI-TOF MS-based identification Using our in-house database to identify the 422 strains included in the panel, a clear separation was observed between the highest scores corresponding to true identifications at the species level and the highest scores corresponding to a false genus identification. Conversely, it was markedly more difficult to define a satisfying cut-off that differentiated between the highest scores corresponding to an accurate species identification and the highest scores corresponding to an accurate genus but false species identification (Fig. 1a ). Using the commercial database led to a similar observation, although the separation between the three curves was less pronounced (Fig. 1b ).
Algorithm performance and identification score threshold Analyses of the identification performances for the 422strain panel with the Bruker database and the in-house database. The percentage of identified strains and PPVs at the species and genus levels obtained using the 10 distinct identification algorithms were assessed on the 422-strain panel using the Bruker database (Table  3 ) as well as the in-house reference database (Table  4 ). Notable differences in successful identification rates were highlighted between the 10 algorithms (up to 22% difference between the best identification rate and the worst one for one defined threshold). In particular, with both references databases, the algorithm that selected the best results among four spots (D1) increased the accepted identification rate (i.
e. , percentage of assessed strains that fulfilled the criteria for identification at either the genus or species level) (A < B1 < C1 < D1) without notably changing the identification PPV. By contrast, the inter-spot concordance criteria did not increase the identification PPV at the species level and reduced the accepted result rate (D4 < D3 < D2 ≈ D1). The main difference between the results obtained with the two databases was the percentage of accepted identifications, which was markedly lower with the Bruker database than with the in-house reference database (up to 59. 8% difference between the two databases). Additionally, while the PPV for genus level identification was 100% for both databases using the 1. 7 threshold, regardless of the other decision criteria applied, the PPV for species level identification was better with our in-house database than with the Bruker database (from 0.
13 units for the D4 algorithm to 0. 18 units for the A1 and B1 algorithms). Using the inhouse reference database, the D1 combination with a threshold at 1. 7 yielded 87. 41% of accepted identifications, with a species identification PPV of 0. 89. We noted an increase in the species PPV with threshold values higher than 1. 7, but the loss in percentage of identification is important. Using the identification score threshold of 2. 00, as recommended by the manufacturer, led to a 23. 75% decrease in accepted identifications and only a 2% increase in the PPV. The "highly probable species identification" criterion by Bruker (i. e. , a score above the threshold of 2. 3) yielded a species PPV of 0. 92 but only 18. 05% of accepted identifications (76 strains) using our inhouse database. The identification results were even less acceptable when using the commercial database, with only 44 strains (10.
Description of the misidentifications obtained with both databases For 32 strains, the identification best score using the D1 algorithm was higher than the 1. 7 threshold, although the species level result was incorrect using the in-house database. For all of these strains, proper identification at the species level was impeded by the close similarity of the MALDI-TOF MS spectra of some species belonging to the same species complex (as it is the case with Fusarium solani and its closely related species Fusarium petrolophilum). The details of these strains are presented in Table  5. The same finding was observed for 39 misidentifications obtained using the Bruker database (Table  6 ). Analyses of the identification performances for the restricted panels with the Bruker database and the in-house database The robustness of the identification algorithms was tested by performing the same analysis on a restricted panel including only the strains that were represented by at least three distinct strains in the reference spectrum libraries (the restricted panel used with the Bruker database (n = 191) is different than the one used with the in-house database (n = 280) due to the differences in databases composition).