dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2004; Kato et al. , 1990) , Taiwan  (Kamiza et al. , 2015) , Canada  (Campbell et al. , 2007)  or Australia  (Kune et al. , 1989). Three articles potentially sharing data for Canadian subjects, reported results on BMI for men and women combined  (Campbell et al. , 2010; Win et al. , 2011)  or not  (Campbell et al. , 2007) : an overlap between results of the two articles whose subjects were recruited by the same registry  (Campbell et al. , 2010; Win et al. , 2011 ) cannot be excluded. Although two articles may present an overlap for subjects from Hawaii (Le  Marchand et al. , 1999; Joshi et al. , 2015) , their analyses are different. The risk of colon and rectum cancers combined was estimated in twenty articles, the risk of colon cancer in thirteen articles and the risk of rectal cancer in three articles.
Strengths and limitations of the review The presented study has several methodological strengths, namely 1) the focused review question; 2) a comprehensive and systematic literature search; 3) the use of explicit and reproducible eligibility criteria; and 4) selection and extraction of data in duplicate. Concerning limitations, this systematic review did not provide a quantitative evaluation of data. Moreover, epidemiological data were mainly obtained from retrospective studies which are prone to recall bias. The included studies had other obvious limits: low number of subjects with FH of CRC, and heterogeneity according to the exposure categories, and/or adjustment variables. In addition, several studies were based on potentially overlapping populations which made it difficult to summarize the current findings for some factors.
Data The measurements were taken at Col de Porte, which is located at 1325 m altitude in the Chartreuse mountain range, France. An exhaustive description of the study site and the long-term measurement instruments is provided in  Morin et al. (2012). The long-term measurements used in this study are 2 m air temperature, surface temperature, direct and diffuse broadband incident short-wave radiations, snow depth and snow water equivalent (SWE), snowfall and rainfall rates.
Finally, the measured spectra are analysed with respect to the presence of liquid water (step 4). Step 1: estimate the scaling factor A A seasonal value of A is estimated using Eq. (  8 ) with r diff = 1 (selected fully cloudy days from visual inspection of the photographs and θ s smaller than 65 • ) and a non-linear least squares method (provided by Python scipy. optimize. leastsq function). To avoid avoid multiple solutions (A and c imp ) in case of moderate to high amounts of impurities in snow, only the spectra from the beginning of season were used for this estimation. Quantiles 25 and 75 of A distribution are used to propagate uncertainties on the near-surface and surface properties predicted from the spectra. Step 2: estimate optimal SSA and c imp Once the value of A is set, optimal SSA and log 10 (c imp ) values are estimated from the spectrum within 400-1050 nm along with K using Eq.
, 2006) , we apply the following method to distinguish wet from dry snow. The spectra are first averaged using a 20 nm moving window average in order to reduce noise before minimum calculation. We then compute the wavelength of minimum albedo in the 1000-1050 nm range and apply the following criteria to detect liquid water presence: argmin λ∈[1000,1050] nm (α(λ)) < λ water. (9) λ water threshold has been set after studying the distribution of argmin(α) on the whole spectra dataset. 0. 943. This value is close to one, which indicates that the actual instrumental response deviates only slightly from the ideal one. The spread of A values is small (quantile 25, q 25 , of 0. 920 and quantile 75, q 75 , of 0. 964). This spread could probably be attributed to residual measurement artefacts such as deposition of precipitation particles on the measurement head, small direct incoming radiation and reproducibility errors due to the optical switch  (Picard et al.
The average slope and aspect are in agreement with what can be visually estimated from Fig. 1. The seasonal evolution of slope and aspect is related to snow evolution (the red line in Fig. 6b  shows the measured snow depth). Slope is smaller just after a precipitation event and increases during the melt season. For comparison, the same method has been applied to the albedo database measured in Dome C and described in  Picard et al. (2016a). The snow surface is horizontal at large scale but the surface can be locally rough due to wind-drift effects. The method applied to Dome C data during the 2012-2013 season leads to slope angles between ±2 • , thus providing an insight into the accuracy of the method.
Concluding remarks This study introduces a continuous dataset of spectral albedo acquired from January to May 2014 from 400 to 1050 nm at the Col de Porte alpine site as a follow-up of the study conducted using the same instrument at Dome C in Antarctica during 3 years  (Picard et al. , 2016a). The alpine snowpack radically differs from inner Antarctica snowpack and the spectral albedo variations cannot be only explained by the evolution of near-surface SSA and solar zenith angle. We investigated how the variations of measured spectral albedo can be explained by the evolution of other factors such as ef-fective impurity content, surface slope and aspect and presence of liquid water. For this, we used analytical formulation of spectral albedo as a function of these near-surface parameters. Results show that the measured spectra are accurately simulated using this formulation.
However, optimal near-surface SSA predicted from the spectra exhibits strong seasonal and diurnal cycles that can be related to snow and meteorological conditions. Near-surface optimal effective impurity content also exhibits strong variations along the season that can be related to surface enrichment process due to melt and Saharan dust deposition events that frequently occur in the French Alps (Di  Mauro et al. , 2015). Such time series of spectral albedo are required to understand and quantify the evolution of snow albedo in relationship with snow surface variables such as SSA and surface impurity content. They are also a unique opportunity to better understand the evolution of near-surface SSA and effective impurity content during the snow season. They provide a unique dataset to evaluate and refine detailed snowpack model such as Crocus  (Vionnet et al.
Acknowledgements. CNRM/CEN and LGGE are part of  Labex OSUG@2020  (investissement d'avenir - ANR10 LABX56 ). This study was supported by the  ANR  programs  1-JS56-005-01   MON-ISNOW  and  ANR-16-CE01-0006  EBONI and the LEFE programs BON and ASSURANCE. The authors are grateful to the  Col de Porte  staff for ensuring a proper working of all the instruments, to EDF-DTG for SWE automatic measurements at  Col de Porte , to  L. Mbemba  for the impurity content in situ measurements, to  Vincent Vionnet  and  Henning Löwe  for helpful discussion on sky view factor and to the two anonymous reviewers for their useful comments on the manuscript. Edited by: M. Schneebeli Reviewed by:  L. Egli  and one anonymous referee.
Study setting & population Sihanouk Hospital Center of HOPE (SHCH) is a 40-bed non-governmental referral hospital for adults in Phnom Penh, Cambodia. Since July 2007, SHCH and the Institute of Tropical Medicine (ITM) in Antwerp, Belgium, have been jointly organising the surveillance of bloodstream infections at this hospital and its associated clinics. For this study, all data collected between 2008-2015 were analyzed. Blood cultures were systematically sampled in all patients presenting at SHCH who were suspected of having sepsis according to the Systemic Inflammatory Response Syndrome (SIRS) criteria  [17]. Recently, new definitions and criteria for sepsis have been proposed such as the Sequential [Sepsis-related] Organ Failure Assessment (SOFA) score  [18]. Over the 8-year period, 18,927 blood cultures were sampled from mostly, but not exclusively, adults  [19].
Assessment of antimicrobial resistance genes and Salmonella Typhi H58 typing Whole genome sequencing was carried out on all 65 Salmonella Typhi isolates and a selection of 26 Salmonella Paratyphi A isolates at the Plateforme de microbiologie mutualisee (P2M) from the Pasteur International Bioresources network (PIBnet, Institut Pasteur, Paris, France). Short-read sequences from 30 previously published Salmonella Paratyphi A genomes were also included  [23]. The run accession numbers and related metadata are detailed in S1 Table. Short-read sequences have been deposited to the European Nucleotide Archive (ENA) (http:// www. ebi. ac. uk/ena) (accession number PRJEB19906). The MagNAPure 96 system (Roche Diagnostics, Indianopolis, IN, USA) was used for DNA extraction, libraries were prepared using the Nextera XT kit (Illumina, San Diego, CA, USA) and sequencing was done with the NextSeq 500 system (Illumina).
Read alignment, single nucleotide polymorphism (SNP) detection and maximum-likelihood phylogeny were carried out as described previously  [23]. Sequence assembly was performed using SPAdes v. 3. 6. 0 [24]. Salmonella Typhi isolates were categorized as belonging to haplotype H58 based on the presence of the H58 specific single SNP (T at nucleotide 252 on the gene glpA corresponding to STY2513 from GenBank accession no. AL513382, Salmonella Typhi CT18)  [25]. Genotyphi (https://github. com/katholt/genotyphi) was also used to classify Salmonella Typhi  [26]. Salmonella Paratyphi A isolates were categorized as belonging to clade C5 (the dominant clade in Cambodia) based on the presence of the C5-specific SNP (G to A at position 2 381 607 within the SPA_RS11495 gene)  [23]. The presence of antibiotic resistance genes was determined with ResFinder version 2.
Data registration, statistical analysis Demographic, clinical and microbiological data were entered encoded into an Excel database that was created for this study (Microsoft, Redmond, WA, USA). The code referring to the patient identity was only known by two medical doctors. Access to the database was restricted to these two medical doctors and patient identifiers were removed prior to analysis. Only the first isolate and associated clinical data for each unique patient was considered. Isolates recovered from a second blood culture drawn within two weeks after the initial one were considered as duplicates, whereas isolates recovered from a repeat blood culture more than two weeks after the initial one were considered as recurrences (either relapse or repeat infections). Statistical analysis was done with Stata 12 (Stata Corp.
Ethics The study was conducted according to the principles expressed in the Declaration of Helsinki and involves use of information that was previously collected in the course of routine care. Ethical approval for the Microbiological Surveillance Study was granted by the Institutional Review Board of the ITM, the Ethics Committee of Antwerp University, and the National Ethics Committee for Health Research in Cambodia. This study and approval includes retrospective review of demographic and clinical data which are part of routine clinical history taking as recorded in the clinical chart. During 2008-2012, enteric fever was caused mostly by Salmonella Typhi (35 cases) and only 12 cases of Salmonella Paratyphi A infection were identified (Fig  1 ). In 2013 however, a sharp increase in the number of Salmonella Paratyphi A cases was observed with a total of 72 unique cases.
Acknowledgments We would like to thank  Barbara Barbe ,  Marleen Verlinden  and  Marjan Peeters  for technical assistance at ITM. We would also like to thank all colleagues involved in blood culture surveillance and clinical care at the  Sihanouk Hospital Center of HOPE  and  Laura Brinas ,  Andrea Alexandru ,  Maud Vanpeene ,  Sobhy Wilhame , and  Vincent Enouf (Institut Pasteur ,  Pasteur International Bioresources network (PIBnet) ,  Plateforme de microbiologie mutualisee  ( P2M ),  Paris, France , for performing the sequencing experiments.
the  Sihanouk Hospital Center of HOPE  is supported by the  Belgian Directorate of Development Cooperation (DGD)  (https://diplomatie. belgium. be/en/policy/development_cooperation/who_we_are/our_ organisation/dgd) through project  2. 08  of the  Third Framework Agreement between the Belgian DGD (Ministry of Development Cooperation)  and the  Institute of Tropical Medicine (ITM). This study was further supported by the  Strategic Network Laboratory Quality Management (LQM)  project (http://www. labquality. be/)( Belgian Development Cooperation) , the  Institut Pasteur and the French government 's  Investissement d'Avenir programme, Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  (http://www. agence-nationalerecherche. fr/? ProjetIA=10-LABX-0062 ) (grant number  ANR-10-LABX-62-IBEID ).
The strain collection also includes the ECOR (72 strains,  Ochman and Selander 1984 ) , IAI (82 strains,  Picard et al. 1999)  and NILS (82 strains,  Bleibtreu et al. 2014  ) strain collections, as well as 290 strains from the LTEE collection ( E. coli Long Term Evolution Experiment)  (Tenaillon et al. 2016). The collection additionally includes 15 strains belonging to other species of the Escherichia genus. The full list of strains, including name, collection of origin and links to relevant databases is provided in Supplementary table 1 and online at https://evocellnet. github. io/ecoref. Such a large and diverse collection of strains serve as the foundation of the development of phenotypic predictive models. Our collection also exhibits a high diversity at the genetic level. We combined already available genomic sequences (374) with newly generated ones (322) to obtain the genomes of 696 strains, after removing strains whose sequence did not match known typing information and duplicate isolates (Figure  1A  and Supplementary table 1 and Methods).
The correlation of phenotypic profiles also clustered drugs with same mode of action (MoA), as shown by comparing the cluster's purity against those of random clusters (Figure  3G ). As an additional benchmark for the accuracy of the fitness responses we tested if pairs of drugs that have correlated fitness effects across our strain collection also displayed correlated profiles in the K-12 knock-out collection of strains. Indeed, those conditions that clustered together in our data also had higher correlations in the E. coli K-12 knockout library than random pairs of conditions (Figure  3H ). Using principal component analysis we concluded that the phenotypic space sampled by the strain collection is largely similar to that of the K-12 KO library (Supplementary figure  3 ). This suggests that the phenotypes exhibited by the natural strains are similar to those reached by the K-12 gene KO, even if the strains possess much larger genetic variability in gene content and nonsynonymous variation.
05 and FoldX ΔΔG < 0. 46 kcal/mol). We hypothesize that the original growth phenotype is either due to this incorrectly classified variant or due to another variant that indirectly acts on the expression of this efflux pump. Of the 5 strains expressing the reference allele of acrB tested in pseudomonic acid, three harbored nonsynonymous variants in their chromosomal copy. One of them (IAI30) was predicted to increase its colony size after the complementation, whereas the other two (ECOR-42 and ECOR-28) were not predicted to be affected by the complementation (Figure  5D ). We mapped those nonsynonymous variants to the three-dimensional structure of AcrB (PDB entry: 2dr6,  Murakami et al. 2006 )  to inspect their potential impact on the protein function (Figure  5G ). Both ECOR-42 and ECOR-28 harbor a single nonsynonymous variant (A915D and T1013I, respectively) in the transmembrane domain of the protein; both variants are predicted to be deleterious (SIFT, p-value ~0.
Contributions: PB and ATy conceived the study, ATy, LHD and MG designed the phenotypic screening, LHD, ATe and MG carried out the phenotypic screening, ATe sequenced part of the strains, MG and JACV analysed the sequencing data, OW and MG precomputed the predicted impact of nonsynonymous substitutions, MG designed the strain collection, analysed the phenotypic data and applied the predictive models, MG, AK and ATy designed the follow-up experiments, AK carried out the follow-up experiments, AK and MW assembled the strain collection, OC and ED provided part of the strain collection, OC, ED and MG verified the consistency of the genome sequences, MG, PB and ATy wrote the manuscript. for each condition the predicted score is computed using the disruption score of the conditionally essential genes.
Mean colony area and the 95% confidence intervals are reported. Significant differences (t-test p-value < 0. 01) between colony area of the strains with the empty and complemented plasmids are reported. "Other complementation" reports strains expressing a different gene than the focal one, indicated between parenthesis. (G) Cartoon representation of AcrB 3D structure (PDB entry: 2dr6). Only one of the three monomers is highlighted in blue; colored spheres represent known non-synonymous variants in one of the strains complemented experimentally. Variants present in strains ECOR-42 and ECOR-28 are in the transmembrane domain, while both variants present in IAI30 are in the porter domain. FDR-corrected p-value <= 0. 01) over total conditions with at least 1% and 5% sick strains. Marker's size is proportional to the of the FDR-corrected p-value.
Computation of all possible mutations and their effect The impact of all possible nonsynonymous substitutions on the reference individual has been precomputed to speed up the lookup process. Functional impact of nonsynonymous substitutions has been computed using SIFT  (Ng and Henikoff 2001)  version 5. 1. 1. Structural impact of nonsynonymous substitutions has been computed using FoldX  (Guerois, Nielsen, and Serrano 2002)  version 4; both 3-D structures present in the PDB database and homology models were used. Homology models were created using ModPipe  (Pieper et al. 2014 ) version 2. 2. 0. Water accessibility of all 3-D structures was computed using FreeSASA  (Mitternacht 2016 ) version 1. 1. Conversion from PDB to Uniprot residues coordinates was derived from the SIFTS  (Velankar et al.
Computation of the disruption score For each strain and each protein coding genes we have computed the overall impact of all nonsynonymous and nonsense substitutions, in a similar approach as the one used for Saccharomyces cerevisiae  (Jelier et al. 2011). The output of each predictor (a deleterious probability for SIFT and a ΔΔG value for FoldX) has been converted to the probability of the substitution being neutral. Mutations with known impact on (neutral) P the reference individual have been downloaded from Uniprot (UniProt Consortium 2015) (N=3673) and used to derive such conversion; since only 580 mutations are reported to have a neutral impact, we added all observed nonsynonymous variants affecting known essential genes (as reported in the OGEE database,  Chen et al. 2016 )  to the list of tolerated mutations.
Use of the disruption score as a functional association predictor We used the proteins pairwise Pearson's correlation of disruption scores as a predictor of genes functional associations. We used four benchmarking sets: operons, as derived from the DOOR database  (Mao et al. 2014)  , protein complexes and pathways derived from the EcoCyc database  (Keseler et al. 2013)  , and protein-protein interactions derived from a recent yeast two-hybrid experiment  (Rajagopala et al. 2014). The distribution of the disruption score correlation for each pair of related genes was compared against the same number of gene pairs randomly drawn from all reference genes. We also assessed the predictive power of the disruption score correlations by drawing a receiving operator characteristics curve (ROC) across each correlation threshold, using the scikit-learn library  (Pedregosa et al.
Final S-scores were quantile-normalized, and significant phenotypes were highlighted using a 5% FDR correction similar to the one used in the E. coli reverse genetic screen  (Nichols et al. 2011)  , using the statsmodels library version 0. 6. 1. The phenotypic data is available in Supplementary material 1. Phenotypic distance between strains was computed using the euclidean distance between the S-scores across all conditions, using the nadist library version 0. 1. 0. PCA (Principal Component Analysis) of the joint data from the K-12 chemical genomics and the E. coli strain collection was carried out by merging the two datasets over the shared conditions (conditions tested in both sets), using the scikit-learn library  (Pedregosa et al. 2011 ) , version 0. 17. 1. Since the PCA method doesn't allow missing values, strains that were not tested in more than 60 conditions (over 161) were removed.
All resulting strains were pinned using a Singer Rotor robot in 10 different conditions, on 2 solid agar plates, so that each strain is pinned at least four times per plate. The plates were incubated at room temperature and multiple photographs were taken until colonies were found to be overgrowing into each other. Iris  (Kritikos et al. 2017 ) version 0. 9. 7 was used to extract colony size from the pictures. and Others 2010) version 0. 18. 0, Biopython  (Cock et al. 2009)   Genomic and phenotypic data, as well as relevant information on how to obtain the members of the strain collection is available at the following URL: https://evocellnet. github. io/ecoref.
Acknowledgements: We are particularly grateful to the various people providing us with many of the strains of the E. coli genetic reference panel, specifically (in alphabetical order):  Alfredo G. Torres ,  Catharina Svanborg ,  David Clarke ,  Erick Denamur ,  Ewa Bok  and  Pawel Pusz ,  Isabel Gordo  and  Lilia Perfeito ,  Jorg Weinreich  and  Peter Schierack ,  KC Huang ,  Lisa Nolan ,  Mark Goulian ,  Mathew Upton ,  Olin Silander ,  Richard Lenski ,  Scott Hultgren  and  Wanderley Dias da Silveira. We thank  Amanda Miguel  for helping in the phenotypic screen. We also thank the  EMBL Gene Core , and especially  Rajna Hercog  and  Vladimir Benes  for the support in genome sequencing. We thank  Ewan Birney ,  Oliver Stegle ,  KC Huang  and  Zam Iqbal  for critical reading of the manuscript.
Code, data and strains collection availability The source code used to perform the analysis reported here and generate the figures is available as Supplementary material 3 and at the following URLs: https://github. com/mgalardini/screenings, https://github. com/mgalardini/pangenome_variation, https://github. com/mgalardini/ecopredict. Code is mostly based on the Python programming language, and using the following libraries: Numpy (Van Der Walt,  Colbert, and Varoquaux 2011) version 1. 10. 4, SciPy version 0. 17. 0, Pandas (McKinney.
Our research focused on the identified deletions and those mutations were subsequently verified by PCR analysis using primers flanking the deleted fragment. For two predicted deletions (on scaffold 1 and scaffold 16), we could not confirm the results obtained by Illumina sequencing as no PCR product was obtained. The recent genome assembly of T. reesei using chromosomal contact data in a computational approach identified scaffolds 1 and 16 to be located on chromosomes number five and seven, respectively  [24] [25] [26]. Upon close examination of our sequencing data using the Integrative genome viewer program (http://software. broadinstitute. org/software/igv/) combined with the information on Fig. 2  Extracellular protein production in the reference strains QM9414, Rut-C30, and QM9978. Protein concentration in the supernatant was determined from cultures on liquid minimal medium supplemented with 10 g/L Solka-floc cellulose/lactose or glucose.