dataesr/scientific-paragraphs-categorization huggingface dataset:
2017 ) and has been shown to perform well on a mock community  (Tedersoo et al. 2015) , despite its tendency to aggregate closely related species  (Ryberg 2015; Bálint et al. 2016). All other settings were left to default. Sequences with a Phred score greater than 30 over 75% of the read length were included in the clustering process. Quality filtering was performed with the QIIME script split_libraries_fastq. py (Fig. 2 ). We also used the R package DADA2  (Callahan et al. 2016)  to correct sequencing errors and to infer exact amplicon sequence variants (Variation=DADA2) (Fig. 2 ). We retained only reads with less than one expected error (given the quality scores; Edgar and Flyvbjerg 2015). Quality filtering was performed with the fastqFilter function. Quality data were lost during the extraction step.
We therefore applied DADA2 only to the fungal ITS1 sequences not extracted with ITSx. This analysis strategy is different from that recommended in the DADA2 tutorial (http://benjjneb. github. io/dada2/tutorial. html). We, therefore, also included an approach adhering to the strategy described in the tutorial (Assembly=CUTADAPT_MERGED) (Fig. 2 ). Primers were removed from both forward and reverse reads, with Cutadapt v1. 13  (Martin 2011). The forward and reverse reads were then truncated (at 200 bp and 180 bp, respectively) and we retained only reads with fewer than two expected errors (as in the default parameters of the filterAndTrim function). Reads were merged after the inference of sequence variation as described in the tutorial. (4) Treatment of chimeric sequences (Chimeras) The chimeric sequences identified were either removed (Chimeras=Removed) or retained in the dataset (Chimeras=Retained) (Fig.
2 ). Detection with QIIME script identify_chimeric_seqs. py was performed on the demultiplexed reads before USEARCH clustering (Fig. 2 ), as recommended in the QIIME tutorial (http://qiime. org/scripts/identify_chimeric_seqs. html). Following VSEARCH clustering, we combined de novo and reference-based strategies for chimera detection. The de novo strategy used the UCHIME  (Edgar et al. 2011)  algorithm implemented in VSEARCH. The reference-based strategy used the ITS1-only UNITE-UCHIME dataset v7. 2 (as of 2017-10-10) as a reference  (Nilsson et al. 2015). Following DADA2 sequence variation analysis, chimeric sequences were removed with the removeBimeraDeNovo function, using the consensus option. (5) Final filtering of the community (Filtering) Finally, we filtered the OTU and ASV tables (Fig.
2 ). Five filtering methods were compared: Filtering=1 involved removing OTUs (or ASVs) composed of a single sequence; Filtering=10 involved removing OTUs (or ASVs) for which less than 10 sequences were obtained when all three replicates were considered; Filtering=RA involved removing OTUs (or ASVs) with a relative abundance lower than 0. 005% of the total number of sequences; Filtering=LULU used the LULU curation algorithm  (Frøslev et al. 2017)  to collapse erroneous OTUs (or ASVs) into their parent OTUs; Filtering=All involved keeping all OTUs or ASVs, regardless of the number of sequences obtained. Representative sequences were assigned to taxa with the QIIME script assign_taxonomy. py, with BLAST v2. 2. 22  (Altschul et al. 1990 ) and default QIIME parameters (e-value < 0. 001; identity ≥ 90%) against the local database of Sanger sequences for the fungal strains of the mock community.
The LULU curation algorithm was applied with both default settings and a set of parameters adjusted to the features of the mock community. Three parameters can be tuned in LULU: the minimum sequence similarity between a 'potential daughter' and its 'potential parent' (default 84%), the minimum ratio of parent OTU abundance to daughter OTU abundance in all samples (default 1) and their minimum co-occurrence rate across samples (default 95%). Increasing the first parameter is only advised when the barcode region has little variation or when few PCR and sequencing errors are expected, and changing the second parameter is generally not recommended  (Frøslev et al. 2017 ). Therefore we tuned the third parameter. We lowered its value to 66. 6% to account for the small number of samples in our study (3 replicate samples per bioinformatic approach).
Ribosomal RNA gene copy number information was not available for each strain and was not used to adjust the expected number of sequences. All three criteria theoretically range between 0 and 1. They equal 1 when the algorithm successfully identifies all members of the mock community. However, maximum sensitivity may be below 1 if the sequences of some fungal strains are absent from the raw Illumina dataset. We, therefore, estimated the total number of strains present in the raw dataset, by aligning the forward and reverse MiSeq sequences with the ITS1 Sanger sequences, with a similarity threshold of 100% and an alignment length threshold of 90% of the length of the shorter sequence. Alignments were performed with VSEARCH (--usearch_global)  (Rognes et al. 2016) , using the following parameters: --id 1 -userout --userfields query+target+qcov+tcov+id --maxaccepts 20 -top_hits_only.
Assessment of maximum sensitivity The manually curated Sanger database contained the ITS1 sequences of the 189 fungal strains of the mock community (Fig. 1   Only 160 of these 175 unique ITS1 sequences were detected in the raw Illumina MiSeq dataset (Table  S1 ), suggesting that the other 15 strains were either not amplified by the ITS1F-ITS2 primer pair, not sequenced or were sequenced with errors. Eleven of these strains were detected in the Illumina data when the similarity threshold between Sanger and Illumina sequences was lowered to 93. 5% (Table  S1 ), suggesting that their apparent absence was caused by mismatches between the Sanger sequence and the Illumina sequences. Four of these eleven strains had ambiguous bases in the Sanger sequence, preventing a perfect match with Illumina sequences.
These ambiguous bases might be due to the within-strain polymorphism of the ITS region that exists for some fungi  (Fiers et al. 2011). The other four ITS1 sequences absent from the raw Illumina dataset came from the following species: Lepiota clypeolaria, Mycena abramsii, M. galopus and Panellus stipticus. The first species was successfully amplified with the ITS1F-ITS4 primer pair before Sanger sequencing and possessed the exact sequence of the ITS2 primer, suggesting that its absence from the Illumina dataset was caused by DNA pooling biases rather than lack of amplification. In contrast, the sequence of the ITS2 primer was detected with some mismatches for the three last species. Their absence could be due to a lack of amplification by the ITS1F-ITS2 primer pair (Table  S1 ). The maximum sensitivity attainable by any bioinformatic approach (that is, the maximum proportion of fungal strains that could actually be found) therefore ranged from 84.
Influence of Assembly, Extraction, Variation, Chimera and Filtering on sequencing data (1) Influence of Assembly In total, we obtained 143873 paired-end Illumina reads of 250 bp each (Table  S2 ). We obtained 43352, 56406 and 44115 sequences for the three replicates. The mean quality of the forward (R1) reads was slightly higher than that of the reverse (R2) reads (35. 59 versus 34. 31, respectively) (Table  S2 ). The choice of paired-end read assembly algorithm strongly influenced the number, length and quality of the consensus sequences (Table  S2  and Figure  S1 ). FASTQ-JOIN retained on average 53. 1% of the raw reads (whatever the minimum overlapping length), whereas PEAR retained on average 96. 3% of the raw reads (Table  S2 ). Mean sequence quality was also higher for PEAR than for FASTQ-JOIN (Table  S2  and Figure  S1 ).
Discussion Metabarcoding approaches have revolutionized fungal ecology over the last decade  (Hibbett et al. 2009 ) and have become the gold standard for describing the richness and composition of communities and the networks of associations between community members  (Bálint et al. 2016). They have been so successful that fungal ecologists are struggling to cope with the boom in sequencing platforms, bioinformatic pipelines, taxonomic databases and community analysis tools. Benchmark studies and methodological reviews are required to help them make the most appropriate choices (e. g. Lindahl et al. 2013; Bálint et al. 2016; Weiss et al. 2016; Pollock et al. 2018). In this study, we focused on one aspect of the metabarcoding approach, bioinformatic analysis, assessing its effect on the recovery of community richness and composition.
Our analyses revealed that four fungal species (Lepiota clypeolaria, Mycena abramsii, M. galopus, and Panellus stipticus) were absent from the sequence dataset (Fig. 1 ), suggesting a lack of amplification by the so-called "universal" primers  (Bellemain et al. 2010; Tedersoo and Lindahl 2016)  or a sequencing failure  (Nguyen et al. 2015; Palmer and Jusino et al. 2018). DADA2, a clustering-free software package  (Callahan et al. 2016) , effectively recovered the composition of the mock community. The top ten bioinformatic approaches in terms of performance for the compositional similarity criterion all used DADA2 to identify amplicon sequence variants. The total number of ASVs generated by these 10 approaches ranged from 148 to 197, which was therefore of the same order of magnitude as the total number of strains in the mock fungal community (i.
e. 189). We highlighted several options for increasing the efficiency of DADA2 for fungal metabarcoding datasets. Firstly, our results confirm that the use of single forward (R1) reads as input data is a good option  (Nguyen et al. 2015). This made it possible to ensure that strains with longer ITS regions (such as those of the genus Cantharellus, for instance;  Feibelman et al. 1994)  were not excluded. Based on our results, we also recommend retaining the primers for fungal communities amplified with the ITS1F-ITS2 primer pair. Indeed, we found that primer removal did not improve the recovery of mock community composition. These findings may be accounted for by the absence of degenerate nucleotides in the ITS1F-ITS2 primer pair. Primer retention may be relevant in this case, because non-degenerate primers have no impact on the denoising step of DADA2.
The merging of reads after sequence variation inference, as recommended in the DADA2 tutorial (http://benjjneb. github. io/dada2/tutorial. html), did not improve the recovery of the mock community either. The Si5 approach represented one of the best trade-offs between the three selection criteria among the 360 bioinformatic approaches compared. The Si5 approach used single forward (R1) reads as input. Quality filtering, sequence variation analysis and chimera removal were performed with DADA2  (Callahan et al. 2016). ITS1 extraction  (Bengtsson-Palme et al. 2013 ) and downstream OTU table filtering were not required. The Si5 approach recovered the ITS1 regions of 80 out of 87 Ascomycota strains, 83 out of 99 Basidiomycota strains and all 3 Mucoromycota strains (Fig. 1 ), suggesting that there was no detection bias against Ascomycota strains despite the intron insert downstream of the ITS1F primer binding site that might impair their amplification (see  Taylor et al.
They could also include error-correction methods alternative to that of DADA2, such as UNOISE2  (Edgar 2016) , or recent clustering approaches, such as OptiClust  (Westcott and Schloss 2017)  or SeekDeep  (Hathaway et al. 2018) , or consider reference-based clustering approaches  (Cline et al. 2017; Halwachs et al. 2017 ; but see  Westcott and Schloss, 2015). All the data required for the extension of our methodological comparison are provided. Illumina dataset are indicated with a cross and those that were recovered by the recommended bioinformatic approach (Si5, Table  3 ) are indicated with a black circle. (or ASVs) retrieved by the most sensitive approach (Se1; Table  1 ), the most precise approach (P1; Table  2 ), the approach with the best performance in terms of compositional similarity to the mock community (Si1; Table  3 ) and the bioinformatic approach recommended in this study (Si5; Table  3 ).
Acknowledgments We thank  Matthieu Barret ,  Martial Briand ,  Lucas Auer ,  Gregory Gambetta ,  Guilherme Martins ,  Frédéric Barraquand  and  Tania Fort  for useful discussions on the first draft of the manuscript. We also thank the editor,  Peter Kennedy  and one anonymous reviewer for helpful comments of the submitted version. We are grateful to the  INRA MEM metaprogramme (Meta-Omics of Microbial Ecosystems)  for financial and scientific support. The mock community sequencing was funded by the  INRA MEM   MetaBAR  project (PI: MB) and the bioinformatic analyses were performed as part of the  INRA MEM Learn-biocontrol  project (PI: CV). Additional funding was received from the  LABEX COTE  ( ANR-10-LABX-45 ) and the  LABEX CEBA  ( ANR-10-LABX-25-01 ). CP's PhD grant was funded by the  INRA and Bordeaux Sciences Agro (BSA).
and Table S1). Several strains had identical Sanger sequences for ITS1: two strains from the genus Alternaria, six from the genus Botrytis (B. calthae, B. pseudocinerea, B. ranunculi and three strains of B. cinerea), two strains from two different species of Colletotrichum (C. destructivum and C. higginsianum), two strains of Craterellus cornucopioides, four pairs of strains from the genus Fusarium (F. acuminatum and F. avenaceum, F. langsethiae and F. sporotrichioides, F. oxysporum and F. commune, F. verticillioides and another species of the F. fujikuroi species complex), two strains from two different species of Lepista (L. irina and L. nuda) and two strains from the genus Zymoseptoria. The Sanger database, therefore, contained 175 unique ITS1 sequences.
We selected 50% of the animals with OD c or qPCR values that less confidently suggested MAP infection (that is, 50% of animals with OD c < 10 and 38 ≥ C t > 35, and 50% of animals with 30 ≥ OD c ≥ 10 and C t > 38). We selected only 20% of the animals with results that suggested MAP negativity at time 1. In total, 1,475 animals met these criteria and were included in the set of animals to continue with follow-up. After the initial herd assessment, individual blood and fecal samples were collected from the selected individuals 3 more times, each time targeted in a 4-mo interval (range 3 to 9 mo). Final Study Sample and Follow-Up. Of the 1,475 animals selected to continue with follow-up, 1,312 were sampled more than once, and 782 completed the 4 sampling events. Table  3  shows the number of animals per herd that successfully provided 4 data points for the description of fecal shedding patterns and serological patterns.
Serological patterns were defined based on the number of MAP-antibody-positive events determined by serum ELISA (categories A0 to A4, meaning zero positive samples to 4 positive samples). Individual Cow Information. The age, breed, and parity of the cows were known from the national database SNIG of the Centre de Traitement de l'Information Génétique. The age in months at time 1 was calculated; thereafter, animals that continued the study were classified into age categories (in years) for data analyses. Breeds in the initial study sample were Prim'Holstein (818 individuals), Normande (498 individuals), crossbreeds (122 individuals) and Pie Rouge des Plaines (37 individuals). To explore the effect of parity on fecal shedding and serology, we created the following categories: nulliparous cows, nulliparous cows that became primiparous during the study period, primiparous cows, and multiparous cows.
Age at time 1 and parity were treated as categorical ordinal variables. Data Visualization and Statistical Analysis. We plotted response variables (fecal shedding traits, serological patterns) against the categorical explanatory variables (age, breed, parity) using mosaic plots implemented in R Software (R Core Team, 2016) with the packages ggplot2  (Wickham, 2009)  and ggmosaic  (Jeppson et al. , 2017). Mosaic plots are useful for visualizing categorical data: they consist of a set of rectangles (or "tiles") that represent the cells in a contingency table, and the area of each tile is proportional to its corresponding cell  (Hofmann, 2008). We built 2-way contingency tables to plot 1 explanatory variable versus 1 response variable each time, and to test the null hypothesis of independence in these 2-way tables.
To test whether there was a significant departure from independence (that is, an effect of the explanatory variables on the response variables), we aimed to capture deviations from zero in the Pearson residuals (r), a measure of discrepancy between observed and expected values. For this purpose, the statistic we tested was the maximum of the absolute values of Pearson residuals (M), because it is "particularly suitable for identifying the cells responsible for the 'dependence'"  (Zeileis et al. , 2007). Following the instructions from Zeileis   -+--, +---,--*-, *---,---+   et al. (2007, 2017)  for each combination of response and explanatory variable, we performed a permutation test of independence of the maximum statistic using the package vcd  (Meyer et al. , 2016)  in R software  (R Core Team, 2016).
For the present study, we investigated a subset of 34 fecal samples that were too numerous to count by FC to determine the bacterial load by means of bacterial enumeration (data not shown). Out of these, 29 showed a value of >1,000 cfu/g, and thus were either heavy or super-shedders as defined by  Fecteau and Whitlock (2010) ; our qPCR cutoff value for high shedding successfully detected 23 of those 29. Thus, the semiquantitative use of qPCR can aid in the detection of high shedders in control programs. Heavy shedders are thought to contribute largely to contamination of the farm environment, and they can also transmit MAP to other adults  (Pradhan et al. , 2011; Schukken et al. , 2015). However, the magnitude of the effect of MAP super-shedders has been questioned  (Mitchell et al. , 2008; Slater et al.
ACKNOWLEDGMENTS The data from the national database SNIG were contributed by the  French departmental agriculture agencies , the performance-recording organizations institutions, and the artificial insemination centers. The authors acknowledge funding from the  INRA   metaprogram "Integrated Management of Animal Health " for the " GISA-PICSAR " project, from the  Regional Council of Pays de la Loire , from  APIS-GENE , and  GDS France  for the "PARADIGM" Project. The funders had no role in the production, analysis, interpretation, or reporting of the data. The authors also thank 2 anonymous reviewers for their useful contributions.
That is why optimization problems of the offline and online phases are considered separately. In this case, disruption reports are received during crises, a dummy node is created temporarily at the disrupted area (i. e. , on the route between the warehouse and the demand point). Available routes from that dummy node to other demand nodes with the same order are identified. Then the new destination will be selected after ensuring that its path is available. This is when the network disruption can occur in both phases. The disturbance in the offline phase is predicted by the experts, whereas, in the online phase, interruption reports are obtained using satellite data (i. e. , disorders and blockings in fragile and vulnerable places (e. g. , bridges, tunnels and freeways). It should be noted that after receiving a blocked warning message, the repairing operations starts in order to unblock the route (though the program will not stop till the path is repaired).
Model verification In this section, the problem is tested in small sizes by using the AUGMECON-2 method applying BARON solver to validate the proposed mathematical model. We have used GAMS software version 24. 7. 4 on an intel CORE i7 CPU computer set with 8 Gigabytes of RAM. As an appropriate method of producing the optimal-Pareto set in MOIP problems, AUGMECON2 was presented by  Mavrotas and Florios (2013) , being an improvement of their latter proposal, AUGMECOM  (Mavrotas, 2009). The improvements caused a far better quality of Pareto solutions and a significant reduction in the calculation time by avoiding redundant repetitions. As the first step of this method, the most important objective function is considered as the only objective function of the equivalent single objective model and the two other OFs are applied as the constraints.
Computational experiment 5. 1. Managerial insight A case study of Seattle is presented, in the beginning, to validate the mathematical model of section 3. The 24. 7. 4 version of the GAMS software helped us to solve the presented an MINL mathematical model for 14 nodes, including 5 warehouses and 9 demand zones (according to the case study data). Afterwards, the sensitivity analysis is presented, taking into consideration the changes of the TH method's major parameters. To prove the significance of the study, the goals of the presented two-phase mathematical model, as the planning of crisis management in pharmaceutical supply chain, are listed below: • Impressively reducing the amount of unmet demand by considering the second phase of mathematical model in order to avoid the probable disruptions.
Comparative study In this section, the performance of the DE algorithm  (Zhalechian et al. , 2017a ) is assessed on a number of randomly generated data sets in a comparative study. The data sets comprise 20 test problems ranging from small-to medium-sized cases (i. e. up to 25 demand nodes). The attained solutions, by SADE and the GAMS software, are compared with each other for small-and medium-sized instances. Since GAMS is incapable of solving medium-sized problems, the DE algorithm is developed to assess the proposed SADE algorithm performance. To specify the SADE adjusted parameters, we use the RSM. Table  2  demonstrates the SADE adjusted parameters. In Tables 3, the solutions obtained from the GAMS software and the two developed algorithms (i. e. , SADE) are compared for instances up to 25 demand nodes as well as nine establishing warehouse nodes.
We calculate the mean value of the relative percentage gap for the five replications of each test problem and recorded it as the APRG. In addition, the required CPU time to realize the optimal solutions and the average CPU time of SADE are disclosed in columns "Exact time" and "Time", respectively. According to Tables 3, the APRG between SADE and the optimal solution is 0. 57%. Since the APRG is less than 4%, it can be deduced that both of the algorithms can find a near-optimal solution for small-and medium-sized problems. The test problem size #10 K| | = 20, | | = 8L is the last one that can be worked out by the GAMS software. More complex test problems (i. e. , problems #11 to #30) cannot be worked out by the GAMS software because of the high required memory.
Effects of the demand and maximum storage capacity of suppliers According to the assumptions based on the case study data (z = 70 , =40 and i=1#g=3#h=8#w=3#p=5#St=4) and the numerical results obtained from the 24. 7. 4 version of the GAMS software, we analyze each of the objective functions' sensitivity to demand and the maximum storage capacity of suppliers. We consider 20 scenarios in this section (based on different values for these two parameters) that the alterations of the first and second objective functions are depicted in Figs. 7  and 8. The associated numerical results can be found in Tables  4  and 5. As can be seen in Table  4  and Fig. 7 , the scenarios are based on the ascending order of the maximum storage capacity ( L. By increasing the amount of maximum capacity, the first objective function value (i.