dataesr/scientific-paragraphs-categorization huggingface dataset:
Saccharomyces cerevisiae growth in chemostat versus batch This dataset was originally published by  Nookaew et al. (2012)  and contains gene expression measurements of the yeast S. cerevisiae obtained using RNA-Seq from two conditions: growth in a batch culture and growth in a chemostat culture. In a batch culture, a small number of cells are inoculated into a nutrient-rich medium, and allowed to grow at maximal rate until the nutrients are exhausted and the growth curve achieves saturation. In contrast, in a chemostat culture, nutrients are added to the culture at a fixed flow rate while the biomass and the products of metabolism are removed from the vessel at the same flow rate to maintain a fixed culture volume. Growth will settle to a steady state value and cells will continue to divide indefinitely.
Escherichia coli mercury exposure The second dataset analyzed, originally published by  LaVoie and Summers (2018) , demonstrates E. coli's response to mercury exposure. Mercury is a toxicant that negatively impacts the health of both microscopic and macroscopic organisms, and induces a broad cellular response in E. coli. Gene expression was measured using RNA-Seq in two conditions: an unexposed control and a culture exposed to mercuric chloride (HgCl 2 ). For this dataset, the optimum of Equation (  8 ) was not unique, and in total, there were 1684 alternative optima. However, across all these solutions, 1120 of 2578 reactions were present as coloured in all solutions, and 1338 in none. Accordingly, analysis of the results was done using a consensus across all optima which for most reactions uniquely determined their colour.
Here, we tested a proof of concept dataset of yeast growth in chemostat mode versus batch mode. The shift in metabolism between the two types of growth is known to affect the entire TCA cycle, as it is reflected by the up and down-regulated genes seen in dark red and dark blue (INPUT), respectively. In this example, all reactions had at least one differentially expressed gene and are thus a priori coloured. MOOMIN then confirms these a priori colours as well as chooses a direction for reversible reactions so that stoichiometric balance is maintained. For examples of MOOMIN also colouring a priori grey reactions (see Figs  3  and 4 ). The metabolite abbreviations are defined in Supplementary Tables Fig. 3. Predicted flux changes for the production of key metabolites related to mercury stress response of E.
Saccharomyces cerevisiae ethanol stress The final dataset we analyzed compared the response to ethanol stress under aerobic growth of two strains of S. cerevisiae with differing levels of ethanol tolerance: a tolerant strain Y7568 (PAP) and a susceptible strain NCYC3290 (WA). This data was originally published by  Sardi et al. (2018)  (GEO Accession ID: GSM3318043). Although ethanol stress has been very well studied in the past decades in yeast, different levels of tolerance between strains are not yet completely understood. For instance, it has been reported that one of the main consequences of ethanol accumulation inside yeast cells results in ROS generation which, in turn, is responsible for inhibition of cell viability and cell death  (Perez-Gallardo et al. , 2013). Sardi et al. focussed on comparing the transcriptomic profiles of the strains stressed with ethanol by using the same strain under no stress as control.
, 2016) , notably due to the direct protection against ROS generation. Indeed, this is an example of how the method is able to find new hypotheses not clear from only DE data and shows that incorporating the metabolic network structure into the analysis of transcriptomic data can help to connect isolated differentially expressed genes in a biologically meaningful way. Another reason for the difference in susceptibility predicted by MOOMIN might be a higher availability of cytoplasmic trehalose. Several studies suggest that trehalose promotes the survival of yeast cells during ethanol stress, stabilizing or repairing proteins denatured by ethanol  (Bandara et al. , 2009; Ding et al. , 2009). The transcriptomic data showed up-regulation of trehalose degradation in the cytoplasm (R_TREH) and down-regulation in vacuole (R_TREH_v).
Comparison of the outputs from the full and the topological formulation In order to better understand the differences between the outputs from the original problem [Equations (  8 )-(  19 )] and the topological approximation (see Supplementary Materials), we analyzed in detail both solutions for the S. cerevisiae resistance to ethanol dataset. Out of 1576 reactions analyzed in the network, 89% were in accordance between the topological results (consensus of 48 outputs) and the stoichiometric output (Supplementary Tables). Out of the 174 reactions with different outputs, 21 were exchange boundary reactions and 23 were extracellular transport, which means they resulted from different outcomes from within the network. From the remaining 130 intracellular reactions, 26 were considered inconsistent among the 48 topological solutions; this means that they were chosen at times as blue and red in at least two different outputs.
Conclusion In this paper, we have introduced a new computational method for the analysis of gene expression data that makes use of a genomescale metabolic reconstruction of the organism under study: MOOMIN. The method takes as its input the results of a differential expression analysis comparing the gene expression between two different conditions and a metabolic network, and produces a hypothesis of a metabolic shift: a qualitative change in the activity of reactions indicating which parts of the metabolism increased, decreased or remained unchanged in flux between the two states. We have described the theoretical framework behind MOOMIN and its implementation in the language of Mixed-Integer Linear Programming. We applied the MOOMIN method to three distinct datasets from both eukaryotic and prokaryotic organisms and were able to extract interesting information that were not possible to infer only by analyzing the DE data itself.
In the latter two datasets, investigating E. coli's response to mercury and S. cerevisae's tolerance to ethanol, by analyzing at the same time the network information and the differentially expressed genes, MOOMIN inferred pathways that were already shown to be involved with the analyzed conditions but were not detected as differentially expressed by our GO enrichment analysis or by the authors from both publications, namely the GSH production in E. coli in response to mercury and the arginine production in strains of yeast resistant to ethanol. The method was also able to infer pathways expected to be differentially expressed and that were also enriched in our GO analysis, but in a more connected way, focussing also on genes that possibly associate these seemingly unrelated functions and connecting upand down-regulated genes.
A good example of this feature is seen in Figure  3 : MOOMIN recovered a connected subnetwork in which the TCA cycle and amino acid metabolism were directly linked to the production of GSH and GABA. Altogether, our analyses show that by coupling the network topology and DE data, MOOMIN is able to both further confirm results that can be obtained using traditional tools of DE analysis, as well as discover new information such as metabolically regulated reactions that cannot be straightforwardly inferred from gene expression data alone.
Objectives: The purpose of this study was to compare the efficiency of mould identification of two matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS) systems e Vitek MS (VMS) and Microflex LT (MLT) e and the MSI application. Methods: Moulds were collected retrospectively and prospectively to display epidemiological diversity of a microbiology laboratory. All of them were identified via sequencing. Strains were then identified using the VMS v3. 0, the MLT, and the MSI software applied on MLT spectra. Rates of correct identifications to the species, to the complex, and to the genus level were compared with the molecular reference standard. Results: A total of 102 isolates were collected. The rate of correct identification to the species level with the MLT was 42.
2% (43/102) with a threshold of 1. 7 (vs. 16. 7% (17/102) with a threshold of 2. 0, p < 0. 05). The VMS performed better than the MLT with a threshold of 1. 7 for species (49. 0% (50/102), p 0. 33) and complex level identifications (71. 6% (73/102) vs. 54. 9% (56/102), p < 0. 05). However the highest performances were observed when the MLT spectra were analysed via the Mass Spectrometry Identification (MSI) software reaching 90. 2% (92/102) of correct identification to the species, 92. 2% (94/102) to the species complex and 94. 1% (96/102) to the genus level. Conclusions: The VMS performed better than the MLT for mould identification. However, it remains of utmost importance to expand commercial databases, as performances of the MLT highly improved when using the MSI software and its extended database, reaching far above the VMS system.
The sequences were aligned and compared with GenBank reference sequences using BLAST searches, CBS Knaw Database (http://www. cbs. knaw. nl/collections/ BioloMICSSequences. aspx), French National Reference Center for Mycosis database for ITS sequences (http://fungibank. pasteur. fr/ BioloMICSSequences. aspx?file¼MOLDseq&file¼YEASTseq), and FUSARIUM-ID for TEF sequences (http://isolate. fusariumdb. org/).
Vitek MS v3. 0 system The strains were grown at 35 C on Can2 (bioM erieux) for 24e48 hours, depending on growth. Fungal hyphae and spores were collected gently at the surface of the colony in order not to collect any agar. An extraction process using the new Vitek MS Mould Kit was used as recommended by the manufacturer (bio-M erieux). One microlitre of the supernatant was then deposited in four replicates on a target plate and air dried. One microlitre of acyano-4-hydroxycinnamic acid (HCCA) matrix was added, thus permitting recrystallization with the sample. For each acquisition group, a strain of Escherichia coli (ATCC8739) was used as calibrator in each experiment. Samples were processed in the VMS spectrometer using the manufacturer's standard settings. Spectra were analysed using VMS automation control and Myla software.
This included IVD database version 3. 0. (bioM erieux) containing 79 references. According to the manufacturer, values between 60. 0 and 99. 9 indicate a reliable discrimination of species. However, we chose to use a more discriminant cut off of 95% for identification as acknowledged by previous use with yeasts  [15]. Only the deposit with the best identification score was taken into account by analogy with previously published literature about Bruker MALDI-TOF  [16].
Statistical analysis All isolates were analysed using the MLT and the VMS systems, as well as the MSI software. The results obtained using these three libraries were then classified into five categories according to score, identification results, and concordance/discordance with molecular typing: correct at the species level, correct at the complex level, correct at the genus level, false at the genus level, and identification criteria not met. 'Correct' corresponds to concordant with the molecular identification at either the species, the complex, or the genus level whereas 'false' means discordant with the molecular identification at the genus level. 'Identification criteria not met' corresponds to an identification score below the defined threshold. Differences between spectral analysis results were analysed with the chi-square test, and p < 0.
Description of the species identified A total of 102 isolates were collected and included during the study. They represented 20 genera comprising 45 taxa, all of which were identified via DNA sequencing. Details on the species variety included in this study are shown in Table  1. Twenty-one taxa included in this study were not present in the VMS database, 21 taxa were not in the Bruker database, and only six taxa were not in the MSI database (Table  1 ). Overall 77 isolates were present in the VMS database, 65 in the Bruker database, and 95 in the MSI database.
Global results The percentage of isolates that did not meet the identification criteria differed greatly between the database/threshold association used, ranging from 81. 4% (83/102) for the MLT system using the Bruker library with a threshold of 2. 0, to as low as 5. 9% (6/102) for the MLT system associated with the MSI software. When comparing the VMS to the MLT using the Bruker library with a 2. 0 recommended threshold, we observed significant differences in favour of the VMS for all categories (genus level, p 3. 0 Â 10 À14 ; complex level, p 9. 4 Â 10 À15 ; species level, p 2. 2 Â 10 À7 ; identification criteria not met, p 1. 1 Â 10 À14 ). Using a threshold of 1. 7 increased performances of the MLT with the Bruker library for all categories compared with a threshold of 2. 0 (genus level, p 1.
7 Â 10 À9 ; complex level, p 3. 1 Â 10 À8 ; species level, p 6. 5 Â 10 À5 ; identification criteria not met, p 1. 7 Â 10 À9 ). However, the MLT using a threshold of 1. 7 with the Bruker library still remains significantly inferior to the VMS at complex-level identification (p 0. 01), whereas for the other categories there is no significant difference (genus level, p 0. 08; species level, p 0. 21; identification criteria not met, p 0. 05). Finally, the highest performances were observed when the MLT spectra were analysed via the MSI software (MSI vs. Bruker library using a 2. 0 threshold: genus level, p 1. 6 Â 10 À27 ; complex level, p 1. 1 Â 10 À26 ; species level, p 6. 5 Â 10 À26 ; identification criteria not ; identification criteria not met, p 5. 9 Â 10 À9 ). Additionally, the use of MSI is significantly superior to the VMS (genus level, p 1.
Database-related results Looking at database-related results, VMS results are significantly better than the global results for all categories (genus level, p 7. 2 Â 10 À5 ; complex level, p 7. 2 Â 10 À5 ; species level, p 0. 03; identification criteria not met, p 3. 7 Â 10 À5 ). Results for MLT using a 1. 7 threshold are significantly better only to the species level identification (genus level, p 0. 15; complex level, p 0. 07; species level; p 2. 5 Â 10 À3 , identification criteria not met, p 0. 15). The results obtained using a 2. 0 threshold did not differ significantly when comparing database related results to global results (p < 0. 05 for all categories). However, database related results were significantly better than global results when using the MSI software for all categories (p 0.
02 for all categories). Comparing the VMS database related results to the MLT using the Bruker library with a 2. 0 recommended threshold, we observed significant differences in favour of the VMS for all categories (genus level, p 1. 0 Â 10 À16 ; complex level, p 2. 7 Â 10 À17 ; species level, p 8. 9 Â 10 À7 ; identification criteria not met, p 1. 8 Â 10 À18 ). Comparison of VMS results to the MLT using the Bruker library with a threshold of 1. 7 showed significant differences in favour of the VMS in all categories but identification to the species level (genus level, p 1. 1 Â 10 À4 ; complex level, p 5. 1 Â 10 À5 ; species level, p 0. 86; identification criteria not met, p 3. 2 Â 10 À5 ). Using a threshold of 1. 7 still increases the performances of the MLT with the Bruker library for all categories compared with using a threshold of 2.
0 (genus level, p 9. 0 Â 10 À7 ; complex level, p 8. 8 Â 10 À7 ; species level, p 4. 8 Â 10 À6 ; identification criteria not met, p 9. 0 Â 10 À7 ). Finally the best results are obtained when using the MLT spectra with the MSI software (MSI vs. VMS: genus level, p 6. 2 10 À4 ; complex level, p 3. 3 10 À3 ; species level, p 5. 2 10 À8 ; identification criteria not met, p 1. 3 10 À3 ; MSI vs. Bruker library using a 2. 0 threshold: genus level, p 6. 1 Â 10 À23 ; complex level, p 1. 0 Â 10 À22 ; species level, p 6. 9 Â 10 À22 ; identification criteria not met, p 6. 1 Â 10 À23 ; MSI vs. 1. 7 threshold: genus level, p 2. 0 Â 10 À8 ; complex level, p 4. 6 Â 10 À8 ; species level, p 3. 4 Â 10 À8 ; identification criteria not met, p 2. 0 Â 10 À8 ).
Discussion In this study, we aimed to compare the two commercially available database/software associations for MALDI-TOF MS, namely the VMS from bioM erieux and the MLT from Bruker Daltonics. Yeast identification has been shown to be highly efficient and accurate  [19]. Conversely, mould identification remains underevaluated, particularly when using commercially available databases  [19e21]. Regarding the VMS only a few studies have focused on specific taxon such as Aspergillus  [22, 23]  or dermatophytes [24e26], and only one study, to our knowledge, has evaluated precisely this system on the species diversity observed in a medical mycology laboratory  [27]. Previously lacking a reliable algorithm of spectral analysis for mould identification, the VMS system has recently been updated to a newer version, namely v3.
0, with a new algorithm and an enlarged database. This major update requires evaluation. Recently, the use of extensive reference spectrum databases has been widely encouraged  [7, 9]. Thus Rychert et al. [28]  evaluated the performances of this new version reporting a rate of 91% of correct identification of moulds to the species level. But in contrast to our study, they analysed only isolates that were previously known to be represented in the database. This highlights our results because in a clinical setting analysed isolates are not always present in the database. In contrast to previous versions, the VMS has greatly improved (data not shown) considering mould identification. Results show that VMS is superior to the MLT using the manufacturer's recommended threshold of 2. 0 at all taxonomic levels (p < 0.
05 for all four categories). As reported previously the use of a 1. 7 threshold significantly increased the accuracy of the MLT system for all identification levels (identification to the species, 16. 7% (17/102) to 42. 2% (43/102); p < 0. 05) as previously reported  [16, 29]. However, overall satisfactory identification seems in favour of the VMS compared with the MLT system using a threshold of 1. 7, with a rate of correct identification to the complex level significantly in favour of the VMS (71. 6% (73/102) vs. 54. 9% (56/102); p < 0. 05). Indeed, looking at the other categories, the VMS system performs slightly better than the MLT e threshold of 1. 7 e but with no significant differences (p > 0. 05). In order to evaluate more precisely the algorithm as opposed to the database, strains not belonging to the database were discarded and identification accuracy results updated.
VMS obtained better results than MLT using a 2. 0 threshold for all identification levels. Moreover, VMS performed better than MLT using a 1. 7 threshold for all identification levels but to the species level. Only one identification was false, at the genus level, and was reported with the VMS. This discrepancy occurred for a Rhizopus oryzae that was identified as an Aspergillus brasiliensis. Macroscopic and microscopic examination of the subculture used for MALDI-TOF was congruent with DNA sequencing of R. oryzae. New acquisition of this strain did not yield any results. R. oryzae identification using the VMS regularly does not give results in routine use (data not shown). Increasing the number of strains per taxa in the spectral database could be useful in preventing this kind of misidentification.
Considering isolates that did not meet the identification criteria, they spanned from 27. 5% (28/102) for the VMS to as high as 81. 4% (83/102) for the MLT using a threshold of 2. 0. When discarding isolates not present in the different databases, these rates decreased to 2. 9% (3/77) for VMS and to 72. 3% (47/65) for MLT. This decrease is due to the use of a limited reference spectrum database. However these three 'no results' isolates with VMS (two A. tubingensis and one A. flavus) might be due to issues during the extraction process. Looking at the Bruker results, the high rates of identification criteria not met might be due to spectral analysis algorithm issues. Since MSI identifications were obtained from the same Bruker acquisition that yielded the MLT results, we can rule out extraction issues.
Globally, the VMS has a lower rate of unidentified isolates than the MLT system. The newly available update version of the VMS allowed the identification of more than 70% of isolates to the genus level. However, there remain almost 49% unsatisfactory identifications (correct at the genus but not at the species level or no identification or incorrect at the genus level). Most of these 'correct at the genus or complex level but not at the species level' correspond to species complex issues. In fact the VMS database frequently relies on a complex level reference spectrum without a specific intracomplex spectrum (13 A. niger complex, two A. terreus complex, two Rasamsonia argillacea complex, one Rhizopus arrhizus complex, one Fusarium solani complex). As only a few of these species complex have been investigated for antifungal susceptibility (e.
g. Aspergillus section Fumigati and Aspergillus lentulus), it remains of the utmost importance to expand the number of reference spectra of species belonging to a species complex. Looking at the Bruker database, intracomplex spectra are mostly used. Five strains were only identified at the genus level (two Penicillium brasilianum identified as P. discolor, a P. sayulitensis identified as P. funiculosum, a P. chrysogenum identified as Penicillium sp. , and a Curvularia spicifera identified as C. pallescens). Only P. chrysogenum is present in the Bruker database. Using the MSI database, two strains were correctly identified only at the genus level (F. oxysporum identified as F. ananatum and Penicillium sayulitensis identified as Talaromyces siamensis), the latter being not present in the database.
Looking at isolates not present in the database, MLT using a threshold of 1. 7 was able to identify correctly 15 isolates to the genus or complex level (Aspergillus mangaliensis, A. intermedius, A. tubingensis, A. calidoustus, Rasamsonia aegroticola, P. brasilianum, P. sayulitensis, and C. pallescens). All these identification errors involve closely related species and they should have no impact on the therapeutic regimen. In order to reduce these misidentifications, and as supported by others, there is a considerable need to expand MALDI-TOF databases in order to increase the efficiency of these systems. This is possible using the Saramis system for research use only, but this is manageable only for specialized laboratories and is very time consuming. Looking at the MLT system, it has been shown that its database could easily be enhanced by means of the addition of an in-house reference spectrum  [7, 8, 16].
Recently, Normand et al. [18]  showed the great potential of a new web application for the identification of yeasts and moulds available to medical mycologists (MSI software). These results are confirmed in our study in which the MSI software greatly improves the MLT system's performances (p < 0. 05) looking at correct identification at the species level (16. 7% (17/102) to 90. 2% (92/102)), at the complex (17. 7% (18/102) to 92. 2% (94/102)), at the genus (18. 6% (19/102) to 94. 1% (96/102)), or strains for which the identification criteria could not be met (81. 4% (83/102) to 5. 9% (6/ 102)). The use of this web software applied to MLT spectra is greatly superior to the performances of the VMS. Accurate identification of the species is achieved in 90. 2% (92/102) of isolates compared with 51.