dataesr/scientific-paragraphs-categorization huggingface dataset:
Protein crystallization experiments and data collection The tcDAC2 enzyme in complex with Quisinostat (QSN) or TB56 inhibitors was prepared for crystallization by mixing the purified protein (concentration of 0. 12 mM to 0. 3 mM) with a three or five-fold molar excess of the inhibitors and incubated for 1 h at 4 C. Crystallizations were performed in 96-, 48-or 24-well plates (MRC). Crystals of the tcDAC2/QSN complex were obtained in 5% PEG 6000 and 0. 1 M HEPES pH 7. 0 or pH 7. 5. Crystals of the tcDAC2/TB56 complex were obtained in 8% PEG8000, 0. 2 M MgCl 2 , and 0. 1 M Bis-tris or Bis-tris propane pH 6. 5. Crystals used for X-ray data collection were briefly transferred into reservoir solution supplemented with 22% glycerol or PEG200 and flash-frozen in liquid nitrogen. Crystallographic data were collected at 100 K on Swiss Light Source (SLS) synchrotron beamline PX-III and SOLEIL synchrotron beamline PROXIMA2.
Images of 25 fields per well were acquired on an Operetta Image System (PerkinElmer) and analyzed using the Harmony software to discriminate nucleus, cytoplasm and the spots delimited for the intracellular amastigotes. The number of cells and amastigotes per well were determined and doseresponse curves were plotted using GraphPad Prism version 7. 05. Non-linear regression was calculated to determine CC 50 and EC 50 for cells and parasites, respectively. The inhibition ratio (in %) was calculated based on the mean of cells or amastigotes from the infected untreated wells.
Mouse infection assays Mouse infection assays were performed with three groups of five mice each of 5-week old Balb/C females infected by intraperitoneal injection with 2x10 5 T. cruzi Dm28c trypomastigotes expressing firefly luciferase  (Henriques et al. , 2014). One group comprised the untreated controls and a second group was treated with the reference drug benznidazole, while the third group received TB56. TB56 and benznidazole were prepared in 7% DMSO (v/v), 40% (2-Hydroxypropyl)-b-cyclodextrin (v/w) in DPBS. Intraperitoneal injections were performed from day 4 to day 8 post-infection with 50 mg/kg/day of each compound. Mice from the control group were treated with vehicle only. Parasite load was determined by luminescence quantification using D-luciferin administered at 150 mg/kg 10-20 min before data acquisition using a IVIS Spectrum CT (Perkin-Elmer) on dorsal and ventral positions, on days 3, 7 and 9 post infection.
Images were processed using the Living Image v. 4. 3 software. Regions of interest (ROI) were selected to the full body including tail. Background auto luminescence was calculated using the same ROI size where indicated. Total flux [photons/second] was considered for calculations and average radiance [photons/sec/cm 2 /steradian] for image units. Statistical analysis was performed by using one-way ANOVA with spherical data and Tukey's multiple tests in GraphPad Prism v. 7. 05.
A visual reduction of luminescence levels can be observed both for TB56-and BZN-treated mice. (G) Luminescence quantification of the images shown in (F), confirming a significant inhibition of T. cruzi in TB56-treated mice relative to the control group at 7 dpi, albeit weaker than for the BZN-treated mice. (H) Weight of the mice used and reported in (F) and (G) measured at the start and at the end of the experiment (0 and 9 dpi). The differences observed are statistically non-significant. All assays in (A)-(D) were performed with two to six biological replicates tested in technical quadruplicates (A), one to five biological replicates tested in technical quadruplicates (C), and technical quadruplicates (B and D). In (A)-(D), data are represented as mean ± SD. In (G) and (H), data are shown for each individual mouse, represented by an individual symbol, and the mean and SD are represented, respectively, as light gray horizontal and vertical lines, n = 5.
110129. Research Council (CNPq), the Research Foundation from the state of Parana (FapPR), and the INOVA research program from FIOCRUZ. The authors acknowledge the support and the use of resources of the French Infrastructure for Integrated Structural Biology FRISBI ANR-10-INBS-05 and of Instruct-ERIC. The authors thank the Program for Technological Development of Tools for Health-PDTIS-FIOCRUZ for the use of its facilities. W. S. and K. H. acknowledge the financial support by the European Regional Development Fund of the European Commission. M. J. and A. M. were supported by the Deutsche Forschungsgemeinschaft -Project ID 192904750 -CRC 992 Medical Epigenetics. We wish to thank members of the Swiss Light Source (SLS) and SOLEIL synchrotrons for the use of their beamline facilities and for help during data collection.
ACKNOWLEDGMENTS This work and the authors of this manuscript have been supported by funding from the  European Union 's  Seventh Framework Programme  for research, technological development, and demonstration under grant agreement no. 602080  ( A-ParaDDisE ). C. R. , M. M. , E. R. -M. , and T. B. S. were supported by institutional funds from the  Centre National de la Recherche Scientifique (CNRS) , the  Institut National de la Santeet de la Recherche Medicale (INSERM) , and the  Universitede Strasbourg. C. R. acknowledges the support by the  Fondation ARC. R. J. P. was supported by institutional funds from the  CNRS , the  Institut Pasteur de Lille, and Lille University. N. I. T. Z. , G. F. A. P. -C. , E. P. G. -S. , and M. M. M. were partially supported by the  Brazilian National.
Questionnaire distribution The survey was open to everyone with access to the anonymous survey link. This link was distributed per email to healthcare professionals involved with PKU patients via the Metab-L and SSIEM mailing lists. In the invitation email, the goal of the study as well as the approximate time it would take to complete the survey was mentioned. A reminder was sent two weeks after the first invitation. After five more weeks, a second reminder was sent, specifically targeting SSIEM members from countries from which no response had yet been received. All invitees were encouraged to forward the invitation email to colleagues. Data were collected from March 8 to May 23, 2019.
Data analysis Only complete questionnaires were analysed. IP addresses, location data (latitude and longitude), survey starting dates and times, and respondent names and main functions were checked to control for multiple entries from the same individual. Multiple responses per center were allowed because entries from different healthcare professionals working in the same center sometimes gave markedly different answers to the questionnaire. Since some answers to open questions covering the same subtopic overlapped, we analysed the overall survey results per subtopic and not per specific question. The following additional measures that were taken to enable analysis of the dataset:  [1]  if a respondent gave the same answer multiple times in different open questions, redundant answers were removed;  [2]  if a respondent gave a range for a cut-off value, the lowest number in that range was used;  [3]  answers regarding dietary intake given as mg Phe were converted into grams natural protein (using a conversion factor of 50 mg Phe for 1 g of natural protein);  [4]  since we aimed to analyse general healthcare practices, answers that were related to specific cases were not analysed;  [5]  answers that were unrelated to defining BH 4 responsiveness were removed;  [6]  responses to open questions were grouped.
Declaration of Competing Interest RAFE has received financial support from Biomarin for attending symposia. AMJvW has received a research grant form Nutricia, honoraria from Biomarin as speaker, and travel grants from Nutricia and Vitaflo. AMB has received a speakers fee from Nutricia and has been a member of advisory boards for Biomarin. MG received honoraria and was a consultant for: Nutricia International/Danone, Merck-Serono, Mead Johnson, BioMarin and Vitaflo. JCR has been a member of the European Nutritionist Expert Panel [Biomarin], the Advisory Board for Applied Pharma Research and Nutricia, and received honoraria as a speaker from APR, Merck Serono, Biomarin, Nutricia, Vitaflo, Cambrooke, PIAM and Lifediet. FJvS is a member of scientific advisory boards for PKU and aminoacid defects that are supported by Agios, Applied Pharma Research, Arla Food Int, BioMarin, Eurocept, Homology, Lucane, Nestle-Codexis Alliance, Nutricia, orphan Europe, Rivium Medical BV, Vivet, has received research grants from Alexion, BioMarin, Beatrix Research Fund, Codexis, ESPKU, NPKUA, NPKUV, Nutricia, Sobi, Tyrosinemia Foundation, Vitaflo, ZONMW, and has received honoraria as a consultant and speaker from Applied Pharma Research, Biomarin, MendeliKABS, Nutricia, Pluvia, SoBi, and Vitaflo.
Acknowledgements We sincerely thank all healthcare professionals who participated in this survey, including  James B. Gibson, MD ,  Bert Verhage ,  Dr. Mary Ann Abacan ,  Samantha A. Schrier Vergano ,  J. V. Joergensen ,  Yilmaz Yildiz ,  Beth Golden ,  Jamie Jocis ,  Marie Lefrancois ,  Laurie D. Smith PhD MD ,  Duncan, MPH, RD ,  Prof. Laurie Bernstein ,  Sylvia Stockler , Susanne.
Introduction Although the concept has been proposed for more than 40 years for microbiology applications, MALDI-TOF MS has only started to be used extensively over the 10 last years for microorganisms' identification in clinical laboratories  [1] [2] [3] [4]. The highly specific protein spectra generated by this technology is like a barcode (also named MSP by Bruker, for Main Spectra Profiles) which allows the identification of microorganisms up to the species level within a few minutes. The accuracy of identification relies on previously created databases (also known as 'libraries') that include thousands of reference spectra obtained from strains strictly identified by DNA sequencing. More recently MALDI-TOF MS commercial assays have also been successfully used to evidence antibiotic resistant phenotypes in bacteria, or to accelerate the identification of microorganisms directly from positive blood cultures.
Using direct deposit, time-to-result from colony is less than 15 min. In addition to its rapidity, the other main advantage of MALDI-TOF MS is its accuracy. It improved yeast identification compared to common phenotypic techniques by discriminating very close species or subspecies. For instance, depending on the database, MALDI-TOF MS makes it possible to distinguish species among the Candida complexes such as C. parapsilosis complex (C. parapsilosis stricto sensu, C. metapsilosis and C. orthopsilosis), C. glabrata complex (C. glabrata s. s, C. bracarensis, C. nivariensis) or the C. haemulonis complex (C. haemulonis s. s. , C. pseudohaemulonii and the emerging species C. auris). The species within and the C. neoformans and the C. gattii complexes can also be distinguished according to the recently changed taxonomy  [5, 8, 9].
Both databases of the two main MALDI-TOF MS systems currently available, MALDI Biotyper (Bruker Daltonics GmbH, Bremen, Germany) and VITEK MS/MS Plus (bioMérieux, Marcy l'Etoile, France), include common yeasts isolated in clinical microbiological laboratories. While there may be some differences in nomenclature (anamorph/teleomorph) or species between these two databases, their performances for common yeast identification appear similar with high rates of identification, from 83 to 99%, for common yeasts (see review Patel 2019 for more details  [5] ). The Autof MS 1000 system (Autof Acquirer database V2. 0. 18, Autobio Diagnostics Co. , Ltd, Zhengzhou, China) also appears suitable for identifying common yeasts  [10]. Comparing performances of the different MALDI-TOF MS systems is rather challenging because of the multiplicity of extraction procedures and reference techniques, as well as the evolution of databases.
Limits and Solutions MALDI-TOF MS presents only a few limitations for common yeast identification. Misidentifications and no identifications are rare. No identification cases mainly result from the absence of the given yeast species in the database, from the poor quality of the spectra or from undetected polymicrobial colonies. In the latter case, duplicate, repeat testing, or re-isolation of pure CFU often lead to a correct identification. Identification is highly dependent on the quality of the databases (number of species, number of spectra per species, etc. ). The large majority of common yeast species are included in these databases, but as they are constantly evolving, regular updates are necessary. Apart from the commercial IVD and RUO databases, user-developed on-line databases are freely available (Microbenet developed by the CDC (https://microbenet.
cdc. gov; accessed on 3 May 2021) or the Mass Spectrometry Identification platform (MSI, available online at https://msi. happy-dev. fr/ (accessed on 3 May 2021)), or can be developed for a specific project when commercial databases are challenged, as can be the case for rare yeasts (Table  1 ). For instance, such a database was recently developed to improve the identification of South-American strains of C. auris which were not included in the MALDI-TOF MS Bruker Biotyper OC 3. 1. 66 database  [13]. MALDI-TOF MS identification of Cryptococcus neoformans subspecies can also be challenging and requires pre-extraction and development of such user-developed bases as well as peak analysis process  [5, 9, 14]. In addition, databases are most often developed using culture conditions and long protein extraction procedures that can differ from the ones routinely used in microbiological laboratories, which explains why identification performances are not always perfect.
Spectra obtained after direct deposit can present important noise whereas those obtained using rapid formic acid extraction seem to be more compatible with these databases  [6]. Several other factors can influence the quality of the spectra, and notably the quality of the deposit on the target. Even though this may seem trivial, it requires training to transfer the right amount of colony on the plate. Adequate training may also improve the performances of yeast identification after direct deposit  [5]. Regular maintenance of the MALDI-TOF MS system, and especially the laser calibration, is also crucial for the quality of the obtained spectra  [15]. If reagent costs are limited (restricted to internal control and matrix solutions, plates and devices for the deposit of colony and reagents), the MALDI-TOF system itself remains expensive for laboratories of low-income countries  [16].
For instance, the Bruker filamentous Fungi database (V3) covers 189 species, and the VITEK MS database only 79 fungal species  [24]. Hence, if the most common molds are generally well covered, things become more complicated when it comes to rarer ones or cryptic species. The identification thresholds proposed by manufacturers may also not be adapted to this specific use  [25, 26]. Finally, a recent multicenter study identified the spectra acquisition parameterse. g. , the peak evaluation parameters or the signal noise threshold-and the instrument maintenance as factors that can limit identification performance  [27].
Regarding the identification thresholds, when using a MALDI Biotyper (Bruker Daltonics GmbH, Bremen, Germany), several authors suggest that, as for yeast, reducing the species cutoff proposed by Bruker from 2 to 1. 7 increases the number of correct identifications with no significant impact on the misidentification rate  [25, 26]  (Table  1 ). The deficiencies of commercial libraries have compelled laboratories to develop their own databases. Some of these libraries are commercially or freely available, such as the NIH Mold Database  [23] , Microbenet developed by the CDC which provides reference spectra for more than 400 FMI, such as rare dimorphic fungi, or the MSI platform (Table  1 ). The latter is currently the most comprehensive database covering more than 1000 fungal species (including dermatophytes), and clearly improves the routine use of MALDI-TOF MS for mold identification.
Using this library, Normand et al. reached 87. 35% of correct identifications at the species level on a panel of 501 clinical isolates, vs. 60. 24% or 25. 7% using the Bruker database and cutoffs of 1. 7 and 2. 0, respectively  [31]. Based on our experience of the routine identifications of clinical filamentous fungi, we compared the Bruker and the online MSI databases using 265 spectra (Bruker MBT System). We also found that our Bruker database (V3, 9888 entries, 1340 fungi) lacks reference spectra for specific genera such as Galactomyces, Corynascus, Paecilomyces, Dicyma and Acrodontium, making them either unidentifiable or misidentified (unpublished data). In our hands, MSI outperformed Bruker with 76% of identification to the species level versus 54%, respectively. In addition, Bruker's database offers limited discrimination among the Aspergillus genus.
Notably, in the Nigri section (A. tubingensis and A. welwitschiae misidentified as A. niger), the Fumigati section (A. thermomutatus misidentified as A. lentulus) and the Nidulantes section (A. spinulosporus misidentified as A. nidulans) for which antifungal susceptibility differs greatly  [19, 32, 33]. Overall, our results confirmed a previous study showing that the MSI database provided more accurate identifications than Bruker and that the systematic combination of the two databases significantly improves the rate of correct identifications for filamentous fungi  [34]. However, a recent evaluation of the MSI platform on 245 Aspergillus isolates covering 56 cryptic species and 13 sections reported that although 99. 6% of identifications were correct at the section level, only 66. 1% of isolates were correctly identified at the species level  [35].
Routine Use As detailed in the previous paragraph, an optimal identification of common filamentous fungi with MALDI-TOF MS still needs some adaptation regarding the culture media or the database used (Table  1 ). When it comes to rare filamentous fungi, protocols are the same as for common ones, but the main limit is the rarity or the absence of reference spectra in the commercial databases. Nevertheless, unexpected species are sometimes evidenced in a specific clinical context. As an example, due to the fact that the species Schizophyllum commune was present in the Bruker library in 2016, we and others could identify this fungus as a common but previously poorly described species involved in sinusitis or bronchopulmonary diseases  [37, 38]. S. commune usually do not sporulate under classical culture conditions which makes morphological identification almost impossible.
Limits As previously mentioned, the main limit in the identification of uncommon filamentous fungi is the rarity of reference spectra in commercial databases. In fact, a non-negligible number of spectra exist in the Bruker library, but a high number of those are from fungal species that are not of medical importance, but more designed for plant health approach, or food industry  [40]. Of note, thanks to the regular updates of commercial databases, this drawback is less disabling than before, but still not entirely satisfactory.
Solutions Again, some solutions need to be found to optimize identification of rare FMI. We were confronted with this issue in 2018, when we failed to identify a fungal like infection of the cornea in a young patient  [41]. All attempts to identify this mycelium obtained from the corneal biopsy on the basis of microscopic morphology or by MALDI-TOF MS failed. Although the spectra were nice and clean, they could not be attributed to any of the microorganisms present at that time in the Bruker Taxonomy (7815 MSP), Bruker Filamentous Fungi (364 MSP), NIH mold (365 profiles) or MSI fungal (1026 species) databases. Confirmation of the species was finally achieved by sequencing and revealed the fungal-like oomycete named Pythium insidiosum. Treatment was consequently changed and the patient's eye was saved, albeit just in time.
Here, a non-negligible amount of time was lost because no reference spectra for P. insidiosum were present in the various databases commonly used at that time for microbial identification  [40]. Therefore, after confirmation by sequencing, we created our own reference MSP on the Microflex massspectrometer, according to Bruker's MSP creation protocol (v1. 1). This reference spectrum was not only added to our local database but also shared on the new version of the MSI database, to facilitate future diagnosis of pythiosis for both our institute and other healthcare centers  [41]. Creating home-made 'libraries' is time-consuming and requires technical skills, and yet it remains the best solution to implement new rare species of medical importance (Table  1 ). This strategy was recently used in 2016 in our laboratory in the context of a large epidemiological study of sporotrichosis and chromoblastomycosis in Madagascar  [42] , which aimed at facilitating the diagnosis of these endemic dermo-invasive mycosis.
For that purpose, and because reference spectra were extremely rare in the commercial databases at that time (2 MSPs in the Bruker database and 1 profile in the NIH database for Sporothrix sp. , and none for the different agents of chromoblastomycosis), we generated 20 and 11 reference spectra from reference or previously sequenced strains for Sporothrix schenkii, and chromoblastomycosis causative agents (i. e. , C. carrionii, F. nubica, F. pedrosoi, and F. monophora), respectively  [43, 44]. These homemade libraries now allow us to accurately and rapidly identify these agents, without the need for DNA sequencing.
8% protocol using the same rapid protocol as the one developed later by Bruker in the Rapid Sepsityper  [59]. Results were comparable to the ones obtained in the following study using Rapid Sepsityper  [54, 59] , confirming that home-made SDS 1. 8% protocol is suitable for both bacteria and yeast identifications. Bruker has also developed a specific software for the analysis and interpretation of spectra obtained directly from blood cultures, which takes into account that spectra obtained from pathogens isolated from blood cultures are less 'clean' than the ones obtained from CFUs on a Petri dish. We also evaluated the potential benefit of this specific MBT Sepsityper RUO software compared to the standard MBT Compass-IVD software for spectrum analysis from positive blood culture to reliably identify bacteria and yeasts to the species level without modifying cut-offs provided by the manufacturers.
Considering yeast identification, the MBT Sepsityper-RUO module provided significantly higher percentages of reliable identifications to the species level than the MBT Compass-IVD module. However, only the algorithms used for interpretation differ between these 2 software: the analyzed spectra are exactly the same. Before the commercialization of this specific module, we and others had shown that lowering the thresholds and defining new criteria for interpretation were suitable for routine microorganisms identification at the species level when dealing with blood cultures, for instance a score between <2 but >1. 7 and a species proposed at least four times in a row in the identification process  [53, 60, 61]. In brief, for a given spectra obtained from a blood culture, the MBT Sepsityper RUO software could give a higher identification score than a standard software, but our personal opinion is that lowering the identification scores with well-defined and validated limits is equivalent (Table  1 ).