dataesr/scientific-paragraphs-categorization huggingface dataset:
We estimated the bioavailability of these different formulations of procaine BP to be approximately 100%. This high bioavailability of 100% for procaine BP administered IM has previously been reported by others  (Firth et al. , 1986). However, the total bioavailability of procaine BP was surprising in the case of the USA1 formulation, for which plasma concentrations were much lower than for the other investigated formulations (Swedish, French, and Japanese). The explanation is that the USA1 data were collected only during the first Associated CV% and SE as a measure of precision of theta estimates were not computed due to the size of the model that would have required several weeks of computation by a bootstrap method. The disposition parameters (CL, CL2, CL3, Vc, V2, and V3), as well as the "source of dataset" covariates for the clearances under IV for Swedish, USA1, and USA2 horses, were frozen at the optimal values obtained with the analysis of IV data only (see Table  3 ) and are not reported again in this table.
Mean absorption time (MAT) was computed from the corresponding estimated 1/Ka and taking into account the covariate, if any. 12 h after administration and showed a very flat initial profile. Consequently, a very slow absorption rate constant was estimated for USA1, with a mean absorption time (MAT) of 230 h, in comparison to approximately 21 h for the other three formulations. In other words, the NLME model predicts that, for USA1, BP plasma concentrations will be maintained at this low level for several days; however, in the absence of data beyond 12 h, it is advisable to remain cautious about this conclusion. A very long MAT was also estimated for the benzathine BP Duplocilline (107 h) delivered IM, which is consistent with what has been reported for benzathine BP in cattle  (Papich et al.
, 1994). Our data support their conclusion, which is that BP plasma concentrations Left: 90th quantiles of fAUC/MIC (log10 scale) vs. MIC (log2 scale) for procaine BP at a daily dose of 12. 360 mg/kg (22,000 IU/kg) for three days (72 h). The pharmacodynamic target (PDT) was fixed to 72 h. These curves were obtained via Monte Carlo simulations (n = 5,000) of the population model, with global estimates of the pharmacokinetic parameters of BP from procaine BP from the French (black curve), Japanese (purple curve), Swedish (red curve), and USA1 (green curve) datasets, each nation having been simulated with its own covariate for plasma clearance. It can be noted that the French, Japanese, and Swedish data lead to the same results (PK/PD cutoff value of 0. 25 mg/L), whereas the USA1 data are very different, with a PK/PD cutoff value close to 0.
0625 mg/L. Right: the corresponding probability of target attainment (PTA). It can be noted that the French, Japanese, and Swedish data lead to the same results (PK/PD cutoff value of 0. 25 mg/L), whereas the USA1 data are very different, with a PK/PD cutoff value of 0. 0625 mg/L for a PTA of 80%. Probability of target attainment (PTA) for 30% (left) and 40% (right) of the dosing interval for procaine BP at a daily dose of 12. 36 mg/kg (22,000 IU/kg) for three days (72 h). The pharmacodynamic target (PDT) is set at 30% or 40% of the dosing interval (DI), which means that the MIC selected as the PK/ PD cutoff value must guarantee that the free plasma BP concentration is at least equal to or higher than this MIC over 30 or 40% of the DI (i. e. , 21. 6 and 28. 8 h in toto over 72 h) in 90% of horses.
These curves were obtained by Monte Carlo simulations (n = 5,000) of the population model, with global estimates of the pharmacokinetic parameters of BP from French (black curve), Japanese (purple curve), Swedish (red curve), and USA1 (green curve) datasets, each nation being simulated with its own covariate for plasma clearance. following benzathine BP administration remained below the level of susceptibility for most pathogens. For sodium BP, IM bioavailability was estimated at 89%, and a MAT of 4 h was observed after a short delay following a rapid initial phase of absorption. This indicates a flip-flop process for a fraction of sodium BP, allowing therapeutic concentrations to be maintained over the 12-h dosing interval. For penethamate, IM bioavailability was incomplete (68. 8%), with a relatively long MAT (28-65 h).
It can be noted that this formulation gave rise to relatively significant local reactions, and aside from the fact that it does not contain procaine, which is an advantage for doping control in racehorses, this formulation has lower performance in terms of BP exposure than procaine BP formulations. The main result of this meta-analysis is that a PK/PD cutoff value of 0. 25 mg/L can be guaranteed in 90% of horses for the usual dosage of 12. 4 mg/kg per day with the three formulations of procaine BP that have been documented over a sufficient period of time to qualify the full plasma concentration profile (the France, Sweden, and Japan datasets). In contrast, the PK/PD cutoff value is only around 0. 0625 mg/L in the case of penethamate and the combination of procaine and benzathine BP, which may still be acceptable for treatment of more sensitive pathogens such as Streptococcus spp.
We gathered Sjögren's-related symptoms such as sicca, parotid gland enlargement and extraglandular manifestations of pSS including occurrence of lymphoma at any time before or after diagnosis. Biological and immunological features included anti-nuclear antibodies (ANA), anti-SSA/Ro, anti-SSB/La, anti-DNA, anti-Sm and anti-RNP, gammaglobulins and β2microglobulinaemia titres, complement levels and presence of cryoglobulinaemia. We gathered histological data of accessory salivary glands biopsies. We assessed EULAR Sjögren's Syndrome Disease Activity Index (ESSDAI) at each consult with available information and retained each category's maximum ESSDAI score during the follow-up. We then calculated a cumulative ESSDAI (cumESSDAI) score per patient as the sum of each category's maximum score during the follow-up.
Statistical analysis Our main analysis consisted of comparing patients of AA and Caucasians. We subsequently explored demographic, clinical and biological characteristics in patients of AA (sub-Saharan African and Afro-Caribbean) and of Caucasian ancestry. Lastly, we explored clinical and biological parameters associated with a cumulative clinESSDAI ≥5. We used as outcome clinESSDAI rather than ESSDAI in order to explore gammaglobulin titres, cryoglobulin and low complement level. Data are presented as mean±(SD) or medians (IQR) as appropriate for continuous variables and number (%) for qualitative variables. Wilcoxon rank test and one-way analysis of variance tests were used to compare continuous variables. χ 2 or Fisher's exact test was used to compare categorical features. In order to investigate clinical and biological parameters associated with an.
The Al 2 O 3 -SiC-C or ASC castables are used in the main runners of blast furnaces. Most of the papers published to date focus on laboratory corrosion tests because access to the post-mortem samples of ASC castables is difficult. However, investigation of post-mortem samples is crucial in reducing the wear of ASC castables. The main runner is divided into the turbulence, middle, and non-turbulence zones. The microstructures of post-mortem samples located in working linings of different zones were investigated by SEM/EDS technique. The thermodynamic simulations were carried out in different systems, such as slag/refractory and iron/refractory, using the FactSage software. The SEM/EDS results showed the formation of phases with low melting points, such as gehlenite and anorthite, together with spinel solid solutions in the matrix and calcium dialuminate near the alumina aggregates.
This research focuses on the corrosion mechanism of ASC castables by implementing post-mortem analysis and thermodynamic computations. First, the microstructure of the post-mortem samples of ASC castables from different (turbulence, middle, non-turbulence) zones of the main runner was investigated by SEM/EDS. Next, thermodynamic simulations were performed with the FactSage commercial software. Based on the principle of the main runner of the blast furnace, the thermodynamic computations investigated phase equilibria with the presence of slag or iron in contact with ASC castables at high temperatures and during the cooling steps. According to local information, the minimum and maximum temperatures of the runner are 1440 • C and 1580 • C, respectively. However, the (average) working temperature measured for the runner is around 1505 • C.
Fig. 2  shows a schematic side view of the main runner of the blast furnace. Liquid slag-iron mixture is introduced through the tap hole in the runner (zone A), and slag and iron are separated at the end of the runner (zone C). The simulation technique is a powerful tool commonly used to analyse and predict the behaviour of fluids, especially in severe environments such as blast furnace runners, where measurement is difficult. Therefore, a computational fluid dynamics (CFD) multiphasic study was carried out to understand the flow distribution of cast iron and slag when the stream arrives at the main iron runner. The model considered comprised the internal walls of the main iron runner, the cast iron, the slag, and an air layer above. ANSYS Fluent software  [22]  was used with a k-ω SST law for the turbulence, which is a two-equation eddy-viscosity model combining the Wilcox k-omega and the k-epsilon models.
Zone Sample No. Lining loc. Adhesion of corrosive agents A 1 Slag ++ 2 Slag/iron +++ 3 Iron - B 1 Slag + 2 Slag/iron + 3 Iron - C 1 Slag line - 2 Slag/iron - 3 Iron -Fig. 9. Schema of post-mortem brick A-2. The databases implemented were "FactPS", "FToxid", and "FTmisc". Specifically, the liquid solution phases selected in the calculations were FToxid-SLAGA for liquid slag description and FT-misc-FeLQ for liquid metal iron. Calculations for solid phases included "FToxid-SPINC", "FToxid-MeO_A", "FToxid-Mel_A" and "FToxid-Mull" to represent the spinel, monoxide, melilite, and mullite solid solutions, respectively. All other solid phases were considered as stoichiometric compounds. Two types of calculations were performed. The first type consisted of adding slag or iron stepwise into a system composed initially of pure ACS castable to calculate the nature and amount of the phases resulting from the equilibrium and thus simulate the corrosion reactions.
Conclusion Based on the CFD simulations, the main runner of the blast furnaces was divided into different zones concerning the movement of molten media (slag and iron), namely: the turbulence zone (A), the middle zone (B), and the non-turbulence zone (C). The "flow-back" co-circulations of the stream of the slag-iron mixture in the turbulence zone (A) exposed the walls of the main runner to the refreshed corrosion medium. The microstructural investigations of the post-mortem Al 2 O 3 -SiC-C (ASC) samples from different zones of the runner were carried out by SEM/EDS techniques. Next, the experimental results were compared with those simulated by the thermodynamic software of FactSage. It was found that the simulated results correlated with the results obtained from the microstructural characterizations of the post-mortem samples of ASC castables.
02 TeV using the GEANT3  [30]  or the GEANT4  [31]  transport codes (see Section 3. 3), or from fits to the 13 TeV data sample. While the tail parameters can be extracted in p T and y intervals in the MC for both signal shapes, the 13 TeV data sample only constrains the tail parameters when an extended Crystal Ball fit to the integrated mass spectrum is performed. Therefore, when using tail parameters from data, the same set is applied to all the p T and y intervals. Various functions successfully model the background in the invariant mass range 2 < m µ + µ -< 5 GeV/c 2. To extract the J/ψ signal, either a pseudo Gaussian varying linearly with the mass or the ratio of a first order to second order polynomial is used as a background shape. For the ψ(2S) signal extraction, either a pseudo Gaussian varying linearly with the mass or the combination of a fourth order polynomial with an exponential function is used to describe the background.
The ϒ(1S) mass and width are left free in the fit, while the ϒ(2S) and ϒ(3S) masses are bound to the ϒ(1S) one by fixing the mass difference between the states according to the PDG values  [29]. The width of the ϒ(2S) and ϒ(3S) signals are also bound to the ϒ(1S) one by factors, σ MC ϒ(nS) /σ MC ϒ(1S) , obtained from MC simulations. Two alternative width scalings, namely σ ϒ(nS) = σ ϒ(1S) and σ ϒ(nS) = σ ϒ(1S) × (2 × σ MC ϒ(nS) /σ MC ϒ(1S) -1), are also considered. The ϒ(nS) signal tail parameters must also be fixed while fitting. By default, they are fixed to the values extracted in each given p T and y range from MC simulations performed with the GEANT3 transport code. The same shapes are used for the three ϒ resonances. The systematic uncertainty related to the choice of the tail parameters is evaluated for each resonance on the p T and y integrated mass distribution by using several sets of tail parameters that were generated from the fit of the 13 TeV data sample taking into account the correlation among the parameters via the covariance matrix.
The first source of uncertainty is evaluated by using minimum bias triggers issued either by the V0 or the T0 detectors. The two methods are in agreement within 0. 5%. This systematic uncertainty is therefore consistently neglected for all resonances and the method which uses the T0 detector is used as the main one since it gives the result with the smallest statistical uncertainty. The second source of uncertainty is the dominant one and arises from the uncertainty on the T0-trigger cross section. It amounts to 1. 8%. This uncertainty is fully correlated as a function of p T and y for a given state and also fully correlated among all the quarkonium states. The systematic uncertainty on (A × ε) related to the parametrization of the signal input p T and y distributions has two components. The first one arises from the fact that the corrected yield used to tune the MC input shape in the iterative procedure is obtained from a data sample with an associated statistical uncertainty.
The uncertainties from renormalization and factorization scale and parton distribution function on prompt and non-prompt J/ψ production are considered as uncorrelated. In Figs. 2, 3 , and 4 the data are compared with the models described above when calculations are available. A good description of the p T and y distributions of the data is obtained by the NRQCD models for p T > 3 GeV/c for the model from Butenschön et al. and p T > 5 GeV/c for the model from Ma et al. The NRQCD+CGC model describes well the data as a function of p T and y for p T < 8 GeV/c. The ICEM model also gives a good description of the data as a function of y and p T for p T < 15 GeV/c. It overestimates the data for p T > 15 GeV/c. Finally, the CEM NLO calculation underestimates the cross sections for 4 < p T < 10 GeV/c and reproduces the data at higher p T.
Humans are constantly exposed to micromycetes, especially filamentous fungi that are ubiquitous in the environment. In the presence of risk factors, mostly related to an alteration of immunity, the non-dermatophyte fungi can then become opportunistic pathogens, causing superficial, deep or disseminated infections. With new molecular tools applied to medical mycology and revisions in taxonomy, the number of fungi described in humans is rising. Some rare species are emerging, and others more frequent are increasing. The aim of this review is to (i) inventory the filamentous fungi found in humans and (ii) provide details on the anatomical sites where they have been identified and the semiology of infections. Among the 239,890 fungi taxa and corresponding synonyms, if any, retrieved from the Mycobank and NCBI Taxonomy databases, we were able to identify 565 moulds in humans.
Systematic Literature Review and Database Creation First, all fungi names and synonyms were collected on both Mycobank (https:// www. mycobank. org/, accessed on 15 November 2019) and NCBI Taxonomy (https:// www. ncbi. nlm. nih. gov/taxonomy, accessed on 15 November 2019) that represent the state of the art of taxonomy of microfungi, updated on 15 November 2019. From Mycobank, the downloaded fungi taxon names and synonyms were provided in the worksheet https://www. MycoBank. org/images/MBList. zip accessed on 15 November 2019. From NCBI Taxonomy, the query used was Fungi[subtree] AND species[rank] AND specified  [prop]. Python script using the Biopython package  [11]  was also implemented to fetch synonyms from NCBI Taxonomy. We have aggregated and deduplicated these two fungi name listings in order to obtain a single list of 239,890 fungi taxa and corresponding synonyms, if any (Figure  1 ).
For each fungus name in the list, we used a Python script and Biopython package  [11]  to query PubMed to find bibliographic references that mention the fungi name or its synonyms associated with the term "human" in the article title (TI), abstract (AB), author-supplied keywords (Other Term (OT)) or the Medical Subject Headings' (MeSH) terms. The syntax of the queries was dynamically built using this pattern (fungi_name_or_synonyms [TIAB] OR fungi_name_or_synonyms[OT] OR fungi_name_or_synonyms  [MeSH] ) AND ("Human"[TIAB] OR "Human"[OT] OR "Humans"  [MeSH] ). Based on the query performed on 15 November 2019, 7428 fungi taxa were found with at least one PubMed reference.
Manual Database Incrementation In the MS Access database (MS Access 2013 TM, Microsoft), each of the 7428 fungi had a record from a page linked to the PubMed-relevant references. For each PubMed reference, an analysis of the title and/or abstract and/or whole paper was performed manually to ensure that it was isolated from humans. This process was time-consuming. Only An MS Access ® database (Access 2013, Microsoft) was set up on 16 June 2020, with these 7428 recorded fungi names. Using this relational database management system, a link for each taxa corresponding to the query described above gave access to the relevant PubMed references and made it possible to study them one by one in order to identify and collect the relevant information.
Manual Database Incrementation In the MS Access database (MS Access 2013 TM, Microsoft), each of the 7428 fungi had a record from a page linked to the PubMed-relevant references. For each PubMed reference, an analysis of the title and/or abstract and/or whole paper was performed manually to ensure that it was isolated from humans. This process was time-consuming. Only references present in PubMed before  16  June 2020 were taken into account in order to have the same PubMed content for each fungal species. After analysis, 6516 fungal taxa that were ultimately not found in humans were excluded. They included fungi of food, therapeutic or environmental interest, or those involved in domesticated animal diseases. Synonyms, when not isolated from humans but associated with a species involved in humans, were also excluded.
Data Analysis The MS Access ® database (Access 2013, Microsoft) was converted into two Excel files (Excel 2013, Microsoft). In the first file, the number of PMIDs per taxa was calculated by the anatomical site where the fungi were isolated. In the second, the number of PMIDs per fungus was given for six major fungal categories (i. e. , Aspergillus, dematiaceous, Fusarium, mucorales, Penicillium and Pseudallescheria/Scedosporium) according to the infections associated with the isolation of the fungus, as stated by the authors of the article. If more than one case was described in a publication with the same anatomical site, it counted as one publication because of a single PMID.
Figures All figures were produced using the online tool, Wordart (https://wordart. com/). The size of the name of each species was proportional to the number of times it occurred in the database. The genus Aspergillus is represented in red, the dematiaceous fungi in brown, the mucorales in blue, the genus Fusarium in turquoise, the Scedosporium/Lometospora species complex in purple, the genus Penicillium in green and the others in black.
It is now necessary to go further in our work for each anatomical site in order to distinguish colonisations from infections, in order to help clinicians interpret positive results and to assist with the diagnostic management of patients. Similarly, we did not distinguish between diagnosis by molecular biology and macroscopic identification, although it has been shown that potential errors were found in macroscopic identifications, mostly between close species within a section or species complex  [4]. More, even in molecular identification, it was pointed out that 20% of the sequences available in public databases are unreliable  [220, 221]. We have based our repertoire according to state of the art at the time of reported publications. Therefore, in Table  1 , we have chosen to group species by sections, sharing morphological similarities, in order to bypass this limitation.
Finally, one of the limitations of this publication is its temporality. As explained in Section 2, only references present in PubMed before 16 June 2020 were taken into account in order to have the same PubMed content for all fungi species. However, medical mycology is dynamic, and new organisms constantly need to be accounted for by both clinicians and microbiology laboratories  [222]. Moreover, with the COVID-19 pandemic, a new risk factor has emerged  [223]. Publications reporting the detection of filamentous fungi in humans have multiplied, reporting, for example, the emergence of mucormycosis and COVID-19associated pulmonary aspergillosis throughout the world  [224, 225]. It will therefore be necessary to update this data regularly.
Acknowledgements We are grateful to  Amelia Pope  for editing the paper. The elaboration of this review was partly supported by the  Horizon Europe  project wildE (grant agreement No. 101081251 ). Funding This research was funded through the  2015-2016   Biodi-vERsA Call for Research Proposals  under the '' SPONFOREST '' Project  ( BiodivERsA3-2015-58 ).
Discussion An old story: each species has his own song timing and large-eyed species sing earlier In this study, we used several metrics to measure the song timing of different species, going beyond measurements of the time of rst song of each species per station. Using this diversity of metrics, we were able to classify species by song time and duration. Our approach is above all a re nement to reveal that each species has its own song timing, as already shown several years ago (e. g. ,  Allard 1930; Allen 1913 ). However, the robustness of our classi cation is con rmed by comparing our results with other studies. From unpublished data from Lluisia, Aubin and Sueur, Gil and Llusia (2020), found a similar sorting of the 11 species shared in their study and our study (Linear model based on the time ranks of the rst song of the species of the two datasets: F 1,9 =22.
increases as song index song index the duration (DUR) (CDI) of the song increases https://observatoire. loire-atlantique. fr/44/les-cartes/l-occupation-des-sols-en-loire-atlantique/r_8326) and the third one was extracted from a vector database produced by the IGN (BD-TOPO®) in 2018. Tra c noise. In this study, we are employing the L50 indicator to translate the sound levels. This indicator is linked to the noise level surpassed for 50% of the measurement time, expressed in dB. This indicator holds the advantage of being resilient to sound events and providing a relatively accurate representation of the overall sound level (Can et al. 2016 ). Given the spatial gradient sampling in this study, and with tra c noise being the primary sound source in urban and suburban environments, we can state that in our dataset this indicator exhibits a high correlation with tra c noise levels.