dataesr/scientific-paragraphs-categorization huggingface dataset:
MALDI-TOF MS Identification The fungi were incubated on SDA GC at 30 • C for five to eight days. Once grown, colonies were identified with matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS), using the procedure described in  Cassagne et al. (2016)   [23]. The Microflex LT TM instrument and the MALDI Biotyper TM system (Bruker Daltonics GmbH, Bremen, Germany) were used, along with the manufacturer's and inhouse reference spectra databases, as described in  Normand et al. (2017)   [24].
Energy-Dispersive X-ray Spectroscopy (EDX) Analysis Fresh cultures of the three strains were fixed in 1 mL of 2. 5% Glutaraldehyde in 0. 1 M Sodium Cacodylate buffer for at least 1 h. A cytospin was performed with 200 µL of the fixed fungal solution and centrifugation at 800 rpm for eight minutes. EDX was achieved with an INCA X-Stream-2 detector (Oxford Instruments) associated with TM4000 Plus scanning electron microscopy and AztecOne software (Oxford Instruments, Pasadena, CA, USA). The chemical mapping was performed blindly and took into consideration all chemical elements. The results for the weights and atomic percentages of chemical elements for each strain were subjected to principal component analysis with the XLSTAT (Addinsoft, Paris, France) software.
75 µL forward/reverse primer) to a total volume of 25 µL/well. The PCR program for all gene amplifications was as follows: an initial denaturation step at 95 • C for 15 min, followed by 39 cycles of: 1 min denaturation at 95 • C, 30 s annealing at 56 • C, 1 min extension at 72 • C, and a final 5 min extension at 72 • C. The amplicons were visualized on 2% agarose gel with Sybr Safe DNA TM gel stain (Invitrogen, Waltham, MA, USA) using the Safe Imager 2. 0 Blue-Light Transilluminator TM (Invitrogen). Sequencing was performed on a 3500 Genetic Analyser TM (Applied Biosystems, Inc. ). The sequences were assembled and corrected using Chromas Pro 2. 0 and analysed using the BLASTn tool with the reference data available from the GenBank database of the National Center for Biotechnology Information (NCBI).
Phylogenetic Analyses Five phylogenetic trees were built. The first one was a multilocus tree constructed after concatenating the ITS, B-tub2, and D1/D2 nucleotide sequences of the clinical isolate, two reference strains, and sequences of other Coniochaeta species obtained from the GenBank database. The four other trees were each built with only one locus and included several Coniochaeta species also collected from GenBank database (accession numbers are detailed in Table  2 ). All sequences were aligned with Muscle (a tool available in MEGA 11 software). Phialemonium obovatum CBS 279. 76 was used as the outgroup. The maximum-parsimony phylogenetic trees were constructed with the default settings and branch-robustness estimation was tested using 1000 bootstrap replications with the molecular evolutionary genetics analysis (MEGA) software version 11.
Primers Sequences Targeted Regions References ITS1 TCCGTAGGTGAACCTGCGG 18S-5. 8S [26] ITS2 GCTGCGTTCTTCATCGATGC 18S-5. 8S [26] ITS3 GCATCGATGAAGAACGCAGC 5. 8S-28S [26] ITS4 TCCTCCGCTTATTGATATGC 5. 8S-28S [26] ITS1 TCCGTAGGTGAACCTGCGG 18S-5. 8S, 5. 8S-28S [26] ITS4 TCCTCCGCTTATTGATATGC 18S-5. 8S, 5. 8S-28S [26] Bt-2a GGTAACCAAATCGGTGCTGCTTTC B-tub2 [27] Bt-2b ACCCTCAGTGTAGTGACCCTTGGC B-tub2 [27] EF1-728F CATCGAGAAGTTCGAGAAGG TEF1 [28] EF1-986R TACTTGAAGGAACCCTTACC TEF1 [28] D1 AACTTAAGCATATCAATAAGCGGAGGA 28S [11] D2 GGT CCG TGT TTC AAG ACG G 28S [11] Table  2. GenBank accession numbers of the reference strains used for the phylogenetic analyses.
MALDI-TOF MS Identification The MALDI-TOF MS identification score (log score < 1. 90) was below the limit required for obtaining a good identification. The isolate was identified at the genus level as Coniochaeta sp. identification score values were generated from MALDI-TOF MS spectra of the new isolate and the two other reference strains from the DSMZ collection, Coniochaeta mutabilis DSM 10716 and Coniochaeta hoffmannii DSM 2693, which were collected and have been entered in the MALDI-TOF MS database. The weights and atomic percentages of chemical elements resulting from the chemical mapping performed on the three species of the Coniochaeta genus displayed three distinct profiles. In the principal component analysis (Figure  3 ), each Coniochaeta species was very distant from the others, demonstrating highly heterogeneous chemical profiles.
DNA Sequencing The sequences were analysed using the BLASTn tool with the reference data available from the GenBank database of NCBI. The BLASTn of the new isolate showed a percent identity ≤97% for the four gene markers. Notably, the best-match pairwise identity was 96. 51% for C. rhopalochaeta CBS 109872 with ITS (NR172554. 1), 96. 41% for C. deborreae BE19 001008 with TEF-1a (MW890087. 1), 97. 45% for C. deborreae CBS 147215 with D1/D2 (NR076709. 1), and 94. 44% for Cosmospora inonoticola 9361 with B-tub2 (KU563621. 1).
(Holotype IHEM 28559/PMML0158, stored in a metabolically inactive state. ) GenBank: OM366153 (ITS), ON000097 (B-tub2), OM640093 (TEF-1a), OM366268 (D1/D2). Description: the macroscopic features were characterised by a rapid growth time on SDA GC medium at an optimal temperature of 25 • C. However, Coniochaeta massiliensis was not able to grow at 4 and 45 • C. Colonies were first white to beige, both on the surface and on the reverse after four to five days of incubation, then turned light orange to salmon. Colonies were flat and moist, with a glabrous aspect. There was no aerial mycelial growth. The microscopic features were characterised by the presence of wide septate hyphae, numerous cylindrical adelophialides (short phialides without septum), discrete phialides with conical tips, exhibiting ellipsoidal to cylindrical and rarely curved conidia, nonseptate with thin and smooth conidial walls (2 to 3 by 6 to 10 µm).
Several conidia were observed aggregating on the side of the hyphae and most often in clusters. Collarettes were present, but chlamydospores formation was not observed. The Biolog TM carbon-source assimilation profile showed that C. massiliensis PMML0158 can assimilate different carbon substrates, such as 2-keto-D-gluconic acid, D-gluconic acid, Additional specimen examined: C. mutabilis: Reference: Sweden. Origin of isolation unknown, before 14 June 1996. (DSM 10716-EMPA 573, S24 E. ) GenBank: OM366154 (ITS), ON000098 (B-tub2), OM640094 (TEF-1a), OM366269 (D1/D2).
D-ribose, D-xylose, D-glucosamine, D-cellobiose, D-melibiose, Palatinose, Turanose, Lsorbose, and β-methyl-D-glucoside. Based on this phenotypic analysis, C. massiliensis PMMFL0158 appears similar to C. hoffmannii DSM 2693. Host: Human Additional specimen examined: C. Hofmann: Reference: Country of origin unknown. Treated pine stake, before 8 July 1983. (DSM 2693-ATCC 34158-SP33-4. ) GenBank: OM366155 (ITS), ON000099 (B-tub2), OM640095 (TEF-1a), OM366270 (D1/D2).
Acknowledgments: The authors gratefully acknowledge wholehearted and fruitful discussions with Pierre Pontarotti concerning phylogenetic analysis and the technical support of  Anthony Fontanini  for electron microscopy investigations. We also sincerely thank  Takashi Irie ,  Kyoko Imai ,  Shigeki Matsubara ,  Taku Sakazume ,  Toshihide Agemura , and the  Hitachi team in Japan  for the collaborative study conducted together with  IHU Méditerranée Infection  and for the installation of TM4000 Plus microscopes at the  IHU Méditerranée Infection facility.
Data Availability Statement: The Coniochaeta massiliensis holotype is available in the IHU MI (no. PMML0158) and IHEM (no. 28559) strain collections. The nucleotide sequences are available in GenBank (accession nos. OM366153, ON000097, OM640093, and OM366268). The datasets analysed in the current study are available from the corresponding author upon reasonable request.
Statistical Analysis All statistical analyses were performed using SPSS software (SPSS INC, Chicago, IL, USA, version 24. 0) and Stata software (Stata corp, College Station, Texas, USA, v. 14). The reliability of each psychological measurement (self-administered questionnaire) was gauged by computing Cronbach's alpha and all alphas were above 0. 74, which indicates good reliability. The description of the population was complemented by a flow chart showing PND trajectories (Figure  1 ). (PTSD).
Statistical Analysis All statistical analyses were performed using SPSS software (SPSS INC, Chicago, IL, USA, version 24. 0) and Stata software (Stata corp, College Station, Texas, USA, v. 14). The reliability of each psychological measurement (self-administered questionnaire) was gauged by computing Cronbach's alpha and all alphas were above 0. 74, which indicates good reliability. The description of the population was complemented by a flow chart showing PND trajectories (Figure  1 ). For the description of the sample at VI, we have completed the presentation of the sociobiographical data by separate comparisons on MT score at VI and POMS sub-scores at VI according to the PND status after delivery. Each comparison was applied using ttest or Mann-Whitney test if normal distribution was not found.
PND status was evaluated using a score above 11 on the EPDS that is recognized as the sensitive and specific For the description of the sample at VI, we have completed the presentation of the sociobiographical data by separate comparisons on MT score at VI and POMS sub-scores at VI according to the PND status after delivery. Each comparison was applied using t-test or Mann-Whitney test if normal distribution was not found. PND status was evaluated using a score above 11 on the EPDS that is recognized as the sensitive and specific threshold for PND  [28]. The PDN + group consisted of women with a score on the EPDS above 11 at least at one of the post-birth visits (VB1, VB2, VB3 or VB4). The PND -group consisted of women with EPDS score always below the threshold (11) whatever the post-birth visits.
Data and methods The study area is based on an ALS flight performed in February 2019 in North-eastern France, which covers a forested area of 18,646 ha, with a mean pulse emission density of 16 points per m 2. Within this area, a set of 487 systematic field plots were carried out in the Mouterhouse forest (5,324 ha) during the winter of 2019-2020 (Fig. 1 ). The Mouterhouse forest consisted of broadleaved, mixed, and coniferous stands (respectively 43 %, 23 % and 34 % on an area basis) that were considered as representative of the whole ALS area. The main species are Scots pine (Pinus sylvestris), sessile oak (Quercus petraea), beech (Fagus sylvatica), Douglas fir (Pseudotsuga menziesii) and Norway spruce (Picea abies). Diameter (starting at 17. 5 cm) and tree species were measured in these calibration plots of 15-meter radius spaced at every ~ 300 m, on a systematic grid.
As shown in Fig. 1 , no field plots were available for 71 % of the ALS acquisition area (the orange section). This situation offers the opportunity to examine the impact of model application outside of its "spatial" calibration area. Standard area-based ALS metrics (ABA) were computed using the R package lidR  (Roussel and Auty 2021). Metric computation was performed over the field plots, as well as over a raster grid of 30 m resolution for the whole ALS area. A simple working linear regression model  (Magnussen et al. 2012 , Bouvier et al. 2015)  was adjusted to estimate basal area (G), quadratic mean diameter (Dg) and tree density per hectare (N) separately. The retained working model parameters (Eq. (  1 )) included 3 independent variables: mean (Hmean), and standard deviation (Hsd) of all ALS pulse heights and the average slope (Slope) of the pixels (or of the field plots), computed from the digital terrain model (DTM) using the R package terra  (Hijmans 2021 ).
The working model is as follow: (N, G, Dg) ~ β 1 H mean + β 2 H sd + β 3 Slope + ε (1) where β i are model parameters obtained for each dependant variables separately (N, G, Dg). Models' performance was evaluated using root mean square error (RMSE) computed from the validation samples, as: RMSE = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ∑ n i=1 (y i -ŷi ) 2 n √ (2) where n is the number of validation field plots, y i the observed values of field attributes and ŷi the predicted values. RMSE was further divided by the validation attribute mean to obtain a relative RMSE. Using the calibration auxiliary space of the model independent variables, convex hulls were computed for all field calibration configurations associated to the different sampling efforts compared. The "geometry" package  (Habel et al. 2019 ) was used to build convex hulls around the the auxiliary space of the calibration domains.
The volume of the convex hull was also extracted from the scaled auxiliary space. Then, validation plots or pixels of the AOI were classified as being located inside or outside the hulls using the "inhull" function of the same package. For the Mouterhouse forest, as well as for the extended ALS area, the proportion of extrapolated pixels were computed. The yaImpute R package  (Crookston and Finley 2008) , was then used to obtain their Euclidian distances (using scaled X variables) to the nearest calibration plot. The same operation was performed for the validation plots which were also used to compute residuals from the models' predictions (Eq. (  3 )): residual i = (y i -ŷi ) (3) where, y i is the observed value, ŷi is predicted value. Means and standard deviations of all these indicators were obtained for each level of sampling intensity by aggregating replicates and were reported in the relevant figures.
g. , with less than 25 plots to calibrate a model) it was possible to obtain a bias larger than 10 % for a given replicate (Fig. 3b ). As opposed to RMSE, the mean proportion of extrapolated pixels remained relatively high at low sampling intensities (Fig. 3c ). For example, with less than 100 calibration plots, more than 25 % of the pixels over the Mouterhouse forest, or 29 % over the whole Lidar area were located outside the auxiliary space defined by the calibration hulls. Only minor differences in proportion of extrapolated pixels between the two AOI examined were observed, slightly more extrapolated pixels (ca. 3 %) were observed within the extended AOI, as opposed to the Mouterhouse forest area. With the full calibration dataset (487 plots) the proportion of extrapolated pixels was less than 18 % (Fig.
Acknowledgements We acknowledge the  Laboratoire Interdisciplinaire des Environnements Continentaux (CNRS et l'Université de Lorraine)  for supplying the ALS data and the  Agence Territoriale de Mulhouse (ONF)  for the field data. We thank  Steen Magnussen  for enlightening discussions. We thank  Alain Munoz ,  Flavien Lamiche , and  Vincet Pérez  ( ONF ) for their help in preparing and supplying data. Funding ONF Département RDI is supported by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  ( ANR-11-LABX-0002-01 ,  Lab of Excellence ARBRE). Ankit Sagar  received the financial support of the  French PIA project  " Lorraine Université d'Excellence" , reference  ANR-15-IDEX-04-LUE , through the project  Impact DeepSurf. Olivier Bouriaud  acknowledges support from the project funded  Ministry of Research, Innovation and Digitalization within Program 1 -Development of national research  and development system, Subprogram 1.
g. ,  Sing et al. 2016; Pinhas et al. 2019, and references therein). Unlike most ultra-short period (P ≤ 1 day) Hot Jupiters, WASP-43b is only moderately irradiated to an equilibrium temperature of ∼1400 K. It thus resides in a temperature range Notes. The filekey is the unique identifier associated with each dataset processed by the CHEOPS Data Reduction Pipeline (DRP). ( a) The cadence is 0. 025 s longer than the exposure time due to the transfer time needed from the image section to the storage section of the CCD. where cloud condensation can occur on the planetary dayside, setting the object apart from ultra-hot Jupiters with extremely hot daysides that are therefore likely to be clear (e. g. ,  Helling et al. 2021). In an effort to understand its atmospheric physicalchemical environment, this planet has been targeted by Spitzer (e.
2021). One of the main themes of its science program is the characterization of exoplanet atmospheres  (Benz et al. 2021). This consists of observations of secondary eclipses of hot Jupiters (e. g. ,  Lendl et al. 2020; Hooton et al. 2022 ) across a wide range of temperatures and planetary surface gravities, full or partial phase curves of the most compelling targets (e. g. ,  Deline et al. 2022) , and detailed observations of ultra-hot super-Earths (e. g. ,  Morris et al. 2021). In this paper, we study the phase curve and measure the geometric albedo of WASP-43b. To this purpose, we analyze CHEOPS observations of the optical secondary eclipses of WASP-43b, jointly with data obtained by the Transiting Exoplanet Survey Satellite (TESS,  Ricker et al. 2014 ) and with a revision of the publicly available HST WFC3/UVIS observations of the planetary eclipse.
We also homogeneously analyze the archival near-IR eclipse observations of the system to estimate the thermal emission in the optical bands. This analysis is necessary to disentangle the reflective contribution to the observed eclipse depth from the thermal emission component. All the datasets are presented in Sect. 2, together with the data reduction. In Sect. 3, we provide the spectroscopic characterization of the host star. We describe the modeling of the light curves (LCs) in Sect. 4, while in Sect. 5, we discuss the results of our analysis. In Sect. 6, we draw our conclusions.
TESS observations TESS  (Ricker et al. 2014 ) observed the WASP-43 system in sector 9 (from February 28 to  March 26, 2019; orbits 25 and 26)  and in sector 35 (from February 9 to March 06, 2021;orbits 77 and 78). Using the package lightkurve (Lightkurve Collaboration 2018), we retrieved the 2 min cadence Simple Aperture Photometry (SAP) and Pre-search Data Conditioning Single Aperture Photometry (PDCSAP), which is corrected for instrumental systematics and for contamination from some nearby stars  (Smith et al. 2012; Stumpe et al. 2012 Stumpe et al. , 2014)). We rejected all the data that are flagged by the pipeline. During sector 35 the telescope experienced a technical issue with the thermal stability and the pointing, which introduced trends and additional noise in the LC 2. To avoid these data, we also clipped out the photometry collected between BJD 2 459 255 and 2 459 256 and between BJD 2 459 266.
Spectroscopic observations We searched public archives for high-resolution spectroscopic data for WASP-43. We ended up using three exposures observed with the Ultraviolet and Visual Echelle Spectrograph (UVES) which are public in the ESO archive, taken under the program 090. C-0146. The instrumental configuration was Red Arm @580nm with a slit of 0. 3", which provided a coverage 472-683 nm with a resolution of R = 107200. The exposure time was 691 s for each spectrum. The individual signal-to-noise ratio (S/N) is ∼40, while the S/N of the combined spectrum is ∼70.
Characterization of the host star To determine the stellar parameters, we used the astroARIADNE package 3  (Acton et al. 2020) , a recently developed code that automatically retrieves photometric data (when available) from catalogs such as ALL-WISE, APASS, Pan-STARRS1, SDSS, 2MASS, and Tycho-2. With the distances from Gaia, as well as the available maps of dust distribution, the photometric data can be fitted to different stellar models using Bayesian model averaging in order to find the best model for the stellar parameters. With this method, we obtained T eff = 4240 +30 -20 K, log(g) = 4. 71 ± 0. 09 cgs, [Fe/H] = 0. 15 +0. 10  -0. 04 dex, d = 87. 2 ± 0. 5 pc and A V = 0. 05 +0. 04  -0. 02 , where the errors are internal to the statistical calculations. We also used the IDL package Spectroscopy Made Easy (SME) to synthesize models of the observed spectrum, which tests several atmospheric models  (Valenti & Piskunov 1996; Piskunov & Valenti 2017) , to eventually select the one best (i.
e. , in agreement with the observations). The code utilizes atomic and molecular line lists from VALD  (Piskunov et al. 1995). In the case of WASP-43 we found that the model best agreeing with the observed spectrum are the MARCS 2012 models  (Gustafsson et al. 2008; Fig. 1). While keeping the turbulent velocities V mac and V mic fixed at values from  Gray (2008) , we determined the v sin i ⋆ = 3 ± 1 km s - 1  , while the other derived spectroscopic parameters have greater uncertainties than the previously determined ones, so the earlier ones are preferred. To compute the radius of WASP-43, we used a Markov chain Monte Carlo (MCMC) modified infrared flux method (IRFM;  Blackwell & Shallis 1977; Schanche et al. 2020). By building spectral energy distributions (SEDs) from stellar atmospheric models and stellar parameters derived via our spectral analysis, we compared the determined synthetic fluxes to observed broadband photometry to calculate the apparent bolometric flux 3 https://github.
As for the phase variations, the model also allows for an angular lag with respect to the planet's orbital phase through the extra parameter, namely, Θ lag. Thus, the complete model depends on a total of 15 parameters. To reduce the dimensionality of the problem and to avoid degeneracies which may occur among the model parameters, we locked some of them by means of prior knowledge coming from previous studies of the system. We fixed both F d (ϕ) and F e (ϕ) in Eq. (  1 ) using the estimates of K RV and M ⋆ reported in Table  2. For the other input parameters, we proceeded as follows. We used the throughput of CHEOPS and TESS publicly available at the SVO Filter Profile Service  (Rodrigo et al. 2012; Rodrigo & Solano 2020)  and computed the passbanddependent u LLD using the LDTk package 5. We computed y GD in the TESS passband by a trilinear interpolation of the table provided by  Claret (2017) , assuming the stellar parameters listed in Table  2.
Regardless of the source of the trends, we modeled them as a Gaussian process (GP,  Rasmussen & Williams 2006; Gibson et al. 2012 ) with a Matérn 3/2 kernel, which quantifies the covariance between two observations at times t i 1 and t i 2 as: k(t i 1 , t i 2 ) = h 2       1 + √ 3|t i 1 -t i 2 | ρ       exp       - √ 3|t i 1 -t i 2 | λ       + j 2 o δ i 1 ,i 2 , ( 2 ) where h is the amplitude of the GP and λ is its timescale  6. In order to take into account any additional white noise, either instrumental or astrophysical, not included in the uncertainties, we added the diagonal elements j 2 o δ i 1 ,i 2 where o denotes the TESS orbit  25, 26, 77, and 78. To trace any long-term activity signal, we also included in the model a linear function of time t for each TESS orbit: l o (t) = c 0,o + c 1,o • (t -t m,o ), (3) where the subscript o denotes again the orbit of interest, while t m,o is the mid-time ((t max + t min )/2) of the corresponding orbit o.
With this representation, it is straightforward that the constant term refines the normalization of the LC, while the linear term traces any slope in the data. We fit the data by maximizing in the parameter space the log-likelihood function given by: ln L = - n 2 ln(2π) - 1 2 ln(det K) - 1 2 r T • K -1 • r, ( 4 ) where r is the array of length n containing the residuals with respect to the model, while K is the covariance matrix obtained with the kernel in Eq. (  2 ). For the log-likelihood maximization we used a homemade code written in the Python3 language. Our code reads in the prior distributions, one for each free parameter in the model, which define the boundaries of the parameter space within which the maximum likelihood location is searched for (Table  4 ). Then, using the python package PyDE 7  , the code searches for the maximum likelihood location, which is used to initialize the Monte Carlo fit.