dataesr/scientific-paragraphs-categorization huggingface dataset:
MH is adapted to the culture of nonfastidious bacteria like Achromobacter. Extraction was performed according to Bruker instructions (MSP creation protocol V1. 1). Briefly, a 1-mL loop of bacteria was suspended in 300 mL ultrapure water (HPLC grade) then mixed with 900 mL of 90% ethanol and centrifuged (12 000 g for 2 min). The pellet was dried, resuspended in 50 mL of 70% formic acid (Optima™, Fisher chemical)/acetonitrile (Carlo Erba Reagents) (V/V) and centrifuged again. The supernatant was used for further analysis. Data acquisition was performed according to Bruker instructions (Maldi Biotyper protocol V. 2. 4). For each extract, the supernatant was spotted eight times (1 mL) on a disposable target, coated with 1 mL HCCA matrix solution (a-cyano-4-hydroxy-cinnamic acid, Bruker Daltonics) and air-dried.
The target was inserted in MALDI-TOF/MS Microflex LT (Bruker Daltonics), and FlexControl software (version 3. 3, Bruker Daltonics) was used. Each spot was measured three times, resulting in 24 spectra by extract. Each spectrum was generated with 240 laser shots (six series of 40 laser shots) using linear positive mode with mass range m/z 2000 to 20 000, an acceleration voltage of 20kV in FlexControl software. Calibration was performed with the bacterial test standard calibrant (BTS) (Bruker Daltonics, Germany). The resulting 24 spectra were carefully inspected using flex-Analysis software (version 3. 4 Bruker Daltonics), according to Bruker's recommendations. At least 20 spectra were selected to generate a single MSP onto the MALDI Biotyper 4. 1 software (Bruker Daltonics) using the Biotyper MSP Creation Standard Method with default parameters (25% desired peak frequency minimum and 70 maximum desired peak number for the MSP).
The MSPs were used to generate a new local database. After constitution of the novel Achromobacter database, each individually created MSP was analysed against the database itself to check whether the best match was obtained with one MSP belonging to the same species. Evaluation for routine identification by direct colony transfer (Supplementary Material Table  S1 ) Fifty isolates used for MSP creation (the 18 type strains and representative strains of all species) were selected to test the direct colony transfer method and to assess the potential influence of culture media widely used for clinical sample analyses (sheepblood agar and Drigalski agar, BioM erieux). Isolates were tested in duplicate by direct colony transfer: colony fragments from overnight cultures at 37 C were spotted onto the target and overlaid with 1 mL of HCCA-matrix.
MS analysis was performed as described above but with a single measure of each spot. The spectra obtained were compared to the newly generated database. The algorithm generated a log score value ranging from 0 to 3. 0. We used the criteria provided by Bruker to interpret the score: a log score 2. 0 indicated 'high confidence identification' at the species level, a score value between 1. 7 and 2. 0 indicated 'low confidence identification' and a score value <1. 7 'no organism identification possible'.
Accuracy of the newly generated database and comparison with default Bruker database (Supplementary Material Table S2) A total of 167 Achromobacter isolates (clinical and environmental) belonging to the 18 species were tested for database verification and comparison with the Bruker default database: A. aegrifaciens (n ¼ 7), A. agilis (n ¼ 1), A. animicus (n ¼ 2), A. anxifer (n ¼ 1), A. deleyi (n ¼ 3), A. denitrificans (n ¼ 3), A. dolens (n ¼ 6), A. insolitus (n ¼ 8), A. insuavis (n ¼ 18), A. kerstersii (n ¼ 1), A. marplatensis (n ¼ 5), A. mucicolens (n ¼ 14), A. pestifer (n ¼ 1), A. piechaudii (n ¼ 1), A. pulmonis (n ¼ 3), A. ruhlandii (n ¼ 13), A. spanius (n ¼ 6) and A. xylosoxidans (n ¼ 74). Among these 167 isolates, 38 had already been used for the creation of the MSPs (Supplementary Material Table  S2  and Fig.
S1 ). All isolates were cultivated (overnight at 37 C) on sheep blood agar, and 48 of them also on MH and Drigalski agar (although MH and Drigalski are not approved media for the use of IVD-CE Bruker database). Each isolate was tested in duplicate by direct colony transfer. Each spectrum was compared by the MALDI Biotyper software both to the default Bruker database (DB-7712 MSP) and to our database, and the best matches and scores were gathered.
Verification of the newly generated database When analysing each individual MSP against the newly generated database, the best match corresponded to an MSP belonging to the same species and a score 2. 0 in 94. 6% of cases (194/205) including the six MSPs of the novel species 1. The remaining cases involved the five species represented only once in the database (2. 4%, 5/205), two isolates not matching with a score 2. 0, and four isolates matching with another species (score 2. 0), including one A. ruhlandii isolate that matched with A. xylosoxidans. Identification performed by direct colony transfer of the 50 isolates led to 100% matching with their own corresponding MSP or another MSP from the same species with an excellent degree (scores 2. 0), indicating that the direct transfer method is suitable.
Accuracy of species identification by default Bruker database (Table 1) Briefly, the default Bruker database resulted in a correct identification (i. e. score 2. 0 and concordance with nrdA sequencing) in only 50. 9% of the isolates (85/167). Surprisingly, the species was misidentified in 17. 3% of the isolates (29/167) despite high confidence scores (2. 0). All of these erroneously matched with A. xylosoxidans except for one strain of A. piechaudii that was identified as A. spanius. These errors mostly involved species not included in the database, with the exception of A. ruhlandii and A. piechaudii. The other isolates led to scores <2. 0 (25. 2% (42/167) low confidence, and 6. 6% (11/167) no match). The isolates not identified (score <1. 7) belonged to species not found in the Bruker database.
Accuracy of species identification with the local database (Table 1) Overall, 99. 4% (166/167) of the isolates were correctly identified with our local database (i. e. score 2. 0 and concordance with nrdA sequencing). The only discrepancy observed involved a single isolate out of 167 (0. 6%) corresponding to one A. ruhlandii isolate for which the best match was obtained once with A. xylosoxidans (with A. ruhlandii as the second-best match with a score 2. 0). We therefore reiterated spots for all of the 13 A. ruhlandii isolates and observed that A. xylosoxidans erratically matched with high confidence scores as best or second-best match for four strains. For these 13 isolates, we also tested the extraction method (used for database creation), which did not improve the results obtained. We did not detect any species-specific peaks in the spectra.
Discussion In this study we constructed the first MALDI-TOF database to include 18 species currently described within the genus Achromobacter using a wide collection of isolates identified with a reference method (nrdA sequencing). Because the pathogenicity of these bacteria remains controversial in CF patients, there is a strong need for developing rapid and accurate Achromobacter species identification methods that are not currently available for routine laboratory analysis. Concerning MALDI-TOF MS, the commercially available libraries are incomplete: MALDI Biotyper® (Bruker) and VITEK® MS databases include respectively only six and two Achromobacter species. Here we underscore the large number of erroneous identifications obtained with the Bruker database, despite high confidence scores, leading mostly to the misidentification of certain isolates as A.
xylosoxidans, as previously described  [24]. One might assume that the spectra from isolates belonging to species not included in the database would not find a match, but this was not the case for isolates belonging to A. insuavis, A. dolens and A. pestifer. In these cases it was not possible to determine a score cut-off for distinguishing erroneous and correct 'A. xylosoxidans' identifications. Users should be aware of the risk of misidentification. Moreover, the isolates belonging to species present in the Bruker database were not always correctly identified. Surprisingly, none of the 13 A. ruhlandii isolates was correctly identified. This issue has already been pointed out by Rodrigues et al. in a study including nine isolates of A. ruhlandii  [21]. Finally, 6. 6% of the isolates (11/167) led to scores <1.
7, preventing genus identification. There may be several reasons for the issues with the Bruker database. First, the number of isolates included in the database is limited and some species are absent, as already highlighted by Papalia et al. [24]. Secondly, with the exception of the type strains, the method of species identification of the isolates is not provided by the manufacturer, making it difficult to ascertain reliability. This also applies to the VITEK® MS system. To overcome the weaknesses found in the existing databases, first we included only isolates identified by reference methods using the pubMLST database  [10]  and representatives of 18 species (corresponding to all the species that could be identified with this database at the beginning of the study). Second, the database was built up with a large number of MSPs (205) created with a wide variety of clinical and environmental isolates, either from our collection or from other collections in Denmark, Belgium, France, and the USA.
We built MSP dendrograms, but we noticed that the results depended on the parameters and the set of strains included for their construction (data not shown) despite the excellent results of species identification obtained with our database. This might be explained by the fact that Bruker algorithms for dendrogram construction and bacterial identification are not based on the same parameters. With the use of our database, the rate of correct identifications jumped from 50. 9% (85/167) (Bruker default database) to 99. 4% (166/167) with high confidence scores (score 2. 0) for all 167 isolates tested by direct colony transfer from sheep-blood agar. The use of Drigalski agar, which is not validated by Bruker, yielded lower scores, which could be explained by the drier texture of the colonies. We recognize that our database has certain limitations.
First, some species are rarely found in clinical samples  [10,22e25]  and therefore are not easily available from laboratory collections. As a result, few isolates were included in the database for some species: only one representative for A. agilis, A. anxifer, A. kerstersii, A. pestifer, and A. piechaudii. In addition, accuracy was verified with fewer than three isolates for the five species listed above as well as for A. animicus. In the future, additional strains belonging to these species or to novel species should be included when available to expand the database. Second, the results were not satisfactory for some A. ruhlandii isolates. Indeed, the only incorrect species identification with our database (0. 6%, 1/167) involved a single isolate of A. ruhlandii erroneously identified as A.
xylosoxidans, and we observed this possible misidentification in the MSP evaluation step. Gomila et al. also noticed that "A. xylosoxidans and A. ruhlandii showed a close relationship by MALDI-TOF MS analysis" in a study including ten A. ruhlandii isolates  [30]. The inclusion of more strains of A. ruhlandii in our database might help to rectify this problem. On the contrary, for the 74 A. xylosoxidans isolates identified with our database, A. ruhlandii was never proposed with a high confidence score. Therefore, another method (nrdA analysis or MLST) should be used when both A. ruhlandii and A. xylosoxidans are presented as the best and second-best match with high confidence scores. In conclusion, we built a reliable database for the identification of clinical or environmental Achromobacter.
Introduction: the need for multi -sensor forest structure datasets Following the urgent need for improved mapping of vegetation structure  (Le Toan et al. , 2011)  to better quantify global carbon stocks and fluxes from land use change  (Houghton, 2005)  and impacts on ecosystem services and forest resources  (Bustamante et al. , 2016) , NASA and ESA have developed three spaceborne missions -NASA Global Ecosystems Dynamics Investigation (GEDI,  Dubayah et al. , 2020) , NASA-ISRO Synthetic Aperture Radar Mission (NISAR,  Rosen et al. , 2016)  and ESA BIOMASS  (Quegan et al. , 2019)  -to be launched between 2018 and 2022. By virtue of the different sensitivities to forest structure combined with overlapping coverage at different geographic and time scales, NISAR, GEDI and BIOMASS are slated for new remote sensing analysis and scientific discovery that were not possible to date or with each mission alone.
, 2017). In anticipation of the three missions, there was a need for field and airborne measurements of forest structure and condition, as well as new forest structure retrieval algorithms across a wide range of tropical forest conditions. As a result the European Space Agency (ESA), United States National Aeronautics and Space Agency (NASA), French Aerospace Lab (Office National d'Etudes et de Recherches Aérospatiales; ONERA), German Space Agency (Deutsches Zentrum für Luft-und Raumfahrt; DLR), Gabonese National Park Agency (Agence Nationale des Parcs Nationaux; ANPN), the Gabonese Earth Observation Agency (Agence Gabonaise de l'Etude et Observation Spatiale; AGEOS) and multiple international University partners collaborated on the AfriSAR campaign, to acquire coincident calibration and validation datasets that would facilitate comparison between the airborne, field and spaceborne data.
, 2018a; Denbina et al. , 2018b). This dataset also includes various intermediate PolInSAR products including radar backscatter, coherence, and viewing and terrain geometry. Canopy height was derived from the multi-baseline UAVSAR data by inverting the RVoG model. Kapok uses the SLC stack to calculate a multilook PolInSAR covariance matrix for each pixel in the imagery. Kapok also resamples the available vertical wavenumber, look vector, and geolocation information to have the same dimensions as the multilooked UAVSAR image stack. After calculating the multi-look covariance matrix, a coherence optimization procedure was performed to find the PolInSAR coherences with the largest separation in the complex plane, followed by an estimation of the interferometric phase of the ground surface beneath the forest canopy, as in the standard three-stage RVoG model inversion procedure  (Cloude and Papathanassiou, 2003).
, 2018b. For each pixel, the algorithm uses machine learning to choose the interferometric baseline expected to provide the best canopy height estimate. This selection is primarily based on the characteristics of the observed PolInSAR coherence region, in addition to other parameters such as k z and radar backscatter. A sparse subset of coincident LVIS RH100 data, similar to the point density expected from the GEDI mission, was used to train the classifier at approximately 250-m spacing in both azimuth and range directions  (Denbina et al. , 2018b). After training, for each pixel the baseline selected by the classifier was used to invert forest height from the RVoG model, as described in the previous paragraph. This product helps demonstrate the potential of fusing multi-baseline PolInSAR with data from GEDI or other future spaceborne lidar missions.
PAI is defined as one half of the total plant element area per unit ground surface (m 2 m -2 ;  (Gower and Norman, 1991). 3) Footprint summary data of total recorded energy, PAI, CCF, and vertical plant area volume density (PAVD, m 2 m -3 ) profiles in 10 m vertical bins (i. e. 0-10, 10-20, 20-30 and above), and foliage height diversity (FHD) -a canopy structural index that describes the vertical heterogeneity of the PAVD profile  (MacArthur and Horn, 1969). The algorithm to derive vertical canopy profile metrics from waveform lidar is well developed  (Armston et al. , 2013a; Ni-Meister et al. , 2010a , 2010b; Tang et al. , 2012)  and requires estimates of the following parameters to compute: (i) the integrated laser energy returns from the canopy R v (z) and ground R g ; (ii) the ratio of canopy and ground reflectance ρ v /ρ g ; (iii) the leaf area angle projection coefficient, G(θ), representing the fraction of canopy element area projected perpendicular to the view direction to the total canopy element area; and (iv) the clumping index, Ω(θ), describing the spatial distribution pattern of canopy elements.
e. trunks, branches, leaves). Unlike Lidar, which results from intercepted surfaces, including leaves, L-band radar tomography penetrates deep into the canopy with greater sensitivity to large branches and trunks. The vertical resolution is driven by the length of the longest interferometric baselines in the tomographic stack and is therefore coarser than in the lidar data (ie m resolution in TomoSAR vs mm to cm in Lidar). The spacing between the interferometric baselines determines the height of ambiguity, which was set to be greater than the expected height of the forest. The three dimensional focusing of an image stack requires that each image has a common phase reference, which is especially difficult in the airborne case, since errors in the knowledge of the platform position are typically a large fraction of the size of the radar wavelength.
Dataset intercomparison We compared the accuracies and sensitivities of the height and AGBD products by extracting the values of the RH100, CHM PLAnT , CHM Kapok , CHM Fusion data (for height) and AGBD LVIS , with the small footprint lidar datasets by  Labriere et al. (2018)  and plotting them against each other. To achieve this, we extracted all points covering the overlapping areas between LVIS and the radar products. The values of each point were then plotted and basic statistics calculated  (r2, intercept, slope, RMSE, residual error,. Crossovers between LVIS and discrete return ALS data were used for comparison of equivalent products for each dataset at the Lopé, Mondah/Akanda, Rabi and Mabounié sites. To ensure both datasets were aligned, horizontal offsets were calculated by maximizing the correlation between real and ALS simulated LVIS waveforms  (Blair and Hofton, 1999; Hancock et al.
In situ aboveground biomass density We measured 6692 trees from 139 species in Mondah, with DBH values ranging from 5 cm to 198. 4 cm and maximum measured heights of 59. 23 m. Mean AGBD was 103. 2 Mg ha -1 and ranged from 3. 26 Mg ha -1 to 267. 5 Mg ha -1. All vegetation characteristics and estimates of AGBD were reported at multiple scales: 0. 0625 ha, 0. 25 ha, and 1 ha. These data are available on the ORNL DAAC  (Fatoyinbo et al. , 2018)  and were used to validate and calibrate the NASA AfriSAR higher level data products described below.
UAVSAR canopy height UAVSAR height product accuracies were assessed through a regression with LVIS RH100 metrics. Comparisons of the three UAVSAR canopy height products with LVIS are shown in Fig. 3  and Fig. 5. The UAVSAR Canopy height models performed better in Pongara than in Lopé, likely due to the flat topography of mangrove forest of Pongara. The Lopé and Mondah sites are characterized by large topographic gradients and many areas of steep slopes. The UAVSAR's CHM Fusion canopy height product is the most accurate, as compared to LVIS RH100, with the highest r 2 (0. 84 in Pongara and 0. 74 in Lopé), lowest RMSE (around 27%) and low bias (3. 65% for Pongara). Good agreement between these datasets is expected as the CHM Fusion product used a sparse subset of LVIS RH100 data to train the classifier.
Discussion The 2016 NASA AfriSAR mission was the first simultaneous acquisition of polarimetric SAR, waveform lidar and field data in support of the upcoming NISAR, GEDI and Biomass missions and the first coordinated campaign for measurements of forest structure properties across multiple international space agencies. While similar campaigns flying LVIS and UAVSAR have been carried out, such as the 2009 and 2010 DesdynI Cal/Val campaigns in Howland, Harvard and Penobscot Experimental Forests  (Montesano et al. , 2013) , the amount of data collected and area covered was much lower than accomplished by AfriSAR. Furthermore, previous campaigns did not include multiple baseline or tomographic SAR acquisitions. Here, we collected over 7000 km 2 of Lidar and 30,000 km 2 of PolSAR data, covering 30% of the Gabonese territory and all of the major terrestrial ecosystems of Central African Region.
The AfriSAR data have been key in advancing forest structure retrieval algorithms, image processing software, spaceborne data simulation and biodiversity mapping methodologies amongst others. In  Denbina et al. (2018b) , the new Kapok software package to generate canopy height from repeat-pass UAVSAR data was developed using UAVSAR and LVIS airborne acquisitions over Pongara and Lope National Parks, while the ISCE-PLANT software was developed over Lope, Mondah and Rabi sites. Similarly, this dataset was instrumental in developing new machine learning approaches to fuse SAR and Lidar data  (Denbina et al. , 2018b , (Pourshamsi et al. , 2018). Here the subsampled LVIS data was used to select the best baseline configuration and k z value, i. e. to determine whether a large or shorter baseline configuration between two UAVSAR acquisitions should be used in multiple baseline PolInSAR processing.
On the Lidar side, the AfriSAR data was one of the key datasets used for validation of the GEDI-simulator  (Hancock et al. , 2019a) , pre-launch calibration and validation of GEDI Level 2 footprint product algorithms (Hofton and  Blair, 2019; Tang and Armston, 2019) , and is an ongoing key component of GEDI's post-launch calibration and performance assessment strategy  (Dubayah et al. , 2020). It has also been used in combination with other SAR datasets, such as Sentinel-1, to generate country wide  (Lang et al. , 2019)  and site-wide  (Pourshamsi et al. , 2018)  canopy height and AGBD  (Marshak et al. , 2020)  estimates. Areas of dense canopy cover as found in Gabon may sometimes present a challenge for lidar measurements, particularly over complex topography. LVIS was designed to be sensitive enough to detect a ground pulse in canopy cover of up to 99% and comparisons with smallfootprint systems have shown this to be true  (Hofton et al.
, 2002). However, certain environmental conditions such as steep slopes or lowlying canopy material such as shrubs can weaken already weak ground returns to the point where automated ground finding algorithms misidentify the ground. Here, LVIS was deployed over the most challenging forest conditions -the combination of high canopy cover, wide ranges of topographic relief and different types of forest types and densities. Despite these conditions we showed a high degree of consistency in estimating canopy structure parameters from airborne waveform lidar data. We also found excellent agreements with PAI profiles derived from terrestrial laser scanner (TLS) even at a high vertical resolution (1 m)  (Marselis et al. , 2018). In sum, our results highlight the fidelity of LVIS-based vegetation structure products and strengthens the confidence in our data processing algorithms.
The extensive UAVSAR collection over a wide range of forest types and biomass added important new sites for the NISAR mission calibration and algorithm development, with the addition of 15 new 1-ha plots in lower biomass areas and extensive airborne Lidar data needed for AGBD calibration and validation. Indeed, while a breadth of field measurements was previously available in Central African forests, 90% of all plots were in high biomass forests (over 200 Mg/ha). Through the additional field data collected here we have expanded the range in AGBD measurements available for the tropics with plot AGBD densities ranging from 50 to 250 Mg/ha. In the case of UAVSAR, this was the first extensive PolInSAR and tomographic experiment over tropical forests. While TomoSAR and PolInSAR processing have been carried out before with UAVSAR  (Hensley et al.