dataesr/scientific-paragraphs-categorization huggingface dataset:
X-Ray crystal structure of cubamers in complex with PvLDH The crystal structure of PvLDH in complex with the 1501s cubamer was solved at 2. 5Å resolution (PDB entry 6TXR; Fig. 1  and SI Appendix, Table  S3 ). As expected, the overall structure of PvLDH is similar to that previously reported (PDB entry 3ZH2) indicating that binding of cubamers does not affect the 3D structure of this protein. Difference Fourier electron density maps indicate the presence of two cubamers which bind to the target protein at each end of the tetrameric PvLDH (Fig. 1a ), in some ways reminiscent of the canonical DNA aptamers we had previously selected binding to PfLDH(4). The 5' end of the cubamer showed two base-paired loops in close interaction with the surface of PvLDH, whilst the 3' end of the cubamer extended away from the protein surface and formed a duplex with an aptamer bound to a second protein molecule in the crystal packing (SI Appendix, Fig.
Cubamer biophysical characterization by surface plasmon resonance (SPR) SPR measurement was collected using a Biacore X100 instrument (GE Healthcare). PvLDH was captured on the surface of NTA sensor chip (GE Healthcare). A running buffer containing 25 mM Tris-HCl, pH7. 5, 100 mM NaCl, 20 mM imidazole, 0. 005% (v/v) Tween 20 and 0. 5 mg/mL BSA was used for ligand capturing. The surface of NTA chip was primed with running buffer and PvLDH cubamer, 1501s, was injected in at a flow rate of 30 μL min -1 and at 25 °C. All data were referenced for surface without captured cubamer and blank injections of buffer. Sensorgrams were analyzed with Biacore X100 Plus Package evaluation software (GE Healthcare).
Crystallization and data collection and structure refinement Crystallization screening trials of the protein-aptamer complex (14mg/ml) were carried out by the sitting drop vapor-diffusion method with a Mosquito automated nanoliter dispensing system (TTP Labtech, Melbourn, UK) at 291K. Sitting drops of 400 nL were set up in Greiner plates for 672 commercially available screening solutions using a 1:1 mixture of protein sample and reservoir well solution (150 µL). The plates were stored in a RockImager (Formulatrix, Bedford, USA) automated imaging system to monitor crystal growth. The crystals appeared after 3 months in crystallization condition 52 of Index HT kit (Hampton Research) containing 45% (v/v) 2-Methyl-2,4-pentanediol, 0. 2 M Ammonium acetate and 0. 1 M Hepes pH 7. 5. The fished crystals were flash-cooled in liquid nitrogen directly, as the condition of crystallization served also as cryoprotectant.
Diffraction data were collected at beamline PROXIMA-1 (SOLEIL synchrotron, St. Auban, France) and processed with XDS  (60)  and Aimless  (61). The structure of the PvLDH-cubamer complex was solved by the molecular replacement technique using a PvLDH tetramer (PDB entry 3ZH2) as search model with Phaser  (62). A complete model of the complex was obtained through interactive cycles of manual model building with Coot  (63)  and reciprocal space refinement with Buster(64) and phenix. refine (65). The complex's DNA moiety was gradually traced into difference electron density maps during this process. X-ray data collection and model refinement statistics are summarized in Table  S2. Figures showing the crystallographic model were generated and rendered with Pymol (Schrodinger, LLC) and/or Chimera  (66).
Acknowledgments We thank the  Shirley Boyde Trust  for their generous support through a  Shirley Boyde Foundation  Grant to J. A. T. and acknowledge  Institut Pasteur  funding to M. H. , A. H. , P. R. , and F. L. -A. J. A. T. also acknowledges the  University of Hong Kong Outstanding Young Researcher Award   2015-16  and the  University of Hong   Kong Outstanding Research Student Supervisor Award   2016-2017. J. A. T. also acknowledges  HKU Seed Fund for Basic Research  grants  201711159185  and  201611159269. We thank the staff of the platform for production and purification of recombinant proteins of  Institut Pasteur  for technical assistance during the expression and purification of the proteins steps. The authors are grateful to the staff of the crystallography platform at  Institut Pasteur  for carrying out robot-driven.
On the other hand, installing a radar at the bottom of the valley provides high-resolution and quality data required for vulnerable and densely populated Alpine valleys, but the QPEs are limited in the latter due to beam blockage by surrounding mountains. In Europe, MeteoSwiss has the longest-standing experience in operating radars in mountainous regions. The Swiss C-band radar network in the Alps  (Joss and Lee, 1995; Germann et al. , 2006)  is one of the highest in the world and is coping with the associated altitude dilemma by using a large number of plan-position indicator (PPI) scans (including negative elevation ones) aimed at determining high-resolution vertical profiles of reflectivity. Sophisticated radar-rain gauge merging techniques and echotracking techniques, as well as numerical prediction models outputs  (Sideris et al.
Dataset Table  2  shows the main characteristics of the nine convective events considered for the study of the φ dp -PIA relationship in rain, by using the XPORT radar data. A stratiform event, which occurred on 3-4 January 2018, is also considered for a preliminary study of the φ dp -PIA relationship in the ML, with both the MOUC and the XPORT radar data. Figure  2  presents a time series of one of the most intense convective event (21 July 2017) and the stratiform event. In both cases, the total rain amount observed at the IGE site was about 35 mm, but in 3 h, with two peak rain rates of about 40 mm h -1 for the 21 July 2017 convective event, while the 3-4 January 2018 stratiform event lasted more than 12 h with an average rain rate of about 3 mm h -1. The two events also differ in their vertical structure.
Some characteristics of the nine convective events considered in the study of the φ dp -PIA relationship in rain. The ML detection was performed with the 25 • elevation angle measurements of the XPORT radar using the algorithm described in  Khanal et al. (2019). The total rain amount and the maximum rain rate are recorded at the weather station available at the XPORT radar site at IGE. The maximum PIA is derived from the MRT by considering the 7. 5 2200-2800 m a. s. l. after 04:00 UTC, passing progressively at the level of the MOUC radar in the meantime. As an additional illustration of the dataset, Fig. 3  gives two examples of XPORT PPIs at 7. 5 • elevation angle for moderate (left) and intense (right) rain during the 21 July 2017 event. As a clear feature, one can see that, for this elevation angle, the radar beam is fully blocked by the Chartreuse mountain range in the northern sector.
One can notice the good stability of the mean reflectivity values between the two events, which is an indication of both the radar calibration stability during the period and the moderate impact of the mountain surface conditions already evidenced in previous studies  (Delrieu et al. , 1999; Serrar et al. , 2000). The standard deviations of the reflectivity time series range between 0. 2 and 0. 9 dBZ, and the mean 10 %-90 % interquantile range is equal to 1. 03 dBZ. Due to limited data availability, a simpler approach was implemented for the selection of the MOUC mountain reference targets. Here again, the raw reflectivity data were accumulated and averaged, but only over the period of 3 January 2018, 19:00-23:55 UTC, preceding the rise of the ML at the level of the MOUC radar. It was snowing during this period at the MOUC radar site.
The example on the left-hand side corresponds to a moderate PIA (5. 4 dB, when considering all the gates of the radials comprising the target) and the right-hand side example corresponds to one of the highest PIA value observed (27. 6 dB) in our dataset. We tried to limit, as far as possible, the radial extent of targets (less than 2000 m) and/or multipeaks targets, such as the one shown in the left-hand side example, in order to limit positive bias on MRT PIA estimates. The top graphs of Fig. 5  give two examples of apparent reflectivity time series during the events of 21 July 2017 and 20 July 2018, together with the mean, 10 % and 90 % quantiles of the dry weather apparent reflectivity. For both cases, the XPORT data acquisition started a bit after the actual beginning of the storm. Therefore, the dry weather reference values were estimated with data collected after the event, i.
4. 2. However, we have to mention the following three points here that may limit the validity of such inferences for the MOUC radar configuration compared to the XPORT one: (i) the MRT PIA estimates may be positively biased by radome attenuation, (ii) the polarimetry-derived PIA estimates may be affected by δ hv contamination in the ML, and (iii) nonuniform beam filling effects probably become significant for the 20-40 km range considered, leading to a smoothing of the radar signatures. There is no evidence so far of the first two points in the available dataset; this may be due to the moderate intensity of this precipitation event.
Study of the k-K dp relationship in rain from in situ DSD measurements Before presenting the analysis of the φ dp -PIA relationship in rain and in the melting layer based on the estimates for all the mountain targets and time steps available for the two sets of events, we study in this subsection the k-K dp relationships that we were able to derive from the DSD measurements collected at ground level at the IGE site. For all the events, precipitation was in the form of rainfall at this altitude. As for the scattering model, we used the CANTMAT version 1. 2 software program that was developed at Colorado State University by Chenxiang Tang and Viswanathan N. Bringi. The raw Parsivel 2 DSD measurements have a time resolution of 1 min. The volumetric concentrations were computed with a 5 min resolution and binned into 32 diameter classes with increasing sizes from 0.
125 mm up to 6 mm. The CANTMAT software uses the T-matrix formulation to compute radar observables such as horizontal reflectivity, vertical reflectivity, differential reflectivity, copolar cross-correlation, specific attenuation, specific phase shift, etc. , as a function of the DSD, the radar frequency, air temperature, oblateness models (e. g. ,  Beard and Chuang, 1987; Andsager et al. , 1999; Thurai and Bringi, 2005) , and canting models for the rain drops and the incidence angle of the electromagnetic waves. Figure  8  displays the empirical k-K dp pairs of points obtained for the convective events (left) and the stratiform one (right) as well as the fits of least square linear models and power law nonlinear regressions. Based on the literature review mentioning an almost linear relationship between k and K dp at X band  (Bringi and Chandrasekar, 2001; Testud et al.
From this dataset, it was possible to derive the evolution of PIA (r M ) and φ dp (r M ) values as a function of the altitude within the ML. The evolution with the altitude of the ratio of the mean value of PIA (r M ) over the mean value of φ dp (r M ), as a proxy for the slope of a linear k-K dp relationship within the ML, was also considered. Since the ML width varied during the ML rising, we found it necessary to scale the altitudes with respect to the ML width. The three variables considered present a clear signature as a function of the scaled altitude. In particular, the PIA/φ dp ratio peaks at the level of the ρ hv peak (somewhat lower than the Z h peak), with a value of 0. 42 dB per degree, while its value in rain just below the ML is 0. 33 dB per degree. The latter value is consistent with the slope of the linear k-K dp relationship (0.
62-1. 44]). However, we did not find evidence of δ hv signatures in the raw ψ dp (r) profiles, and we are confident in the ability of the regularization procedure to filter them in a rather satisfactory way if they eventually occur. Although the experimental configuration for the study of attenuation in the ML presents some limitations (e. g. , possible radome attenuation and NUBF effects), the preliminary results presented here will be deepened by processing a dataset of about 30 stratiform events with the presence of the ML at the level of the MOUC radar. Data availability. Data can be made available by the first author upon request. Author contributions. GD developed the concept of the article, realized the codes and performed the data processing; GD also wrote the article and handled the review process.
AKK was involved in the development of the codes (ML identification, Phidp processing, etc. ); NY shared his experience of the RHytMME radars and provided the MOUC radar data and the preliminary version of the Phidp regularization procedure. FC is a research engineer who has built the XPORT radar and is now responsible for the current updating program and the data acquisition. BB and NG contributed to the article through scientific discussions and amendments to the paper.
Acknowledgements. We are grateful to  Patrick N. Gatlin  ( NASA Marshall Space Flight Center, Huntsville, Alabama, USA ) for providing the CANTMAT version 1. 2 software developed at  Colorado State University by Chenxiang Tang  and  Viswanathan N. Bringi , whom we also thank. The RadAlp experiment is cofunded by the  LabEX OSUG@2020 of the Observatoire des Sciences de l'Univers de Grenoble , the  Service Central Hydrométéorologique et d'Appui à la Prévision des Inondations (SCHAPI)  and  Electricité de France/Division Technique Générale (EDF/DTG). Review statement. This paper was edited by  Gianfranco Vulpiani  and reviewed by three anonymous referees.
Assembly and Binning Primary assembly was carried out from full individual Illumina AL-W and AL-N read data sets (NCBI Bioproject PRJNA315555) with  MEGAHIT (v. 0. 3. 3; Li et al. 2015)  without further filtering and with default parameters except for the k-mer range, which was set to k-min ¼ 27 and kmax ¼ 123 in 10 k-mer size increments. Candidate Chloroflexi contigs were identified as described in  Saghaı ët al. (2016)  and corresponded to contigs longer than 10 kbp, where the majority of genes was affiliated to Chloroflexi and where Chloroflexi genes represented at least 20% of all genes (the actual range was 20-69% of chloroflexal genes). Phylogenetic affinity was determined based on high similarity to sequences in the RefSeq database (minimum best hit identity >50% over at least 80% of the gene length).
The clustering was performed on nucleotide tetramer frequency distributions computed with the tetra-mer_freqs_esom. pl script  (Dick et al. 2009 ) using a window size of 5,000 nt. ESOM training parameters were set to kbatch training method, 140 Â 250 mesh size, radius start 50, 20 training epochs. All other parameters were set to their defaults. The visualization of the ESOM maps was done with a U-Matrix background and inverse gray-scale gradient coloring (supplementary fig. S2 , Supplementary Material online). The completeness and contamination of the bins were assessed using CheckM with Chloroflexi as lineage marker  (Imelfort et al. 2015). Average nucleotide identity (ANI) values were calculated using the OAU program (v 1. 2;  Yoon et al. 2017). The five assembled bins are available at NCBI Bioproject PRJNA579290.
Genome Annotation Gene prediction was performed on the newly assembled contigs using  PRODIGAL (v. 2. 6. 3, "metagenomic" mode; Hyatt et al. 2010). For a more accurate functional annotation, we used the amino acid sequence of predicted genes. The annotation was done using the BlastP command implemented in DIAMOND (v. 0. 7. 9, with a maximum e-value of 10 À 5 ;  Buchfink et al. 2015)  with the 2014 edition of the COGs database  (Galperin et al. 2015) , supplemented with a set of genes from available Chloroflexi genomes (supplementary table 1, Supplementary Material online). In the subsequent analyses, we retained only the best hit to represent each annotated gene.
Reconstruction and Phylogenetic Affiliation of Chloroflexi Genomes We applied compositional binning to sequence reads mapping to contigs affiliating to Chloroflexi from five microbialite metagenomic data sets in Lake Alchichica (AL-W, AL-N-1, AL-N-5, AL-N-10, and AL-N-15) and several reference genomes. The ESOM visualization maps (supplementary fig. S2 , Supplementary Material online) of all data sets showed two major features. First, each reference genome was contained in a single, well-defined cluster. Second, the candidate Chloroflexi contigs displayed a similar pattern across all five data sets, with three to five clusters (named A-E; supplementary fig. S4 , Supplementary Material online). However, comparison of the cumulative size of the cluster contigs, estimated completeness and estimated contamination collectively suggested that only cluster A contigs could be considered as candidate bins (supplementary tables 1 and 3, Supplementary Material online).
Interestingly, an analysis of sequence divergence revealed a very high level of similarity across the five data sets (AL-W and AL-N-1, AL-N-5, AL-N-10, and AL-N-15) in this cluster. Both amino acid and nucleotide sequences showed that 92% of all genes were shared between data sets and nearly identical (i. e. , average and median percentages of gene alignments maximum bitscores approaching 100% for amino acids and nucleotide sequences; fig. 1  and supplementary fig. S5A-F , Supplementary Material online). Furthermore, computed ANI values between all draft genome pairs (>99%; supplementary fig. S5G , Supplementary Material online) were higher than commonly used speciation cutoff values (95-96%;  Goris et al. 2007; Richter and Rossell o-M ora 2009). Even though the contigs could not be merged to form a single reliable scaffold, a synteny analysis conducted on the individual contigs at nucleotide level showed that these were highly conserved along most of their length among the different data sets (fig.
In brief, these numbers were consistent with the total nucleotide length, contig, and gene number of each data set, AL-W being the least and AL-N-15 the most profuse data sets, respectively. However, these differences did not seem to be randomly distributed among COG categories (supplementary fig. S8 , Supplementary Material online). Leaving aside the "RNA processing and modification" category that represents only five genes, the most salient features were as follows. First, the two largest bins, AL-W and AL-N-5 (table 1), showed that M/D genes were A Novel Microbialite-Associated Phototrophic Chloroflexi Lineage GBE Genome Biol. Evol. 12(7):1207-1216 doi:10. 1093/gbe/evaa122 Advance Access publication 16 June 2020 distributed across all categories, with only a few exceptions. Second, AL-W appears to have an extra set of genes in the "Translation, ribosomal structure and biogenesis" and, to a lesser extent, the "Amino acid transport and metabolism" categories, which is due to the presence of a duplicated ribosomal protein operon in this data set.
Acknowledgments This work was supported by the  European Research Council  Grants ProtistWorld (PI P. L. G. , Grant No. 322669 ) under the European Union's  Seventh Framework Program , the RTP G enomique environnementale of the  CNRS  (project  MetaStrom , PI D. M. ), and the  ANR  project  Microbialites  (PI P. L. G. ).
| Thematic analysis The transcribed data were entered into the qualitative data analysis software NVivo 12 (NVivo qualitative data analysis software; QSR International Pty Ltd. Version 12, 2018) for data management, and analysed thematically for codes and themes. Thematic analysis of data followed six steps as described by Braun and Clarke; familiarizing oneself with the data, generating initial codes, searching for themes, reviewing themes, naming, and defining themes. 26 Two researchers (M. S. O. and N. P. ) were involved in the thematic analysis of the data. The initial codes were developed inductively, in that they were driven from the data and not by any pre-existing theory or coding framework. The initial codes were identified by M. S. O. , iteratively refined within the research team, and collated into potential, and final themes by both M.
| FRAM: Building the FRAM model The FRAM model was built using the FRAM model Visualiser tool. There are five steps involved in developing a FRAM model. The first step is to identify the primary purpose of the FRAM analysis and identify functions that are essential for work to be carried out. In this case, FRAM was used to demonstrate co-administration practice. The second step is to identify the functions that are required for everyday activities and how each function relates to another. Functions in this context refer to people's actions to achieve or perform a specific task either individually or collectively. The functions were identified by was obtained from all participants before taking part. Each participant was allocated a number to ensure anonymity during coding processes, and all data kept confidential.
Research papers on dermatology nowadays tend to focus on the dermoscopy modality used to perform automatic classification of lesions. Most of them obtain acceptable results with melanocytic pathologies  [3]. Older methods focus on finding the most pertinent combination of preprocessing steps and handcrafted features to be used in a machine learning scheme  [4, 5]. By contrast, most recent methods use deep learning approaches, and they have yielded impressive results in this discipline  [6]. In this particular study, the authors used an Inception-V3 architecture pre-trained on the "ImageNet" database  [7] , and they fine-tuned this model on a dataset of 129,450 clinical images containing 2032 different skin lesions and distributed across 757 classes. They carried out the classification at different taxonomy levels, and at the first level of classification (Non-neoplastic versus benign versus malignant), they achieved an accuracy of 0.
Only images considered relevant for the diagnosis by two out of the three investigators were retained, at different depths of the skin: the epidermis, the dermal-epidermal junction (DEJ), and the dermis. However, most of the information was acquired at the DEJ. Each RCM image corresponding to a horizontal 920 µm × 920 µm section of the skin at a selected depth with a lateral resolution of 1 µm and axial resolution of 3 µm to 5 µm. For specifications, these images have a spatial resolution of 1000 × 1000 pixels with quantification on a single 8-bits channel. The relative position between images of a single patient was unknown, and this could not provide any further knowledge in the next part of this work. Furthermore, the metadata available regarding the age of the patients was not used as the purpose of this work was to evaluate the relevance of image classification techniques, although it was initially provided to the experts during their assessments.
These data include 223 lesions from 201 patients, for a total of 7846 RCM images. Generally, one lesion is equal to one patient so we will discuss of a lesion as a patient in the next paragraphs. Each of these cases varies between 2 and 833 images (with a mean of 35 and a standard deviation of 64). For each patient, the data provided a diagnosis based on clinicopathological correlation that served as a reference basis and there was the following distribution of cases: • 135 patients had "malignant" tumors: 115 LM/LMM and 20 basal cell carcinoma (BCC). • 88 patients had "benign" tumors: mainly represented by solar lentigines. No collision tumors were included in this series. Also, the study  [27]  evaluated 21 experts, and they achieved a mean sensitivity of 0. 80 (range 0. 66-0. 90, standard deviation 0.