dataesr/scientific-paragraphs-categorization huggingface dataset:
Confocal laser scanning microscopy (CLSM) and correlative with scanning electron microscopy (SEM) For standard CLSM analyses, cells concentrated using Cell-Trap units were deposited on a glass slide, covered by a coverslip and sealed with nail polish. Samples were observed by CLSM using a Zeiss LSM 710. Excitation was performed at 405 and 488 nm and emission spectra were measured for each pixel of the images over the 405-720 wavelength range using a 34channel Quasar T-PMT detector. Data were processed using the Zeiss Zen software. For correlative CLSM-SEM, cells were 140 first washed with milliQ water and then deposited on a coverslip and left to dry. The coverslip was mounted onto a KorrMik Life Sciences sample holder and the correlative Shuttle and Find software implemented in ZEN 2012 was used.
For the search of silicate rings in samples, backscattered electron (BSE) images were acquired using an angle selective backscattered (AsB) detector at an accelerating voltage of 10 kV and a working distance of ~7. 5 mm and a 60 nm aperture at high current. The elemental composition of mineral phases was determined under the same conditions as BSE imaging by energy dispersive x-ray spectrometry (EDXS) using an EDS QUANTAX detector 155 (Bruker). EDXS data were analyzed using the ESPRIT software package (Bruker). https://doi. org/10. 5194/bg-2023-90 Preprint. Discussion started: 9 June 2023 c Author(s) 2023. CC BY 4. 0 License.
Semi-quantitative analyses of EDXS spectra were performed using the JEOL Analysis Station software. This was based on the use of K-factors. After subtracting out the background noise in the EDXS spectrum, the software performed a Gaussian fit of selected elemental peaks and calculated the area under the peaks. From this, the atomic percentage of each selected element was assessed. EFTEM elemental mapping of C, Fe and Mn was performed using the 3-window technique  (Hofer et al. , 1997). This technique requires three energy-filtered images: two 170 positioned before the ionisation edge (pre-edge images), which serve to calculate the background, and one positioned just after the edge (post-edge image). Calculated background image was subtracted from the post-edge image to give an elemental map, in which changes in background shape were considered.
These 16S rRNA gene sequences were used to screen a large dataset of 16S rRNA gene metabarcoding sequences of plankton from 11 Mexican lakes, including La Preciosa  (Zeyen et al. , 2021; Iniesto et al. , 2022). We detected sequences closely related 235 (>99% identity) to those of the silicate-ring-producing cyanobacteria in nine of these eleven lakes: La Preciosa, Alchichica, Atexcac, Aljojuca, Alberca de los Espinos, Pátzcuaro, Quechulac, Tecuitlapa and Yuriria (Supplementary Table  2 ). These sequences were particularly abundant (representing >1% of all bacterial metabarcoding reads) in La Preciosa, Alchichica, https://doi. org/10. 5194/bg-2023-90 Preprint. Discussion started: 9 June 2023 c Author(s) 2023. CC BY 4. 0 License. Atexcac, Aljojuca, and Alberca de los Espinos, sometimes reaching frequencies up to 15%.
No Mn or Si enrichment was reported by any of these studies but mineralogical analyses were too limited to be conclusive. One difficulty in inferring phylogenetic affiliations is 325 that sequence data for all these cyanobacterial morphotypes is not available. Although the morphological features of picocyanobacteria alone cannot be used to reliably discriminate between different taxa  (Callieri et al. , 2012) , the morphotype https://doi. org/10. 5194/bg-2023-90 Preprint. Discussion started: 9 June 2023 c Author(s) 2023. CC BY 4. 0 License. observed in La Preciosa resemble Cyanocatena cells  (Hindák, 1982) , at least regarding their biomineralization pattern. Therefore, we propose to ascribe our new 16S rDNA sequence to this genus, as Cyanocatena sp. The taxonomic affiliation of this sequence allows to start investigating the potential prevalence of this unusual biomineralization process in different lakes 330 by the analysis of massive metabarcoding datasets Although we ignore the molecular mechanisms of this biomineralization process, several potential functions can be speculated for this trait: (i) High amounts of Fe and Mn are needed by cyanobacteria compared with other microorganisms  (Raven et al.
Acknowledgements This study was supported by the  INSU programme Interrvie , the  ERC  grants  Calcyan  (PI: K. Benzerara, Grant Agreement no. 307110 ) and  Plast-Evol  (PI:  D. Moreira , Grant Agreement no. 787904 ), and the  Agence Nationale de la Recherche  (PI:  P. López-García ,  ANR Microbialites , grant number  ANR-18-CE02-0013-02 ). We thank  Lucie Franco  and  Michael Bourges  for helping with FACS experiments;  Mélanie Poinsot ,  Jena Johnson ,  Daniel Nothaft  for help in the field;  Franck Bourdelle  for 375 help on STXM experiments;  Mickael Trichet  for help with lyophilisation. The authors acknowledge  SOLEIL  for the provision of beamtime.
In addition to the variability in the conditions of preparation, we also consider the speciation heterogeneity within a given sample by adding to the dataset the spectra recorded at the center/core or lobe/border of the extrudate. Figure  5  displays the EXAFS spectra and FT associated with the dataset of the wet samples. Among the spectra presented in Figure  5 , the spectrum belonging to the core of the IWI sample (Figures  5(a1 ) and (b1)), already presented in Figure  4 , clearly stands out from the rest of the dataset. In contrast, for most of the wet samples, there is a high degree of similarity of EXAFS spectra and associated FT moduli (Figures  5(a2 ) and (b2)). PCA/SVD analysis has been performed to the EXAFS spectra of the wet samples. PCA is a dimension-reduction technique for correlated data whose goal is to determine the minimum number of components necessary to describe the majority of the variance of the dataset and leave apart the components that mainly describe the noise of the data.
A similar methodology to the one applied to the impregnation cell was used to validate the drying cell, i. e. , PCA analysis, reproducibility and speciation. Figure  10  compares the EXAFS spectra and associated FT moduli measured for the dried samples. The shape and intensity of the EXAFS oscillations in the 5-11 Å À 1 region display strong variance after drying (Figure  10(a) ). In comparison with the FT  in interaction with the support. However, the still remaining contribution at 3. 1 Å, which is associated with the Mo second nearest neighbors, evidences that condensed species are still present after drying and in larger abundance than the one reported for the maturated IWI. Regarding the EXAFS spectra of the dried samples, the PCA analysis reveals that 4 PCA components would be necessary to explain most of the variance of the dataset shown in Figure  10  (scree plot and PCs available in Figure  S5  in SI).
In this case, it is not so obvious which Mo-species, among the 11 references building our EXAFS spectra library, will be assigned to these components. The characterization, by Raman microspectroscopy, of the same samples, will help to significantly reduce the number of references to be considered for the LCF of EXAFS data measured at the end of drying. The Raman spectra, recorded for lab-dried extrudates obtained for different impregnation conditions, is presented in Figure  11(a). The Raman line at around 974 cm À 1 can be assigned to the presence of the lacunary/substituted Keggin's HPA and/or Dawson's HPA. In addition, the shift of the main peak from 940 to 936 cm À 1 from IWI to EI 4WRV, together with the increase at 892 cm À 1 , suggests an increase of MoO 4 2À in relation to Strandberg and/or polymolybdates.
The measurement is performed across the 1. 6-1. 7 mm extrudate length within a 300 × 300 μm 2 squared region on the sample, delimited by two sets of horizontal and vertical slits before I 0. Except when mentioned, the measurements were performed at the center of the extrudates. Raman spectroscopy of the catalysts was performed with a Horiba SOLEIL spectrometer at 532 nm and using a 500 nm-1800 g/mm diffraction grating. The catalyst was placed on a glass microscope slide for the analysis. Before spectra acquisition, a calibration using a silicon reference wafer was performed. Raman spectra were acquired between 100 and 1100 cm À 1 with a X20 objective, with a spatial resolution of 50 μm. The laser power used was 7. 5 mW with an exposure time of 40s in acquisition mode of point by point. LabSpec 6 software was used for spectra acquisition and preprocessing of the spectra including removal of spikes and baseline correction.
Further processing included normalization of the spectra to the more intense Raman line in the 900-1000 cm À 1 range, which rescaled the intensity of data to [0,1], and smoothing using a gaussian-weighted moving average over each window of 4 points in Matlab®. The solutions were analyzed with a Kaiser RXN1 spectrometer, equipped with a 532 nm laser delivering 60 mW to the fiber coupling probe head equipped to a 1 = 4 '' optical immersion probe with a sapphire window. The Raman spectra were acquired between 200 and 4000 cm À 1 using a holographic dispersive grating. Processing included baseline correction in LabSpec 6 and normalization with rescaling the intensity data to [0,1] in Matlab®. Processing of the XAS spectra included the merge of consecutive 250 ms spectra recorded with ascending energy using IC in order to improve the S/N ratio, without compromising too much the timeresolution necessary for the description of the dynamic processes of interest.
During the spectrum normalization, the so-called edge jump, corresponding to the difference between the linear pre-edge function and the smooth post-edge background, is calculated. Both the preand the post-edge are determined at 20003. 9 eV. In a transmission experiment, according to the Beer-Lambert law, at which the X-ray absorption spectroscopy obeys, the edge jump is dependent on the quantity of absorbing atoms in the thickness x of the sample probed by the X-rays. EXAFS extraction and FT calculations were performed using Jupyter notebooks based on the Larch Python® scripts  [27]  and adapted by one of us to the outcome of the normal_gui interface. The processing of hundreds of spectra at a time can be then performed. The extraction of the EXAFS spectra from the XAS starts by a preview, in which the extraction parameters for a selected spectrum are tuned.
This includes a two-step process where the EXAFS spectrum is extracted by subtracting from the previously normalized spectrum a background using a spline function which approximates the atomic absorption. The result is plotted as a function of k, the photoelectron wave-vector, considering a threshold energy E 0 which must be selected by the user and according to the relation given by Equation (5). The EXAFS spectrum is weighted by k 3 , which intensifies the damped sinusoidal waves of the EXAFS spectra at large k values. k ¼ ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffiffi 2 � m � ðE À E 0 Þ=� h 2 q (5) The Analyses of the EXAFS spectra include firstly PCA, in order to have a first estimation of the number of different species accounting for the data variance and, consequently, the number of references to consider in the fitting without overfitting the data and, secondly, LCF of the spectra.
These analyses are done in Matlab® using two Graphical User Interfaces developed by T. Putaud, XAS_SVD and MATCLAX and the spectra from a library from C. Lesage. [16] The assessment of the LCF model is performed regarding the R factor (R f ). The R f is given by Equation (  6 ): R f % ð Þ ¼ 100 � ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffiffi P j e j À r j À � 2 P j e 2 j s (6) where e j and r j represent the j th index of the vectors corresponding to the experimental spectrum and the reconstructed spectrum, respectively. The index j varies from 1 to 331 which corresponds to the k scale from 0 to 16. 5 Å À 1 with 0. 05 Å À 1 step. The closer R f is to 0 %, the better the model.
The work at the ROCK beamline was supported by a public grant overseen by the French National Research Agency (ANR) as part of "Investissements d'Avenir" program (reference: ANR-10-EQPX-45). The authors are very grateful to the SOLEIL's mechanical service,  Carlos de Oliveira ,  Sylvain Bonnin ,  Armindo Marques ,  Jessy Bouillaud  and, in particular, Pascal Prout, for their diligence and competence in the cell manufacturing.
Procedures All data were collected via a Google Form link, between July and August 2022. The survey needed between 10 and 15 min to be completed. The project was advertised on social media and the research team approached friends and family members; the link was shared among those who accepted to participate through social media applications (WhatsApp, Instagram, Facebook). Consequently, participants were asked to forward the link to other friends and family members they know, explaining the snowball technique. Inclusion criteria for participation included being of a resident and citizen of Lebanon, aged over 18 years, and willing to participate (Further details about data collection can be found in  [102] ). After providing digital informed consent, participants were asked to complete the instruments described above, which were presented in a pre-randomized order to control for order effects.
Analytic Strategy Data treatment. There were no missing responses in the dataset. To examine the factor structure of the SSS-CD, we used an EFA-to-CFA strategy [107]. To ensure adequate sample sizes for both EFA and CFA, we split the main sample using an SPSS computer-generated random technique; sample characteristics of the two split-halves are reported in Table  1. There were no significant differences between the two subsamples in terms of mean age, t(568) = 0. 984, p =. 326, education level χ 2 (1) = 2. 22, p =. 136, marital status χ 2 (1) = 0. 002, p =. 965 and the distribution of women and men, χ 2 (1) = 0. 296, p =. 587. Exploratory factor analysis. To explore the factor structure of SSS-CD, we computed a principal-axis EFA with the first split-half subsample using the FACTOR software  [108, 109].
e. , ≥ 0. 4)  [116]. Confirmatory factor analysis. We used data from the second split-half to conduct a CFA using the SPSS AMOS v. 29 software. A previous study suggested that the minimum sample size to conduct a confirmatory factor analysis ranges from 3 to 20 times the number of the scale's variables  [117] , which was surpassed in our study. Our intention was to test the model extracted from the EFA. Parameter estimates were obtained using the maximum likelihood method and fit indices. The normed model chi-square (χ²/df ), the Steiger-Lind root mean square error of approximation (RMSEA), the Tucker-Lewis Index (TLI) and the comparative fit index (CFI). Values ≤ 3 for χ²/df, and ≤ 0. 08 for RMSEA, and 0. 90 for CFI and TLI indicate acceptable fit of the model to the data  [118]. Gender invariance.
Two sets of six choice tasks were experimentally designed using the Ngene software so that the respondents' choices provide the maximum information regarding their preferences over the attributes and levels. All choice tasks were rationally consistent, with lower levels of reduction in farmers' use of glyphosate being associated with lower increase in price of weekly shopping basket of food. S1 Table in the S1 File provides a full description of the 12 choice tasks and the way they were grouped in the two choice sets of 6 choice tasks. Our sample of respondents consists of 2050 households' primary grocery shopper, aged 18-70 years-old. Respondents were recruited by the survey agency IPSOS, which abides by the ICC/ESOMAR International Code on Market and Social Research regarding ethics in social sciences research.
CPR genomes contain a high percentage of genes that code for proteins with unknown functions; "orphan", also known as hypothetical proteins  [15]. A large comparative genomic study identified many functional domains that can confer antibiotic resistance activity in the hypothetical proteins  [16]. New technologies, Microorganisms 2023,  11, 1933  2 of 14 such as high-throughput methods, have largely contributed to the increasing number of new genomic sequences added to public databases, and powerful computational tools have created important links between molecular biology and enzymology  [17]. However, despite many advances, the current data reveal significant gaps in knowledge on CPR, especially the functional role of hypothetical proteins and enzymes with antibiotic activity in terms of their colonization capacity and environmental expansion.
Interestingly, 14 MBL-like sequences (22 copies) did not have the group-B-specific pattern "H-x-[HDE]-x-D-H" (Figure  1 ). It is noteworthy that the distribution of unique and duplicated CPR MBL-like sequences across different motifs was the same. Therefore, the multiplication of these sequences in the CPR genomes did not significantly highlight a specific repeated type of sequence or a specific character in the MBL-like motif between CPR genomes. Furthermore, the length of the identified unique CPR MBL-like sequences had an average of 425 amino acids, ranging from 70 to 1038 amino acids (Figure  2 ). These sequences were almost two-and-a-half times longer than the bacterial sequences of the betalactamase database (BLDB) (average length of 266 amino acids [ranging from 235 to 428 amino acids]) (Figure  2 ).
MBL-like sequences from CPR showed a small percentage of similarity, ranging from 20% to 40%, with those from bacteria (Figure  3 ). This high divergency in protein sequences was also found among CPR MBL-like sequences in all superphyla (with a mean of 44. 9% [18. 75% to 100%] of similarity percentage) (Figure  3 ). In contrast, there was a high similarity percentage between MBL sequences for those of BLDB (with a mean of 94. 6% [34. 7% to 99. 6%]) (Figure  3 ). Furthermore, the length of the identified unique CPR MBL-like sequences had an average of 425 amino acids, ranging from 70 to 1038 amino acids (Figure  2 ). These sequences were almost two-and-a-half times longer than the bacterial sequences of the beta-lactamase database (BLDB) (average length of 266 amino acids [ranging from 235 to 428 amino acids]) (Figure  2 ).
These CPR MBL-like sequences, with the 24 MBLs expressed in vitro (n = 24) and the MBL sequences from BLDB (n = 761), were found to exhibit a conserved and common functional protein fold according to the Conserved Domain Database (CDD) search (see Table  S3  in the Supplemental Material). This common fold, "cl23716", has a metallohydrolase activity that carries out multiple biological functions. For this purpose, we again searched for the presence of different protein domains in this common fold, "cl23716", by reusing the CDD search (Table  S3 ). Our findings showed that the CPR MBL-like sequences had the highest number of different types of protein domains (n = 101, mean = 8 domains/sequence [1 to 31]) compared to the expressed one (n = 22, mean = 2. 5 domains/sequence [2 to 7]) and those of BLDB (n = 19, mean = 3.
Thus, 134 and 102 CPR sequences shared 4 and 17 protein profiles with 12 sequences of those already expressed in vitro and 116 BLDB sequences, respectively (Figure  5 ). There were only 2 common protein profiles between 2 expressed sequences and 72 of BLDB (Figure  5 ). It is notable that CPR had a significant number of unique protein profiles (n = 593) in comparison to 29 unique profiles in BLDB (n = 554 sequences) and 6 in the expressed sequences (seven sequences) (Figure  5 ). These unique CPR protein profiles were found in 92% of MBL-like sequences (n = 3087) (Figure  5 ). We mapped the large dataset of MBL-like sequences from CPR onto clusters of those expressed in vitro in the sequence similarity network (SSN) to visualize their connectivity. By integrating CPR sequences into the expressed sequences, we were able to distinguish those which were not yet characterized, allowing us to make functional hypotheses.
Recent studies reported that distinct environments and microorganisms are reservoirs for novel beta-lactamase encoding gene precursors  [16, 44, [59] [60] [61] [62] [63]. This large dataset of uncharacterized MBL-like sequences from CPR could present an ideal candidate to elucidate this functional diversity. Functional exploration can focus on the in vitro test of one sequence from each cluster to enable a functional prediction of all sequences from this cluster. Given that MBL proteins are promiscuous enzymes, there is a strong chance of finding their native activity when studying the MBL-like sequences from CPR genomes. Our findings contribute to the comprehension of the potential multifunctionality exhibited by the MBL-like sequences found in CPR genomes, extending beyond their involvement in beta-lactam resistance.
Data Set Curation for Protein Function Profiling In order to compare the CPR MBL-like sequences with other MBL sequences, we retrieved the 24 expressed MBL sequences in vitro from the Swiss-Prot database (manually annotated and reviewed)  [28]  and the MBL sequences from the beta-lactamase database (BLDB) (all MBL available up to 1 November 2021) (n = 761)  [69]. We identified the different functional domains that are present in the common "cl23716" MBL fold of all query sequences (n = 4134) using the CDD search with a maximum e-value of 0. 0001. In this paper, we always refer to similar functional domains (with the same accession number), when conserved patterns of amino acids were detected in a protein query sequence based on local multiple sequence alignments of NCBI CDD search. The analysis of the different protein sequences of the MBL superfamily for the three groups of sequences enabled the identification of the common and unique protein domains found in each sequence.
Microorganisms 2023 , 15 Figure 2. 2023152 Figure 2. Boxplot showing the length of metallo-beta-lactamase (MBL) sequences of the beta-lactamase database (BLDB) and CPR MBL-like sequences, based on the number of protein domains detected according to the Conserved Domain Database (CDD) search. CPR sequences are shown in black while those of BLDB are in red.
Acknowledgements: We thank  Christian Felber ,  Nikolas Vellnow , and  Dita B. Vizoso  for assistance during the experiment, and  Jürgen Hottinger ,  Daniel Lüscher , and  Urs Stiefel  for technical support. This study was funded by a grant from the  Swiss National Science Foundation  to L. S. ( SNF  grant  31003A-143732 ).
Patients The current study used data from a pooled dataset comprising patients with CHB who discontinued NUC therapy as part of studies or clinical practice in centres in Europe and Asia. 2, 3, 8  Patients were eligible for the current analysis if they had been treated with only NUCs (no history of pegylated interferon add-on was allowed) and if they (1) were HBeAg negative at the time of treatment initiation, (2) had available data on HBV DNA levels before therapy initiation, (3) had undetectable HBV DNA at NUC cessation, and (4) had available data on both HBsAg and hepatitis B core-related antigen (HBcrAg) levels at treatment cessation. Based on these selection criteria, we identified 757 patients for further analysis. This research was conducted in accordance with both the Declarations of Helsinki.
>20,000 IU/ml), and stratified by EOT HBsAg (<100 and >100 IU/ml) or HBcrAg levels (undetectable and detectable) and compared using log-rank tests. We also performed multivariable Cox regression analyses, which were adjusted for previously reported predictors of outcome after therapy withdrawal, including antiviral agent, patient ethnicity (or HBV genotype), age, sex, EOT viral antigen levels, and baseline and EOT ALT levels. Analyses were performed using SPSS version 28. 0 (IBM, Armonk, NY, USA). All statistical tests were two-sided and were evaluated at the 0. 05 level of significance.