dataesr/scientific-paragraphs-categorization huggingface dataset:
For each array, we quantified the sequence identity of its repeats with all repeats of the typed CRISPR repeats. We used a global alignment with no gap end penalty and equal gap creation and extension penalties (-3) using the module pairwise2 from Biopython (function align. globalxs). We took the best hit among those scores. If the identity score was higher than 74%, we classed the array of repeats to the subtype of the best hit.
Phylogenetic analyses We identified the families of orthologous proteins present in >90% of the genomes (when larger than 1 Mb) of two phyla: Firmicutes (3003 genomes), and Proteobacteria (6986 genomes). The genomes were obtained from Gen-Bank's RefSeq dataset as indicated above. The orthologs were identified as reciprocal best hits using an end-gap free global alignment, between the proteome of a pivot and each of the other strain's proteomes (as in  (31) ). Escherichia coli K12 MG1655 and Bacillus subtilis str. 168 were used as a pivot for each clade. Hits with fewer than 37% similarity in amino acid sequence and >20% difference in protein length were discarded. The persistent genome of each clade was defined as the intersection of pairwise lists of orthologs that were present in at least 90% of the genomes representing 435 protein families for Firmicutes and 387 for Proteobacteria.
The types IV, V and VI are extremely rare in the current genome database--they were found in fewer than 70 genomes--and are mainly restricted to a few clades (Proteobacteria, Actinobacteria, Bacteroidetes). Some subtypes are present in many clades--I-B, I-C, II-C, III-A, III-B, III-D--while others are clade specific, e. g. subtype I-D is mostly found in Cyanobacteria and Archaea, II-A in Firmicutes and II-B in Proteobacteria (Figure  1B ). The relative abundances of CRISPR and Cas subtypes are close to those reported previously  (8, 30) , although with our larger dataset type II appear more abundant than type III in Bacteria, in contrast with other studies  (8, 13, 38). Overall, the number of systems per genome does not show systematic variations with genome size, except that small (<1 Mb) genomes rarely encode these systems (Supplementary Figure  S1A ).
We observe that most bacterial genomes lack cas clusters, but 9% have more than one cluster. To understand if epistatic interactions between different systems could explain the co-occurrence of these multiple cas clusters, we analysed the co-occurrence of all pairs of Cas types. We used phyloglm to integrate the information of the phylogenetic structure in the evaluation of these associations  (39). Since phylogenetic inference of all the prokaryotes is very inaccurate, we restricted our analysis to Firmicutes and Proteobacteria, the two clades with more genomes in our dataset (78% of the total). We inferred 100 phylogenetic trees for these clades (to account for uncertainty in phylogenetic inference), and used them to test the associations between every pair of systems (Figure  2A  and B ).
The latter result, is consistent with previous findings  (42). In consequence, the presence, proximity and subtype of cas clusters impact the number of repeats in CRISPRs. Several natural and synthetic systems revealed that CRISPR arrays can be used in trans by cas genes  (7, 43). We therefore tested whether CRISPRs distant from CRISPR-Cas loci could function with the latter for immune defence. If true, one would expect that the repeats of these CRISPRs should be similar to those of the cas clusters in trans. To test this hypothesis, we typed the CRISPRs outside CRISPR-Cas loci and checked if they matched the subtype of CRISPR-Cas systems in the genome. We typed these CRISPRs using the information on the best hit of the repeats of the CRISPR to a databank of 4978 unique repeats that could be unambiguously assigned to a Cas subtype (because they were taken from CRISPRs of CRISPR-Cas loci with one single cas cluster, Supplementary Table  S2 ).
We used a logistic regression to set the identity score threshold above which a best hit could be reliably associated with a Cas subtype. We chose a threshold that corresponds to an identity score of 74% (Figure  4B , Supplementary Figure  S3A ). The analysis of the original dataset shows 3961 correct and 843 incorrect assignments (accuracy of 82%). This method could be useful in metagenomic studies, where most of the detected elements are effectively orphan because most contigs are very small. We show in supplementary material a proof of concept for the classification of CRISPRs from metagenomics data (Supplementary Text1, Figure  S3C  and Table  S6 ). The method allowed the assignment of subtypes to 54% of the orphan arrays and 81% of the distant arrays (Figure  4C ). The different levels of success in typing the two classes of CRISPR may be explained by the presence of spurious CRISPR arrays in the orphan dataset  (44) , i.
CRISPRs in mobile elements match chromosomal Cas systems It has been shown that bacterial lysogens are more likely to encode CRISPR-Cas systems than bacteria devoid of prophages  (28). Recently, the ICP1 phage of Vibrio cholerae was shown to carry a CRISPR-Cas system to subvert its host immune defences  (19). Following on these studies, we wished to assess the frequency with which CRISPR-Cas systems occur in phages and if there are significant associations between these systems and those of the host. We quantified the occurrence of CRISPR-Cas systems in the 2498 phages of RefSeq. Using PHACTS, we could characterize the lifestyle of more than half of the phages, among which 42% are virulent and 58% are temperate. We found only one complete CRISPR-Cas system and 21 CRISPRs in these genomes (Figure  6 ).
We then analysed the sequences of 26987 prophages to search if temperate phages that successfully infected a host were more likely to encode CRISPR-Cas systems than the other phages. We only detected 27 complete CRISPR-Cas systems, seven cas clusters and 212 CRISPRs. These values are very similar to those identified in the phage dataset, once the different number of elements is accounted for. We conclude that, within the limits given by the size and diversity of current genome databases, CRISPR-Cas systems are extremely rare in phages. Their frequency in prophages is not significantly different from that of the average temperate phage, suggesting that prophages carrying these traits do not significantly increase in frequency by natural selection on the host. We then turned our attention to plasmids.
We observed that genomes encoding CRISPR-Cas systems are slightly more likely to have plasmids (37%) than the others (34%, 2 P < 0. 0001). We searched for CRISPR-Cas systems among 11 805 plasmids that were sequenced in the wholegenome projects analysed in this study. This means that we know the chromosome of the bacterial host of every plasmid in the database. We found 173 complete systems and 150 CRISPRs in plasmids devoid of cas clusters (Figure  6 ). Plasmid CRISPRs have on average 16 repeats, significantly fewer than chromosomal arrays (20 repeats) (Supplementary Figure  S5A  Mann-Whitney, P < 0. 001). The relative abundance of subtypes is also significantly different on plasmids and chromosomes (Supplementary Figure  S5B ). In particular, no plasmids encode type II-A CRISPR-Cas systems while type IV systems are encoded almost exclusively on plasmids, as described previously  (45).
DISCUSSION We detected and analysed thousands of CRISPRs and cas clusters in fully sequenced bacterial and archaeal genomes. We used the entire RefSeq complete genome database, and therefore there was no bias in our analysis of the data, apart from the bias of the database itself, which is known to over-represent cultivable Bacteria over other Prokaryotes. We have not used draft genomes because they would have greatly increased the sampling biases, since the drafts database is overwhelmingly dominated by a dozen species of bacterial pathogens, and because one cannot reliably analyse chromosomal distances between genetic elements in drafts nor to discriminate accurately plasmids from chromosomes. It is possible that extremely rare systems in our dataset--types IV, V, and VI--are more frequent in poorly sampled clades.
However, it should be noted that in our study these three systems tend to be overrepresented in phyla that are well sampled in the database (Proteobacteria, Actinobacteria and Bacteroidetes). Further work and much broader samples will be needed to understand if these sys-tems are rare and why this is so. In this study, their low frequency resulted in no significant association with other systems. Our analysis also revealed that 18% of CRISPRs have fewer than five repeats. One third of these small CRISPRs were in CRISPR-Cas loci, but part of the other small CRISPRs might be false positives. To control for their impact on our results--when necessary--we made replicates of the analyses using only CRISPRs with five or more repeats. These analyses resulted in qualitatively similar conclusions (Table  S3 ).
We used CasFinder to identify and type cas clusters, which was previously shown to provide accurate classifications  (29). As our detection takes into account the architectures and signature proteins of cas clusters, it provides a robust subtype assignment compared to a previous study where subtypes were only inferred from Cas1  (13). To associate the CRISPRs outside CRISPR-Cas loci to Cas types we used the sequence similarity to a database of known repeats. This part of our method might gain from the diversification of the genome database, since rare CRISPR-Cas systems are under-represented. A larger reference database might also allow to define subtypespecific sequence identity thresholds. Such developments could pave the way to understand if our inability to type half of the orphan CRISPRs (because they lack high sequence similarity to known repeats) is due to the lack of a sufficiently comprehensive repeat database or to the rapid evolution by drift of orphan arrays.
Consistent with previous analyses  (8, 13) , we observe that most bacteria lack CRISPR-Cas systems. This is puzzling, because most species have mobile genetic elements against which these systems might presumably provide protection. For example, half of the genomes in the database are lysogens  (28) , and many have plasmids (this work). If CRISPR-Cas are universally efficient immune systems, why is it that most bacteria lack them? And why is that many of the remaining bacteria have small CRISPRs, which presumably provide protection against few MGEs? This cannot be caused by phylogenetic inertia, i. e. the fact that certain lineages have not developed such systems, since CRISPR-Cas systems are frequently transferred across phyla  (14, 15, 46, 47). Instead, it has been proposed that the deleterious effects of CRISPR-Cas systems on the host, either by spacers targeting the chromosome  (48) , or by interfering with DNA repair functions (49) could explain the relative paucity of CRISPR-Cas systems across Bacteria.
Distant CRISPRs often have repeats compatible with the cas locus in trans. This may be explained by several mechanisms. Multiple CRISPRs with similar repeats may arise from the fission of an ancestral single CRISPR. They may also result from multiple acquisitions of a similar CRISPR-Cas systems that leads to loss of one of Cas locus and maintenance of the cognate CRISPR because it is functionally compatible with the existing complete CRISPR-Cas locus. Finally, recent data suggests that non-specific integration of spacers at non-CRISPR locus could lead to the creation of CRISPRs with repeats similar to the existing CRISPR-Cas system  (56). The compatibility between distant CRISPR and CRISPR-Cas systems may also result from natural selection. If MGEs bringing into the genome isolated CRISPRs use them to co-opt the CRISPR-Cas system of the host, then the repeats of their CRISPRs must be compatible those of the host for co-option to work.
Finally, a recent study has shown that CRISPRs spacers can facilitate horizontal transfer of neighbouring bacterial genes by a process resembling specialized transduction  (60). One is tempted to speculate that the presence of multiple CRISPRs in the chromosome could be selected to increase the local rates of genetic exchanges. Little was known about the frequency of CRISPR-Cas in phages and plasmids, in spite of the previous studies describing their existence and relevance  (16) (17) (18) (19). While this paper was in review, an analysis was published showing that most of the phage-encoded CRISPR-Cas loci lack the genes required for adaptation, suggesting that, like we observed for plasmids, phages tend to encode either the interference mechanisms or just the CRISPR  (21). Interestingly, this study showed that most spacers of phageencoded CRISPRs have good matches in the database, usually matching other phages or prophages  (45).
It must however be considered that phages rarely encode such systems. A recent preprint identified CRISPR-Cas systems in phages with very large genomes  (61) , but even in this case their frequency was very low. Here, we show that CRISPR-Cas are almost never carried by the phages available in the genome databases. This does not invalidate the previous results showing that CRISPR-Cas carried by phages may provide the latter with a mechanism to escape host innate immunity  (19). Yet, if the current phage database is representative of the natural diversity of these elements, such mechanisms are rarely used by phages and may be specific of a few families. Interestingly, the much higher frequency of plasmids carrying CRISPR-Cas and especially CRISPRs compatible with the chromosomal cas clusters opens the possibility for plasmids to manipulate the host immunity by using the host Cas proteins and their own CRISPRs.
Although there is no incompatibility between them, i. e. no inversion of sign in the significant associations, many interactions that are significant in one clade aren't in the other. This may result from different effects. The power of the statistical tests is higher when the two systems are abundant. Since, as shown in Figure  1 , the frequency of the sub-types is very different in the two phyla, the associations that can be detected given the current datasets are different. This may explain why many interactions concern the system IB in Firmicutes (where it's abundant) and not in Proteobacteria (where it's rare). Furthermore, the negative interaction between system I-B and II-A could hardly have been observed in Proteobacteria, because II-A is almost absent in the phyla. Hence, it's not clear at this stage if such interactions are phyla-specific or just reflect the underlying distribution of sub-types.
This may explain why we found many more positive than negative associations between Cas subtypes. Different systems may provide different degrees of protection, e. g. by targeting DNAs or RNAs in a specific or non-specific manner  (71, 72). The existence of different systems may also allow bacteria to counteract the action of anti-CRISPRs in MGEs. Indeed, recent data revealed the presence of numerous systems with the ability to counteract the action of CRISPR-Cas systems  (22, (73) (74) (75). These systems tend to be Cas-type specific  (76) , and the diversification of the repertoires of Cas systems provides a way to increase the likelihood that the MGE does not escape immune response. Complex CRISPR-Cas loci may produce more efficient immune response when they reflect functional associations between CRISPR-Cas systems of various types.
FUNDING Pasteur Institute  and the CNRS;  European Research Council (ERC)  under the Europe Union's  Horizon 2020 research and innovation program  [677823];  French Government's Investissement d'Avenir program  and by  Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  [ ANR-10-LABX-62-IBEID ];  INCEPTION  project  [PIA/ANR-16-CONV-0005 ]; This work used the computational and storage services (TARS cluster) pro-vided by the  IT department at Institut Pasteur, Paris. Funding for open access charge:  Pasteur Institute.
In contrast, the Nahr Ibrahim basin represents the managed basin of this study. It is a Lebanese basin known for excessive erosion rates  [12]  that have led to significant land degradation  [61]  and landslides  [62]  coupled to a typical Mediterranean unequal land occupation distribution that has expanded due to the absence of land use planning  [20]. The workflow of this chapter consists of using the CORINE erosion model  [48]  given its relative accuracy with respect to simple data requirements consisting of climate, slope, soil properties and vegetation cover, and its widespread application  [63]. Erosion assessment in the Claise basin serves to respond to DCE recommendations for assessment of the effect of hydromorphological altering structures on basins. For Nahr Ibrahim, the CORINE model serves as a tool for mapping land degradation as function of soil erosion.
topography, obtained through slope angle calculation given its pronounced effect on soil erosion, particularly when a certain critical threshold is exceeded [48]; 4. vegetation cover, obtained from Land Use and Land Cover (LU/LC) maps given their effect on soil fixation via their roots and by reducing rainfall splash effect  [5, 93]. The respective input layers were extracted from the databases and inputted into the Raster Calculator tool of ArcGIS for computation and application of the basis, equations and workflow for the CORINE presented in Figure  2. Each index was computed after classification into the corresponding CORINE categories, into the erodibility, erosivity and topography components which in turn are part of the potential soil erosion risk formula (Figure  2 ). Having obtained the potential soil erosion risk map, overlaying the vegetation cover layer allowed the computation of the actual soil erosion risk maps.
Input data and database description Data availability and quality are one of the main governing factors for any modeling study. The main reason behind the choice of the CORINE model is the data-scarcity state of Nahr Ibrahim where several input data for physical modeling are either lacking or insufficient. Therefore, with respect to the data requirements of the CORINE model, Table  2  presents the input data for each study area. CORINE model methodology, adapted and modified from CORINE (1992); C: clay, S: sand, Si: silt, L: loam, P i : total precipitation in month i, Pi: mean annual total precipitation, t i : mean temperature for the month i, and k i defined as the proportion of the month in which 2t i -P i > 0. Soil Erosion -Rainfall Erosivity and Risk Assessment 10.
Alternative simulations for comparison Analyzing trends obtained from SAFRAN database and applied to the Claise revealed a decreasing trend of precipitation and increasing trend of temperatures. Given the evaporative regime of ponds, an alternative scenario simulating the absence of ponds was obtained. The latter was input, as the alternative vegetation cover, into the CORINE model for comparison with the current condition erosion map in order to determine the impact of pond presence/absence. For the Nahr Ibrahim basin, the CORINE erosion map provided a tool for land degradation mapping. In analogy to the LDN concept at the scale of soil loss, the land use/land cover, actual soil erosion, national land capability classification and organic C map were intersected to reveal sustainability distribution.
Conclusions As a first step, a simple data demanding CORINE model was used to assess erosion risks of two different geographical settings represented by the Claise and Nahr Ibrahim basins. Several pronounced differences between the two settings were observed as a result of a completely different natural setting and opposing land cover/management practices. A number of conclusions may be drawn from this study; these are listed under fundamental and contextual settings. 1. Fundamentally, despite the abundance of several erosion models, given the data-scarcity of Nahr Ibrahim and for the purpose of comparison between the two study areas, the relatively simple data demanding CORINE model was used. As a first step, the CORINE erosion model proved to be a robust tool for evaluation of the spatial distribution of erosion risks despite its empirical nature where CORINE established maps have shown sufficient accuracy when verified on field and crossed with ancillary maps.
Acknowledgements This research is part of a PhD thesis funded by the  National Council of Scientific Research-Lebanon (CNRS-L) ,  Agence Universitaire de la Francophonie (AUF) ,  Lebanon  and the  Lebanese University. It is also part of the  Dynétangs  project funded by the  Centre-Val-de-Loire region. The Authors would like to extend their gratitude to the editors of the book and to the  Brenne Regional Natural Park  for their help in weather station maintenance, and to  Dr. Ihab Jomaa  of the  Lebanese Agricultural Research Institute (LARI)  for providing weather station data.
Sample and Procedure Our sample consisted of 454 children (48% boys) from six elementary schools, including three state (N = 256) and three church schools  (N = 198). The age of the respondents ranged from 8 to 11 years (M = 9. 85) Children attended grades 3-5, in general corresponding to age categories 8-11-year olds. The data were collected between January and February 2017. A researcher distributed the questionnaires, while the teachers were not present in the classroom. The researcher read the questions aloud to children age 8-9, who then answered the question by indicating their response on the measure. Respondents had one class (45 min) dedicated to completing the questionnaire, with the average time for questioning being 30 min. Participation in the survey was anonymous and voluntary.
90, goodness-of-fit index (GFI) ≥ 0. 90 a Tucker-Lewis index (TLI) ≥ 0. 90, a root-mean-square error of approximation (RMSEA) ≤ 0. 08 and a standardized root-mean-square residual (SRMR) ≤ 0. 06  (Hu and Bentler 1998; Kline 1998; Schermelleh-Engel et al. 2003; Tabachnick and Fidell 1996). Analyses were performed using the statistical software package IBM SPSS version 23 and Mplus software version 6. 12. In the fourth step, we identified inter correlations between the scales and the overall score of FGLL using the correlation analysis. At the same time, we identified differences in the type of school using the Mann-Whitney U test.
Strengths and Limitations This study has several important strengths that should be mentioned. Since there are no instruments in the Slovak language for measuring spirituality in childhood, this study is the first study that works with the FGLL as a tool for measuring children's spirituality in the Slovak environment. The Slovak version of FGLL had suitable psychometric properties and could become a reference tool for studies about spirituality, spiritual well-being of young children in Slovakia. On the other hand, a limitation of our study could be that the participants were not selected by means of representative sampling, which limits the generalizability of these findings. Furthermore, 1 3 Journal of  Religion and Health (2020 ) 59:1421 -1433  our data were based on self-reports of children, which can be inaccurate or influenced by social desirability.
Acknowledgements This work was partially supported by the  Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic ; by the  Slovak Academy of Sciences, Reg. No. 1/0932/16. Compliance with Ethical Standards 1 3 Journal of Religion and Health (2020) 59: 1421-1433  Tammeh, M. A. , Akhondzadeh, K. , Ehsani, S. R. , Shamsi, M. A. , & Mohammadnejad, E. (2016). The concept of spiritual well-being from the viewpoint of nurses caring for heart disease patients. International Journal of Humanities and Cultural Studies, 3,  2019-2116. Vandenberg, R. J. (2006). Statistical and methodological myths and urban legends. Organizational Research Methods, 9, 194-201. West, S. G. , Finch, J. F. , & Curran, P. J. (1995). Structural equation models with nonnormal variables: Problems and remedies.