dataesr/scientific-paragraphs-categorization huggingface dataset:
Code availability. The source code for ORCHIDEE-MICT-OP revision 6850 is available via https://forge. ipsl. jussieu. fr/ orchidee/wiki/GroupActivities/CodeAvalaibilityPublication/ ORCHIDEE-MICT-OP-r6850 (last access: 23 July 2020; Xu,  2020). This software is governed by the CeCILL licence under French law and abides by the rules of the distribution of free software. You can use, modify, and/or redistribute the software under the terms of the CeCILL licence as circulated by CEA, CNRS and INRIA at the following URL: http://www. cecill. info (last access: 20 July 2021). Data availability. The CRUNCEP data and the HWSD v1. 2 data  (Nachtergaele et al. , 2010 ) are available at ftp://nacp. ornl. gov/synthesis/2009/frescati/temp/land_use_change/ original/readme. htm (Viovy, 2011)  and at http://www.
Nevertheless large-scale studies establishing the early-life virome composition and structure are sparse, and human virome studies in general have been challenged by the viral "dark matter" problem  19. The dark matter problem is a phenomenon where only a small fraction of nucleic acid sequences can be traced back to any known virus because sequence databases currently under-represent the diversity of viruses in natural environments. Attempts at de novo virus discovery directly from the virome data have been limited by the lack of universal viral marker genes, which makes it difficult to distinguish viral sequences from contaminating DNA. In addition, de novo classification of novel viruses has been held back by the lack of criteria and standardised methods. Although much progress has been made in recent years  [20] [21] [22]  , virome studies are still catching up.
Study population COPSAC2010 is a population-based birth cohort of 700 children recruited in pregnancy where we study the causes for chronic inflammatory diseases. The children are monitored by clinicians who diagnose eventual conditions, and multi-omics data is collected routinely to shed light on disease mechanisms. A total of 647 children had a fecal sample obtained at 1 year of age where the virome was characterised, and deep metagenomes for the samples were sequenced in parallel  31. Identifying the viruses and resolving their taxonomies Virome extractions are known to contain large amounts of bacterial contaminating DNA  32  and the virome dark matter problem  19  makes it difficult to discern novel viruses from contaminants. We elected to resolve the virome dark matter by assembly, clustering and successive rounds of manual curation.
vOTU host distributions mimic sample bacterial composition Bacterial hosts for the vOTUs were predicted using 318k CRISPR spacers from our metagenome assembled genomes (MAGs)  31  and the 11M spacers from the CRISPR spacer database  33  as well as by using WIsH  34. The three host predictions were merged by selecting the last common ancestor (LCA). 63% of the vOTUs yielded host predictions at the bacterial genus-level, while 77% were predicted at the bacterial order-level (Figure  3A ) and 79% at the phylum level. Bacteroides was by far the most common bacterial host genus followed by Faecalibacterium and Bifidobacterium. At the order level, more than half of all vOTUs had Clostridiales as hosts, with Bacteroidales covering just 20% (Figure  3A ). These differences mirror the corresponding pattern for the bacteria found in the metagenome, where Bacteroides are abundant while Clostridiales are diverse (Figure  3B ).
The Crassvirales clade as a whole covers 15% of the caudoviral MRA, with the previously proposed families making up 6%. Other caudoviral families overshadow Crassphage families in our data, and Crassvirales as an order is less prevalent than the novel Gramvirales, Picovirales and Ullmannvirales. Benchmarking virome decontamination software A series of virus discovery and decontamination tools have been published recently  21, 38, [50] [51] [52] [53] [54] [55] [56]  and they have already seen widespread application by the viromics community. Several large gut phage databases have been released that were built on predictions from such tools  22, 36. Yet, little is known about their efficacy in identifying novel viral clades or their ability to weed out contaminating DNA. The manually curated nature of our virome data set made it well suited for independently testing the performance of these tools.
Discussion We have presented here the largest human virome study published to date, covering fecal samples from 647 1-year-old infants where DNA viromes were sequenced to an average depth of 3 Gb per sample. This study additionally represents three major advances in descriptive viromics. To our knowledge, this has been the first exhaustive attempt at resolving virome dark matter. We were able to match 93% of the reads to either viral or bacterial DNA, leaving only 7% as unaccounted dark matter. Secondly, we performed the first reported comprehensive taxonomic resolution of a virome data set. This led to the identification of 232 novel viral families, representing a major expansion of known phage taxonomy. Lastly, we generated an overview of phage lifestyles for an entire ecosystem. The analysis shows that the bulk of phage diversity in the infant gut is composed of temperate phages, even if less diverse virulent phages can be more abundant.
All reference phages belonging to the family infect Lactococcus while our vOTUs were predicted to infect Streptococcus, but this could be an artefact caused by the lack of lactococcal CRISPR spacers in our host prediction database. Streptococcus, although very prevalent in the children, may not be abundant enough to support the high counts of virulent Skunaviridae. We also did not find any strong (anti)correlation between Streptococcus and Skunaviridae counts in the data. Thus, it remains a possibility that these phages were ingested as dairy products and survived the digestive tract, as has also been proposed earlier  57. That they end up as the most abundant viral family in our study is still surprising, but could be explained by the overall phage load in the human gut. Our epifluorescence VLP counts place gut viruses at a billion per gram of feces, or at least an order of magnitude lower than the density of gut bacteria.
Although the large terminase subunit (TerL) was the most conserved protein in our caudoviruses, its gene was frequently exchanged such that even members of the same viral genus would carry different TerL homologs (Figure  S5 ). Notable examples of this phenomenon are found in Gramvirales. Thus, the practice  26, 28  of using TerL phylogeny to classify caudoviral phages can sometimes produce confusing results. As shown by Yutin et al. 28  , Crassvirales is not TerL monophyletic and non-Crass phages often encode TerLs that wind up in the middle of the crAss TerL tree. The recent introduction of ε-crassviridae into Crassvirales  28  likely illustrates this problem, as our results indicate that the family is not a crAssphage family. Finally, we found that the latest generation of metagenome virus discovery tools such as CheckV, viralVerify and VIBRANT, in conjunction with ViromeQC should account for most sequences in one's virome data.
This recent development begins to question the continued relevance of the viral dark matter problem for DNA viromics at least. Although the sensitivities of the tools against our data never got close to 100%, most of the sequences missed by the best tools were just too short to pass the imposed quality thresholds. Thus, their predictions were good and certainly easier to obtain than manual curation. On the other hand, the performances of the alignment-free methods were very close to random with our dataset, and it appears that nucleotide-level motifs do not carry enough information to distinguish viruses from their abundant hosts.
The top 400 ranked clusters were inspected visually for two viral signatures, namely conservation of contig sizes and of gene content. There were diminishing returns beyond the top 400 mark and the remaining decontamination clusters were assumed to represent contaminants. Species-level (95% ANI) deduplication of contigs into OTUs was done using BLAT  71. Decontaminated vOTUs and reference phage species  93  were pooled and the APS tree and gene orthology (VOGs) were recalculated. VOGs were aligned against Pfam  72  , CDD  73  , COG  74  and TIGRFAMs 75 using HH-suite3  76  to gain functional annotations. The APS tree was cut using phylotreelib (https://github. com/agormp/phylotreelib) to reproduce existing phage taxonomy  28, 77. Family visualisations (Figure  S2 ) were used to 1) further curate each individual vOTU in order to separate confirmable viruses that had structural VOGs, from vOTUs representing small fragments or various virus-related MGEs that did not harbour genes coding for any structural proteins.
The three predictions were integrated using the LCA of the two most closely matching predictions, as an error-correction strategy, since all three methods would occasionally mispredict. Bacterial contamination was estimated for each virome sample using ViromeQC  38  along with a custom approach where we leveraged the metagenomes cognate to each virome: Reads were mapped from both fractions against 16S DNA  78  and cpn60  79  and the degree of contamination was calculated as the ratio between the two fractions. Abundances of vOTUs in each sample were determined by mapping sample reads to sample contigs using bwa mem -a 80 , then using msamtools profile to determine depth and length-normalised relative abundances with iterative redistribution of ambiguously mapped reads (https://github. com/arumugamlab/msamtools).
The obtained contig abundances were then aggregated at the OTU level in order to obtain vOTU abundances per sample. vOTU abundances were aggregated at the family and order levels using phyloseq  81  to obtain the statistics used for figures 4 and 5. A list of VOGs matching to integrase and recombinase protein families was first curated, then used to predict whether complete vOTUs within viral families were temperate or virulent. Families where more than 95% of complete vOTUs did not harbor an integrase were deemed virulent, whereas for temperate families at least 50% of complete or incomplete vOTUs needed to encode an integrase. The versions of virus discovery tools used for benchmarking were DeepVirFinder v1. 0, VIBRANT v1. 2. 1, VIRSorter 1. 0. 6, VIRSorter2 v2. 0 commit 22f6a7d, Seeker commit 9ae1488, PPR-Meta v1.
The aggregate protein similarity (APS) tree Sharing features with DICE  82  and VIPtree  83  , the APS tree methodology was first developed  68  for systematic and automated classification of prokaryotic defence systems and mobile genetic elements that were traditionally classified by manual inspection. The tree was found to reproduce existing manual classes and sub-classes when cutting it with global cutoffs  68, 84. The tree also predicted deeper relationships between classes that were experimentally validated later  85. The method uses as input, an all-against-all protein alignment search, preferably with sensitive alignment software like FASTA  67  , although BLASTP or DIAMOND also work. For all viruses being compared, all constituent viral proteins are searched against each other. Next, virus to virus similarity scores are tallied by aggregating the alignment scores for all their constituent proteins.
Next, using the Bray-Curtis dissimilarity  86  , a distance matrix is constructed between all viruses, which is then used as input for constructing a neighbour joining tree. We used RapidNJ (https://github. com/somme89/rapidNJ) for the latter. After manually rooting the tree, it can be cut at fixed distances from the root to reproduce classes at required levels. To this end we developed "treetool" described further below. Using more sensitive alignment software (like FASTA) generates more reliable distant protein alignments, making deeper cuts less noisy. In this study we found that cuts down to the viral order-level were reproducing existing and proposed taxonomy, making such cuts useful for defining novel taxa as well.
Protein annotation, clustering and visualisation Protein coding genes for representative contigs during initial decontamination, and for vOTUs and reference phages later were predicted using Prodigal  66. All protein sequences were subject to an all-against-all sequence alignment using FASTA  67. Protein alignments between viruses were used to cluster viruses into taxa using the APS tree as described above and below. But the alignments were also used to define VOGs de novo using a previously described orthology detection pipeline involving protein alignment coverage cutoffs  70  and Markov Clustering  87. Multiple sequence alignments (MSAs) and phylogenetic trees were constructed  88  from the protein sequences corresponding to each VOG. MSAs were used for profile-profile alignments against the Pfam 72 , COG  74  , CDD  73  , TIGRFAMs  75  and PhROGs  95  databases using HHblits 76 in order to annotate them and determine their functions.
The clustering step itself was carried out with a perl one-liner applied on top of an all-against-all BLAT output, where all assembly contigs had been used as input. PhyloTreeLib. py for exploring and cutting the APS tree phylotreelib (https://github. com/agormp/phylotreelib) is a Python library developed in-house for manipulating or extracting information from phylogenetic trees. Here, we used phylotreelib. py via its command line front-end treetool. py (https://github. com/agormp/treetool). After constructing the APS tree it was visually inspected in FigTree (https://github. com/rambaut/figtree/) to find a suitable outgroup. Suitable outgroups comprised branches originating directly from the stem of the tree. The stem was easily visible due to the high diversity of sequences used to construct the APS tree.
Next, treetool. py was used with the --root option to root the APS tree based on the selected outgroup. Next, a list of accession numbers was made containing all members of the Herelleviridae family, along with separate lists for members of different subfamilies and genera within the Herelleviridae. Another list was made containing all known crAssphages at the time of the analysis. Then, treetool. py was used along with the --cladeinfo option to retrieve the distances from the root to the branch encompassing the leaves in the lists. Next, treetool. py's --clustcut option was used to cut the tree at the above distances in order to obtain clades of vOTUs and reference phages corresponding to viral orders, families, subfamilies and genera. The distances we used to cut the tree were 0. 250, 0.
In parallel, reads from both the viromes and metagenomes were mapped against a database of the bacterial cpn60 core gene from cpnDB  79  , using bowtie2  92. As we did for the 16S-based contamination estimate, the proportion of reads mapping to this database in the virome over the metagenome for each sample was counted as the estimated bacterial contamination ratio by cpn60 content. Although the two estimates, by 16S and cpn60, were in general agreement, they also sometimes differed. The mean between the two estimates was used as the "metagenome core gene depletion" contamination-estimate used in Figure  S4. Estimating sample vOTU abundances vOTU counts were generated by mapping the reads of each sample against only the assembled contigs from that particular sample. Mapping was performed using the bwa mem -a flag  80.
Contig counts were generated with msamtools profile (https://github. com/arumugamlab/msamtools), which computes relative abundances of each contig, normalising by contig length and mapping depth while iteratively redistributing non-uniquely mapping reads. Abundances of contigs corresponding to vOTUs were aggregated by leveraging the 95% ANI dereplication cluster membership information computed previously. Counts for contigs that did not correspond to any vOTU were assumed to represent contaminant DNA and gathered as an independent contamination estimate. The vOTU to sample relative abundance matrix, or OTU table, was loaded into a phyloseq object in R and agglomerated at the viral family and order-levels to compute the statistics used in Figures  4  and 5.
Acknowledgments This work is supported by the  Joint Programming Initiative  ' Healthy Diet for a Healthy Life ', specifically here, the  Danish Agency for Science and Higher Education ,  Institut National de la Recherche Agronomique (INRA) , and the  Canadian Institutes of Health Research  ( Team grant on Intestinal Microbiomics, Institute of Nutrition, Metabolism , and  Diabetes , grant number  143924 ). M. B. D. is recipient of graduate scholarships from the  Fonds de Recherche du Québec -Nature et Technologies  [259257] as well as Sentinel North and is a recipient of the  Goran-Enhorning Graduate Student Research Award  from the  Canadian Allergy, Asthma and Immunology Foundation. JT is supported by the  BRIDGE Translational Excellence Program  (bridge. ku. dk) at the  Faculty of Health and Medical Sciences, University of Copenhagen , funded by the  Novo Nordisk Foundation  (grant no.
Load and CF Time Series Following Tantet et al. [23] , regional VRE CF time series from 2010 to 2019 (10 y) are estimated at an hourly sampling from the climate data provided by the MERRA-2 reanalysis. This is done per grid point of the climate data, with each grid point being associated to a region, as illustrated in Figure  2. The result is averaged per region and adjusted using a Ridge linear regression with cross-validation to CF data from 2014 to 2019 (6 y) provided by the French transmission system operator, RTE (https://opendata. reseaux-energies. fr/explore/dataset/fc-tc-regionauxmensuels-eolien-solaire/information/?disjunctive. region, accessed on 1 June 2021). The averages of the regional CFs ( H i ) are represented in Figure  3a, b. Similarly, the thermosensitive demand model developed by Tantet et al.
[23]  is used to estimate national demand time series computed from the MERRA-2 temperature data and fitted against the demand data from RTE (https://opendata. reseaux-energies. fr/explore/dataset/ eco2mix-regional-cons-def/information/?disjunctive. libelle_region&disjunctive. nature, accessed on 1 June 2021). These estimations are perfomed using the e4clim modeling platform  [33]  introduced by Tantet et al. [23].
reseaux-energies. fr/explore/dataset/parc-prod-par-filiere/information/ (accessed on 1 June 2021) and https://opendata. reseaux-energies. fr/explore/dataset/importsexports-commerciaux/information/ (accessed on 1 June 2021). The maximum regional VRE capacities (x max i ) are taken from ADEME  [35]  and are represented in Figure  3e, f. These values are for industrial-scale onshore wind and PV installations. They exclude rooftop residential PV.
Solver The optimization problems are solved using the Pyomo  [36, 37]  interface to the Ipopt algorithm  [38]. For a given value of α, a numerical approximation of the optimal solution is found in a few minutes on a computer with an Intel Core i7 processor of the 8th generation (8 cores) and with 32 Gb of random access memory. This optimization module is integrated to the new version of the E4CLIM modeling platform  [33]. The problem is solved independently for different values of α ranging from 1. 0 × 10 -6 to 6. 0 × 10 -3 with a step of 1. 0 × 10 -4 A C/MWh 2. The robustness to sampling is tested in Appendix C by comparing numerical results obtained from load and CF time series of increasing duration estimated from the MERRA-2 data. The choice of using only 10 years of data is found to lead to differences of only a few percents compared to using 5, 15, or 20 years and does not affect the conclusions of this study.
Data Availability Statement: The result data is produced by the E4CLIM open-source software available under GPL license at https://doi. org/10. 14768/20191105001. 1 (accessed on 1 June 2021) based on geographic data available at https://www. data. gouv. fr (accessed on 1 June 2021), MERRA-2 climate data available at https://disc. gsfc. nasa. gov/datasets (accessed on 1 June 2021), capacity-factor and load energy data available at https://opendata. reseaux-energies. fr/ explore/dataset/fc-tc-regionaux-mensuels-eolien-solaire/information/?disjunctive. region (accessed on 1 June 2021) and https://opendata. reseaux-energies. fr/explore/dataset/eco2mix-.
DNA methylation (DNAm) is commonly assayed using the Illumina Infinium MethylationEPIC BeadChip, but there is currently little published evidence to define the lower limits of the amount of DNA that can be used whilst preserving data quality. Such evidence is valuable for analyses utilizing precious or limited DNA sources. We used a single pooled sample of DNA in quadruplicate at three dilutions to define replicability and noise, and an independent population dataset of 328 individuals (from a community-based study including US-born non-Hispanic Black and white persons) to assess the impact of total DNA input on the quality of data generated using the Illumina Infinium MethylationEPIC BeadChip. We found that data are less reliable and more noisy as DNA input decreases to 40ng, with clear reductions in data quality; and that low DNA input is associated with a reduction in power to detect EWAS associations, requiring larger sample sizes.
Background Illumina Infinium MethylationEPIC BeadChips have been used extensively in epigenetic studies. Although Illumina recommend using at least 250ng of DNA on their BeadChips, there has been little published work examining the possibility of using less DNA than this. As DNA methylation (DNAm) profiling becomes more widespread, there is a need to ensure robust and reliable data can be generated from precious (e. g. , clinical or historic) or limited (e. g. , archaeological) biosamples. Three previous studies have assessed the effect of low levels of input DNA on the Illumina Infinium HumanMethylation450 BeadChip by generating data from multiple dilutions of the same biological samples. The first reported that correlations between genome-wide DNAm profiles remain above 0. 96 for dilutions containing as little as 10ng of DNA  [1] ; the second reported correlations with input of 1 µg for total input as low as 10ng remained above 0.
92  [2]. The third and most recent reported that input of 125-500ng total DNA resulted in highly replicable DNAm data for measurements taken over two days; and that 63ng and under total DNA input resulted in less replicable data  [3]. However, no study has yet investigated the expected increase in signal variability or noise induced by low input DNA and its impact on statistical power to detect associations with DNAm; this is important because a number of studies have demonstrated that many probes on these BeadChips have low reliability, particularly where DNAm sites are either highly methylated or unmethylated and have low variance  [4] [5] [6] , and conceivably this might be exacerbated by low levels of input DNA applied to the BeadChip. Additionally, no comparison of data generated using different input levels has yet been carried out using a large population dataset.
Here we assess whether low yields of input DNA are sufficient to reliably detect associations with DNA methylation measured using the Illumina Infinium MethylationEPIC BeadChip. The study consists of two parts: an initial analysis, where we assess reliability and noise within a single sample at three DNA concentrations; and a subsequent assessment of total input DNA on data quality and power to detect EWAS associations, using an independent population-based DNAm dataset of 328 individuals from the My Body My Story (MBMS) study  [7]. We believe this is the first study assessing the impact of low input DNA explicitly utilizing data from a large and socially diverse cohort.