dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, two different sets of parameters were necessary for specific and efficient detection for single PLA (puncta of high density) and dual/negative PLA signal (puncta of low density). In contrast to ImageJ and ICY, BOPSS uses a machine-learning algorithm to learn the properties of the puncta based on the data. It measures a variety of features for each cluster of pixels and uses the distributions of these features to contrast between negative controls and samples. It processes color images containing cells and PLA signal without relying on any userdefined parameters, but rather on the specification of negative control and experimental images. On a standard laptop PC (2. 6 GHz CPU), we analyzed the output results for 10 megabytes of images in 5 s. In a pretest with three full counting images of each PLA condition, single PLA, dual PLA and negative control, this all-in-one script completed machine learning-based optimization and quantification automatically.
At the same time, BOPSS significantly improved detection of dense single PLA signal, leading to comparable results for single PLA as ICY_S (with optimized parameters for single PLA) (Figure  4P : BOPSS). Therefore, BOPSS achieved a favorable S/N for dual PLA similar to that of ICY_D (Figure  4Q ) by reducing nonspecific detection without underestimating real PLA puncta, and simultaneously overcame the underestimation issue for single PLA. In addition, BOPSS led to comparable results as manual counting of PLA puncta (Supplementary Figure  2 ), and was much more consistent than manual quantification, which is an advantage for processing a large dataset in an unbiased manner. Thus, overall, BOPSS seems to be a straightforward approach for achieving completely automated, efficient and specific detection of PLA signals and worked equally well for both single PLA (puncta of high density) and dual (or negative) PLA signal (puncta of low density).
Massive MIMO system-level simulator The first open-source system-level simulator for maMIMO systems was delivered as supplementary material to the book  [3]. This MATLAB simulator supports the state-of-the-art methods for channel estimation, receive combining, transmit precoding, and power allocation, as well as quantifying the corresponding achievable rates. While some of these methods are tailored for statistical channel models, with known first-and second-order moments, in this paper, we use methods that support any channel model. More precisely, we consider least-square channel estimation (under the presence of pilot contamination) and regularized zero-forcing (RZF) precoding. The power allocation is selected to maximize the product of the users' effective SINRs, which is essentially the same as maximizing the sum rate, except that there is a "bias" towards giving higher rates to the most unfortunate users than they would get with pure sum-rate maximization (which could lead to zero rate for the weakest users).
Simulation methodology and scenario To evaluate the performance of a maMIMO system, two different scenarios have been considered. A single-cell multiple simultaneous user environment is first evaluated for the minimum conditions similar to favorable propagation (FP) that can be achieved. Then, a multi-cell network of maMIMO macro BS is considered to evaluate the performance of the network considering signal processing schemes for different antenna array shapes. The first scenario provides insights into the minimum horizontal separation required for a maMIMO BS to be successfully able to differentiate between users. Whereas, the second scenario evaluates these results in a large-scale realistic maMIMO environment with 3D spatial distributions of the users. The selected scenarios utilizes 3D geographic map data from New York City, which includes buildings of varying heights.
The selected area contains various different geographic features like narrow and wide streets, open squares, and cross sections. This map data makes it possible to obtain realistic and highly variable channel predictions for the considered scenarios. To evaluate the favorable propagation conditions, three different sites as shown in Fig. 1 , are selected. At each site, two simultaneous users are considered to be located in close proximity to each other. In Fig. 2 , a depiction of the gradually increasing linear distance between the users is shown. The deterministic channels are then extracted from the ray-tracing tool for these user positions in close proximity. These channels are then evaluated for the level of favorable propagation in Section 5. Further, to analyze the network-level performance, a network of macro-cell maMIMO BSs is considered.
The human gut phageome varies according to the age of individuals. The infant phageome is highly dynamic and associated with early life changes in its composition [8]. In contrast, the phageome of adults is stable  7. Healthy adults have a gut phageome mainly constituted of temperate DNA phages belonging to the Caudovirale order (Siphoviridae, Myoviridae, Podoviridae)  3, 8. There is a high inter-individual variation between the phageome of healthy individuals  9  , but a recent study showed that a small set of phages are found in the majority of healthy people  10. One of these is the crAssphage, a 97 kbp Podoviridae phage that is highly abundant and ubiquitous in the human gut metagenome  11. Our limited knowledge about gut phages is reflected in the limited host taxonomic span of sequence databases and results in that the vast majority of reads in human viromes cannot be annotated into a functional or taxonomic category  12.
We excluded all contigs with less than three open reading frames (ORFs) because these analyses are prone to contamination with small amounts of DNA from bacteria and because very small contigs are hard to identify as phages. This reduced the dataset to 3,369 contigs that represented only 3% of the number of contigs, but were mapped by 84. 5% of all reads. Hence, this threshold reduces dramatically the number of contigs, but not the number of reads actually used in the analysis. To compare the samples in terms of richness (the number of contigs and their content), each sample was rarefied at 322,285 reads. The contigs were then classified as bacteria, phage with family, putative phage, other contamination or non-attributable (NA), using the flowchart described in the Methods (see Fig. 1a ).
The number of contigs, and their categories, were very diverse across the different methods (Fig. 1b ). Method III presented the smallest number of contigs, in opposition to methods II and IV. The majority of contigs could not be assigned to Figure  1. Algorithm to explore contigs and comparison between the five different phage isolation methods (I to V) used to analyse the faeces sample AA 0 of the volunteer AA. (a) Flowchart for the taxonomic attribution of the contigs with a complete method. It permitted to classify a contig according to the following groups: "phage with family", "putative phage", "bacteria", "contamination", "non-attributable (NA)" according to wGRR strategy, Virsorter software, HMM bacterial markers and 16S profile and NCBI database. (b) Number of contigs with a minimum of three ORFs, after a rarefaction step, classified following the procedure in (a).
The effect of sequencing depth. The depth of sequencing is often decided based on budget constraints. Nevertheless, it is important to assess how much is lost or gained when making this decision. To tackle this point, we sequenced the same sample extracted with method IV at a high coverage (HC) (87,148,950 reads) (Table  1 ). Statistics comparing this dataset and the previous one (LC, 74 times fewer reads) are described in supplementary material (Table  S3 ). Each dataset was assembled separately, the LC dataset produced 952 contigs (with at least three ORFs) and the HC dataset produced 16,608 contigs (with at least three ORFs). We searched for phages (phages with family or putative phages) on these two pools of contigs using the developed flowchart (Fig. 1a ), and found 237 phage contigs in the LC dataset versus 1994 in the HC dataset (Fig.
2a ). The number of phage contigs, weighted by the number of reads mapping on them, was very large in both datasets, even if the HC dataset showed more contaminants or unassigned contigs (Fig. 2b ). The distribution of sizes of these contigs was significantly different: phage contigs from the HC dataset were significantly longer (mean size of 7,926 bp vs 6,163 bp, P < 0. 001, Wilcoxon test, Fig. 2c ). Hence, the HC analysis has 79 times more reads, but the average contig is only 28. 6% larger. We pooled phage contigs (phage with family and putative phage) from the two datasets and clustered them using CD-HIT. We found 189 clusters that included contigs from both datasets. We found 1803 phage contigs that were exclusively present in the HC dataset, showing that increasing the depth of sequencing significantly increases the number of novel phage contigs.
On the other hand, 41 phage contigs were exclusively present in the LC dataset. To enquire on the possibility that these 41 contigs were only partly homologous to contigs in the HC dataset, leading them not to cluster together, we re-ran the CD-HIT with a local alignment (option -G 0) and with a less strict coverage on the shorter contig of 50% instead of 95%. This resulted in only 14 contigs exclusively present in the LC dataset (Table  S4 ) representing 2% of the LC reads. Hence, very few contigs assembled in the LC dataset are absent from the HC dataset. We quantified the gains associated with increased sequencing by simulating different sequencing depths. This was done by randomly selecting reads (in-house script) from the sample IV (HC sequencing). We performed steps from 1% to 90% in steps of 10 (Table  S5 ).
Each subset was assembled and analysed independently (in the same way as before). To test the reliability of this simulation method, we plotted on the same figure the number of phage, bacteria and NA contigs for simulated datasets and for the LC dataset. The number of contigs identified in the LC dataset is very close to the curve fitting the results of the simulations from the HC dataset (Fig. 3b ), confirming the adequacy of our simulations. Expectedly, the number of contigs increases less than linearly with the number of reads: smaller datasets have proportionally more contigs. Interestingly, the abundance of phage contigs, relative to bacterial and unassigned contigs, decreases as the sequence depth increases. This suggests a lower diversity of phage DNA, relative to other elements of the community, and faster saturation of this population.
S3 ). These samples always clustered together in each individual, contrary to those of the other two methods. In summary, MDA severely decreased the genetic diversity and the reproducibility of the method, and method IV without MDA showed high reproducibility and ability to discriminate between individuals. The wGRR strategy informs the phageome. We developed a method to attribute a family, a lifestyle or a host to contigs. We searched for sequence similarity between the contigs and (pro)phages of three databases (phage Refseq database, prophages from HMP bacteria and prophages from RefSeq bacteria, see Methods). These results were used to compute a score of similarity called weighted gene repertoire relatedness (wGRR) for each pair of contig/phage. This corresponds to the assignation of the properties of the best hit (phage, prophage) to the global characteristics of the contig (family/host/lifestyle attribution), which facilitates processes that aim at classing the entire contig.
To quantify the quality of such assignments, we defined a wGRR cut-off per database. We computed the wGRR of each pair of phages of the Refseq database and found that a wGRR cut off of 5. 2 gives 95% correct predictions of phage family (Fig. S4a ). However, this cut-off is valid only for comparisons between complete phages. We produced sets of fragments of phage genomes of different sizes to simulate phage contigs of different size. We then recalculated the wGRR cut-off between the complete phage sequences and these different sets (Fig. S4b, c ). This produced a smooth increase of the wGRR threshold value with decreasing contig size that could be fit with a negative exponential function (Fig. S4d ). We used a similar approach to define wGRR thresholds for lifestyle and host attributions (Table  S6 ).
The use of MDA (SP30 and SP90) drastically increased the proportion of ssDNA phages (Inoviridae, Microviridae) and almost excluded dsDNA phages (Fig. 5b ). The predicted lifestyle of the contigs was also very different between methods, with a dramatic increase in contigs classed as virulent phages in samples with MDA; a consequence of the fact that most (73%) ssDNA phages of phage Refseq database are classed as virulent in PHACTS  21  (Fig. 5c ). MDA was also accompanied by an increase of Bacteroidetes phages (Fig. 5d ), in agreement with works identifying many Microviridae in this clade 2.
Biological insights. Our analyses revealed the following categories in the ABC-Reference-Contig-Catalog: 57. 6% of the contigs could not be assigned, 18. 5% were classed as putative phages, 15% as bacteria and 8. 3% to a known family of phages. Importantly, the order of decisions in the decision algorithm used in Fig. 1a  has little impact on the overall conclusions since very few contigs are ever classed as phage and bacterial or phage and contamination (all details are in Fig. S5 ). Intriguingly, 218 out of 506 contigs classified as "Phage with family" were not detected by Virsorter software (Fig. S5 ). These contigs are small and this is probably the reason why they were missed by Virsorter (Fig. S6a ) even if they have homologs of phage genes. To check that these 218 contigs provide reliable information, we re-analysed Fig.
5  (panels b-d) after removing the contigs missed by Virsorter. The results are similar (Fig. S6b-d ), showing that the wGRR approach provides a means of classifying the phage contigs and of identifying novel ones. We found that only 0. 41% of the contigs corresponded to other types of contamination: 2 contigs matched eukaryota DNA, 1 contig matched archaea DNA and 22 contigs had sequence similarities to environmental DNA from uncultured bacteria found by metagenomic methods. Most contigs did not match any database but when we looked at the relative abundance of reads mapped on ABC-Reference-Contig-Catalog, we observed that in method S the vast majority of reads mapped on putative phage or phage contigs (Fig. 5a ). We studied more precisely the composition of the gut phageome of the three volunteers when the procedure did not include MDA (Fig.
S7 ). Here, we observed that Caudovirales were largely predominant, but within this group each volunteer had a different predominant family (Podoviridae for AA, Myoviridae for BB and Siphoviridae for CC). The analysis of the phage lifestyle showed that nearly half of the phage contigs could not be classified according to the lifestyle (46. 6%) (Table  S7B ). The vast majority of the remaining contigs were classed as temperate phages in the non-amplified dataset, although most of the times as "non-confident". Lastly, most predicted hosts of the gut phages correspond to bacterial phyla frequent in the gut (Firmicutes, Bacteroidetes, and Proteobacteria)  22, 23  (Table  S7C ). The crAssphage is a 97 kbp bacteriophage that is frequently found at high abundance in human faeces  11. It infects Bacteroides intestinalis  24.
Accordingly, the majority of contigs in our samples have no homology to known phages (or bacteria). All the methods had a centrifugation step that was followed by the filtration of the supernatant; this was performed to eliminate debris. Bacteria were subsequently eliminated by a filtration step and each specific step of phage concentration were performed. We observed a high variability in terms of contigs provided by the five methods, which highlights the difficulty of comparing phageomes obtained with different procedures. The small size of contigs is quite typical of metagenomics datasets. The extraction techniques may lead to damage and fragmentation of the DNA and contribute to the existence of so many small contigs. If so, the effect is similar for the five extraction techniques because we find small contigs in all the samples.
Hence, MDA should be avoided, at least when studying the dsDNA component of this community. Phages evolve rapidly by exchanging functional modules by recombination  27  , and genome databases do not adequately represent the diversity of the phage world. As a consequence, phage genomes have many genes lacking clear homology to known genomes. To analyse the gut phageome, a global genomic similarity approach can provide interesting additional information relative to approaches based on local similarity. This spurred our approach of assembling reads into contigs and then search similarity to known phages using a global approach (the wGRR index). This index has been previously used to analyse several types of mobile genetic elements, including phages  28, 29. Here, it allowed the inference of the family, lifestyle and host phyla of many phage contigs, representing a large fraction of the reads.
This method is complementary to typical individual protein-based sequence similarity analyses since the wGRR index collects information on the presence of homology and degree of identity of the set of genes of the contig. This global index is thus particularly appropriate to make classifications of the entire contigs. Its limit is that if one wishes to change the reference database, then the fit of a new wGRR cut-off equation will be needed. For example, since the beginning of our work, several novel programs were published to identify prophages in bacterial genomes with higher sensitivity or specificity than PhageFinder. We made a rapid analysis of the effect on the wGRR method of changing the source of prophages by using Phaster 30 instead of PhageFinder. It showed that the introduction of a more accurate method further improves the analysis of wGRR, since the threshold upon which classifications can be made decreases (Fig.
S9 ). The wGRR method is general and can be used with any prophage finder method. The majority of the reads present in samples mapped phage or putative phage contigs, supporting the quality of our isolation method. Nevertheless, the majority of the contigs could not be classed. Most of them are small and poorly covered, they account for a small amount of reads (Fig. S1 ), which may explain the lack of homologous sequences in the databases. Finally, some contigs were classed as of bacterial origin. It is not possible to determine the relative frequency of them corresponding to bacterial contamination or to virions carrying bacterial DNA arising from generalized transduction. The phageome of each individual is unique  7. This study shows that Bray-Curtis dissimilarity between two gut phageomes content is much smaller for samples from the same individual than for samples from different individuals.
DNA sequencing. Phage DNA was sequenced with the Illumina MiSeq Nano V2 PE_250 bases method using the Kit Nextera XT with an input of 1 ng DNA, called low coverage (LC) sequencing. Phage DNA from method IV was also sequenced with a deeper coverage with Illumina Hiseq2500 PE_250 bases, called high coverage (HC) sequencing. All samples were sequenced using paired-end reads (numbers of reads mentioned in this manuscript always refer to paired-end reads). Genome sequences. The sequences and corresponding annotations of 1,943 complete bacteriophage genomes were retrieved from GenBank Refseq (ftp://ftp. ncbi. nih. gov/genomes; last accessed November 2016) called phage Refseq database in this paper. The order, the family, the type of nucleic acids and the bacterial host were also extracted from the GenBank files of each phage (when available).
This dataset contained 1,756 Caudovirales (90%) of which 3 main families were identified, i. e. : 53% were Siphoviridae, 27% Myoviridae, and 19% Podoviridae. In order to create prophage databases we retrieved bacterial genomes from different locations: (i) 5562 complete bacterial genomes from Refseq GenBank (ftp://ftp. ncbi. nih. gov/genomes/, last accessed in November 2016) and (ii) 457 draft genomes of the Human Microbiome Project (HMP) (http://hmpdacc. org/ HMRGD, last accessed in December 2015). wGRR. The weighted gene repertoire relatedness (wGRR) is a similarity score between two genetic elements. We used blastp v. 2. 6. 0 (default parameters) to compute sequence identity between proteins encoded in pairs of contigs (A and B). We kept all bi-directional best hits with an e-value lower than 10 -5.
The wGRR score is computed as: = ∑ × wGRR id A B A B ( , ) min( , ) 100 i p i i where A i and B i is the pair i of homologous proteins present in A and B, id(A i ,B i ) is the percent sequence identity of their alignment, and min(A,B) is the number of proteins of the contig encoding the fewest proteins (A or B). The wGRR varies between zero and one hundred; it represents the fraction of homologs in the smallest of the two contigs weighted by their sequence similarity  28. Prophage identification. We identified 9927 prophages in the chromosomes of complete bacterial genomes from GenBank Refseq using Phage Finder v4. 6 33 as in  34. Many of these prophages were very similar. We clustered them with a wGRR cut off of 95% and we took a representative of each cluster (6426 phages). We used Virsorter v.
1. 0. 3  35  to identify prophages in draft genomes because this software searches for phages in draft genomes and metagenomic datasets (contrary to Phage Finder). We excluded the putative prophages classed under categories 3 and 6 of VirSorter, which often correspond to other mobile genetic elements (e. g. pathogenicity islands). We thus identified 1243 putative prophages in the draft genomes of the Human Microbiome Project (HMP) (http:// hmpdacc. org/HMRGD, last accessed in December 2015). Prediction of phage lifestyles. The lifestyle of phages from phage Refseq database was predicted using PHACTS v. 0. 3  21. Predictions were considered as confident only if the average probability score of the predicted lifestyle was two standard deviations (SD) away from the average probability score of the other lifestyle, as recommended by the authors (who claim a precision rate of 99% with this parameter).
Using these criteria, we classified 54% of phage Refseq database into 420 virulent and 629 temperate phages. Trimming, assembly, and mapping of reads. The sequence reads were trimmed (i) to remove the Illumina adapters using Adapter Trimming with cutadapt (v1. 12)  36  with default parameter values (ii) and reads with low quality were removed using UrQt (v1. 0. 18)  37  with parameters:-min_QC_length 80-min_read_size 100-min_QC_phred 20. In order to optimize the assembly step, we reduced the redundancy of the reads with the Khmer software (v2. 0)  38  , using a k-mer size of 20 and a median k-mer coverage level of 20, which makes a digital normalization and a trimming of sequences based on the k-mer abundance. The resulting reads were assembled using SPAdes 3. 10. 1  39  with -meta option.
Gene prediction was done using Prodigal (v2. 6. 2)  40  with -p meta option. We excluded genes lacking start and stop codons. In order to focus our analysis on contigs sufficiently large to study genetic contexts, we excluded contigs with less than 3 open reading frames (ORFs). We used CD-HIT (v4. 7, option -g 1, -c 0. 95, -aS 0. 9 or option -g 1 -G 0 -c 0. 95 -aS 0. 5 for local alignment) 41 to define families of contigs. For each resulting family, we took a representative contig (the largest) to create a non-redundant contig catalog. We then mapped the samples' reads on this "contigs reference catalog" using bowtie2 (v2. 2. 9-local-sensitive-local options)  42  and exploit SAM files with samtools (v1. 3. 1 with the following commands: views, sort, index, idxstat)  43. As result, we obtained a matrix representing the number of reads of a sample (columns) mapping each contig reference catalog (rows) in the dataset.