dataesr/scientific-paragraphs-categorization huggingface dataset:
Tumor samples were obtained as core biopsies or surgical resection performed by interventional radiologists or surgeons. Samples were immediately formalin fixed and paraffin-embedded (FFPE) or snap frozen following tissue collection. The single-cell RNA sequencing B-cell analysis used a dataset from 32 metastatic melanoma patients (n=48 samples) treated with anti-PD1 (n=37), anti-CTLA4 (n=2), or anti-PD1/CTLA4 (n=9) 38. Patient response was determined by RECIST criteria: CR and PR for R or SD and PD for NR. For the analysis we focused on individual lesions and classified them into two categories: Responder (R; n=17) including CR and PR samples; Non-responder (NR; n=31) including SD and PD samples, based on radiologic tumor evaluations. Samples were collected after patients provided a written consent for research and genomic profiling of collected tissue as approved by the Dana-Farber/Harvard Cancer Center Institutional Review Board (DF/HCC protocol11-181)   and UT MD Anderson Cancer Center (LAB00-063 and 2012-0846).
Acknowledgements: The correlative research was funded by philanthropic support from the  MD Anderson Melanoma Moon Shot Program , The  Parker Institute for Cancer Immunotherapy , the  Dr. Miriam and Sheldon G. Adelson Medical Research Foundation , and the  AIM at Melanoma Foundation. It was supported by  The Immunotherapy Platform  and the  Core grant   CA016672 ( SMF)  to support the Sequencing and Microarray Facility at  UT-MDACC  as well as the the Office of the Assistant Secretary for Defense for Health Affairs grant ( W81XWH-16-1-0120  and  W81XWH-16-1-0121 ). BAH received support from  National Institutes of Health   T32 CA 009599  and the  MD Anderson Cancer Center  support grant  P30 CA016672. SMR received support from  National Institutes of Health   T32 CA 009666. EZK is supported by  National Institutes of Health  grant  T32 CA009599.
( NCT02210117) whereby adults with metastatic RCC without prior immune checkpoint therapy and anti-VEGF therapy were enrolled and randomized 2:3:2 to receive nivolumab (3mg/kg q2wks x3 doses), nivolumab+bevacizumab (3mg/kg q2wks x3 +10mg/kg x3) or nivolumab+ipilimumab (3mg/kg q2wks x3 1mg/kg x2), followed by surgery (cytoreductive nephrectomy or metastasectomy), or biopsy at week 8-10, and subsequent nivo maintenance therapy up to 2 years. Response was assessed at 8 weeks and then at ≥12 weeks by RECIST 1. 1 criteria. Clinical response data collection is still ongoing at this time. For this current correlative study, response was defined as achieving a complete or partial response. Pre-and post-treatment blood and tumors were obtained for correlative studies by IRB-approved lab protocol PA13-0291.
Contribution of plasmidome and mobile elements to FPEassociated stress survival The plasmidome of the 94 L. monocytogenes genomes were de novo reconstructed and the gene content investigated to untangle the MGEs contribution in the longterm survival of clonal groups. Combining plasmid-SPAdes  [58]  and MOB-suite  [59]  algorithms, different plasmids were assembled and further compared with a comprehensive plasmid database (Table  1 , Additional file 7). A large 90. 8 kbp plasmid sequence (split into two contigs) was shared by ~92% of CC204 food and FPEs isolates (n = 22) from plant A and B, and ~43% of CC155 (n = 6) isolates. Interestingly, all but one plasmidharbouring isolates from CC155 were from the FPEs of plant B. Although conserved across different CCs, the 90. 8 kbp plasmids were found almost identical (99.
All these isolates were collected at the beginning of the sampling timeframe  (1998) (1999). A total of 98 and 89 CDSs were predicted for the 90. 8 kbp and 81. 6 kbp plasmids, respectively, including genes implicated in stress response such as Cd-resistance determinants cadA2C and the efflux pump bcrABC conferring resistance to BC (Fig. 3 ). One of the first plasmid harbouring the cadA2C/ bcrABC resistance markers was pLM80 from the L. Table  1  In silico reconstruction and typing of the plasmidome and comparison with publicly available sequences from public databases NCBI and PLSDB. The plasmids reconstructed by plasmidSPAdes  [58]  and MOB-recon  [59]  are reported for each L. monocytogenes CC with predicted mobility based on the MOB-typer module  [59]. Only the public sequences with ANI > 99.
9% and mash distance < 0. 002 were reported from NCBI and PLSDB databases  [60] , respectively monocytogenes strain H7858 responsible for the 1998-1999 hot dog outbreak  [24]. High degrees of identity (> 99. 9% ANI over ~96% coverage) were identified between plasmids from CC7, CC204 and CC155 plasmids and the 82. 2 kbp pLM80 (2 contigs: NZ_ AADR01000010, NZ_AADR01000058). Therefore, we considered them as pLM80-like plasmids. The pLM80like plasmids showed high identity (99. 9% ANI) also with highly conserved plasmids described in ST204 strains from food products and FPEs in Australia and Ireland  [17] , as well as in ST5 and ST204 strains from an Austrian cheese processing facility  [21]. Interestingly, the time of sampling of these published genomes spans from 2000 to 2012. A highly conserved 62.
The pLM80-like plasmids were genetically close (mash's distance < 1. 77 × 10 -2 ) to different publicly available Listeria plasmids (Table  1 ) from PLSDB plasmid database  [60]. In particular, a plasmid from CC7 was nearly identical (ANI > 99%; 100% coverage) to the 81. 5 kbp pCFSAN004330 (acc. No. NZ_CP020834. 1) and pLSI1 (acc. No. MH382833. 1). The CC204 and CC155 plasmids showed higher similarity (ANI > 99%; described the L. welshimeri strain 40/07 as BC-and Cdresistant, as well as the bcrABC/cadA2-harbouring plasmids as potentially highly transferable  [62]. These authors also defined the ~150 kbp pN1-011A and pCFSAN021445 as composite plasmids resulting from the recombination of a pLSI1-like plasmid and a small cadA1-harbouring plasmid similar to pl2015TE24968 from L. monocytogenes strain 2015TE24968 (acc.
No. CP015985). Two publicly available L. monocytogenes plasmids, the 62. 2 kbp pGMI16-004 (acc. No. NZ_CP028184. 1) and the 67 kbp pCFSAN022990 (acc. No. NZ_CP020829. 1), were reported as genetically close (mash distance < 2. 41 × 10 -3 ) to the pLM6179-like plasmid from CC121 with a 99% ANI and 99-97% coverage. Interestingly, pGMI16-004 was found in the L. monocytogenes strain CFSAN054109 (GenBank acc. No. GCA_003030165. 1) collected in Denmark from smoked fish in 2015.
The region size, phage components, insertion site, number of CDSs, name and percentage of the major phage organism, as well as the GC content were listed in Additional file 9. Besides the chimeric nature of these regions, a major Listeria phage organism (homologous to more than 50% of the total number of CDS of the region) was identified in the majority (> 78%) of the prophage dataset. The most commonly identified Listeria phages were: A600 [NC_009815], A118 [NC_003216], vB LmoS 188 [NC_028871] and LP-101 [NC_024387]. However, homologies to phages from other bacterial species such as phage 315. 2 [NC_004585] from Streptococcus pyogenes were also reported (Additional file 9). PHASTER identified unique sets of prophages characterising strains from each clonal group, which presented from one (e.
31. 5 kbp) genomic island that we named Listeria genomic island 3 (LGI3). The LGI3 includes 29 predicted CDSs and chromosomally integrates the Cd-resistance determinants cadA1C, flanked by the recombinase hin and the transposase Tn3 (Table  3 ) typical of pLM6179 plasmid. All the genomes containing the cadA1C-harbouring genomic island were members of CC101. In these strains, the genomic island is inserted upstream of the internalin gene inlJ (Lmo1413 homolog of strain EGD-e) and includes also genes putatively involved in DNA integration, conjugation, translocation and recombination (Fig. 5 , Table  3 ). In comparison to the whole NCBI database, only the L. monocytogenes reference strain ATCC 51775 (acc. No. CP025222. 1), isolated from dairy products in Belgium in 1990-1992, showed sequence similarity (99.
De novo assembly and genome annotation An in-house developed pipeline called ARTwork (Assembly of Reads and Typing workflow)  [47]  was used for the generation of draft genome assemblies. Briefly, short paired-end reads are decompressed, the coverage of reads is estimated with BBmap  [84]  to exclude reads lower than 20X, and the others are normalized at 100X with Bbnorm (part of the BBMap v35. 85 package; http:// sourceforge. net/projects/bbmap). Reads are then quality checked and a report is produced using FastQC v0. 11. 8  [85]  before trimming of adapters and low-quality reads (i. e. minimal length of 50 bp and quality score of 20) with Trimmomatic v0. 38  [86]  Contigs are therefore generated using the SPAdes v3. 9 assembler  [87]  and the MinHash v2. 0 estimation of Jaccard similarity index  [88]  is applied to identify the closely related reference genome from an internal database of high-quality complete reference genomes.
Reference-based scaffolding and gap closure steps are then performed with MeDuSa  [89]  and GMcloser  [90] , respectively. Then, scaffolds shorter than 200 bp are trimmed with Biopython and annotation is performed with Prokka v1. 13. 4  [53]. Finally, a qualitative evaluation of the assembly is then performed by computing various metrics with QUAST  [91]. High-quality draft genomes of 94 L. monocytogenes strains were obtained with ARTwork pipeline (Additional file 3 and Additional file 4), whose source code is available at https://github. com/afelten-Anses/ARtWORK. FASTQC and QUAST metrics were combined by MultiQC  [92].
Finally, SNPs and InDels are stored by the iVARc-Call2 workflow in genome Variant Call Format (gVCF) files for each sample, a single VCF file for the whole genome dataset, a single FASTA file of concatenated core variants and another single FASTA file of pseudogenomes. The pseudogenomes were used for the generation of a phylogenomic tree with the IQ-tree software v1. 6. 7  [97]  using the MoledFinder fast model-selection option  [98]  with an ultrafast bootstrap (UFboot) approximation approach  [99]  of 1000 bootstrap replicates. The interand intra-cluster pairwise SNPs differences were calculated and plotted using the R packages "ggplot2", "dplyr" and "harrietr", with a script adapted from the R package harrietr (https://github. com/andersgs/harrietr). In order to support the robustness of the phylogenomic reconstruction, another core genome SNPs-based phylogenomic reconstruction was also performed using Snippy v4.
Plasmidome reconstruction The plasmidome (i. e. total plasmid populations in a given environment) of the 94 L. monocytogenes strains was de novo predicted using a combination of the bioinformatics tools plasmidSPAdes  [58]  and MOB-suite  [59]. As first, the plasmidSPAdes algorithm was used with default settings for extracting and assembling of putative plasmids from short reads, mainly based on differential estimations of median coverage from chromosomal and plasmid sequences. Then, the recently developed MOB-suite software was used to predict the transmissibility of the plasmidSPAdes assembled sequences. Based on the presence of relaxase, mate-pair formation and oriT sequences, the plasmid is classified as "conjugative" (i. e. if it includes both a relaxase and a mate-pair formation marker), "mobilizable" (i.
e. if it includes only a relaxase or an oriT) and "non-mobilizable" (i. e. if both a relaxase and an oriT are missing). Moreover, the MOB-recon algorithm was used to identify plasmidic contigs from the draft genomic assemblies generated by ARTwork in order to confirm the performance of plasmidSPAdes assembler. The BLAST-based MOB-recon tool uses markers from sequence databases of known replicons and relaxase in conjunction with a reference database of clustered plasmids provided by MOB-cluster  [59]. Finally, the PLSDB web-resource  [60] , a comprehensive large-scale database including 13,789 (November 2018) bacterial plasmid complete sequences, was used for a large-scale comparative analysis to retrieves any plasmid records (and its meta-information) similar to the herein assembled plasmids.
Prediction of prophages In order to identify the putative prophage sequences harboured by the L. monocytogenes strains analysed in this study, the 94 draft genomes were submitted through the URL API to the PHASTER (PHAge Search Tool -Enhanced Release) server  [63]. This application scores prophage regions as "intact", "questionable" or "incomplete" based on several criteria such as the number of CDSs homologous to certain phages, the percentages of CDSs that match a certain phage (i. e. major potential phage), the size of the phage region and specific phagerelated keywords (e. g. tail, head, capsid, portal, terminase, integrase, etc). The functional annotations of CDSs homologous to known phages are also provided. Intact and questionable regions with sequence length over 20 kbp were considered for the prophage profiling of the genome dataset and the average nucleotide identity (ANI) computed for each genome pairwise using Fas-tANI v2.
It is dicult to dene. As a rule of thumb, this paper proposes to choose the value that yields an average number of neighbors between 1 and 2 % of the data. Combining distance and shared neighborhood. In  [22, 23]  the index used penalizes the connectivity, assessed by the number of shared neighbors, by the distance as follows: c d (c i , c j ) = i∈ci j∈cj b ij + b ji d(i, j) |c i | |c j | , b ij =    1 if j ∈ N k (i) 0 otherwise (6) The cardinality is only used to compute the mean of the distribution. The result is very sensitive to the number of neighbors which is dicult to set in the general case. Moreover, the computation of the neighbors is quite slow even for moderately sized datasets. Silhouette. The Silhouette index  [24]  for a cluster, c i , is computed as the average of a value, s, that characterizes each data point, p, of the cluster, c.
This precision was not required for the clustering algorithm and the proposed f sl is appreciably 310 faster. The hierarchical process is described in Algorithm 3. 3: for all (i ∈ c) do 4: noise dens c [i] = j( =i)∈[1,|c|] H(2d c -d(i, j)) d c max(d c , d(i, j)) c [i] = F ALSE 9: if (dens c [i] ≤ Q 1 -α(Q 3 -Q 1 )) then 10: noise c [i] = T RU E 11: end if 12: end for The whole distribution is taken into account to identify noise items. The noise detection is based upon the interquartile range. A data item, x, is labeled as noise when dens(x) < Q 1 -α(Q 3 -Q 1 ), line 9. It has to be highlighted that this density is relative to each group and not to the whole dataset. This is essential to manage clusters with distinct densities. α is dened as an internal parameter of Algorithm 1 in line 3. To display the outliers in the boxplot, the value of α = 1.
Complexity analysis. The hierarchical algorithm (Algorithm 3) complexity is O(n 3 ). The rst step (Algorithm 2) is likely to speed up the process by achieving several mutual mergings at each iteration. However, as the number of mergings is data dependent, the whole complexity remains that of the hierarchical algorithm. In our implementation, the distance matrix is stored yielding a space complexity of O(n 2 ). The algorithm can however be run without the storage of the distance matrix. This would allow for managing larger datasets but with an increase in the running time. For large datasets, it is recommended to preprocess the data with sampling techniques to improve tractability. Smart algorithms such as ProTraS  [20]  are available.
Numerical experiments The KdMutual was then compared to rival algorithms using two types of data: 2D-benchmark datasets known in the literature and data generated using the genRandomClust R package 2. All the methods were evaluated until d = 10 and only the best ones for d = 50 and d = 100. The clusters have dierent sizes, densities and separation levels. The data were standardized and when the data size was higher than 5000 a sampling using the ProTraS algorithm  [20]  was carried out in order to limit the runtime. The objective of the quantitative comparison was to check whether each of 2 https://www. r-project. org/ HierOpt k  [18]  KdMutual k others, j, is less than a threshold value, i is labeled as noise. Tests on the sixteen datasets showed that the best conguration always involved the same smallest value: 1.
475 The merging is done according to increasing values of mnv and, in the event of equality, increasing values of the distance between neighbors, until a desired number of clusters is reached. No specic procedure is proposed for noise or outlier management and their presence is a potential source of failure. Two improved versions of the Density Peaks algorithm  [29, 11] , DP introduced in Section 1, were considered. In  [29] , the threshold distance d c is now automatically set using the potential entropy of the data eld from the original dataset. Moreover, the local density is now estimated using a Gaussian function instead of the classical nearest neighbor count. This important change was also implemented in the pioneering version of the algorithm for this study. In the comparative density peaks algorithm  [11] , the idea is to consider the parameter θ i = δ i -τ i instead of δ i , τ i being the distance between the point i and its nearest neighbor of lower density.
Comparison of criteria using twelve benchmark datasets Twelve 2-dimensional datasets, representative of the diversity of situations a clustering algorithm has to cope with, were selected. They are plotted in Figure  9. 2 , are usually considered for testing new clustering algorithms but they do not represent the diversity of cases a clustering algorithm has to tackle. One homemade dataset, with well separated clusters but of dierent sizes, was added to complete this diversity. The selected data include some variability in cluster shape, size, density, amount of noise and degree of separation. The datasets plotted in the rst row of Figure  9 , from D1 to D4, show a diversity of shapes. In the second row, from D5 to D8, the shapes are quite simple, with dierent elongation and some 3 https://cs.
joensuu. /sipu/datasets/ 4 https://github. com/deric/clustering-benchmark/blob/master/src/main/resources/datasets/ overlap between groups. In the last row, the datasets include some noise with a diversity of shapes, D9 and D10, or well separated clusters, D11 and D12. The Mutual Information index for the 12 datasets and the 15 algorithms is reported in Table  3. The diversity of shape, density and size is discriminating. For each dataset, 545 the minimum values are plotted is bold font. The methods mainly based on distance are unable to manage non spherical shapes, and the peak density family also has some trouble when the clusters are of various shapes and close to each other, as illustrated with D9. The means and standard deviations of three indices for this experiment are 550 reported in Table  4.
The mean for the Mutual Information Index ranges from 0. 362 for Singlelinkage to 0. 990 for DBSCAN. Three other algorithms have a mean higher than 0. 95: Munec, HierOpt and KdMutual. The three indices yield similar results: Single-linkage obtains the poorest value whatever the index while the best ones 555 are achieved by the identied group, DBSCAN, Munec, HierOpt and KdMutual. The proposal is part of the group of the most accurate algorithms able to deal with such a diversity of situations. These results show that KdMutual is highly eective when dealing with 2Ddata bases containing clusters of various sizes, shapes, densities and in presence 560 of noise.
High dimension data with dierent group separation levels The genRandomClust R package 5 was used for partition generation. This is an implementation of the method proposed in  [35]. The degree of separation between any cluster and its nearest neighboring cluster can be set at a specied value regarding the separation index proposed in  [36]. The cluster covariance matrices can be arbitrary positive denite matrices. The eigen method is used in the experiment. It rst randomly generates eigenvalues (λ 1 ,. , λ p ) for the covariance matrix then uses columns of a randomly generated orthogonal matrix, Q = (α 1 ,. , α p ), as eigenvectors. The covariance matrix is then built as Q • diag(λ 1 ,. , λ p ) • Q T. The package uses the basic parameters for cluster generation such as the number of clusters, the space dimension and their respective sizes but also allows for variability management.
To each dataset 20% of noise is added. The generated groups are spherical based, more or less elongated. The diculty stems from density, level of separation and the large amount of noise. An example in dimension 2 is plotted in Figure  10. 5 https://www. r-project. org/ Figure  10 : A 2-dimensional dataset generated using the genRandomClust R package. For each conguration, the result is averaged over 10 runs. With dim = 2 590 and SepV al = 0. 1, some algorithms have a Rand Index lower than 0. 5 as shown in Table  5. In high dimension spaces the distance measure becomes less meaningful, this is known as the curse of dimensionality and was analyzed in various contexts  [37]. The high dimensional space issue can be addressed using either subspace selection or space transformation techniques.
Statistical results: synthesis To assess how signicant the dierences between KdMutual and the other studied algorithms are, a Wilcoxon signed-rank test was performed on the mean Mutual Information index values. The data sets for which results are not discriminant are not considered. This is the case with the R-data with d > 8 and low separation level: only three methods reach acceptable scores. The data sets generated from the R-package are split in two categories. In the rst category, all the results using sepval = 0. 1 and sepval = 0. 8. These results show that Kdmutual is extremely competitive as it is better (at a condence level α = 0. 05) than its competitors in many cases. The congurations for which the alternative hypothesis is rejected are in bold font in Table  640  8. For 2D-data, the Kdmutual performances are not statistically dierent from those ofDBSCAN, DP-DataField, Munec and HierOpt.
Performance of the inference method We checked the ability of the inference method to retrieve parameters under which we simulated artificial datasets (Material & Methods). Parameters of the substitution process (κ, α, β) are retrieved quite precisely on each of these simulated datasets (Fig. 3 ). Parameters (λ, d) corresponding to the birth-death process and parameter (ν) corresponding to the spike process, have broad posterior distribution, which is expected since each dataset corresponds to only one simulated tree with few branching events (tree size ranged from 12 to 66, with a median of 31. 5, Fig. 3 ). Importantly, the number of spikes, which is our parameter of interest, can be rather well recovered. Our method aims at distinguishing spikes from clock effects. However if the spike amplitude is too low relatively to the stochasticity of the molecular clock, we should not be able to detect spike events (see Section 2.
The results of the inference on simulated datasets show that the method is able to perfectly identify spikes as soon as κ is above a certain threshold κ0 and is below a certain threshold ˆ 0. Decreasing κ below its threshold makes the effect of spikes indistinguishable from the inherent noise of the (even strict) clock, which results in false negatives (true spikes not detected). Increasing above its threshold makes the variations of the clock indistinguishable from spikes, which results in a number of false positives increasing with (false spikes inferred). Specifically, for a fixed value 0. 08 (per bp per My) of the substitution rate µ, the spikes in a 2 kb long sequence on a phylogeny with 32 tips and crown age 8. 78 My are perfectly inferred (no false negative, no false positive) as soon as κ > κ0 , for a clearcut threshold κ0 ≈ 0.