dataesr/scientific-paragraphs-categorization huggingface dataset:
However, these metrics decrease as the target size increases. The success rate falls below 50% for 10 Kb and 20 Kb targets. The assembly accuracy remains high for 5 Kb and 10 Kb targets (89. 1% and 88. 2% resp. ), but drops to 50% for the largest target size (20 Kb) (top part of Figure  2 ). A similar trend is observed for the 10x Genomics H. numata dataset, but with a smaller decrease in performance with the target size, as the success rate remains high for 1 to 10 Kb targets (70. 2% for all three sizes combined, bottom part of Figure  2 ). Analyzing the successful assemblies, we can observe that they were obtained with different k-mer sizes (-k ) depending on the target size (Figure  3 ). We recall that MTG-Link automatically tests several k-mer sizes in decreasing order, and stops when a good quality assembly is obtained.
Barcode-based subsampling analysis The main feature of MTG-Link is its read subsampling step based on the barcode information of the linked-reads. For all the combined target sizes of the stLFR H. sapiens dataset, 855 barcodes were selected on average from the target flanking regions, resulting on average to 111,905 reads given as input for the de novo assembly. The number of selected barcodes is quite variable between the targets, with for instance a standard deviation of 407 barcodes for the 63 10Kb targets (Figure  4A ). However, the number of selected barcodes seems not to be directly linked to the assembly success as the missing and erroneous assemblies do not show either much less or much more selected barcodes (Figure  4A ). Similarly, the distributions of number of selected reads are similar between the three assembly result status, as also shown by the high correlation between the number of selected barcodes and the number of reads extracted from this selection (Additional file 1: Figure  S1 ).
In MTG-Link, the main parameter governing the barcode selection is the size of considered flanking regions (parameter -flank ). Indeed, when increasing this parameter, the number of selected barcodes increases but it does not impact substantially the assembly performances (Figure  4A  and B ; see also Additional file 1: Figure  S2 ). In other words, even if we select more barcodes to increase the size of the subsampled read sets, the success rate and the assembly accuracy do not increase.
Comparison with other approaches To our knowledge, no other local assembly tool using linked-read data been developed. Therefore, MTG-Link was compared to short-read local assemblers that can not take advantage of the barcode  information of the linked-reads. This comparison is therefore meant to highlight the added value of this type of data. MTG-Link was first compared to MindTheGap  [30] , as this is the tool used in the local assembly step of our method, thus enabling to evaluate the benefit of the read subsampling step. MTG-Link was also compared to the most recent short-read local assemblers: ABYSS-Sealer  [13]  and GAPPadder  [14]. However, we did not manage to make GAPPadder work on our datasets as it is no longer maintained, so we will present only the results obtained with ABYSS-Sealer.
For all target sizes, MTG-Link outputs more successful assemblies with a better assembly accuracy than the two short-read local assemblers MindTheGap and ABYSS-Sealer (Figure  2 ). On the stLFR H. sapiens dataset, for all target sizes combined, MTG-Link shows a success rate of much better than the success rates of 5. 2% and 12. 3% for MindTheGap and ABYSS-Sealer respectively. Besides, the differences tend to increase with the target size. In particular, no target over 10 Kb could be assembled by any of the two short-read local assemblers MindTheGap and ABYSS-Sealer. Therefore, MTG-Link outperforms these two tools. Similar results were obtained for the 10x Genomics H. numata dataset. The computational performances (speed and memory usage) of MTG-Link are of the same order of magnitude of those of the short-read local assemblers MindTheGap and ABYSS-Sealer (Additional file 1: Tables  S1  and S2 ).
Application of MTG-Link to the reconstruction of insertion variants We applied MTG-Link to reconstruct the alternative sequences of known large novel insertion variants in human HG002. From the gold standard SV callset provided by the Genome In A Bottle consortium on the HG002 individual  [35] , we selected all novel insertion calls that were larger than 250 bp (N=151). All insertion sequences are resolved in this dataset, as they were called mainly with long-read sequencing data. Therefore, we were able to evaluate the assembled sequences obtained by MTG-Link and assess the sensitivity and accuracy of the method with respect to various insertion features (size, genotype, repeat context of insertion site). The results are presented in Figure  5. On the 151 insertions, MTG-Link has a success rate of 45.
Discussion and conclusion We provide a novel tool for local assembly that is dedicated to linked-read sequencing data. We validated this tool on several datasets, composed of real linked-read data and randomly selected loci for which the sequence to assemble is known, to precisely estimate its success rate and assembly accuracy. The results described above show that our method can be applied on different organisms, as well as on different linked-read sequencing technologies, to obtain better assemblies than with other existing local assemblers. To our knowledge, this is the first local assembly tool dedicated to linked-read data. We have therefore compared our tool MTG-Link to generic short-read local assembly tools that could not use the long-range information given by each read barcode. In particular, we compared MTG-Link to MindTheGap, which is one of the component of the MTG-Link pipeline.
Indeed, barcode-based read recruitment is limited by the size of the long DNA molecules obtained during the linked-read library preparation. As a matter of fact, increasing the flanking region size did not improve the results, since barcodes of interest are more likely to be found near the target sequence than far away. The target size limit is thus likely to depend on the size distribution of the long DNA molecules. Here, for molecules of average size around 55 Kb (around 70 Kb and 40 Kb for the stLFR H. sapiens and the 10x Genomics H. numata datasets respectively), MTG-link was still able to successfully assemble around 20% of the 20 Kb target sequences. Decreasing the k-mer value still allows the assembly of some of the larger targets and Figure  3  shows that there is not a single k-mer size that outperforms the others for any target size; hence the importance of testing several k-mer sizes to optimize assembly results.
This is performed automatically and efficiently in MTG-Link thanks to the last step of the pipeline, the qualitative evaluation of each obtained assembled sequence. The second feature impacting the performances of MTG-Link is typical to any de novo assembly tool: it is the presence of repeats in or around the target sequence. This is particularly illustrated when comparing the results obtained for the assembly of insertion variants in the human HG002 individual, whether the insertion site is located in a repeated region or not (Figure  5 ). Notably, this repeated feature impacts both the success rate and the accuracy of the method. As a matter of fact, among the non-assembled 10 Kb targets of the randomly selected loci of the human dataset, 30% were actually assembled successfully by MTG-Link but returned among other possible solutions.
Even if some targets are particularly difficult to assemble, we proved the usefulness of MTG-Link in two biological applications, namely to characterize large insertion variants and to improve the contiguity of a locus split into several scaffolds in an initial assembly. Large insertion variants are one of the most difficult structural variant types to discover and fully characterize in re-sequencing datasets  [37] , because the alternative allele, the inserted sequence, is most often not contained in read mapping results. In particular, many short-read SV callers can predict insertion site locations on the reference genome but are not able to resolve the whole inserted sequence  [36]. Our results show that MTG-Link could be combined to short-read SV callers to improve their characterization of insertion variant calls.
Funding This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Marie Sk lodowska-Curie  grant agreement No  764840 , and from the  French ANR   ANR-18-CE02-0019 Supergene grant. A CC-BY public copyright license (https: //creativecommons. org/licenses/by/4. 0/)has been applied by the authors to the present document, in accordance with the grant's open access conditions.
We use cohort data on infants for whom microbiome data was available at 6 and 9 months after birth, as well as information on mode of delivery and diet changes over time. In particular, MNDA-based prediction models outperform traditional prediction models based on individual-specific abundances, and enable the detection of microbes whose neighbourhood dynamics are informative of clinical variables. We further show that similarity analyses of individuals based on microbial neighbourhood dynamics can be used to find subpopulations of individuals with potential relevance to clinical practice. The annotated source code for the MNDA framework can be downloaded from: https://github. com/H2020TranSYS/microbiome dynamics Conclusions: MNDA extracts information from matched microbiome profiles and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data.
Data Pre-processing and Exploratory Analysis Selecting informative individuals and taxa and filtering out random noise was achieved with the following prevalence filter: only amplicon sequence variants with a prevalence exceeding 15% survived the filtering. Prevalence indicates the percentage of samples in which a microbe was detected. The average sequencing depth was 57392 read counts, while the range was  [11123, 105921] , with an interquartile range of 25346. Pre-processing was done on the merged data of 155 newborns across months 6 and 9. No infants were removed. Out of 1144 taxa, only 95 (8%) remained after data pre-processing. These 95 taxa were considered for subsequent analyses. In addition, we defined two new classes of microbes: appearing or disappearing microbes -taxa that meet the 15% threshold at month 9 (appearing), or at month 6 (disappearing), but were filtered out by joint-time point pre-processing.
Global Microbial Network Construction For the remainder of this manuscript, we refer to a global network as a network computed over a set of independent individuals. For instance, such a set can refer to all infants at month 6 after birth. We computed a global network for each time point, by selecting microbial taxa as nodes and calculating association strength via microbial sssociation graphical model analysis (MAGMA)  [39]  on all newborns at each timepoint (81 at timepoint 6m and 74 at 9m). MAGMA uses copula Gaussian graphical models for which marginals are modeled with zero-inflated negative binomial generalized linear models, and sparseness is induced via a graphical Lasso strategy. From a practical point of view, we used the rmagma R package to derive MAGMA-networks (https://gitlab. com/arcgl/rmagma).
Since the degree of co-occurrences of microbes may be highly variable, we proposed to use a weighted random walk algorithm to take the edge weights into account. To move from node i and determine the next step in a simple random walk algorithm, all the neighbouring nodes have the same probability of visit P (i -→ j) = 1 ∥Ni∥ , where N i is the set of neighbours (distance 1) of node i with operator ∥. ∥ counting its members. In the weighted version of this algorithm, the probability of moving from node i to node j is proportional to the edge weight w ij linking node i to node j: P (i - → j) = w ij Σ j∈Ni |w ij | (2) Although the idea of weighted random walks is not new, to our knowledge, no customized code was available for their use in our framework. Our code is provided as part of a GitHub repository that covers the workflows used in this manuscript (https://github.
15 seconds. MNDA analysis, including random walk probability calculation and EDNN training, is the most time-consuming step in our analysis. In particular, on a MacOS (version 12. 4) and R version 4. 0. 5 (2021-03-31), random walk probability calculation took 28. 1 seconds for each multilayer network of 95 microbes; each round of EDNN training took 5. 7 minutes for 69 individuals. The measurements were done through the Sys. time() function in R. Embedding individuals into a joint space is less computationally intensive than creating an embedding space for each individual separately. It also ensures that cosine distances are comparable across individuals (and thus can be used to cluster individuals). However, on the downside of this, when the initial dataset is enriched with additional individuals, the joint embedding space will differ, and individual predictions may change.
Since ISNs are computed outside the MNDA framework, our pipeline can be combined with any type of ISN, as long as the edges are individualspecific. Compared to the work done by Mac Aogáin et al. [24] , our work has some important differences. First, the authors construct microbiome co-occurrence networks on CLR-transformed abundances and Pearson correlation. Pearson correlation is the base measure of association implemented in the LIONESS software; which was initially showcased on gene expression data. Friedman et al. [61]  showed that even though correlations on the CLR transformation are more accurate than Pearson correlation, they are not as accurate as the SparCC algorithm. Second, the authors continued their analysis with edges as units of analysis. Our work exploits individualspecific networks to define individual-specific ASV neighbourhoods (hence sets of edges).
Standard microbial abundance changes over time and MNDA dynamics of microbial interactions can be seen as alternate representations of the same underlying process. [72] , we compute the β-diversity with a PCA from the mixOmics package on the CLR transformation of the microbiome data. The axes are the first two principal components and account for 13% of the total variance. Individuals with missing phenotypes (1 for diet) are excluded from the analysis Figure  6  Global MAGMA networks calculated on all subjects retained in the LucKi cohort at 6 (81 subjects) and 9 months (74 subjects) after birth, i. e. , available cases at each timepoint. Color code corresponds to phylum classification. The thickness of an edge corresponds to the strength of association. Microbial taxa are organized on a circle according to phylum membership at timepoint 6m (A) and 9m (B).
Funding This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  H2020   Marie Sk lodowska-Curie  grant agreement No  860895  (TranSYS) [B. Y. , F. M. , B. S. , K. v. S. ]. The  LucKi Gut study  was funded by a grant from The  Netherlands Organization for Health Research and Development (ZonMw)  through the  European Union Joint Programming Initiative - A Healthy Diet for a Healthy Life  (received by J. P. and M. M. ; project:  529051010 ). N. v. B. is supported by a  Kootstra Talent Fellowship  from the  Faculty of Health, Medicine and Life Sciences of Maastricht University.
Availability of data and materials The global network figures are generated by the rMAGMA graphical tool and the igraph R package. The averaged co-expression networks are generated by the R package ggraph. The source code of the MNDA pipeline in R is freely available on GitHub (https://github. com/H2020TranSYS/microbiome dynamic). The LucKi cohort dataset is available upon request from the Euregional Microbiome Center (www. microbiomecenter. eu).
4, 5, 13, 14  In addition with commercial software, some laboratories have developed their own segmentation algorithms or computational tools to extract and quantify vasculatures. 6, [15] [16] [17]  Finally, around fifteen thresholding techniques are available with the open-source freeware ImageJ, belonging to 3 categories of the Sezgin and Sankur's classification, based on 1 the shape of the pixel gray-level histogram (the properties of the gray-level histogram such as its peaks and valleys), (2) the clustering analysis of the pixel gray-level histogram (the gray-level histogram is considered as corresponding to two clusters of pixels representing the object and the background), and (3) the entropy of the pixel graylevels of the image (the image is considered as composed of ''information'' associated to the signal but mainly of noise, with a certain degree of uncertainty on which gray-level correspond to what).
Also, some researchers have developed their own software to study a vascular network 8 or methodologies,  18  sometimes complex for a biologist to use. We have developed our own standardized thresholding method based on the statistical analysis of the voxel gray-level histogram, which can be performed using ImageJ plugins. 19  We will present first this method, then summarize the principles of the most used thresholding plugin commercially available, Imaris software (Bitplane, Switzerland) FilamentTracer function, and finally the entropy-based automatic thresholding techniques, which are available with ImageJ.
Thresholding using Imaris software FilamentTracer function A survey of the literature on optical clearing and 3D imaging of vascular networks indicates that the most of the articles use, for the segmentation step, Imaris software (Bitplane, Switzerland) FilamentTracer function. It has have been used to isolate the vascular network in several organs such as the heart,  4  the liver and the lung,  5  the pancreas,  13  the brachial and inguinal skeletal muscles,  14  and the kidney. 9  Imaris is a commercial software allowing 3D and 4D image analysis, and FilamentTracer is an extension initially allowing the automatic tracing of neurons and the detection of their spines, but extended for the tracing of any filamentous structures as blood vessels. Using FilamentTracer requires first to enter the diameter of the largest and thinnest vessels, usually estimated on 2D images.
On this information, two threshold values are set automatically by the software to create the vessel skeleton. Then, a last threshold is automatically set to adjust the vessel diameter. These thresholds can be manually modified by the experimenter. FilamentTracer is easy to use, but its main weakness (in addition to the fact that the algorithm used to generate the threshold values is unknown by the experimenter) is that it is based on the initial visual, hence subjective, estimate of the vessel diameter by the experimenter and, sometimes, ''ad-hoc'' eye-based ''correction'' of the threshold values.
Purpose Data sharing is essential in health science research. This has also been acknowledged by governments and institutions who have set-up a number of regulations, laws, and initiatives to facilitate it. A large number of initiatives has been trying to address data sharing issues. With the development of the FAIR principles, a set of detailed criteria for evaluating the relevance of such solutions is now available. This article intends to help researchers to choose a suitable solution for sharing their health data in a FAIR way. Methods We conducted a systematic literature review of data sharing platforms adapted to health science research. We selected these platforms through a query on Scopus, PubMed, and Web of Science and filtered them based on specific exclusion criteria. We assessed their relevance by evaluating their: implementation of the FAIR principles, ease of use by researchers, ease of implementation by institutions, and suitability for handling Individual Participant Data (IPD).
Results We categorized the 35 identified solutions as being either online or on-premises software platforms. Interoperability was the main obstacle for the solutions regarding the fulfilment of the FAIR principles. Additionally, we identified which solutions address sharing of IPD and anonymization issues. Vivli and Dataverse were identified as the two most all-round solutions for sharing health science data in a FAIR way. Conclusions Although no solution is perfectly adapted to share all type of health data, there are work-arounds and interesting solutions to make health research data FAIR.
Therefore, developing systematic data sharing is crucial, but it must also be done according to community standards in order to be efficient. These considerations led to the development of the FAIR principles in 2016  [11]. FAIR stands for "Findable", "Accessible", "Interoperable", and "Reusable". Each term covers a precise set of criteria that must be respected to design a FAIR data sharing environment. It has become a reference in research data sharing, so much so that the European Commission elects the FAIR principles as the gold standard for data sharing and adopted them in the EU directive 1024 of 2019  [12]. The RDA also fosters their development by publishing them in their recommendations, and it has been used by a large number of recent initiatives  [13] [14] [15]. Health science researchers face additional specific challenges.
g. , imaging, genomics, and mass spectrometry. Handling these data requires specific expertise and tools which can usually only be found in the specialized, dedicated communities. The objective of this paper is to identify and evaluate technical solutions to implement systematic data sharing in an academic context, in order to help researchers making their data FAIR. We will evaluate various software programs and online platforms used in academic projects to manage and store data through a systematic literature review focusing on the implementation of the FAIR principles and the ability to support sharing of Individual Participant Data (IPD).
Selection process We conducted a systematic literature review of articles detailing, reviewing, or illustrating the use of solutions for data management and sharing. A solution is defined as any infrastructure that can be used to share health research data and meeting the following criteria. Firstly, the solution must be an online platform or a software program. For instance, a solution can be a public online repository where data can be downloaded directly or an open-source software program for data management and sharing that can be installed on the individual institution's IT infrastructure and made available to researchers. Secondly, the solution must implement the FAIR principles. Since the FAIR principles were only introduced in 2016, we extended our research not only to solutions explicitly mentioning the FAIR data principles but also solutions facilitating FAIR alike data sharing.
Search query The search for articles was conducted in PubMed, Scopus, and Web of Science, following an approach based on the PRISMA guidelines  [21]. We searched for articles looking at titles and keywords and comparing those to a combination of relevant vocabulary, using the inclusion criteria detailed above. Articles reviewing multiple solutions of interest were also included, and referred as "reviews" in the following. The detailed search queries can be seen in Supplementary Table  S1. These queries are somewhat different from one database to another, because they have been adapted to each platform. We obtained 1072 solutions from Scopus, 479 from PubMed and 93 from Web of Science.