dataesr/scientific-paragraphs-categorization huggingface dataset:
To make the training step faster, the data were downsampled to 5Hz. Then, each input modality was processed into a dedicated network branch to leverage the different types of data. While the video frames were passed to a VGG19 network  [40] , the kinematic data were passed to an adapted ResNet network  [41]. The last convolutional layer of each modality branch was finally concatenated into a common branch before being split again into separated workflow branches containing their own activation layers (1 for the phase and step recognition, 6 for the activity recognition, and 8 for the multi-granularity recognition). The VGG19 network was initialized with the weights of a pre-trained model on the ImageNet data set. Since the MISAW data set was acquired in phantom surgical settings, the IMPACT team retrained only the last two layers to refine the network for this task.
MISAW data annotation protocol Each session of MISAW (MIcro-Surgical Anastomose Workflow recognition on training sessions) dataset must been manually annotated thanks to *Surgery Workflow Toolbox* [Annotate] software to generate procedural sequences. The sequence have to been annotate at the three following levels of granularity  [1] : • Phases: the main periods of the intervention. A phase is composed of one or more steps. • Steps: a step is a sequence of activities used to achieve a surgical objective • Activities: An activity is a physical action performed by the surgeon and is composed of.
Abstract In ammatory bowel disease (IBD) is a class of chronic in ammatory gut disorders whose aetiogenesis is still unknown. Despite the high number of omics studies, the RNA sequencing (RNA-Seq) data produced for a better IBD pathogenesis understanding cannot be compared because of the experimental variability and different data analysis approaches. To overcome this hurdle, we here introduce the open-source IBD Transcriptome and Metatranscriptome Meta-Analysis (TaMMA) framework, a comprehensive survey of publicly available IBD RNA-Seq datasets. IBD TaMMA will expedite the pro ling of the IBD-associated transcriptome and metatranscriptome, holding out the strong promise of being of high impact for the IBD community.
Main In ammatory bowel disease (IBD), including Ulcerative Colitis (UC) and Crohn's Disease (CD), is a class of multifaceted chronic in ammatory gut disorders characterized by an uncontrolled, resolution-failing in ammation that leads to bowel demages 1. Many efforts have been made to better comprehend IBD etiogenesis, so that, in the last decades, advances in computational biology have boosted preclinical and clinical research studies 2 , generating a high number of RNA sequencing (RNA-Seq) data. Nevertheless, because of the experimental variability and different data analysis approaches, no comparison among the studies has been possible so far. To exploit the efforts made over the years by IBD experts in the eld of Next Generation Sequencing (NGS), we here introduce the rst meta-analysis web app, the IBD Transcriptome and Metatranscriptome Meta-Analysis (TaMMA) platform (https://ibd-metaanalysis.
herokuapp. com/; link is temporarily kept private for the revision process; username: danese; password: steam). IBD TaMMA is a comprehensive survey of publicly available RNA-Seq datasets from IBD-derived and control samples across different tissues, all analyzed with the same pipeline and batch corrected for data harmonization and simultaneous comparison among the different studies. By exploiting this tool, the scienti c community will bene t from a user-friendly, open-source platform where the pro ling of the IBD-associated transcriptome and metatranscriptome will become quicker and statistically more powerful, also holding out the strong promise of being of high impact for the IBD community.
Results Different meta-analyses of IBD patient gene expression pro les from microarray datasets have already identi ed dysregulation of expression of genes encoding for several in ammatory factors and RNAbinding proteins  [3] [4] [5]. Nevertheless, these studies focused on a limited number of genes, lacking not only the whole-transcriptome but also metatranscriptome pro ling, the latter newly emerged as successful to uncover novel gut-populating microbial entities 6. Therefore, to provide a wider picture of the whole-transcriptome and metatranscriptome at different tissue/cell levels in both UC and CD patients, publicly available RNA-Seq datasets were collected and analyzed. Being 26 independent studies, we predicted an experiment-dependent bias which was counteracted through a well-established batch correction algorithm, in accordance with source and tissue of origin.
Of note, we also tried to batch correct the different library construction strategies utilized, but their variance was already fully explained by the source study. The meta-analysis performed was used as the core to design the IBD TaMMA web app (Supplementary Fig. 1a ), which allows quick access to differential gene expression and gene ontology functional enrichment results, among the different conditions (https://ibd-meta-analysis. herokuapp. com/; full guide available at https://ibdtamma. readthedocs. io/). Sample dispersion within the UMAP, easily attainable through the IBD TaMMA platform, shows clustering in accordance with the tissue of origin but not with the source study (Fig. 1a  and Fig. 1b ), indicating successful data harmonization. Consistently, housekeeping gene expression levels were found to be comparable across the different tissues and conditions (Supplementary Fig.
1b ). IBD TaMMA pinpoints a strong differential gene expression among UC, CD, and healthy (control) groups in the ileum, colon, and rectum, as shown in Fig. 1c. Of note, IBD-speci c proin ammatory signatures were con rmed. Indeed, dysregulations between IBD-derived intestinal and the healthy tissues in the expression levels of Tumor Necrosis Factor-alpha (TNF ), Interferon-gamma (IFNG), Interleukin 12B (IL12B), Integrin alpha 4 (ITGA4), Integrin beta 7 (ITGB7), already known as drivers of chronic in ammation and thus exploited as therapeutic targets for IBD patients 7 , were con rmed (Fig. 1d  and Supplementary Fig. 1c ). Likewise, S100A8 and A9 transcripts encoding for the two subunits of the fecal biomarker calprotectin 8 and the recently emerged S100A12 8 were increased in intestinal samples from CD and UC as compared to the healthy (Fig.
Discussion Currently, NGS and multi-omics approaches are accelerating IBD investigations, allowing researchers to simultaneously explore disease intricacy from many points of view 2. Nevertheless, although numerous whole-transcriptome analyses of IBD samples have performed so far, no integrative study has been nalized, thus dispersing both insights and evidence often because of the weak statistical power. IBD TaMMA introduces the rst integrative analysis of all IBD-related publicly available RNA-Seq datasets, computationally uniforming their batch differences due to obvious experimental variability. IBD TaMMA validates speci c pro-in ammatory and microbiota signatures con rming what is already wellestablished in the eld and therefore proving to be a reliable platform of IBD transcriptomic and metatranscriptomic investigations.
Meta-analysis FASTQ reads from 3,853 RNA-Seq data from different tissues, namely ileum, colon, rectum, mesenteric adipose tissue, peripheral blood, and stools, were mined from NCBI GEO/SRA and passed the initial quality lter. FASTQ les that passed the initial quality lter (see metadata table in TaMMA, https://ibdmeta-analysis. herokuapp. com/; link is temporarily kept private for the revision process; username: danese; password: steam) were mapped to the human reference genome and initial gene quanti cation was performed. Since these data came from 26 different studies made in different laboratories, we counteract the presumptive bias through a batch correction in accordance with source and tissue of origin. Once the gene counts were adjusted, samples were divided into groups in accordance with the tissue of origin and patient condition prior to differential expression analysis and gene ontology functional enrichment.
Acknowledgments The authors would like to acknowledge the " Fondazione Cariplo per la ricerca Biomedica"  under the grant agreement # 2018-0112  to FU; " Fondazione AMICI ONLUS " for the research prize 2020 to FU;  INNOVATIVE MEDICINES INITIATIVE (IMI)   2020-2024  ( NUMBER 853995 -ImmUniverse ) to SD and SV.
With the threshold on red fluorescence, cells that contained chlorophyll as well as green fluorescence (same signal as the prey added, YG-beads or FLBs) were considered to be cells containing prey (Figure  S1 ). In addition, to confirm the total concentration of prey added to each flask, the sample was also run with the threshold on green fluorescence. FCM listmodes were analyzed with the Guava easyCyte Suite Software 3. 1 (Luminex Corporation, USA). For each feeding experiment, the ingestion of prey was quantified in each experimental flask by first adding prey and then sub-sampling and fixing after an incubation of 0 (T 0 ), 20 (T 20 ) and 40 (T 40 ) minutes. The T 0 sample accounts for the physical attachment of prey to the cell and therefore the percent of cells ingesting prey corresponds to the percent of cells with prey at T 20 or T 40 , minus the percent of cells with prey at T 0.
Trophic mode predictions from genome and transcriptome analysis Predicted peptides from whole genome (van  Baren et al. 2016 , Worden et al. 2009)  or transcriptome data  (Keeling et al. 2014)  were downloaded from publicly available databases as detailed in Table  S1. Because computational predictions are based on presence/absence information, predicted peptides from independent transcriptome assemblies of the same strain were concatenated to include as much information about each strain as possible. Computational prediction of phagocytotic, photosynthetic, and prototrophic capabilities were completed as in  Burns et al. (2018a). This involved scoring a set of 14,095 protein profile hidden Markov models (HMMs) that were derived by clustering all proteins in 35 reference eukaryote genomes of known trophic mode against all proteins from each test genome or transcriptome.
Final prediction probabil-ities for each test genome/transcriptome were calculated against the reference trophic mode models using a probability neural network. To visualize the prediction output, which exists in four dimensions with three degrees of freedom (phagocytosis, photosynthesis, prototrophy, and a fourth dependent dimension for absence of each trophic mode), predictions were normalized such that the sum of the three predictions plus the probability of not fitting each trophic mode equaled 1 using the relation: 1 -(p phago + p proto + p photo )/3. The fractional independent probabilities of each trophic mode and the dependent absence number were mapped to 4-dimensional color space and projected onto a circle using scripts adapted from the R package "pavo"  (Maia et al. 2013). Scripts are available at https://github.
Acknowledgments This work was supported by  ANR  contract  PhytoPol  ( ANR-15-CE02-0007 ). We thank  Robert Sanders  and  Rebecca Gast  for hosting VJ and training her on techniques to determine mixotrophy in phytoplankton. We also thank  Dominique Marie  for help with flow cytometry,  Christian Jeanthon  for help and advice with fluorescently labelled bacteria,  Sophie Le Panse  from the  Merimage microscopy platform at the Roscoff Marine Station  for assistance with the transmission electron micrographs and the  Roscoff Culture Collection  for providing of the algal strains.
Implementation We implemented spectral partitioning with all thresholding methods and all link prediction measures in Python, 14 and we used python-louvain package as implementation of Louvain. To perform comparison with the state of the art, the source code from Tang et al. (2020) was used as implementation of DTIP_MDHN, the code from  Thafar et al. (2020)  as implementation of DTiGEMS+ and the toolbox from  Buza and Peska (2017)  was used as implementation of ALADIN, BLM-NII, WNN-GIP and NetLapRLS. The evaluation framework was fixed to 5 × 5 where it was required in the aforementioned implementa- tions, and DTiGEMS+ was used with single similarity network for each type of node. All experiments in this article were run on servers with 2 processor units of class Intel Xeon E5-2680 with 48 cores in total and 512 Gb of RAM.
Performance on a bigger data set The five benchmark data sets on which we have reported to far are relatively small and dense, as shown in Table  1. In this section, we therefore contrast those results with those on the IUPHAR data set. The results are shown in Table  4. As Table  4  shows, LPbyCD using either spectral partitioning or Louvain clearly improves on the results of the baseline. Contrary to the results on the benchmark data sets, however, the settings with spectral partitioning outperform the ones with Louvain, even though the two are close for node-community matching. The table also shows a very surprising result in that spectral partitioning with nodecommunity matching does best when not creating communities at all! In that case it is the matching technique that does the heavy lifting and effectively treats each entity as a community of size 1 when the time comes to predict new links.
Relative running times, i. e. running times on IUPHAR divided by running times on the benchmark sets, are shown on the right-hand side of Table  5. With spectral partitioning, we see that size quotients grow faster than running times quotients, with the exception for the NR data set in the CC formulation, which is so small that it can be treated in less than a quarter second in this setting. Another exception is the Kinase data set, running times of which grow faster than edge count. This might have to do with the fact that Kinase is rather unbalanced, with far fewer drugs than targets. These are the cases for JC CC measure, for JC NC quotients of running times are lower than ones of network size for all sets without exceptions. However, JC NC setting with m = 0 , i. e. without community discovery at all, might be not representative enough, the JC CC does not have this limitation.
Performance on generic data sets So far we performed evaluation of LPbyCD approach while solving the drug-target interaction prediction task. This experiment shows that our approach is more general, i. e. it can be used for any kind of bipartite multi-layer data regardless of its origin. The results are summarized in Table  6  with best values for each data set and CD method highlighted in bold. The results are similar to the ones on benchmark sets (0. 89/0. 37 as maximum AUC/ AUPR among generic sets on MovieLens with spectral partitioning and JC NC measure compared to 0. 92/0. 26 the best performance on the benchmark data, on IC, for the same measure). We can therefore conclude that the good results shown above are not due to the specific application domain of biological networks but transfers to other data.
On the MovieLens data set, NetLapRLS is slower than LPbyCD(Spectral part. /JC NC ), but on Unicodelang becomes even slower by an order of magnitude. BLM-NII goes from fourth fastest on MovieLens to sixth fastest on Unicodelang. WNN-GIP has the worst performance and remains the slowest. We use cpu time to illustrate total complexity of each approach implementation when using multi-thread computations. The implementation of LPbyCD(Louvain/JC NC ) does not use this optimization, which is why cpu time and exact time for this approach are equal and its running times are consistently rather low. The only result that is surprising is that the exact time of ALADIN is much higher than its cpu time. We can only conjecture that the cpu time measured by the toolbox with the implementation of this approach is not correct.
To overcome this, we have perform experiments on slightly modified data. 15 This could also the fact that DTiGEMS+, which is very dependent on similarity information, performs worse on Unicodelang. Another issue we faced with ALADIN, NetLapRLS and WNN-GIP on the MovieLens data set, is that we could only run these approaches in quick optimization mode 16 due to incredibly high exact running time in the "full" mode. Taking into account that MovieLens is a bigger set than Unicode by the number of both vertices and edges (Table  1 ), we suspect that means that those approaches (or at least their implementations) are not scalable enough. To sum up, DTIP_MDHN is the clear winner in this comparison. Its complexity and running time are not the best, but can be compensated by the performance it provides.
However, DTIP_MDHN is a latent model based approach, which means that it lacks possibility of interpretability. The latter might not be essential in the user-movie and other recommendation tasks in general domain, but it is very desired functionality in life science applications such as drug-target interaction prediction (the impact from watching a not properly recommended movie is different from one caused by a wrong medication treatment). The other best performing approaches that we tested are data quality dependent (DTiGEMS+, ALADIN, BLM-NII, NetLapRLS) or possess extreme time complexity on a bigger set (ALADIN, WNN-GIP, NetLapRLS). We can conclude that the LPbyCD approach based on the Louvain algorithm with JC NC measure provides the best trade-off in this experiment. Being faster than most of the comparison approaches, it has no limitations regarding the input data and its predictive results are not far from the maximum.
Conclusion We have presented an approach for link prediction in bipartite multi-layer graphs using graph community structure and link prediction measures adapted from those proposed in the literature. We have found empirically that combining the well-known and relatively straightforward Jaccard coefficient, particularly in a BLM formulation, with the Louvain algorithm for community detection allows us to achieve results that are competitive with the state-of-the-art. We have also demonstrated that it is possible to set the parameter values of the community detection techniques via internal cross-validation and that they transfer well to unseen data. In addition, we demonstrated scalability of our approach by performing evaluation on the much larger and sparser IUPHAR data set and comparing the time complexity with benchmark data sets, and assessed interpretability.
The original set description For minimizing evaluation complexity, we base construction of our multi-layer network on the oldest, and thus the smallest, set of the MovieLens group. 18 The version we use includes information about 943 users, 1682 movies and 100,000 movie ratings made by users. In the data, users are presented by age (integer value), gender (binary value), occupation (one of 21 categories) and a post code (in the US or Canadian format for citizens from US and Canada respectively). Movies are described by name, release date, genre (19 multiple categories) and include links to Internet Movie Database (IMDB). 19.
Feature construction To build similarity networks, we first create features to describe users and movies. To achieve that, we exploit available properties of both types of entities. User descriptors To describe users by age, we introduced five age groups for users: less then 18, between 18 and 25, between 26 and 34, between 35 and 49, and 50 and more. The first and the last usually have opposing preferences in movies, the middle groups were selected based on principle of balance (Fig. 6 ). Gender category was used as it is, and occupation as well. Post codes were converted into the exact location names they belong to using publicly available databases. 2021 Then we binarised these locations into two groups: "big city" and "province" based on the top 100 US cities 22 and the top 10 Fig. 6  Handcrafted descriptors for users in the MovieLens data set (the number of instances in each category of each descriptor as well as the amount of unclassified instances, where it is applicable, are shown in grey color) 18 100K Dataset (containing the user ratings from 1998).
19 A popular internet database of movies: http:// www. imdb. com. 20 Database of ZIP codes of the USA states: http:// www. pier2 pier. com/ links/ files/ Count rysta te/ USA-Zip. xls 21 Database of post codes of Canada: http:// www. posta lcode sinca nada. com. 22 According to census of 1990 (published in 1998): http:// www. census. gov/ popul ation/ www/ docum entat ion/ twps0 027/ tab22. txt. 17 http:// group lens. org/ datas ets/ movie lens. Canadian cities. 23 The resulting partitioning produced groups with roughly 20% of individuals from "big cities" and 80% from "province" (Fig. 6 ). To sum up, we encode each user by the vector with 30 binary values: • 5 bits for age (5 bins), • 2 bits for gender (2 categories), • 21 bits for occupation (21 categories), • 2 bits for location (2 groups).
We matched candidate movies by title, year and release date 24 and used edit distance algorithm Fig. 7  Handcrafted descriptors for movies in the MovieLens data set (the number of instances in each category of each descriptor as well as the amount of unclassified instances, where it is applicable, are shown in grey color) 24 The imdb. com was accessed on 19/10/2019. 23 According to census of 1996: http:// en. wikip edia. org/ wiki/ List_ of_ large st_ Canad ian_ cities_ by_ census. (Damerau 1964)  to filter candidates. We then processed the lists of candidates and filled in blanks, manually double checking movie genres. Once IMDB identifiers were collected, the IMDB database was parsed one more time to retrieve additional information about the movies: 25 budget, country of origin, main language, length, type (movie or other), actors, production company and crew members (directors, musicians, operators etc).
pound sterl ingli ve. com/ bank-of-engla nd-spot/ histo rical-spot-excha nge-rates/ usd. 27 The last day of the MoveLens survey for 100K Dataset. 28 http:// www. the-numbe rs. com/ movies/ produ ction-count ries. 25 The imdb. com was accessed again on 25/10/2019. ranges as shown in Fig. Concerning  the last three properties, we have chosen the top n values for actors, companies and crew members, allowing multiple categories. Value of n for each property was selected individually using the principle of minimizing redundancy of vector representation. To sum up, we encode each movie by a vector with 108 binary values: 18 bits for genre that goes with the data, plus 90 bits for the manually created features summarized in Fig. 7.
Materials and Methods 1 Historical data (1992-2014) and egg sampling done for this study (2017-2019) A total of 755 egg masses from historical datasets and recent collections were analyzed in this study. Egg masses originated from 22 sites distributed across the PPM distribution in Tunisia (Table  1 ; Figure  1 ; Table  SM1 ). Historical datasets on egg masses collected in  1992, 1993, 1995, 2010 and 2014  on Aleppo pine stands, Pinus halepensis Miller, were retrieved from institutional reports (unpublished data, INRGREF). These datasets report the length of egg masses, the number of eggs per egg mass, and the phenotype of individual eggs  (parasitized, aborted, sterile, hatched). In addition, we collected egg masses in various locations in  2017, 2018, and 2019. All these egg masses were also collected on Aleppo pine stands, and kept at ambient temperature 25 ± 2°C) at the INRGREF laboratory near Tunis where we followed a protocol similar to that used for historical collections of egg masses.