dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses Normally distributed continuous variables were displayed as mean ± standard deviation, nonnormally distributed variables as median with the first and third quartile (Q1-Q3). Categorical variables were presented as numbers with percentages. Group comparisons were tested using Student's t tests, Mann-Whitney U tests, or Chi-square tests where appropriate. Differences in expression of the 92 biomarkers between patients with AF versus sinus rhythm were tested using Linear Models for Microarray data analysis (Limma) software (version 3. 34. 9), using a log2 fold change cutoff of 0. 2, and a false discovery rate <0. 05 according to the Benjamini-Hochberg method. The biomarkers that were upregulated in patients with AF compared to those in sinus rhythm were further studied by using pathway overrepresentation analysis.
Pathway overrepresentation analysis was performed with knowledge from established pathways in publicly available databases: Gene Ontology (GO), Reactome, and the Kyoto Encyclopedia of Genes and Genomes (KEGG), using Cytoscape (version 3. 7. 1) and plugin ClueGO (version 2. 5. 4). 10, 16, 17  M ltivariable logistic regression was performed to study the association between the biomarkers within pathways and AF status, adjusting for age, sex, body mass index (BMI), heart rate, a history of coronary artery disease, and renal disease. Since we were interested in underlying pathophysiological differences between patients with heart failure with a reduced/mid-range/preserved ejection fraction HFrEF/HFmrEF/HFpEF, we performed the same analyses in these subgroups. Unfortunately, these subgroups were too small to gain results with pathway overrepresentation analyses.
The use of amiodarone was higher in the index cohort compared with the validation cohort. However, despite these differences between the two cohorts, all upregulated biomarkers and pathways found in the validation cohort overlapped with those found in the index cohort. The HFrEF, HFmrEF and HFpEF subgroups were unfortunately too small to yield results with pathway overrepresentation Downloaded from https://academic. oup. com/cardiovascres/advance-article/doi/10. 1093/cvr/cvab331/6409189 by ICL-Alexis Vautrin user on 02 November 2021 analyses. There was, however, no interaction between the amyloid-beta-related biomarkers and the HF subgroups, suggesting a pathophysiological role of amyloid-beta in HF patients across the full LVEF spectrum. Not all (combinations of) biomarkers were annotated by the publicly available databases even though these biomarkers were deemed to be significantly up-or downregulated in our analyses (e.
g. NOTCH3 and IL1RL1), since the content of these databases is based on what is currently known about these biomarkers. Therefore, the results of the current pathway overrepresentation analyses might change over time, when the knowledge on the (combination of) biomarkers has increased. The current findings might reflect underlying pathophysiological processes specific to elderly patients with AF and HF, since the mean age of these patients was 72 and 75 years in the two cohorts respectively. The number of women included in the cohorts was limited (n=462 [index] and n=439 [validation], 32%) and mainly comprised postmenopausal women. Even though we have attempted to define the AF and sinus rhythm group mutually exclusive, it is possible that patients with asymptomatic paroxysmal AF were misclassified.
Whatever the method, the counts are essentially based on the principle of estimation. During our counts, we conducted an individual counting when groups of birds were close to our observation point (J. Blondel, 1969). The equipment needed to count is: binocular, telescope and digital camera. Guides have been used for birds identification: Bird guide-Selection: Heinzel guide to birds of Europe from North Africa and the Middle East -Delachaux et niestlé 2004, Guide Heinzel des oiseaux d'Europe  (Heinzel et al. ,2014 ). Bird's population can be characterized by analyzing the following structural parameters: Abundance "N", Species richness "S". Graphical presentation of the data was based on the development of histograms; curves by Excel 2016. These data have been statistical processing by software R.
Taxonomical classification was done with the qiime classifier (qiime2-2019. 4) trained on the Silva database (Silva_132). Analysis of microbiota composition was carried out in R version 4. 0. 2 and R Studio version 1. 3. 1056 using the phyloseq, vegan, and DESeq2 packages  [75] [76] [77] [78]  on nonrarefied data, including samples with a minimum of 8,000 reads. The dataset had a median of 19,035 and a mean of 20,968 reads per sample with a standard deviation of 8,414. Alpha diversity was calculated based on rarefied data to 8,000 reads per sample (Fig  S1cd ). Predicted functionality of the gut microbiota was assessed using PICRUSt2 generated Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs and pathways. 79.
Lysozyme activity assay in the presence and absence of antimicrobials A. alcalophilum-derived lysozyme was added to an assay plate containing fluorescent-labeled PGN from Micrococcus lysodeikticus (EnzChek Lysozyme Assay Kit, Invitrogen) with or without antibiotics (10 mg/mL neomycin and 20 mg/mL ampicillin) and incubated at 37 degrees for 2 hours. The fluorescence signal (excitation/emission of 485/520 nm) was continuously measured and is proportional to the enzymatic activity. Assays were conducted with lysozyme concentration ranging from 0. 0001 to 2. 5 mg/mL. The data is represented as the normalized linear curve where each data point represents the mean from three independent measurements.
Statistical analysis GraphPad Prism 9 was used to calculate the statistical analysis except for the analysis of the microbiota composition described in the designated section. Each dataset was tested for normality by D'Agostino & Pearson test. Datasets not passing normality (p <. 05) were log transformed prior testing where specific tests are described in figure legends. All repeated-measure ANOVAs include the Geisser-Greenhouse correction for adjusting for lack of sphericity. Statistical significance was considered at p <. 05. In the study of C57BL6/J mice statistically significant differences between HFD+Vehicle and LFD+Vehicle are indicated by gray asterisks and difference between HFD+Vehicle and HFD+Lyso by black asterisks. This study includes n = 10 mice in LFD+Vehicle group, n = 11 in HFD+Vehicle and HFD+Lyso groups.
Acknowledgments We thank  Joanie Dupont-Morissette ,  Christine Dallaire ,  Jenny Rancourt-Mercier , and  Jacinthe Julien  at  IUCPQ  for excellent technical assistance during animal studies. The authors thank the Hist'IM platform ( INSERM U1016, Paris, France ) for their help with histology. Additionally, we thank  Jeanett Christiansen  employed at  Novozymes A/S  for conducting activity assays. Foundation (grant number:  NNF17OC0026698 ), the latter which also supported parts of the experimental costs. The work was also partly funded by the  Canadian Institutes for Health Research (CIHR)  to AM ( FDN-143247 ). BSYC was funded by doctoral scholarships from the  NSERC  and the  Sentinel North program  from the  Canada First Research Excellence Fund. TS and CS were supported by the  German research society DFG /IRTG 1911 Project.
CS is endowed chair of Nutritional Medicine supported by  Fresenius Kabi. BC's laboratory is supported by a Starting Grant from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. ERC-2018-StG-804135 ), a  Chaire d'Excellence  from  IdEx Université de Paris  - ANR-18-IDEX -0001 , an  Innovator Award  from the  Kenneth Rainin Foundation  and the national program "Microbiote" from INSERM; H2020 European Research Council [2018-StG-804135 ]; Kenneth Rainin Foundation  [Innovator Award];  Lundbeck Foundation  [ R232-2016-2425 ]; Novo Nordisk Foundation  [NNF17OC0026698]; Fresenius Kabi ; Chaire d'Excellence  from  IdEx Université de Paris  [ ANR-18-IDEX -0001 ]; Canadian Institutes for Health Research  [ FDN-143247 ];German research society [DFG/IRTG 1911 Project];Sentinel North;NSERC;German research society [DFG/IRTG 1911 Project];.
The X-ray source is AlKα operating at 1486. 6 eV, and the size of the X-ray spot is 200 µm. A neutralizing electron gun was applied. The energy scale was calibrated using the C1s binding energy located at 284. 8 eV. First, a wide range survey of 1,200 eV was conducted to identify all chemical elements with a pass energy of 187 eV. Next, a narrow range survey of each chemical element was conducted at a pass energy of 23 eV to identify the different chemical states, and a quantitative analysis was applied based on the curve fitting of each peak using Multipak software. In addition, time-of-flight secondary ion mass spectrometry (ToF-SIMS) of the steel cylinders and PA66 ring after the tests was performed using a TOF-SIMS 5 (ION-TOF Co. ) to detect the composition of the sliding surface. An area of 100 × 100 µm 2 was used for the spectroscopic analysis, and an area of 500 × 500 µm 2 was used for the chemical mapping.
Protocol Protocol for using MULTILAYER to reveal molecular tissue substructures from digitized spatial transcriptomes 1. The Multilayer toolkit can be downloaded from https://github. com/SysFate/MULTILAYER (Figure  1A ). This is done by clicking in the green 'Code' tab and downloading the zip folder. It contains the data set used as examples in the paper, the gene ontology databases, the tutorial in a pdf format, the MULTILAYER tool, MULTILAYER compressor tool, ''Visium Converter'' tool and a ReadMe file. 2. After downloading the zip folder, extract all files. Anaconda Python Platform MULTILAYER is functional on all operating systems (Windows, Linux and Mac OSX) with Python 3. Python version 3. 8 is recommended. A simplified strategy to run Python on any operating systems is to use Anaconda.
3. Anaconda can be downloaded from https://www. anaconda. com/products/individual according to individual computer specifications. 4. Once Anaconda is installed, open up the Anaconda Navigator to launch the PowerShell Prompt console. 5. Before using MULTILAYER for the first time, it is necessary to install the following dependencies as following: Launching MULTILAYER 6. To launch MULTILAYER, you have to first access the files by tracing the correct path from the PowerShell Prompt Console (Figure  1B ). For e. g. , If the Multilayer. py is located in the Multilayer-master folder, which is located in the downloads folder, type: 7. To launch the Multilayer tool, type or (This will depend on which version of python has been installed. ).
Data collection Spatially resolved transcriptomics (SrT) promotes the transition of histological studies towards a data science context, notably by the generation of digitized maps of tissue architecture. MULTILAYER takes advantage of such a digitized view to reveal molecularly-defined spatial signatures, with the use of agglomerative clustering over contiguous gene expression elements, herein defined as gexels (in analogy to pixels retrieved on digital images). Considering the recent heterogeneity of platforms available for generating SrT data (e. g. , use of DNA arrays  (Rodriques et al. , 2019; Sta ˚hl et al. , 2016)  or microfluidic channels  (Liu et al. , 2020) ), users might refer to the corresponding platforms for the preprocessing steps (spatial barcodes demultiplexing, read counts alignment, gene/transcript read counts association, etc).
In all cases, the spatial transcriptome output requires to be in the format of a matrix composed of columns associated to spatial coordinates and rows to gene identifiers. 8. Depending on the SrT platform in use, such a spatial matrix is generated within the corresponding analytical pipeline or it can be obtained as following: a. In cases in which the structure of the matrix is transposed (i. e. , columns are associated to gene identifiers and rows to spatial coordinates) or where the provided coordinates are not as round numbers; MULTILAYER provides options to handle these types of situations. Within the ''Select dataset(s)'' panel, user can opt to transpose and/or round off the entry matrix (Figure  1D ). At this stage MULTILAYER is not able to recognize the format of the dataset in use.
b. In cases in which the data is available as a three column dataframe (spatial coordinates, gene identifiers and read counts), the ad-hoc module called ''MULTILAYER compressor'', allows to convert it into the required matrix. In addition, this module is able to decrease the resolution of the available data by agglomerating contiguous gexels defined by a user-provided compression factor (refer to section ''processing high-resolution SrT data''). c. In cases in which the data is issued from the commercial solution ''Visium'', the ad-hoc module, called ''VisiumConverter'', allows to generate the required matrix. For this, users need to download the 103Genomics proprietary tool spaceranger from here: https://support. 10xgenomics. com/spatial-gene-expression/software/downloads/latest and proceed with the following Visium generated files: i.
a matrix in h5 format. ii. the spatial imaging data containing the tissue_positions_list. csv file. iii. the feature barcodes matrix files containing the corresponding features. tsv. gz file. On the PowerShell Prompt Console, users can covert h5 matrix files generated by Visium to csv format as following: Where cellmatrix_HDF5. h5 is the h5 matrix files to convert to the csv format. For a detailed description of these steps, users can follow the spaceranger dedicated tutorial here: https://support. 10xgenomics. com/spatial-gene-expression/software/pipelines/latest/output/matrices The obtained csv matrix can be processed by our ad-hoc module, called ''VisiumConverter'' (available at https://github. com/SysFate/MULTILAYER) module as follows: Where -m defines the matrix to be converted; -p: the tissue positions information, -g: the features and -o: the matrix compatible with MULTILAYER.
KEY RESOURCES TABLE STEP-BY-STEP METHOD DETAILS Herein we describe Step-by-step methods for analyzing spatial read counts, from the loading of raw data, to its normalization, differential gene expression detection, gene co-expression pattern mapping and finally revealing the spatial communities corresponding to biologically relevant tissue substructures. To illustrate these various steps, we use as an example, the analysis of the Whole Mouse Embryo data generated by Liu and colleagues  (Liu et al. , 2020). Open data on MULTILAYER Timing: 10 min 1. Use Explore mode for this data. This is used for the analysis of one sample. (Figure  1C ). 3. Select option to transpose matrix. Note: Transposing the dataset is useful when you have a matrix with gene names as columns and coordinates as rows (see data collection).
When it is not performed (but required), the following error comes up -AttributeError: 'NoneType' object has no attribute 'columns'. 4. Optional setting to round off the matrix; default is unchecked. Note: MULTILAYER requires to round out spatial coordinates for the analysis. For this, the tool needs an integer as X & Y (coordinates) which will get rounded off. For eg: a given coordinate '3. 36313. 94' will be converted to '3314'. 5. Indicate maximum matrix size or leave as default option 32 3 32. 6. Click Next. 7. Indicate threshold for up and down regulated differential gene expression; default range is 1 to À1 (in log2). 8. Indicate minimum number of contiguous Gexels; default is 10. Note: In Pattern detection, the tool uses agglomerative clustering on gexels (https:// scikit-learn. org/stable/modules/gen erated/sklearn.
Note: Louvain algorithm is performed over multiple iterations (default value: 15 times) and the most frequent outcomes are retained. This can be seen in the PowerShell prompt console. 33. By clicking the ''Run Communities'' button, MULTILAYER displays a matrix where the spatial communities are highlighted (color-coded gexels) (Figure  7A ). Note: When all communities are displayed, it's possible for communities to overlap. If there are overlaps between communities, the tool will color the gexel as part of the community with the highest similarity. 34. By choosing one community, the over-expressed genes colocalized in that region are listed (Figure  7B ). (C) By clicking on the ''Gene Ontology'' button in (A), a new dialog panel allows to select for a pertinent GO terms database (here Jensen TISSUES).
Processing high-resolution SrT data Step-by-step method of analyzing high resolution data. In this particular case, datasets issued from the technology Slide-seq  (Rodriques et al. , 2019)  is used (Hippocampus Brain section). High resolution data are, in general, stored as a three-column format, instead of a gene/coordinate matrix. Prior to MULTILAYER processing, high-resolution files need to be condensed into larger gexels, thus reducing the number of gexels to display, but simultaneously gain on read counts per gexel.
Compress data with MULTILAYER compressor Timing: 10 min 36. In order to compress the file, it is necessary to open the MULTILAYER compressor tool and also indicate the correct directory path to the data. 37. In order to access the compressor tool, type in the PowerShell prompt console: or For e. g. , (Figure  8A ) Note: The format of the input data has to be in 3 columns, where the first column correspond to barcode coordinates (XxY) with header 'bc'; the second column to genes with header 'gene'; and the third column to the gene counts with header 'count'. The Multilayer_Compressor. py has several arguments: The function -i indicates the input matrix, -o indicates the output matrix, -cx and -cy indicates the compression factor (if -cx and -cy are identical only -cx needs to be defined). By typing into the PowerShell console, you get access to the help section of the compressor.
Note: The Batch mode is useful for analyzing multiple datasets from the same sample. This information can also be downloaded as a table for further analyses (e. g. , graphs display). Furthermore, this panel allows us to access the gene ontology analysis as described for the ''explore'' mode. 41. Provide your dataset by clicking Raw matrix and select the appropriate folder where all datasets to process are available (in this case the ''Prostate Cancer'' datasets). All. tsv files in the selected folder will be considered as the input matrix. 42. All values are kept as default values. 43. Click next. Note: Since multiple files are being processed, this step takes $6 min. 44. You are now able to visualize digitally, the raw counts, normalized counts etc. for individual samples as in the example for Whole Mouse Embryo or a comparison dataset for several samples at once on the basis of their associated spatial communities (Figure  9B ).
Compare multiple spatial communities on MULTILAYER Timing: minutes 45. On the Multilayer interface, select ''comparison datasets'' and select all the files to be compared (''Select All'' button) or make your own selection (Figure  9C ). 46. Default values for community's detection are kept constant for all datasets (weight is checked, multiple iterations (15 times) is checked, threshold is 0%). Furthermore, users can adjust the minimal number of gexels for patterns detection (Figure  9D ). This option is of major interest in case users have previously identified the minimal number of gexels (Figure  10 ). 2). When using a minimal number of gexels of 10 (i. e. , >9) for pattern detection, several gexels within the tissues are not associated to any of the inferred spatial communities (black-colored gexels; Top panels).
By decreasing the minimal number of gexels criteria, it is possible to maximize the number of gexels associated to any of the inferred spatial communities (Bottom panels). Note: Users can empirically evaluate the minimal number of contiguous gexels for the analysis, by evaluating the fraction of gexels that are associated to at least one of the inferred spatial communities. As illustrated in Figure  10 , by decreasing the minimal number of gexels from 10 to 5, the spatial communities retrieved within the displayed prostate tissue sections managed to incorporate most of the gexels of the tissue within the inferred patterns. 47. Run communities and save the. tsv file. Note: When saving the files for the comparison analysis, the tool saves 2 files (ex. save. tsv, save_filter. tsv). 48. An Interactive heatmap comes up with all the dataset communities.
The x-axis represents all the dataset communities, and the y-axis represents all the classes. A class is a group of 1 or several communities. (Figure  9E ). 49. For gene ontology analysis, click GO terms. 50. Select the number of GO terms and database. In this example, top GO terms =5, database= DisGeNET. 51. This gives a heatmap of the GO terms that can be saved as a plot or matrix (Figure  9E ).
EXPECTED OUTCOMES MULTILAYER provides an intuitive platform for processing SrT data issued from a variety of technological platforms (e. g. , DNA-array based methodologies  (Rodriques et al. , 2019; Sta ˚hl et al. , 2016) , microfluidic channels  (Liu et al. , 2020) ). Furthermore, thanks to additional modules, MULTILAYER can also analyze high-resolution data (MULTILAYER compressor module), as well as those issued from the commercial platform ''Visium'' (Visium converter module). As outcome, MULTILAYER processes SrT maps as a digital image, in which users can visualize gene expression signatures, reveal gexel patterns resulting from agglomerative clustering, and even infer biologically relevant tissue substructures. Considering the future needs for processing multiple datasets on a comparative manner, MULTILAYER also provides a ''batch-mode'', allowing to process multiple samples at once, as well as compare them, notably by classifying the assessed tissue substructures per dataset into groups that might reveal common biological functions.
LIMITATIONS The current version of MULTILAYER reveals spatial gene co-expression signatures, but it is not able to infer their related co-regulatory relationships. Furthermore, it is not yet adapted for integrating single-cell transcriptome data. MULTILAYER is currently in progress to be updated with these two aspects. From a technical angle, MULTILAYER is limited with its visual functionalities for datasets represented within matrices > 250 gexels per side. In such a situation, the use of the MULTILAYER compressor module is strongly recommended.
Potential solution To avoid this issue, we recommend using unique identifiers, for instance corresponding to transcripts (e. g. , EnsemblID). Since the gene ontology analyses requires access to the gene symbol ID, an optimal solution is to concatenate both gene symbol and transcript ID into a single identifier separated by a ''_'  ' (e. g. , 'geneSymbol_ensemblID'). MULTILAYER is able to separate such a gene ID structure to recognize the first part of the identifier as the corresponding gene symbol to be used for the gene ontology processing when required. Problem 6 MULTILAYER processing does not provide gene patterns and/or spatial communities.
Figure6. A functional analysis of co-expressed genes revealed by MULTILAYER (A) Gene co-expression analysis for the spatial pattern associated to the gene FABP7. After selecting one of the FABP7 patterns shown in 5b (by clicking on one of the colored gexels associated to the pattern of interest), a gene co-expression similarity (minimal threshold: 10%) is computed. The illustrated matrix, displays gexels occupancy by spatial co-expressed genes with FABP7 (heatmap: similarity level with the query gene). (B) A comprehensive list of the identified co-expressed genes is displayed within the PowerShell prompt. (C) By clicking on the ''Gene Ontology'' button in (A), a new dialog panel allows to select for a pertinent GO terms database (here Jensen TISSUES). By pressing the Run button, MULTILAYER assesses the enrichment for the pertinent GO terms and displays their ranking on the basis of their confidence.