dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements NMR experiments were performed at the ' Service Général des Plateformes, Brest' (SGPLAT). JBB is funded by the  Region Bretagne. CNRS  ( Centre National de la Recherche Scientique ) and the  Université de Bretagne Occidentale (Brest)  are acknowledged for nancial support. LC is thankful for the support of the  Région Bretagne  relating to the project  ConvcatH (Stratégie d'Attractivité Durable)  and the  ANR (Agence Nationale de Recherche)  for funding the project  JCJC OxySplit-H2  ( ANR-20-CE07-0027-01 ).
FUNDING AK and MK acknowledge funding from the  German Federal Ministry of Education and Research, Quantum Futur Program (PQuMAL). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  under grant agreement No. 950618  ( STREAMLINE  project) and No. 947603  ( QFreC  project). BW further acknowledges the support of the  French ANR  through the  OPTIMAL  project ( ANR-20-CE30-0004 ) and the  Région Nouvelle-Aquitaine  (SCIR & SPINAL projects).
Data Preprocessing and Visualization MALDI-MS images are large datasets and enclose complex data. Various approaches are designed to reduce their complexity. For instance, peak picking and alignment reduce the dataset size while preserving relevant information along the spectral dimension  (Alexandrov, 2012). As for their visualization, there are multiple commercial and open-source software available  (Weiskirchen et al. , 2019) , many of which handle the standardized imzML data format  (Römpp et al. , 2011; Schramm et al. , 2012). Spectral visualization strategies, such as Kendrick mass defect analysis, have also been used to deconvolute imaging analysis  (Kune et al. , 2019; Blanc et al. , 2021). These efforts confront a major challenge in MALDI-MSI: a lack of chemical separation.
Data Analysis Data analysis involves methods which facilitate the interpretation and comparison of images. Method choice depends on the experimental design, the nature of the signal, and the scientific goal of the experiment. Dimension reduction techniques, such as principal component analysis  (PCA, Trindade et al. (2018a) ), non-negative matrix factorization  (NMF, Trindade et al. (2018b) ) or t-distributed stochastic neighbor embedding (t-SNE, van der Maaten and Hinton (  2008 )) can further abate the data. PCA and NMF are linear matrix decomposition techniques, whereas t-SNE is a probabilistic, non-linear dimension reduction technique. PCA and NMF decompose the original, large, MALDI-MS image into two smaller matrices, whose product approximates the original image. Common global information shared across ion images are stored in so-called component images.
Clustering methods give simplified representations of the datasets, where similar spectra are grouped together. The output can be visualized as a 2D image where each pixel has an intensity equal to its cluster number. Similarities between spectra can be estimated by various metrics, such as the Euclidean distance, which captures similarities among raw intensities, or the cosine distance, which captures similar trends in the spectra. The k-means algorithm groups spectra according to a predefined number of clusters  (Palmer et al. , 2015) , whereas hierarchical clustering yields a tree of subregions, arranged from broad structures to finer details  (Urbini et al. , 2017). Alexandrov and Kobarg (2011)  proposed a clustering algorithm which limits the impact of noise in MALDI-MS images, where pixels are grouped by the similarities between their neighborhoods.
Supervised machine learning methods, i. e. , methods that use annotated datasets, can recognize co-localized ions from manually annotated images. Ovchinnikova et al. (2020)  found spatial correlations between ion images with a deep learning model using visual similarities. Univariate analysis can be used to find differently-abundant ions across conditions, such as different regions in the images or different samples. T-test is used to highlight statistical differences for a specific m/z between two different samples  (Yajima et al. , 2018) , or regions within a tissue section, whereas the analysis of variance (ANOVA) test can be used to compare three or more regions  (Blanc et al. , 2018). When the normality assumption is not verified, typical in MSI datasets, non-parametric tests, such as Mann-Whitney U-test or Wilcoxon signed-rank tests can be used  (Guo et al.
, 2014). To determine if an ion is a biomarker for a condition, Receiver Operating Characteristic (ROC) curve analysis can be applied  (Hoo et al. , 2017). Data analysis usually returns several m/z features of interest. Metabolite databases can be polled to infer their chemical structure. The choice of the database depends on the type of analyte (e. g. , LipidMaps for lipids, UniProt for proteins, CSDB for glycans, etc. ). A website dedicated to MSI called METASPACE2020 allows for the interrogation of different databases.
MULTIMODAL MALDI-MSI Balanced by optimism in MALDI-MSI and caution with its limitations, researchers have turned to other imaging modalities to compensate. In addition to previously mentioned limitations, MALDI-MSI ion images are not directly indicative of the histological setting, as immunohistochemistry (IHC) could be. Consequently, MALDI-MSI will tend to move toward multimodal imaging which can compensate for these limitations by combining chemical and histological information from various modalities, and which results in a more informative dataset. Multimodal imaging can be used for guided acquisitions. For instance, the acquisition of MALDI-MS images can be guided with microscopy images, so as to restrict the imaged area and reduce the acquisition time and data size  (Patterson et al.
, 2018a; Blanc et al. , 2018). Rabe et al. (2018)  guide the MALDI-MSI acquisition by performing a segmentation of brain tissue based on Fourier transform infrared spectroscopy (FT-IR), reducing time and data size by nearly 98%. Multimodal imaging often combines several imaging modalities and yields integrated multimodal datasets, opening new possibilities for data mining. The methods used depend on the complementary modality associated with MALDI-MSI, the processing strategies and scientific goal (Table  1 ). Integrated multimodal datasets are typically obtained by a three-stepworkflow: multimodal acquisition, registration, or the spatial alignment of imaging datasets, and data analysis to highlight correlations between imaging datasets (Figure  1 ). The objective is to use information from both modalities to evidence complementarities and correlations with different data processing strategies, as described by  Bemis et al.
Acquisition Workflow Biological questions drive the choice of modalities and their implementation. Multimodal acquisitions involve some critical parameters to be considered in terms of analytical strategy: the use of a same sample section or adjacent section, the level of destructiveness of complementary modalities and the order of operations in which the acquisitions occur. Use of same section is advised to establish cellular or subcellular correlations. Adjacent sections are advised when all modalities are destructive, or when it is desirable to keep the sample preparation processes separate. However, the resulting integrated dataset is not reliably interpretable. The significance of MALDI-MSI sample preparation in multimodal experiments cannot be overstated as there will be consequences for downstream analysis.
Both images are different in terms of pixel intensities, so the authors extract binary masks of the sample. These masks are registered by an affine transformation, with an iterative gradient descent optimization of the SSD metric. Castellanos-Garcia et al. (  2021 ) combined MALDI-MS with LA-ICP-MS images, so as to merge the extensive molecular information with supplemental metals analysis. They select matching ion images manually in order to estimate both linear, and non-linear transformations. Abdelmoula et al. (2019)  registered several 2D MALDI slices with a 3D MRI image in order to obtain a volumetric representation of the MALDI dataset. The MALDI image is reduced by the t-SNE algorithm. The corresponding 2D MR image is selected from the 3D volume manually. Then, both linear and non-linear registration methods are applied, using mutual information as a similarity metric.
Hybrid approaches use both iconic and landmark-based registration methods. Patterson et al. (2018a)  registered microscopy and MALDI-MS images, as follows: two microscopy images, before and after MALDI-MSI, were acquired. As they are the same modality, the authors use iconic approaches to register them. The microscopy images are then registered onto an upsampled version of the MALDI-MS image using laser ablation marks as landmarks. This workflow avoids introducing deformation artifacts in the MSI datasets, and preserves the resolution of microscopy images. The quality of the registration determines the reliability of the results of the subsequent data analysis step. Registration evaluation can be achieved through various metrics, such as the Dice coefficient or the F-measure, which both estimate the proportion of overlapping pixels between the deformed and fixed images.
Data Analysis The last step of a multimodal workflow is usually the joint analysis of the images. This involves using information from both modalities to evidence complementarities and correlations. The algorithms depend on the scientific goal and can be classified in three classes, as described previously: (1) class discovery, (2) class prediction, and (3) class comparison. Class discovery finds spatial or spectral similarities in an image, and does not require supplementary information from a complementary modality. It can be achieved by data dimension reduction methods or clustering mentioned previously. Abdelmoula et al. (2019)  found segmentation maps of registered MALDI-MS images by a hierarchical variant of the t-SNE algorithm. Then, spatial correlations are established by Pearson correlation coefficient between the segmentation map and the ion images.
Verbeeck et al. (2017)  automatically identified the spatial distributions observed in registered ion images as a combination of the anatomical structures described in the atlas built from MR images. They then used Wilcoxon rank test to find differently expressed ions between healthy and diseased brain hemispheres. Patterson et al. (2018a)  found spatial correlations between two MS datasets by weighing abundance correlations by their degree of pixel overlap. Bemis et al. (2019)  proposed several models based on hierarchical Bayesian spatial models to find differently abundant ions. They modeled the fact that neighboring pixels have similar spectra, which retrieves differently abundant ions that are missed by other models. Class discovery, prediction and comparison can be integrated in the same workflow.
Jones et al. (2019)  used various methods encompassing all these analyses. They combined MALDI-MS images with various microscopy images (fluorescence and H&E). First, they manually selected ions that visibly colocalize with ROIs in the tissue. This approach was insufficient as it led to subjective results, and was not tractable on large datasets. They performed class discovery by the spatially shrunken centroid clustering algorithm and analyzed the clustering results further by looking up ions that were representative of a cluster of interest. Class prediction achieved by PLS regression, which allows to approximate MALDI ion images from a linear combination of fluorescence microscopy channels. The resulting regression made it easy to find biomarkers that were specifically distributed in a specific fluorescence channel.
PERSPECTIVES Multimodal imaging strategies involving MALDI-MSI alleviate technological issues related to scientific questions, leading to a better understanding of the living. It offers new opportunities in biology by allowing direct correlation between cellular and molecular information, but requires diligent approaches for acquisition, registration and computational data analysis to combine and get the best of the different modalities. Once achieved, these combinations raise new questions and initiate new studies, which would not have been possible with a single modality as shown in Table  1. After highlighting relevant methodological developments, multimodal imaging better contributes or provides evidence towards the biological understanding. Grélard et al. (2021)  correlated water distribution given by MR images with the distribution of specific polysaccharides in MS images.
This study hints at the role of these molecules in the cell-wall porosity on tissue, and corroborates previous observations made in vitro. Strittmatter et al. (2022)  combined MALDI and DESI to study the distribution of a drug and its different metabolites, which cannot be established by a single modality. Then, they combined MSI and IMC to show that drug metabolites co-localize with immunohistochemistry markers for DNA damage. Prade et al. (2020)  correlated multiplexed fluorescent immunohistochemical staining with MS images to identify ions involved in metabolic networks. They showed discrepancies in metabolic distributions according to different cell populations. The capabilities achieved by multimodal imaging workflows are numerous, and many are yet to be fully exploited. The technical progress enacting higher spatial resolution for MALDI-MS images is met with larger dataset sizes.
These datasets do not necessarily fit in memory, thus memory requirements become a bottleneck to several research groups. Bemis and Vitek (2017)  developed an R package which loads larger-than-memory datasets, using on-disk data structures. This effort should be furthered in other programming languages as well. This involves adapting the existing computational methods such that it complies with these data structures, which would require significant work and collaboration from the community. The variety of processing algorithms makes it difficult to pick suitable methods. Deep learning could facilitate the processing and joint analysis of MS datasets. Abdelmoula et al. (2022)  used a deep learning architecture to classify tumor and healthy pixels in MS datasets. One of the benefits of this approach is that it does not rely on spectral and signal processing.
(Continued)Collection of publications that combine MALDI-MSI with another modality. It attempts to classify each paper by its multimodal strategy and scientific goals. The list is separated in vivo/ex vivo and grouped by the spectral nature of its complementary modality, i. e. , whether it is mono-, multi-channel, i. e. , spectra with discrete bands, or hyperspectral, i. e. , spectra with continuous bands. withhigher resolution. MALDI-MSI datasets are large and it is desirable not to interpolate new signal intensities. Thus, MALDI-MSI are generally chosen as the fixed image. The transformation can be rigid (translation and rotation), affine (rigid with scaling, and shearing), projective (affine with perspective) or non-linear. Non-linear transformations account for local deformations in the image.
FUNDING This work was financially supported by the  Agence National de la Recherche (France , Grants  ANR-19-CE29-0010601  " MultiRaMaS "), the  CNRS (International Emerging Actions (IEA) "MultIRaMaS", and Groupement de Recherche "GdR-MSI ",  GDR2125 ). Landry BLANC is supported by a grant from  Gates Foundation  ( INV-004704 ).
, iconic: linear prediction, & interpretation & specific ions (2017) class Wilcoxon rank test comparison MRI Adjacent sections, & Class prediction NMF & inverse NMF, & Colocalizing signal Polysaccharides Plant: wheat grain Grélard et al. , (2021) iconic: linear & non- distance in reduced linear space MRI Adjacent sections, & Class discovery Bisecting k-means, & Colocalizing Proteins Murine kidney Oetjen et al. , (2013) iconic: linear & non- Pearson correlation signal (3D) linear MRI Adjacent sections Class Unpaired t-test Region-specific Proteins Murine brain Sinha et al. (2008) Iconic: linear comparison ions (3D) MRI Adjacent sections, & Overlay N/a Integrated Proteins Murine Attia et al. , (2012) iconic: linear visualization dataset (3D) Ex- H&E Adjacent and same Class prediction PLS Pansharpening, & Lipids, proteins, Murine brain Van De Plas et al.
, Microscopy section, Landmark, & (2018b) iconic: Linear and nonlinear IHC Same section, & Class ROC curves, & Mann Biomarker discovery Proteines Human breast cancer & Rujchanarong et al. , registration not comparison Whitney U test liver (2021) explained IHC Same section, & Class Overlay visualization, & Integrated dataset Lipids Murine brain -Hunter's Dufresne et al. , landmark comparison 2D correlation disease (2017) IF H&E Same section, & N/a Overlay visualization Region-specific ions Lipids Murine brain - Kaya et al. , (2017b) landmark Alzheimer's disease IF Same sections, & Class k-means clustering, & Integrated dataset Lipids Murine brain - Kaya et al. , (2017a) landmark comparison, & t-test Alzheimer's disease class discovery IF Same cells Class discovery Spatial correlation: Colocalizing signal Lipids Single cells Nikitina et al.
, (2020) Registration not Euclidean distance, explained Pearson correlation, & multivariate analysis: K-means clustering IF Same section, & Class Correlation Network, Region-specific ions Metabolites, lipids Murine pancreas cancer Prade et al. , (2020) landmark: linear comparison, & Spearman rank order class discovery correlation, & Mann Whitney U test Ex- Imaging Mass Same section, & N/a Overlay visualization Integrated dataset Lipids Murine Brain, Human Yagnik et al. , (2021) vivo Cytometry registration not tonsil & breast cancer explained FT-IR Same section, & Class discovery Random forest classifier Integrated dataset, Lipids, Eisenia fetida Ritschar et al. , (2022) registration not & colocalizing signal carbohydrates, & explained nucleic acids FT-IR Same section, & Class k-means clustering, & Integrated dataset, Metabolites, & Murine brain, & human Rabe et al.
They are advised for adjacent sections and to accurately model the damages during sample preparation. Registration methods can be classified in two categories, depending on the information they use: landmarks, or pixel intensities (iconic methods). Modality(ies) Multimodal strategy Scientific goal References Acquisition(s) and Data analysis Result(s) Omics Biological background registration method(s) Strategy(ies) Method(s) Raman Same section, Class discovery PCA-PCA correlation Integrated dataset, Lipids, & peptides/ Cell Spheroids Ahlf et al. , (2014) Spectroscopy landmark, & fiducial & colocalizing signal proteins aided Raman Same section, & Class prediction PCA on combined Colocalizing signal Lipids, & peptides/ Murine brain Ryabchykov et al. , Spectroscopy landmark: linear dataset, & 2D proteins (2018) correlation Raman Same section, & Class prediction NMF Data fusion Pansharpening Metabolites, & Murine brain Race et al.
, landmark: linear (2020) DESI IMC H&E -IF Same & adjacent Class discovery 2D correlation Colocalizing signal Drugs Murine Pancreatic Strittmatter et al. , sections, & landmark: cancer (2022) linear DESI Same section, Class discovery Visual comparison Colocalizing signal Lipids, & proteins Murine brain, human Eberlin et al. , (2011) Registration not glioma explained MALDI-MSI Adjacent sections, & Class Multiblock OPLS Pansharpening, Lipids, & proteins Murine hippocampus & Wehrli et al. (2020) iconic: linear prediction, class region-specific ions Rat prostate comparison MALDI H&E Same section, & Class Ion Fold change Integrated dataset, N-glycans, & human carcinomas, & Heijs et al. , (2016) inherently registered comparison calculation region-specific ions, peptides/proteins tissue microarrays & multi-omics LDI Same section, & N/a Overlay visualization Integrated dataset, Metabolites, Lipids Murine brain, & lung Fincher et al.
ACKNOWLEDGMENTS The authors thank the referees for their constructive suggestions and comments. J. Cernicharo  thanks  ERC  for funding support through grant  ERC-2013-Syg-610 256-NANOCOSMOS , and the  Ministerio de Ciencia e Innovación of Spain (MICIU)  for funding support through projects  PID2019-106110GB-I00 ,  PID2019-107115GB-C21/AEI/10. 130 39/501 100 011 033 , and  PID2019-106235GB-I00. This paper makes use of ALMA data from project  2017. 1. 01232. S, of IRAM NOEMA interferometer data from projects  V03E ,  W19DA  and  S20BY , and of Yebes 40-m telescope data from projects  19A003 ,  20A014 ,  20A017 ,  20D023 , and  21A011. ALMA is a partnership of  ESO  (representing its member states),  NSF  ( United States ) and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile.
A reference sample consisting of a yeast protein extract spiked in with different amounts of a phosphomix standard (Sigma/Merck) was distributed to 31 laboratories around the globe. Thirty-six datasets from 23 laboratories were analyzed. Our results indicate the suitability of the PME11 reference sample to benchmark and optimize phosphoproteomics strategies, weighing the influence of different factors, as well as to rank intra and inter laboratory performance. Many aspects of cell biology are regulated by reversible protein phosphorylation networks that involve thousands of phosphorylation events. In the last decade multiple methods have been developed to identify and quantify involved phosphorylation sites, and their modulation and dynamics under physiological and pathological conditions.
Recently, a related study conducted by several laboratories in the frame of the MS Resource Pillar of the HUPO Human Proteome Project has been reported  [4]. In this study, a standard set of 94 phosphopeptides and their nonphosphorylated counterparts, mixed in a neat sample and a yeast background were analyzed. Unlike the HUPO study samples, the samples proposed in the present study allowed for the assessment of the enrichment of the endogenous yeast phosphopeptides, in conditions and amounts similar to a real sample. Besides, the spiked-in phosphopeptide standards were provided in isotopically labeled and unlabeled form, allowing for assessment not only of targeted phosphopeptide analysis, but also to estimate the yield of the enrichment procedures used. Under the coordination of ProteoRed-ISCIII, 36 datasets were received from 23 laboratories (Table  2 ) distributed across Europe -Spain, France, Switzerland, United Kingdom, and Sweden-and USA.
Individual reports including experimental details and results were prepared by each participant in the template specifically design for this experiment. Additionally, MS/MS files (mgf format) were also submitted to the coordination unit for their centralized processing and integration, which will be described elsewhere. Some laboratories provided various datasets that corresponded to different analytical pipelines, which allowed the specific evaluation of the experimental conditions tested as the user and instrument used in these cases were the same. Shotgun analysis results were used to evaluate the general performance of each laboratory in terms of number of yeast phosphopeptides identified, efficiency of the enrichment procedure (phosphopeptides/total peptides ratio) and detection of spiked-in phosphopeptide standards.