dataesr/scientific-paragraphs-categorization huggingface dataset:
Antibodies used for IHC Formalin-fixed and paraffin-embedded WT sliced samples were collected from AHMU. CD14 (Anti-CD14 antibody, Cat. ab182032, Abcam Inc. , Massachusetts, USA) and CD163 (anti-CD163 antibody, Cat. ab182422, Abcam Inc. , Massachusetts, USA) were chosen to reflect the infiltration of monocytes and macrophages  [23] [24] [25] [26] [27]. The expression level of MMP9 was examined by anti-MMP9 antibody (bs-0397R, Bioss Inc, Beijing, China). COX1 (anti-COX1 antibody: ab109025, Abcam Inc. , Massachusetts, USA) and COX4 (anti-COX4 antibody: ab202554, Abcam Inc. , Massachusetts, USA) were chosen to reflect the oxidative phosphorylation level. Following the same procedures as have been reported previously  [28, 29] , positively stained cells were counted by ImageJ software (NIH, Bethesda, USA)  [30].
Statistics T-tests and Wilcoxon rank-sum tests were used for normally or nonnormally distributed data, respectively, to compare the continuous data from the two subtypes. Correlations between two continuous factors were conducted by the Pearson correlation test, and distribution differences between subgroups and predicted response to immunotherapy were analysed by the chi-square test. Time and status Cox regression analysis was conducted to construct the nomogram based on the ''RMS" package, aiming to establish a predictive signature. Patients were separated into high-point (NomoH) and low-point (NomoL) groups for subsequent analysis via the points calculated by the nomogram. The differentially expressed genes (DEGs) between the NomoH and NomoL subgroups were selected by the ''limma" package with the cut-off value of P < 0.
05, |log2-fold-change| > 1. To select the calculation factors affecting prognosis and evaluate the predictive value of our nomogram, Kaplan-Meier (K-M) survival analysis based on overall survival (OS) was performed with the ''survminer" package. Three-year and five-year survival rates were chosen as markers to judge the accuracy of our prediction model by calibration curve with the ''RMS" package (bootstraps with 1000 resamples), and the Hosmer-Lemeshow (H-L) test was applied to detect the performance of fit for the nomogram. The receiver operating characteristic (ROC) curve was generated by the pROC package to assess the prediction ability, and the area under the curve (AUC) was recorded. Statistical significance was considered a two-sided P value <0. 05. All analyses were completed by 3.
To eliminate bias, we included sex, stage, monocytes, and macrophages to calculate the final point value. It is worth mentioning that patients' sex was not considered a significant prognostic factor due to the relatively equivalent distribution in males and females in previous studies  [1, 42]. However, in our research, sex was an independent factor that showed statistical significance in the Cox regression analysis. According to previous studies, different classifications of age led to different outcomes due to the lack of shared criteria  [42] , whereas patient age was not a statistically significant parameter in our study. Although the H-L test and AUCs demonstrated satisfactory discriminative performance in both the validation database and the patients enrolled by us, the different roles sex and age played might be selective bias based on data shapes provided by the TARGET-WT project.
Second, the nomogram model was built upon the gene expression profile and clinical information from the TARGET-WT project and validated by the 7 patients from AHMU-WT cohort. The selective bias based on data shapes might be caused by a limited number of samples, which suggests that more WT patients and databases should be used to validate the effectiveness of our model. Thrid, the comprehensive landscape of TIICs was constructed based on computational calculations, so more studies should be carried out elucidating the potential mechanism by which a lower level of infiltrated monocytes and a higher level of macrophages are associated with the prognosis of WT. Forth, if the nomogram model applied in the clinical, each institute need to construct a baseline cohort to confirm the median cut-off value, no matter used the IHC staining H-score, or the CIBERSORT results from RNA-sequence, due to the impact of different methods, equipment and population.
Acknowledgements This work was supported by the  National Natural Science Foundation of China  [grant number:  81972539 ,  U1732157 ,  82170787 ,  81870519 ,  81630019 ];  Scientific Research Foundation of the Institute for Translational Medicine of Anhui Province  [grant number:  2017ZHYX02 ]; The  Key Project of Provincial Natural Science Research Project of Anhui Colleges  [grant number:  KJ2019A0278 ].
During the last two decades, MALDI-ToF mass spectrometry has become an efficient and widely-used tool for identifying clinical isolates. However, its use for classification and identification of environmental microorganisms remains limited by the lack of reference spectra in current databases. In addition, the interpretation of the classical dendrogram-based data representation is more difficult when the quantity of taxa or chemotaxa is larger, which implies problems of reproducibility between users. Here, we propose a workflow including a concurrent standardized protein and lipid extraction protocol as well as an analysis methodology using the reliable spectra comparison algorithm available in MetGem software. We first validated our method by comparing protein fingerprints of highly pathogenic bacteria from the Robert Koch Institute (RKI) open database and then implemented protein fingerprints of environmental isolates from French Guiana.
Introduction Microorganisms are one of the first sources of enzymes and metabolites with wide applications in the industrial or health fields  [1, 2]. Their identification is necessary to access all the knowledge that has been shared by the scientific community thanks to the literature and open-source databases  [3] [4] [5]. Since Woese's work in 1977, the identification of microorganisms has been based on molecular biology methods, in particular the sequencing of the gene coding for 16S rRNA, in the case of bacteria, or 18S rRNA or internal transcribed spacers (ITS) for fungi, and more recently, whole genome sequencing  [6] [7] [8]. However, these methods still require careful sample preparation and the latter one has a high cost that does not favor its systematic application when dealing with large collections  [6].
Furthermore, the taxonomic resolution offered by 16S rRNA gene sequencing is not sufficient to identify certain bacterial strains, unless third generation sequencers and denoising algorithms are used in combination  [9]. As for fungi, ITS is not a universal barcode because of sequence redundancy between different species-rich genera  [10]. Thus, the identification of microorganisms by MALDI-ToF mass spectrometry has become widely democratized during the last two decades; in the field of clinical microbiological diagnosis and agri-food industry, and finally for environmental studies  [11] [12] [13] [14]. MALDI-ToF mass spectrometry has proven to be a robust, rapid, and low-cost method for isolate identification or dereplication  [15] [16] [17] [18]. This is why one of the current methodologies used for human pathogenic strains consists in implementing private databases with new reference spectra.
This method is experimentally and analytically tedious, in particular because the identification of redundant profiles is based on global similarity analyses by hierarchical clustering. Moreover, visually analyzing these dendrograms involves tedious human intervention due to the difficulty of reading and interpreting them as the amount of data analyzed increases  [19]. Furthermore, MALDI-ToF mass spectrometry identification of representatives of a cultivable environmental microbial community proves to be more difficult than identification of a pathogenic microorganism due to the high proportion of only pathogenic microorganisms in the available databases  [12, 13]. Thus, fingerprint discrimination by detecting specificities could allow a better dereplication  [20]. Among the chemo-informatic pipelines developed to allow classification and/or dereplication of metabolomics data, molecular networking is becoming more and more popular  [21] [22] [23] [24].
Its principle is based on the postulate that structurally close molecules share the same fragmentation pattern in mass spectrometry (MS). Although the construction of molecular networks has been used only for MS 2 data, it is also possible at MS 1 level, according to a direct comparison of MS 1 spectra  [21, 25, 26]. Recently, Dumolin et al. demonstrated the robustness of this methodology through the creation of SPeDe; an open-source software for high-throughput dereplication of isolate fingerprints from MALDI-ToF MS analyses  [27, 28]. In this study, we proposed a methodology based on global similarity recognition of protein and lipid fingerprints from environmental bacteria or fungi where the spectrum/taxonomy match of the isolate is confirmed by sequencing of DNA loci (16S for bacteria and ITS for fungi) correlative to the identity.
We validated our in silico analysis methodology by using open-source data, made available by the Robert Koch Institute (RKI, 13 orders, 38 genera, 568 fingerprints), through an R script and MetGem software  [25]. Finally, we created a dataset to dereplicate bacterial (9 orders, 49 genera, 138 isolates) and fungal (21 orders, 51 genera, 230 isolates) environmental isolates, from the Bank of Natural Substances & Biodiversity (BNSB, CNRS-ICSN, Gif-sur-Yvette, France, https://icsn. cnrs. fr/en/platforms/strain-library (accessed on 1 January 2021) by analyzing their protein and lipid fingerprint by MALDI-ToF MS.
Proof of Concept: t-SNE Algorithm Clusters Bacterial Protein Fingerprints in a Taxonomy-Consistent Manner In order to evaluate the functionality of our methodology, we first used a part of the RKI dataset with the aim of comparing the global similarity of bacterial protein fingerprints. The objective was to know if the t-SNE algorithm used by the MetGem software was capable of clustering MALDI-ToF mass spectra, corresponding to protein fingerprints of microorganisms (Figure  1 ), in order to classify them according to their taxonomy. The t-SNE algorithm is a statistical method for visualizing high-dimensional data that captures local similarities while attempting to preserve the integrity of global structures. For that, we hijacked some MetGem functionalities in order to use the t-SNE algorithm on protein fingerprints from the RKI database.
In fact, MetGem is a software able to build molecular networks and is a powerful dereplication tool in the field of metabolomics  [25]. Here, MS 1 data are visualized following a pre-processing of data under a home-made script in R (for more details, see Materials and Methods, Section 4. 6 and Figure  S1  in Supplementary Materials). In this dataset, we have performed t-SNE visualization of 568 fingerprints belonging to 166 different bacterial strains. Each protein fingerprint of an identified clinical isolate was represented by a node (Figure  2 ). more details, see Materials and Methods, Section 4. 6 and Figure  S1  in Supplementary Materials). In this dataset, we have performed t-SNE visualization of 568 fingerprints belonging to 166 different bacterial strains. Each protein fingerprint of an identified clinical isolate was represented by a node (Figure  2 ).
2% of Bacillales). L (Figure  2 ) are solely composed of fingerprints from isolates belonging to the genus Bacillus (91 fingerprints-53. 8% of Bacillales). The last fingerprints from Bacillales are distributed in the center of the network or in the south region and belong to the genera Bacillus, Lysinibacillus, Paenibacillus, and Staphylococcus (78 fingerprints-46. 2% of Bacillales). The protein fingerprints belonging to the rare orders (55 fingerprints), i. e. , Actinomycetales, Aeromonadales, Campylobacterales, Lactobacillales, Pseudomonadales, Rhodobacterales, Rhodospirillales, Xanthomonadales, of this dataset are scattered in the network (Figure  2 ) but all replicates of an isolate are close in space confirming a high robustness of the sample preparation and MALDI analysis. In order to verify the robustness of chemotaxonomic identifications by fingerprint comparison, we added the protein fingerprints of our environmental bacterial strains to this first network.
Robustness of Chemotaxonomic Resolution by Adding Fingerprints of Environmental Isolates to the RKI Dataset This new analysis was also obtained by visualizing the protein fingerprints of the RKI pathogenic bacteria and those of environmental bacteria through t-SNE algorithm (Figure  2 ). For this purpose, the initial dataset was supplemented with 138 fingerprints of bacteria whose taxonomic affiliation was previously determined by comparing the DNA sequences, encoding 16S rRNA, with those of NCBI. No new order was added to the dataset, majority orders are the same as for the RKI, i. e. , Bacillales, Burkholderiales, and Enterobacterales. Of these 138 fingerprints, 81 nodes (58. 7%) of our dataset join the RKI dataset or form a new Enterobacterales cluster (Cluster M in Figure  3 ). Thus, 44 nodes are isolated (31.
9%) and 13 (9. 4%) are found in clusters consisting of protein fingerprints belonging to bacteria The protein fingerprints belonging to the rare orders (55 fingerprints), i. e. , Actinomycetales, Aeromonadales, Campylobacterales, Lactobacillales, Pseudomonadales, Rhodobacterales, Rhodospirillales, Xanthomonadales, of this dataset are scattered in the network (Figure  2 ) but all replicates of an isolate are close in space confirming a high robustness of the sample preparation and MALDI analysis. In order to verify the robustness of chemotaxonomic identifications by fingerprint comparison, we added the protein fingerprints of our environmental bacterial strains to this first network.
Differentiation of Environmental Bacteria by MALDI-ToF MS Lipid Fingerprint Analysis The purpose of this analysis was to evaluate whether lipid fingerprinting analysis of environmental microorganisms provided better differentiation than the conventional protein fingerprinting approach (Figure  4 ). Thus, 138 lipid fingerprints were performed on the BNSB microorganisms including the same bacteria as those for which the protein fingerprint dataset had been performed (Figure  3 ). Our chemo-informatics pipeline led to the clusterization of the major part of the dataset. Cluster N is composed mainly of Bacillales (24 fingerprints-65% of Bacillales), then Burkholderiales (1 fingerprint-2. 8% of Burkholderiales) and Rhizobiales (1 fingerprint-11. 1% of Rhizobiales). Cluster O is mainly composed of Rhizobiales (8 fingerprints-88.
Differentiation of Environmental Bacteria by MALDI-ToF MS Lipid Fingerprint Analysis The purpose of this analysis was to evaluate whether lipid fingerprinting analysis of environmental microorganisms provided better differentiation than the conventional protein fingerprinting approach (Figure  4 ). Thus, 138 lipid fingerprints were performed on the BNSB microorganisms including the same bacteria as those for which the protein fingerprint dataset had been performed (Figure  3 ). As this methodology was initially applied to our environmental bacteria of our strains collection, we wanted to evaluate if data from fungi could also be efficiently classified. Our chemo-informatics pipeline led to the clusterization of the major part of the dataset. Cluster N is composed mainly of Bacillales (24 fingerprints-65% of Bacillales), then Burkholderiales (1 fingerprint-2.
Discussion The identification and dereplication of environmental isolates is a complex objective due to the analysis of biological objects, i. e. , DNA, protein, lipid, reflecting the taxonomic affiliation of the organisms studied. Today, this methodology is mostly conducted by the dereplication of protein fingerprints obtained by MALDI-ToF mass spectrometry. While this methodology is widely considered to be robust in the clinical context, it is difficult to access the identity of isolates from environmental samples due to the lack of fingerprints of organisms not pathogenic to humans in current databases. In this study, a standardized workflow for extracting lipids and proteins from environmental microorganisms at medium throughput in a single tube of reaction medium was proposed. Then, the pre-processing of the fingerprints from the MALDI-ToF MS analyses using a "home-made" R script and the analysis of the resulting data using the MetGem software was done.
Finally, the reliability of the taxonomic affiliation of the organisms was ensured by the sequencing of the loci corresponding to the 16S rDNA for bacteria or the ITS for fungi. In a first step, we validated our data analysis method by retrieving a portion of the protein fingerprints of highly pathogenic microorganisms from the free RKI dataset (Figure  2 ). The t-SNE algorithm is able to group the RKI protein fingerprints according to the taxonomic affiliation of the studied organisms based on the order for Enterobacterales according to 2 clusters (A and B in Figure  2 ), Burkhorderiales according to 6 clusters (E, F, G, H, I & J in Figure  2 ) and Bacillales according to 2 clusters (K and L in Figure  2 ). If the taxonomic resolution of this representation is validated by the spatial proximity of the nodes corresponding to the same order or genus, the results obtained are biased by the diversity and representativeness of the protein fingerprints present in the analyses.
This problem is often found in environmental sampling campaigns, due to the environmental matrices used, the culture conditions of the organisms and therefore, their physiology in laboratory, without forgetting the sample preparation methodology  [12, 13, 19, 29, 30]. As a second intention, we wanted to know if some protein fingerprints could be implemented and correlated with those already existing in the RKI dataset. Therefore, we acquired protein fingerprints of environmental bacteria maintained at ICSN to adjoin those of the RKI (Figure  3 ). Despite affiliation errors (9. 4%) and lack of matches (31. 9%), 58. 7% of the acquired protein fingerprints were grouped in their corresponding taxonomic order or genus. Once again, the most represented orders were: Bacillales (29. 8%), Burkhorderiales (29.
2%), and Enterobacterales (21. 8%). This demonstrates the possibility of building bacterial protein fingerprint annotation networks by implementing data from the scientific community in a free manner using our workflow. In contrast, the taxonomic resolution of fungal protein fingerprints in our analysis is less efficient than that performed on our bacterial dataset. Despite a greater diversity and a larger dataset (21 orders including 51 fungal genera against 9 orders including 24 bacterial genera), few orders are annotatable except for the Glomerellales which are distributed, mostly, in 2 clusters, R corresponding to the species C. gloeosporioides and U to C. theobromicola, and the Botryosphaeriales constituting the specific cluster V. Moreover, Diaporthales can be annotated according to our approach because of their majority distribution (71.
Spectra Processing Mass spectra from the Robert Koch Institute  [40]  have been sorted so that taxonomic orders or genera are redundant with the taxonomic affiliation of some bacteria studied. Rare orders were deliberately added to the dataset to control the distribution of their corresponding nodes within the constructed molecular networks. In total, 568 spectra were selected, corresponding to 13 taxonomic orders among which: 12 (2. 1%) are Actinomycetales, 4 (0. 7%)-Aeromonadales, 169 (29. 8%)-Bacillales, 166 (29. 2%)-Burkholderiales, 4 (0. 7%)-Campylobacterales, 124 (21. 8%)-Enterobacteriales, 4 (0. 7%)-Lactobacillales, 12 (2. 1%)-Pseudomonadales, 34 (6%)-Rhizobiales, 4 (0. 7%)-Rhodobacterales, 4 (0. 7%)-Rhodospirillales, 24 (4. 2%)-Thiotrichales, and 7 (1. 2%)-Xanthomonadales, i. e. , 38 genera (Table  S1  in Supplementary Material).
The bacteria of the BNSB belong to the 9 following orders: 11 (8%)-Actinomycetales, 40 (29%)-Bacillales, 36 (26. 1%)-Burkholderiales, 32 (23. 2)-Enterobacteriales, 2 (1. 4%)-Lactobacillales, 3 (2. 2%)-Pseudomonadales, 9 (6. 5%)-Rhizobiales, 2 (1. 4%)-Rhodospirillales, 3 (2. 2%)-Xanthomonadales, i. e. , 24 genera (see Table  S2  in Supplementary Material). The combined RKI and BSNB protein fingerprint dataset contains 13 orders, 49 genera, and 706 fingerprints (Figure  3 ). Fungi from our collection belong to the 21 following orders: 16 (7%)-Botryosphaeriales, 2 (0. 9%)-Cantharellales, 5 (2. 2%)-Capnodiales, 1 (0. 4%)-Chaetothyriales, 1 (0. 4%)-Cystobasidiales, 21 (9. 1%)-Diaporthales, 23 (10%)-Eurotiales, 55 (23. 9%)-Glomerellales, 30 (13%)-Hypocreales, 2 (0. 9%)-Magnaporthales, 4 (1. 7%)-Microascales, 1 (0.
4%)-Microthyriales, 7 (3%)-Mucorales, 1 (0. 4%)-Pleosporales, 1 (0. 4%)-Polyporales, 6 (2. 6%)-Russulales, 2 (0. 9%)-Saccharomycetales, 9 (3. 9%)-Sordariales, 1 (0. 4%)-Sphaeropleales, 2 (0. 9%)-Venturiales, and 40 (17. 4%)-Xylariales, i. e. , 51 genera (Table  S3  in Supplementary Material). All spectra are converted in mzXML file format with MSConvert (v. 3. 0. 20344), a tool from ProteoWizard software  [41] , then are processed with a home-made script written in R software with MALDIquant and MALDIquantForeign packages  [42]. Our methodology is the following: first, mass range of the protein mass spectra is adjusted to be between 3. 5-20 kDa in order to avoid the implementation of background noise in our data, then the peaks with a signal to noise ratio ≥6 are selected, the intensities are transformed by applying a square root function, smoothed by Savitzky-Golay method (half-window size = 15)  [43] , then, baseline removal is conducted by Statistics-sensitive Non-linear Iterative Peak-clipping algorithm (SNIP, 30 iterations), finally, spectra are normalized using Total Ion Current (TIC) method.
Conclusions Our study proposed an open-source workflow including a standardized protein and lipid extraction protocol, in a tube of reaction medium, as well as a pre-processing of the data under a home-made R script, and an analysis of the fingerprints obtained by the creation of chemotaxonomic networks under the MetGem software. Our results show that this method can be used to discriminate, at medium throughput, bacterial isolates from protein or lipid fingerprints if there are sufficient representatives of a bacterial order or genus. On the other hand, the application of this same process to tropical filamentous fungi remains to be improved because of the sole clustering of orders or genera overrepresented in our data, i. e. , Botryosphaeriales and Glomerellales. Moreover, the sine qua non condition for the identification of environmental isolates will be the construction of an appropriate spectral database.
To our knowledge, this work is the first to focus on the chemotaxonomy of tropical fungi. Thus, our objective is to provide the scientific community with a free and implementable spectral database of tropical microorganisms' fingerprints with a particular attention to filamentous fungi in order to improve the dereplication processes of environmental strains used following sampling campaigns. These results are encouraging and this method can be used to discriminate two different strains isolated from the same environment and the same host (in the case of symbiotic microorganisms). This first step allows to avoid redundant studies and thus to accelerate the research in the field of natural substances chemistry. Finally, this dereplication based on the calculation of cosine scores enables analysis of large datasets.