dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank all participants for their contribution to this study. Special acknowledgements to  Willem van Wamel ,  Patricia Vellekoop  and  Willemien Zandijk  for laboratory assistance, as well as to  Agnès Nguyen  ( Biofidal, Vaulxen-Velin, France ), for insightful discussions and assistance regarding the metabarcoding strategy used in this paper. This work was part of the  MACOTRA  project, funded by  ZonMw  Grant number  547001006  and  ANR  Grant number  16-JPEC-0006. We acknowledge all members of the MACOTRA study group as listed in supplementary information.
Sample preparation The specimen slide of Alexa Fluor 561 labeled F-actin in the mouse kidney section was purchased from Invitrogen (Cat. No. F24630). DNA origami sample was purchased from GATTAquant (GATTA-STED NANORULER 120 nm, http://www. gattaquant. com/). The two ends of the NANORULER were marked by the dense arrangements of ATTO647N. Organelle imaging in live budding yeast cells: All strains of subcellular organelles were bought from the genomewide GFP-tagged yeast protein localization database  45  (Life Technology, Cat. No. 95702). Appropriate budding yeast strains were selected from the commercial Yeast GFP Clone Collection (Thermo Fisher Scientific, https:// clones. thermofisher. com/cloneinfo. php?clone=yeastgfp) in -80 °C, placed to the YPD agar (1% yeast extract, 2% peptone, 2% glucose, and 2% agar), and cultured in an incubator at 30 °C about 2-3 days.
Acknowledgements This work was supported in part by the  National Natural Science Foundation of China  ( 81890991 ,  61705252 ,  62025501 , 61729501 ,  31971376 ), the  State Key Research Development Program of China  ( 2021YFE0201100 ,  2017YFA0505503 ,  2017YFC0110202 ), the  Beijing Natural Science Foundation  ( JQ18019 ),  CAS Interdisciplinary Innovation Team  ( JCTD-2020-04 ),  Beijing Municipal Natural Science Foundation  ( Z200021 ) and  Shenzhen Science and Technology Program  ( KQTD20170810110913065 ).
STATISTICAL METHODS The analyses of oviposition preference and fecundity (logtransformed number of eggs; Miller 1997) and offspring performance (arc-sine-transformed egg-to-adult survival; McDonald 2014) were performed using R statistical software (R Core Team 2014). For offspring performance, 51 vials had more adults than the number of eggs counted (50 of 541 vials for G0 and one of 602 for G2, with an average egg-to-adult survival observed of 2. 44) due to a difficulty to count the eggs in the vials with very few eggs (average number of eggs of these 51 vials of 3. 65). We assumed an egg-to-adult survival of one in the analyses of the offspring performance for these vials. Doing so is not expected to introduce a bias in the data analysis because such vials were equally represented in all treatments (17, 16, and 18 observations in cherry, strawberry, and cranberry vials, respectively).
Testing for trait differences between original and alternative fruits For each trait and each generation separately, we tested whether populations had a higher preference for or performance in their original fruit than in alternative fruits. First, to visualize the patterns, we plotted the average trait in the medium corresponding to alternative fruits against the same average trait in the medium corresponding to the original fruit for each population. In addition, we computed the weighted correlation coefficient between the average trait in alternative fruits and their average in the original fruits, using the total number of vials for each population as weight, and estimated its 95% confidence interval using the sjstats package  (Lüdecke, 2018). Second, we tested for differences in oviposition preference, fecundity, and offspring performance of populations between original and alternative fruits using the SA method (where S stands for sympatric and A stands for allopatric) detailed in  Blanquart et al.
A new method to detect and quantify local adaptation and adaptive phenotypic plasticity simultaneously To evaluate the relative contributions to preference and performance of local adaptation and adaptive phenotypic plasticity, it was necessary to analyze the data across the G0/G1 and G2/G3 generations. When measured on adults that emerged from fieldcollected fruits (G0/G1), phenotypic differences among populations can be driven both by genetic differences among populations (potentially including local adaptation) and by plastic responses to the natural environment (potentially including adaptive phenotypic plasticity). When measured on adults after two generations of common environment (G2/G3), phenotypic differences among populations are likely to have a genetic basis, as potential maternal and grand-maternal environmental effects are standardized across populations.
A significant difference in the associated F-test with a positive estimate of SAgenetic G0G2 and SAplastic G0 provides support in favor of local adaptation and adaptive phenotypic plasticity, respectively. We either analyzed the entire dataset (12 fruits) or focused on a smaller dataset with the three fruits of interest (cherry, strawberry, and blackberry). Results were similar in both analyses, so we only present the results with only the three fruits of interest. For fecundity, we fitted the same model but without the arena effect. For offspring performance, we fitted the same model with G1 and G3. Offspring performance increases with egg density and levels off at high egg densities (Fig. S12 ). To account for egg density in the statistical analysis of offspring performance, we replaced the arena effect by the log-transformed initial number of eggs (eggs m ).
Testing for correlations between generation and between fitness related traits We tested for two sets of correlations. First, we evaluated whether there is a correlation in oviposition preference, fecundity, or offspring performance of populations between generations (Appendix S6). Second, we examined whether the evolution of preference and performance traits is independent or rather if they coevolve. Specifically, we tested for a significant correlation between oviposition preference and offspring performance between G0 and G1, and between G2 and G3 (Appendix S7). In each of these two cases, we estimated correlations using the total number of vials for each population as a weight and estimating its 95% confidence interval using the sjstats package  (Lüdecke, 2018).
ACKNOWLEDGMENTS We are grateful to  V. Ravigné  and  B. Facon  for insightful discussions;  P. Audiot ,  M. P. Chapuis ,  L. Benoit ,  N. Vieira ,  J. Froissard ,  A. L. Clamens ,  S. Nidelet , and  L. Sauné  for technical assistance;  S. Bonamour  for her comments on the manuscript;  V. Cazalis  and  R. Patin  for their help with drawing the map; and the Sicoly cooperative for providing us with some fruit purees. LO, MG, JF, and AE were supported by the  Languedoc-Roussillon region (France)  through the  European Union  program  FEDER FSE   IEJ 2014-2020  (project  CPADROL ) and the  IN-RAE scientific department SPE  ( AAP-SPE 2016 ). RAH acknowledges support from the  National Science Foundation  ( DEB-0949619 ),  USA Agriculture and Food Research Initiative  award ( 2014-67013-21594 ),  Hatch  project  1012868 , the  French Agropolis Fondation  ( LabEx Agro-Montpellier ) through the  AAP "International Mobility"  ( CfP 2015-02 ), the  French programme investissement d'avenir , and the  LabEx CEMEB  through the AAP "invited scientist 2016.
This confidence score and visual evaluation are crucial for clinical interpretability. Moreover, we used a new efficient feature selection method with a structured constraint for biologically interpretable results. Experimental results on three metabolomics datasets of clinical samples illustrate the effectiveness of our confidence score diagnostic method: Results: The supervised autoencoder provides an accurate localization of the patients in the latent space, and an efficient confidence score. Experiments show that the SAE outperforms classical methods (PLS-DA, Random Forests, SVM, and neural networks (NN)). Furthermore, the metabolites selected by the SAE were found to be relevant.
Recent metabolomic methods using mass spectrometers allow fast and high-resolution detection of massive amounts of metabolites. Therefore, liquid chromatography coupled with high Resolution Mass Spectrometry (LC-MS/MS) based untargeted metabolomics is a very promising omic approach for fundamental research in biology as well as for clinical research applications. A metabolomic analysis is a very sensitive way of revealing biomarkers in physiological or pathological states  [26, 27, 28, 29] , that could be particularly useful for personalized medicine  [30, 31]. In this study, we described in section 2 the SAE method using structured constraints. In section 3 we provide results of experiments on three metabolomic databases. In section 4 we present discussed on these experimental results. Finally, section 5 concludes the paper and provides some perspectives.
Criterion Projecting the samples in the lower dimension latent space is crucial to separate them accurately. Herein we propose to use a neural network autoencoder framework. Let us recall that the encoder part of the autoencoder maps feature points from a high dimensional space to a low dimensional latent space, and that the decoder maps feature points from that low dimensional space to a high dimensional space. Figure  1  depicts the main constitutive blocks of our proposed approach. We have added to our SAE a "soft max" block to compute the classification loss. Let X be the dataset, as an m × d data matrix made of m line samples x 1 ,. , x m. Let y i = j, j ∈ [1. k] be the label, indicating that the sample x i belongs to the j-th cluster. Let Z, be the latent space, X the reconstructed data (Figure  1 ) and W the weights of the neural network.
We used the captum package  [45]  to compute the feature weights of the SAE. We provide comparisons with a PLS-DA using 4 components, with Random Forests using 400 estimators and a maximum depth of 3 (using the Gini importance (GI) for feature ranking), and with a support vector classifier (SVM) with a linear kernel. For the SVM, we perform a cross-validation grid search to find the best regularization parameter C. We provide the statistical evaluation (Accuracy, AUC, and F1 score) using a 4-fold cross validation process. We compare the performances of the different methods using the F1 Score. The F1 Score is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account. Note that the F1 score is more relevant than accuracy, especially for unbalanced datasets.
Diagnosis with confidence score One of the main advantages of an autoencoder is the projection of the data in the latent space, which can easily be visualized if the latent space is of dimension 2. [1] Thanks to this, we propose a clinical diagnosis simulation: having trained a network on a database of patients, we can predict a diagnosis with a confidence score for new patients. To perform this simulation, we removed a patient from each of the k classes from the databases. We then trained the SAE on (n-k) patients and we fed the k "test" patients through the best net. We thus obtained a visualization of the projections of these new "test" patients in the latent space as well as their classification with a confidence score (see figures 3, 7 and 5). The clinician then has a very accurate system to help with the diagnosis.
Metabolomic profiling The SAE was tested on three different metabolomic datasets : the "LUNG" , "BREAST", and "BRAIN" datasets. The LUNG dataset was published by Mathe et al  [46]  and is available at MetaboLights. The BREAST dataset was kindly provided by Dr. Jan Budczies and can be found in the supplementary material of Badczies et al  [47]. The BRAIN dataset was obtained through a study performed in our lab 1 *. The characteristics of the three metabolomic datasets are presented in Table  1. We chose to study these databases for their diversity both in terms of the number of features and number of patients, to test the robustness of our method on different types of databases. The LUNG dataset includes a very large number of patients ( 1,005), with an equivalently large number of features  (2, 944) , and 2 classes.
The BREAST dataset includes a midsize number of patients (271), with a small number of features (161), and 2 classes. The BRAIN dataset includes a limited number of patients (88), with a much higher number of features (7,022), and 2 classes. The LUNG and BREAST dataset were used without additional filtering. For the BRAIN dataset, metabolomic profiling was performed using MZmine (Version 2. 29)  [48]. Data obtained from positive and negative ionization modes were analysed separately. Results obtained with each polarity were combined. Only ions that were detected in all the samples after gap-filing were used.
LUNG dataset The LUNG dataset was provided by Mathe et al. [46]. This dataset includes metabolomic data concerning urine samples from 469 Non-Small Cell Lung Cancer (NSCLC) patients prior to treatment and 536 controls. [1] If the latent space is of dimension k > 2, we can project the latent space on a 2D plot using a PCA. As shown in Table  2  our SAE outperformed PLS-DA, Random Forests, SVM and NN by 4. 58, 9. 58, 9. 63 and 2. 74% respectively for the F1 score. Note that we checked that increasing the number of trees for Random forests from 100 to 400 resulted in a small improvement in accuracy of only 1% while the computational cost increased by a factor of 3.
BREAST cancer dataset The BREAST dataset was published by Dr Budczies et al from Charity Hosptal Berlin  [47]. 204 of these breast cancers were Estrogen Receptor positive (ER+) and 67 Estrogen Receptor Negative (ER-). Expression of the ER, as well as progesterone receptor and HER2 protein are commonly used to define the molecular subtypes of breast cancers. ER positive is a predictive marker for anti-hormonal therapy. The dataset includes a total of 162 metabolites were analyzed using untargeted Gas Chromatography followed by Time of Flight Mass spectrometry (GC-TOFMS). Here, we studied the performances of the supervised autoencoder framework to classify breast cancer patients by ER expression using the same clinical metabolomic dataset.
This cohort is composed of 88 patients, 38 patients with mutated IDH and 50 with wild-type IDH. The dataset includes 7,022 features. Table  6  shows that, despite the small number of patients, the supervised autoencoder outperformed PLS-DA, Random Forest, SVM and NN by 8. 78, 4. 61, 6. 15 and 18. 47% respectively for the F1 score. For this base with few patients the performance of NNs collapses as reported in the literature.
Discussion Our results led us to the following conclusions : • Unlike classical autoencoders that assume a latent space modeling  [23, 24] , we have used a supervised autoencoder (SAE). The real distributions of many datasets, including metabolomics datasets, are far from multi-gaussian mixtures. Therefore, using a non-parametric autoencoder seems more appropriate than forcing a multigaussian distribution upon the data. • Regardless of data size and feature space dimensions, the SAE outperforms all other methods (PLS-DA, Random Forests, SVM and NN). Note that the NN also outperformed classical methods (PLS-DA, Random Forests and SVM), except on small databases. • The trained SAE can already be used to accurately classify new patients (see figures 3, 5 and 7). • The SAE provides high-level distribution visualization of the patients to be diagnosed in the latent space, as well as their classification confidence score, which is crucial for any computerized decision support tool based on clinical metabolomic data.
Indeed, these two features can greatly help a clinician in routine patient care. For instance, if a patient is visualized at the edge of the group, a clinician may want to take extra caution -a nuance only provided by the SAE. • Furthermore, one should note that metabolomics is a very promising approach, particularly adapted to routine clinical practice, since metabolomics analyses are fast and relatively inexpensive. • Interestingly, the SAE combined with a structured projection provides efficient feature selection (Tables  3, 5  and 7). Better yet, we have verified that the selected features in the LUNG, BREAST and BRAIN datasets were known to be biologically relevant metabolites. Efficient feature selection adds interpretability to the model which is crucial for metabolomic studies in biological research or clinical trials.
Conclusion In this paper we have proposed a new and efficient diagnosis method for metabolomics datasets, based on the representation of data on the latent space of a new supervised autoencoder (SAE). In clinical applications, our method provides a diagnosis score to each patient's predicted class. Moreover, from a statistical point of view (Accuracy, AUC, F1 score) our SAE outperformed PLS-DA, Random Forest, SVM, and NN while selecting biologically relevant features.
Funding This work has been supported by the  French government , through  UCA-JEDI Investment  in the  Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-15-IDEX-01. Equipment for this study was purchased through grants from the  Recherche en matières de Sûreté Nucléaire et Radioprotection program  from the  French National Research Agency  and the  Conseil Départemental 06.
50) indicates the high promiscuity, or multi-specificity, of 14-3-3g. This is further illustrated by its binding profile, in which the different phosphomotifs determined are ranked according to their affinity (Figure  2C ). In our library, many proteins had different phosphosites assessed. We could identify two or more "functional" binding sites (pKd ≥ 3. 50) for approximately 60% of the analyzed proteins (Figure  2D ). Among them, some like CDC25B are well known 14-3-3 binders to which our data add new 14-3-3 binding sites to the previously described ones, all susceptible to participate to the 14-3-3-mediated regulation of the protein  41. Other like liprin-a1, ALS2 or the NEK1 kinase, were consistently identified as 14-3-3 binders in several orthogonal high-throughput interactomic studies  3, 4, 42  , yet with no binding site ever identified up to now.
The ten most intense peptide ions in each survey scan with a charge state ≥ 2 were selected for fragmentation. MS/MS spectra were acquired at a resolution of 17,5K at 200 m/z, with a fixed first mass at 100 m/z, AGC was set to 1. 10 5 , and the maximal injection time was set to 100 ms. Peptides were fragmented by higher-energy collisional dissociation with a normalized collision energy set to 27. Peaks selected for fragmentation were automatically included in a dynamic exclusion list for 60 s. To minimize carry-over, three solvent blank injections were performed after each sample. The nanoLC-MS/MS. raw files were converted to peaklist. mgf files and searched with the Mascot search algorithm (local Mascot server, version 2. 5. 1, Matrix Science, London, UK) against a custom protein database containing the sequences of the seven 14-3-3 proteins.
Design of a library of 1032 potential 14-3-3 phosphomotifs To design the most biologically relevant 14-3-3 phosphomotif library, we implemented a strategy that combines both the BioPlex 3 and PhosphoSitePlus 40 databases. Indeed, it was chosen to be as much inclusive as possible, considering that many experimentally characterized 14-3-3 motifs do not follow these classical motif definitions  26, 27. Only 70 of these 301 phosphosites obeyed to the classical 14-3-3 binding motif. For the remaining phosphosites not following this loose 14-3-3 binding consensus definition, the following filtration steps were applied: -All the phosphosites identified by at least 1 low throughput paper in the PhosphoSite Plus database (LTP; determined using methods other than discovery mass spectrometry) were kept.
-All the phosphosites identified by at least 10 high throughput papers in the PhosphoSite Plus database (HTP; determined using only proteomic discovery mass spectrometry) were kept. This threshold was set up because high-throughpout phosphoproteomics data are not always reliable when considered individually  72. Of note, a recent study observed that 14-3-3 binding sites are often more frequently phosphorylated in phosphoproteomic studies  57. -If after these steps the number of selected phosphosites for one protein was <3, we included phosphosites that do not follow the above-described rules, until reaching at least 3 phosphosites / protein. 10-mer phosphopeptides were obtained from the selected phosphosites by extending the natural protein sequence in N-terminal (5 residues) and C-terminal (4 residues) directions, with some exceptions.
Reference Spectral Library Generation Forthy-one DDA and 5 DIA analyses ran on 10 subpools of 96 and 1 subpool of 72 synthetic peptides were used to build a reference spectral library in Spectronaut (v. 15. 0; Biognosys). Searches were ran against a FASTA database containing the 1032 phosphopeptides' sequences and the iRT peptides sequences. Oxidation of methionines was set as a variable modification. NoCleavage was used as digestion enzyme. Data was extracted using dynamic mass tolerances. Identification was performed using 10% precursor q-value cutoff.
DIA data interpretation DIA data interpretation was performed in Spectronaut (v. 15. 0; Biognosys) using the following settings and the same FASTA file as described above. Data was extracted using dynamic mass tolerances. Identification was performed using 10% precursor q-value cutoff. Quantification was performed using interference correction and at least three fragment ions were used per peptide. Quantity values extracted correspond to MS2 XIC peak areas.
Data normalization Many phosphopeptides of the library effectively bound the resin-immobilized 14-3-3, so a global, sample-based normalization was not possible. Therefore, we developed a normalization procedure adapted to our dataset. We identified an ensemble of non-binding phosphopeptides by the following strategy, and used them as internal standards. We reasoned that by plotting the mean intensities of the precursors in a control vs in a 14-3-3 sample, non-binding phosphopeptides would align on a straight-line which slope would depend on the normalization factor to be applied. Thus, we selected precursors with low binding intensities (BI before normalization < 5 th percentile for 14-3-3g, b, h and x; > -0. 5 and < 6 th percentile for 14-3-3t, e and s) and reliable intensity measurements (mean intensity in the control > 3 rd percentile; variation coefficients of the intensities in the control and the 14-3-3 sample both < 5 th percentile).
Based on the direct titrations, the 14-3-3 concentration to be used in indirect FP was chosen to achieve a 80-100% complex formation, and this mixture was used for preparing a dilution series of the competitor (the unlabeled peptide under study; 8 different concentrations). Each fluorescent measurement was done in technical triplicates with a PHERAstar microplate reader by using 485 ± 20 nm and 528 ± 20 nm band-pass filters for excitation and emission, respectively. Analysis were carried out using ProFit, an in-house developed, Python-based fitting program that uses a Monte Carlo approach to take into account the experimental variability  50. The dissociation constants of the direct and competitive FP experiments were obtained by first fitting the direct data with a quadratic binding equation then the competitive data with a competitive equation.