dataesr/scientific-paragraphs-categorization huggingface dataset:
CIDOP-seq analyses Quality controls of CIDOP-seq Fastq files were performed by FastQC. CIDOP-seq Fastq file reads were mapped to the mm10 reference genome using Bowtie2 with default parameters. To determine peaks, CIDOP-seq datasets were processed using MACS2 with -broad and -g mm options. BigWig files were generated using Deeptools bamCoverage with -bs 50 and -normalizeUsing RPKM options.
ATAC-seq analyses Quality controls of ATAC-seq Fastq files were performed by FastQC. ATAC-seq adaptors and ATAC-seq reads were removed or trimmed using Cutadapt. Paire-end reads of ATAC-seq Fastq files were mapped to the mm10 reference genome using Bowtie2 with default parameters. Quality controls of ATAC-seq alignments were performed with ATACseqQC. To determine peaks for ATAC-seq, datasets were processed using MACS2 with default parameters and -g mm and -B options. BigWig files were generated using Deeptools bamCoverage with -bs 50 and -normalizeUsing RPKM options.
OBSERVED AND SIMULATED OVERLAPS For the observed overlap, we analyzed H3K27ac ChIP-seq datasets of embryonic tissues (15. 5 days) and of adult tissues (8 weeks) from ENCODE and we identified enhancers and tissue-specific enhancers. Bedtools intersect was used to quantify the overlap between enhancers or tissue specific enhancers and dual domains (4,774 peaks) in each tissue. For simulated overlaps, we randomly generated 4744 regions of an average size of 420 bp using bedtools random with -n 4744 and -l 420 options. The overlap with enhancers or tissues specific enhancers is computed using bedtools intersect. These steps were iterated 5000 times to obtain a mean of simulated overlaps. The mean of simulated overlaps follows a binomial law B (n;p) where n= 4744 and p is the probability of overlap between a random peaks and enhancers.
Hi-C analysis Raw Hi-C sequencing data were processed using the ''scHiC2'' pipeline. Construction of expected models, Hi-C contact scoring, and Hi-C aggregate plots (peSCANs) were performed using the ''shaman'' R package, v2. 0 (https://tanaylab. bitbucket. io/ shaman/index. html and https://anaconda. org/bioconda/r-shaman). Hi-C map visualization, segmented interaction frequencies, and downstream analyses were conducted using in-house R scripts using the ''misha'' package (https://tanaylab. github. io/misha/ index. html and https://anaconda. org/bioconda/r-misha, v4. 01) to store and access the data. Iced normalized tracks for 25-, 100and 250-kb resolutions were computed with the Hi-C-Pro pipeline using the ice_norm flag on observed data matrices exported from misha tracks. Hi-C-Pro's hicpro2juicebox utility was used to convert the transformed adjacency output of the scHiC2 pipeline into Juicebox format (.
Genome references The mm10 mouse and the dm6 Drosophila reference genomes were downloaded from UCSC genome data (http://hgdownload. soe. ucsc. edu/downloads. html). The Refseq genes was downloaded from Ensembl (https://www. ensembl. org/index. html). The Repeatmasker annotated region (Rmsk 4. 0. 5) was downloaded for RepeatMasker (http://www. repeatmasker. org/species/mm. html). This was then input was divided into 4 family repeats: ERVs, LINE, SINE and Simple repeats. ERVs contains all LTR transposable elements.
ACKNOWLEDGMENTS We would like to thank  Y. Shinkai  for the gift of the inducible Setdb1 KO ES cell line and derivatives and  A. Jeltsch  for the gift of the vectors encoding the CIDOP adapter proteins. I. Forne  and  A. Imhof  are thanked for their help in histone MS analyses. A. B. is supported by  Labex Epigenmed , the  Fondation pour la Recherche Medicale , and an  ERC  CoG METACHROM PhD fellowship. Work in the lab of J. D. is supported by the  ERC CoG METACHROM  ( 724835 ) and  ARC.
Histological Analyses Two serial transverse 10-µm-thick sections were obtained from each GC muscle at -18 • C using a cryostat and mounted on glass slides. Cross-sections were labelled with anti-laminin-α1 (1:20; #L9393, MerckMillipore, Molsheim, France) to outline the fibers and resolved with a secondary antibody conjugated to Alexa-Fluor 488 (Invitrogen, Cergy-Pontoise, France). Images were captured using a high-resolution digital camera coupled to an Axio Vert. A1 inverted microscope (Zeiss, Okerkochen, Germany) at a resolution of 0. 645 µm/pixel. Four fields, each containing at least 200 fibers, were analyzed per muscle. Fiber cross sectional area (CSA) was determined for each fiber using ImageJ 1. 47v image processing software (National Institutes of Health, Bethesda, MD, USA).
The MS/MS spectra search was performed using Mascot (v2. 5. 1) and Peaks (v10) against the Uniprot Rattus norvegicus database (2019/07, 29,944 sequences). The following parameters were considered for the search: precursor mass tolerance of 10 ppm and fragment mass tolerance of 0. 05 Da, a maximum of two missed cleavage sites of trypsin, carbamidomethylation (C), oxidation (M), and deamidation (NQ) set as variable modifications. The minimal peptide length was 5-7 amino acids. Protein identification was validated when at least two peptides originating from one protein showed a significant Mascot score with a false discovery rate (FDR) ≤1%. Label-free protein quantification was performed using Progenesis QI (Nonlinear Dynamics, Waters, Milford, MA). All unique validated peptides of an identified protein were included, and the total cumulative abundance was calculated by summing the abundances of all peptides allocated to the respective protein.
The mass-spectrometry proteomics data were deposited to the ProteomeXchange Consortium via the PRIDE  [63]  partner repository with the dataset identifier PXD032044. Statistical analysis was performed using the "between subject design" option and P-values were calculated by a repeated-measures analysis of variance using the normalized abundances across all runs. The significate threshold was set at P < 0. 05 and a 1. 3-FC cut-off was applied. To complete the analysis, the proteomic results for SPE and MPE were gathered. In the case of duplication between both fractions, the amplitude of the changes is reported for the fraction where the protein was the most expected based on Progenesis QI confidence score and the known protein localization. Note that directional changes were concordant between the two fractions for >75% of the proteins detected in both fractions, and reported as differentially abundant between UND and CTRL rats.
Gene Ontology Analysis Functional annotation was performed according to the Gene Ontology (GO) Biological Processes (BP) using the ProteINSIDE web service (http://www. proteinside. org accessed on 20 April 2021)  [64]. The analysis was performed in Rattus norvegicus, and both muscles were considered independently. Benjamini-Hochberg (BH) adjusted P-values were used to establish lists of significantly-enriched pathways in each dataset compared to the whole genome. The GO_BP overview was carried out using only specifications associated with an adjusted P-value < 0. 001 with a minimum of annotated proteins ≥2. Finally, annotations were completed manually based on UniProt (https://www. uniprot. org/ accessed on 20 April 2021), NCBI (https://www. ncbi. nlm. nih. gov/ accessed on 20 April 2021), and DAVID (https://david.
Statistical Analysis For statistical analysis not related to the proteomic datasets, results were expressed relative to CTRL condition and are presented as means ± standard error of the mean (SEM). An unpaired Student's t-test was used to assess statistical differences amongst means. Statistical analysis of mean grip strength was performed using one-way ANOVA followed by Tukey post hoc tests. The significance threshold was set at P < 0. 05. All statistical analyses were performed using GraphPad Prism 6 software (San Diego, CA, USA).
The surface-subsurface coupled hyperresolution (10 m) distributed hydrological model ParFLOW-CLM is used to simulate the impacts of meteorological variability at spatio-temporal micro-scale on the water cycle. These include 3D simulations with spatially distributed forcing of precipitation, solar radiation and wind compared to 3D simulations with non-distributed forcing simulation. Our precipitation distribution method encapsulates the spatial snow distribution along with snow transport. The model simulates the snow cover dynamics and spatial variability through the CLM energy balance module and under the different combinations of distributed forcing. The resulting subsurface and surface water transfers are solved by the ParFLOW module. Distributed forcing induce a snowpack with a more spatially heterogeneous thickness, which becomes patchy during the melt season and shows a good agreement with the remote sensing images.
In CLM, when pixels cover a large range of elevation, snow fraction is used to account for the surface uncovered by snow. In our study snow fraction was reduced to 0 (no-snow) or 1 (snow) values because our pixels are small enough (10×10 m) that we consider their snow coverage to be uniform. Therefore, CLM can handle the spatial/temporal snow distribution, associated water fluxes (melting, sublimation, infiltration) and evaporative fluxes according to spatial/temporal heterogeneous surface conditions (Temperature, water/snow inputs, incoming radiations, wind and vegetation). After computing the surface exchanges like evaporation, transpiration, snowmelt and precipitation infiltration to and out of the soil, these are applied as source/sink in the Richards equations. A further note on ParFLOW terminology and the model capability is included in the user manual (https://github.
Precipitation The precipitation data from the rain gauge have been first processed to account for the lack of gauge shield  (Klok et al. , 2001). The adopted algorithm follows as: P corr (x 0 , y 0 ) = P (x 0 , y 0 ) * (a + b * u(x 0 , y 0 )) (1) where, P corr is the corrected precipitation (mm), P is measured precipitation (mm) at observation station, u is the wind To account for snow coverage spatial variability on the catchment domain, S c (x, y), the snow precipitation at (x, y) location was calculated using a snow coefficient map C s (x, y) from the ratio between the measured snow height at the gauge to the snow height measured through the laser scan on the same day (21/02/2018) at the end of the accumulation period (Fig. 1 ). The snow height calculated from the laser is the difference of apparent snow height (from laser scan) at the end of the accumulation period and the digital elevation model (DEM) for the surface without snow.
Domain discretization and simulation setup The surface domain of 15. 28 ha was discretized at a hyper-resolution of 10 m with the total number of 84 × 42 × 11 (longitude × latitude × levels) pixels (Fig. 1 ) on a terrain following grid. As mentioned earlier, the model was mainly built and forced using the observations; hence, the input data either are observational data or secondary data derived from observation. These data include the temperature, precipitation, wind, short wave, humidity and pressure plotted on figure 2. These observations data are available at 30 minutes interval from the instruments and few data are even available at a higher temporal resolution and upscaled to the 30 minutes temporal resolution. Leaf area index (LAI) and stem area index (SAI) were calculated from NDVI measurements.
Acknowledgements. This research was conducted within the  Long-Term Socio-Ecological Research (LTSER) platform Lautaret-Oisans , a site of the European Research Infrastructure eLTER. It received funding from the  Lautaret Garden (Univ. Grenoble Alpes ,  CNRS, LAUTARET, 38000 Grenoble, France ), a member of the  Zone Atelier Alpes and the eLTER network and from   ANR  project  ANR-15-IDEX-02.
The use of Artificial Intelligence in medical decision support systems has been widely studied. Since a medical decision is frequently the result of a multi-objective optimization problem, a popular challenge combining Artificial Intelligence and Medicine is Multi-Objective Feature Selection (MOFS). This article proposes a novel approach for MOFS applied to medical binary classification. It is built upon a Genetic Algorithm and a 3-Dimensional Compass that aims at guiding the search towards a desired trade-off between: Number of features, Accuracy and Area Under the ROC Curve (AUC). This method, the Genetic Algorithm with multi-objective Compass (GAwC), outperforms all other competitive genetic algorithm-based MOFS approaches on several real-world medical datasets. Moreover, by considering AUC as one of the objectives, GAwC guarantees the classification quality of the solution it provides thus making it a particularly interesting approach for medical problems where both healthy and ill patients should be accurately detected.
g. , Vitamin D deficiency diagnosis  [1] ). To cope with such issues encountered in many fields of application (e. g. , finance  [2] , bank  [3] , business & marketing  [4] , medicine  [5] ), Multi-Objective Feature Selection (MOFS) problems have been the subject of many research works  [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]. MOFS problems can be formalized as a kind of Multi-Objective Optimization problems  [16]  which consists in minimizing the number of features to select from a dataset while maximizing one or several performance metrics such as accuracy of the decision. The main contribution of this work is the definition of a novel method to solve such MOFS problems. It combines one of the most popular Genetic algorithm (GA) in the literature  [17]  with a Compass based fitness computation method  [18, 19]  promoting optimal solutions respecting a given trade-off between the problem's objectives.
This is obtained by only requiring the setting of a 3D reference vector to define the influence of each objective in the solution. This makes the search direction more explicit and visual for the user. To evaluate the performances of GAwC on various real-world medical datasets, the method is compared with several Multi/Many-Objective Evolutionary Algorithm (MOEA, MaOEA) based state-of-theart competitive methods such as QEISS-like  [6]  or DMEA-FS-like  [10]  methods. The method's different performance metrics -i. e. ; specificity, sensitivity, Matthews correlation coefficient and AUC obtained when applying GAWC on a specific Drug Intake prediction dataset collected by the cardiology department of the University Hospital of Angers in France (CHU Angers) are discussed. The paper is organised as follows: Section 2 reviews the related work and clarifies the background along with the problem.
Section 3 sheds light on the GAwC method while Section 4 depicts how its empirical evaluation against other state-of-the-art methods was conducted. Furthermore, Section 5 provides a discussion which is twofold: 1) it discusses results obtained by GAwC in terms of classification performance metrics only; 2) it gives a clinical view point on the results obtained by GAwC with the ASA Drug Intake (ASA-DI) real-life dataset and especially provides a justification of the results obtained from a clinical point of view. Finally, a section for conclusion and perspectives brings the paper to a close.
g. , NSGA-II  [38] ) and in derivation Many-Objective Feature Selection (MaOFS) methods based on Many-Objective Evolutionary Algorithms (MaOEAs) (e. g. , NSGA-III  [39] ). To the best of our knowledge, among the most efficient methods, that either rely on Non-dominated Sorting Genetic Algorithm II (NSGA-II)  [38]  or on Borg-MOEA  [40] , are W-QEISS,F-QEISS, and W-MOSS  [6]. Those three methods were very recently outperformed in most datasets by two MOEAs methods: W-DMEA-FS and F-DMEA-FS  [10]. Moreover, QEISS-like and DMEA-FS-like methods have been compared to another popular and older methods: mRMR  [41] , and with more traditional methods (e. g. , based on PSO). Since, the four competitive methods presented in  [6, 10]  show better results both in terms of measured accuracy and number of selected features, in this paper GAwC is compared to these methods.
They have been experimented on two binary classification medical datasets (diabetes dataset and the statlog heart dataset) available in the UCI Machine Learning repository. However, those approaches are not medicine-centered and especially they do not consider any relevant metrics that should be taken into account by medical applications i. e. , specificity and sensitivity. Yet, those criteria are important to consider in the field of medicine in order to measure and ensure: 1) the ability to correctly detect ill patients; 2) the ability to correctly reject healthy patients. Since they consider a combination of specificity and sensitivity, ROC curves are typically used to evaluate the quality of a classifier. A ROC curve is a graphical representation used to evaluate the quality of a binary classifier.
Sc Feat = 1 - ρ f 1 (S ) (7) The factor ρ to be applied to high dimensional datasets aims at more rapidly converging on a solution with a low number of features. For example, in  [6]  the minimum number of features is given an upper bound of 20. Thus, herein ρ can be considered both as a way to set an upper bound for the minimum number of features to select and as a sort of learning rate ensuring that convergence on an acceptable optimal solution does not require too many generations. The scoring function used for the maximization of objective function f 2 (S ) (See Eq. (  5 )) during the search process is the "accuracy score" Sc Acc. Since accuracy ranges from 0 to 1 and since it should be maximized, it can directly be used as a score function. Hence, Sc Acc = f 2 (S ) (8) The AUC of a random guess classifier is 0.
The GAwC algorithm The GAwC algorithm is an implementation of the GA inspired by  [17, 25]  and presented in Section 3. 1. In this work it is used to find a Pareto optimal solution to the 3-objective problem consisting in selecting a minimal subset of features to be fed to a classifier while maximizing both, the accuracy and the AUC-ROC score of the classifier. As presented in Section 3. 3. 1 the search of the solution in the search space is directed by a reference vector that indicates a trade-off between all three objectives with which the optimal solution should align. Individuals of the GAwC algorithm are represented by a binary vector of size equal to the total number of features (for example 48 for the ASA-DI dataset). The bits in the vector indicate the presence or absence of each feature in the corresponding individual.
Experimental setup 4. 1. 1. Datasets Datasets have been chosen according to different criteria in terms of scale: number of instances (from 270 to 2126), number of features (from 8 to 166), and number of classes, i. e. , bi-class datasets were exclusively selected since the problem considered here is a binary classification problem from a clinical point of view. Further details concerning the 6 datasets used for experimentation are given in Table  2. More precisely, the evaluation of the GAwC method, presented in this article is based on six real-world datasets in the field of Life Sciences and Medicine: • Breast Cancer 2 is a well-known dataset of 30 continuous features that has been often used in the field of artificial intelligence in medicine  [49] [50] [51]. Its first usage appears in  [52].
The dataset is used to predict whether a given patient has a malignant or a benign tumor. It should be noticed that the features in this dataset are computed from digitized images of a fine needle aspirate of a breast mass. They describe the characteristics of the cell nuclei present in the images. • Cardiotocography 3 is a fetal cardiotocography dataset. The 21 continuous features are used to predict if a cardiotocogram belongs to the "normal" category or to the "suspicious or pathologic" category. The dataset consists of measurements of fetal heart rate (FHR) and uterine contraction (UC) features on cardiotocograms classified by expert obstetricians. Its first usage appears in  [53]. Though this dataset is not used as frequently as the Wisconsin breast cancer dataset, a recent use of it can be quoted for the classification of anticipation of fetal risks using bagging ensemble classifiers  [54].
Fig. 3. Score vectors and fitnesses of the best individuals at generations g i , g i+50 and g i+100. • Diabetes 4 is a dataset from the National Institute of Diabetes and Digestive and Kidney Diseases. Its first usage appears in  [55]  and for recent feature selection methods in  [6, 10]  which are the main competitive methods we compare to. Relying on the 8 categorical features of diagnostic measurements, the goal is to predict whether a patient has diabetes or not; • Heart (Statlog) 5 is a well-known dataset of 13 features (7 categorical, 6 continuous) that has extensively been used in the field of Artificial Intelligence  [56] [57] [58]  and for feature selection  [6, 10]. The goal here is to predict whether a patient has a heart disease or not; • Musk1 6 is a dataset from the AI Group at Arris Pharmaceutical Corporation.
Musk1 is used to scale-up the experiment in terms of number of features (166 continuous features). This dataset, where the goal is to predict whether a molecule will be judged to be a musk or not by a human expert, was originally used by  [59]  and also employed for features selection method evaluation  [6, 10]. Due to the number of features it provides, it represents a high dimensional case that should be experimented in the field of features selection; • ASA (acetylsalicylic acid) Drug Intake (ASA-DI) is a new dataset  [60]  that was collected by the University Hospital of Angers (France) and provided to us for experimentation following a real need: decreasing the number of features to be considered for medical decision while keeping both good accuracy and AUC. Based on 48 (30 categorical, 18 continuous) diagnostic measurements (features), ASA-DI corresponds to a dataset of patients in Cardiology from which the objective is to predict whether a patient has taken ASA or not.
Datasets pre-processing. Due to the imbalance in the distribution of the target classes, a stratified shuffle sampling is used. The Scikit-learn library 7 provides such a package to ensure that relative class frequencies are approximately preserved in each train and validation fold. The GAwC implementation can be set to use a new seed for each run and to save all seeds. To guarantee the reproductibility of results, another setting allows the tool to use a given set of predetermined seeds for the runs.
Technical settings. For all experiments, software methods relying on machine learning have been implemented using the Scikitlearn library and executed on a high performance computing cluster  [61]  of 27 nodes (composed of 700 logical CPU, 2 nvidia GPU tesla k20m, 1 nvidia P100 GPU, 120 TB of beegfs scratch storage). However, both the Genetic Algorithm and the 3-Dimensional Compass used by GAwC, are personal implementations.
3 ). To help choose this parameter, the tool can be set to perform a coarse exploration of the results, for a single run, for ranges of values of θ. In the present experimental case, a high accuracy is a priority and should not be sacrificed for the sake of a lower number Finally, as explained in Eq. (  7 ), the computed number of features score should be scaled in order to bound subset cardinality and define a maximum number of selected features; as in  [6]  where the maximum is limited to 20. For this, constant ρ presented in Eq. (  7 ) is set as follows: • for Breast Cancer dataset, ρ = 4; • for Cardiotocography dataset, ρ = 2; • for Diabetes and Heart datasets, ρ = 1; • for Musk1 dataset, ρ = 16; • for ASA-DI dataset, ρ = 6. Fig. 4. obtained the Compass for 3 enhanced generation from g 1 to g 88 (example from a run using ELM on Musk1 dataset) observable through four points of view.