dataesr/scientific-paragraphs-categorization huggingface dataset:
The library preparations for the genome-wide data were performed according to the SureSelect XT Mouse Methyl-Seq Kit Enrichment System for Illumina Multiplexed Sequencing Library protocol (Agilent Technologies, Santa Clara CA, Version E0, April 2018). A total of 3 mg of footprinted DNA was used as input for bait capture, according to the company's specifications. (# 5190-4836). DNA was first sonicated using a Covaris S220 sonicator (Covaris, Woburn, MA) (duty factor 10%, intensity 4 and 200 cycles/burst for 100 s duration time) to obtain products of 200-300 bp. DNA was then end-repaired, A-tailed and ligated with methylated adapters to create a pre-capture DNA library. Adaptor-ligated libraries were purified using (0. 65X) AMPure XP beads (Beckman Coulter, Inc. , Brea, CA, USA) then quality and quantity of libraries were determined by bioanalyzer using DNA high sensitivity chip (Agilent).
Amplicon bisulfite sequencing To confirm the footprints we observe in bait-capture data, we employed a PCR-based strategy for target enrichment after SMF. This allows for the generation of high coverage SMF data for regions of interest in a cost-and time-effective manner. When picking targets, we focused on TFs with a consensus motif and a matching ChIP-seq signal (16 TFs in total, Table  S2 ). These targets encompass sites where TFs bind in isolation or not bind at all (Table  S2 , Plate 1) as well as sites where binding is observed for multiple TFs (Table  S2 , Plate 2 and Plate 3). We ensured to pick regions with varying features such as NO and distance between the motifs. We laid a focus on capturing a high number of sites for TFs which were perturbed throughout this study, i. e REST and NRF1, while also profiling a sufficient number of sites for other studied TFs.
Confirmation of siRNA knockdowns via qPCR and western blots siRNA transfected cells were pelleted and resuspended in TRIzol reagent (ThermoFisher 15596026) or Protein Extraction Buffer (1% Triton X-100, 150mM NaCl, 50mM Tris pH = 8) for RNA and protein purification, respectively. Upon purification, RNA concentration was measured used Qubit RNA High Sensitivity Kit and 500ng RNA was reverse transcribed using SuperScript IV (ThermoFisher 18091050) with random hexamer priming. cDNA was diluted 20-fold in PCR-grade water and subjected to real-time quantitative PCR (qPCR) using SYBR Green PCR Master Mix (ThermoFisher 4309155) with gene specific, intron-spanning primers. Primers were checked for quantitative range with dilution series, and knockdown data was analyzed using DeltaDeltaCT method. Western blots were used to confirm the knockdowns on protein expression level.
QUANTIFICATION AND STATISTICAL ANALYSIS Alignment and data extraction Data alignment and methylation call SMF data were processed as previously described  (Krebs et al. , 2017). Briefly, raw sequence files were pre-processed using Trimmomatic to remove Illumina adaptor sequences, remove low quality reads and trim low quality bases. The trimmed reads were then aligned using QuasR (using Bowtie as an aligner)  (Gaidatzis et al. , 2015)  against a bisulfite index of the Mus musculus genome (BSgenome. Mmusculus. UCSC. mm10). For other datasets (ChIP-seq, RNA-seq, MNase-seq), reads were aligned using QuasR against the Mus musculus genome (BSgenome. Mmusculus. UCSC. mm10). Context independent cytosine methylation call was performed using QuasR. Custom R functions were developed to determine context dependent (CG, GC) average methylation.
Mapping of TF motifs Position weight matrices (PWM) for vertebrates TFs were downloaded from the Jaspar database  (Mathelier et al. , 2016)  and mapped over non repetitive regions of the mouse genome (BSgenome. Mmusculus. UCSC. mm10) using the matchPWM function of the Biostrings package. PWM with a score of 10 or above were retained for single molecule TF classification. Given that MAX and MYC both bind the palindromic E-box sequence and can't be teased apart from one another in SMF data, we merged all instances for these TFs into MAX::MYC and excluded them from orientation analysis. Conversely, Sox2 and Pou5f1::Sox2 correspond to two separate motifs, mapping to distinct sequences in the genome. Therefore, we kept the instances separate for these TFs and analyzed them as different TF binding events.
For every read covering a cluster, binary methylation information was extracted from the n+2 bins. For each TF in the cluster, we defined the TF bound state as a combination of a footprint at their motif and accessibility at the flanks of the cluster (Figure  S4A ). Since the molecular classifier requires to have informative Cs in each of the bins, increasing the genomic density of the accessibility measures should enhance the number of clusters that can be analyzed with this strategy. We were indeed able to analyze five times more TF clusters when combining information of GCs and CGs from the tandem footprinting dataset in methylation null ESCs  (Figures S4B and S4C). Importantly, SMF signal (Figure  S1C , R = 0. 90) and TF binding frequencies are extremely comparable in WT and DNMT TKO mESCs (Figure  S4C , R = 0.
Data analysis All analyses were performed using R-Bioconductor Comparison with external datasets. Data collection was performed using the qCount function of QuasR  (Gaidatzis et al. , 2015). Reads were counted in a window around each TF motif [-100:100] using the qCount function of QuasR  (Gaidatzis et al. , 2015)  and enrichment over input were derived. Correlations were calculated on log2 transformed data.
Shown is the inverse frequency of methylation (1 À methylation [%]) (blue line). Black boxes represent the location of consensus motifs for TFs. Black arrow indicates the transcriptional start site. Read counts for ChIP-seq of the respective TFs are shown as intensity heatmap (datasets as indicated), revealing strong binding of NFYA and NRF1 but no detectable binding for Sox2 and Stat3. (C-E) TF binding creates discrete footprints detectable by SMF. Composite profile of SMF signal at various (C-E) bound TF motifs (top 5% of the respective TF ChIP-seq) as indicated. Shown is the footprinting frequency (1 À methylation [%]) of individual cytosines (black dots). See also Figure S1.
ACKNOWLEDGMENTS The authors are grateful to  Ferris Jung ,  Leslie Horner , and  Christiane Wirbelauer  for technical assistance and  Bernd Klaus ,  Lukas Burger , and  Charles Girardot  for computational and statistical support. The authors are grateful to  Ralph Grand ,  Duncan Odom , and members of the  Krebs laboratory  for helpful discussions and comments on the manuscript. The authors are thankful to  Veronique Juvin  ( SciArtWork ) for support with artwork and  Guy Riddihough  (LifeScienceEditors) for manuscript editing. Research in the laboratory of A. R. K. is supported by core funding of the  European Molecular Biology Laboratory, Deutsche Forschungs Gemeinschaft  ( KR 5247/1-1 ), and a  Swiss National Fund Ambizione grant  ( PZOOP3_161493 ). The salary of D. I. was supported by a  Swiss National Fund Ambizione grant  ( PZOOP3_161493 ).
Research in the laboratory of D. S. is supported by the  Novartis Research Foundation , the  European Research Council (ERC)  under the European Union's  Horizon research and innovation program  (grant agreement no. 667951 ), and the  Swiss National Sciences Foundation. Work in the lab of Nacho Molina  is supported by the grant  ANR-10-LABX-0030-INRT. Nacho Molina  is supported by a fellowship from the  Theory@EMBL sabbatical program.
In this paper, we investigate the causality in the sense of Granger for functional time series. The concept of causality for functional time series is defined, and a statistical procedure of testing the hypothesis of non-causality is proposed. The procedure is based on projections on dynamic functional principal components and the use of a multivariate Granger test. A comparative study with existing procedures shows the good results of our test. An illustration on a real dataset is provided to attest the performance of the proposed procedure.
This test is explained in detail in  [6]. The proposed test relies on functional dynamic principal components, which are able to capture the variability of functional time series better than functional principal components as demonstrated in  [17]. Once we obtain the dynamic FPCA scores, we make the decision by using a multivariate Granger test on the scores. Aue et al. [18]  use the same idea for predicting functional time series. Our dynamic-FPCA based procedure is publicly available at https://github. com/PyMattAI/DynamicFPCA_Causality. git (accessed on 7 June 2021) In order to compare, we studied another procedure that does not use the functional nature of the data, which is called the classical test. This procedure is based on differentiating the time series and using a multivariate Granger test.
The application of a traditional test and our procedure to the real dataset highlight this fact. X t (u i ) = x (t-1)×m+i. In Section 2, the definition of causality for functional stationary time series and an example in the autoregressive functional processes are introduced. In Section 3, we recall how we can estimate the dynamic FPCA and propose three algorithms for testing the noncausality including the dynamic FPCA-based testing procedure. We conclude this article with Sections 4 and 5, which are developed to empirically analyze the testing procedure, and an application of the procedure to explain the relation between electricity demand and temperature in Australia.
Empirical Analysis 4. 1. Design of the Experiments A simulation study was developed to check the performance of the three procedures. We used R software, namely package "fda"  [20] , "far"  [21]  and "freqdom. fda". To verify the results of our test under the Null and the alternatives, we simulated different scenarios. We used the packages "far" in order to simulate functional time series and to estimate the parameters of the models, the package "fda" to perform the test of equality of covariance and the package "freqdom. fda" to compute the dynamic FPCA scores. Under the null, we used three independent functional time series and one model computed with the "far" package (see "Null 1"). Under the alternative of causality, we tested five different scenarios. We simulated with ρ on a sinusoidal basis that approximates the Hilbert space H.
The level of significance that was studied was α = 0. 05. The two parameters L and q of the Dynamic-FPCA algorithm were chosen according to the freqdom. fda package initializations with q = 30 and L as the minimum between the maximum possibilities and 60 (to avoid high calcuation time). For the analysis of d and d , we chose to give the same value d = d for the sake of simplicity. In order to have an idea of what we are manipulating, we made some plots of different time series; see Figures  3 4 5. Figure  3  represents the whole trajectory of a functional time series simulated with sample size of 100. Figure  4  is the same as Figure  3  but we bring back the sequence series to the same time domain. Figure  5  plots the whole trajectory of two functional time series, which are in a causal relation.
Real Data Illustration The dataset we used is available in the R package "fds"  [22]  and is already studied in  [23]. The article  [23]  deals with the electricity demands and the temperature in Adelaide. This dataset consists of half-hourly electricity demands in Adelaide and temperatures measured at the Adelaide airport from Sunday to Saturday between 6 July 1997 and 31 March 2007 (see Figure  6  for the electricity demand on Thursday and Figure  7  for the temperature on Thursday). We wanted to test the causal relation between these two variables: the electricity demands and the temperatures. To do so, we used the three algorithms. As in article  [23] , we found a dependency between this two variables. Everyday, the temperatures in Adelaide impact the electricity demands in this city.
Patients We analyzed a cohort of 1148 T1-3NxM0 patients with invasive breast carcinoma (NEOREP Cohort, CNIL declaration number 1547270) treated at Institut Curie, Paris, between 2002 and 2012. We included only patients with unilateral, non-recurrent, noninflammatory, non-metastatic tumors with an indication of NAC, for whom pre-and post-NAC data were available for the DCIS component. All patients received NAC, followed by surgery and radiotherapy when indicated. The study was approved by the Breast Cancer Study Group of Institut Curie, and the CNIL gave its authorization for data analysis for research purposes. This study was conducted according to institutional and ethics committee rules regarding research on tissue specimens and patients.
Statistical Analysis The study population was described in terms of frequencies for qualitative variables, or medians, means and associated ranges for quantitative variables. Comparisons of the proportion of samples with a DCIS component before and after NAC were performed with McNemar tests. Factors predictive of pCR were introduced into a univariate logistic regression model. A multivariate logistic model was then implemented. The covariates selected for multivari-ate analysis were those with a p-value for the likelihood ratio test below 0. 05 in univariate analysis. A significance threshold of 5% was used. Analyses were performed with R software, version 3. 1. 2 (RStudio Team (2018). Rstudio: Integrated Development for R. RStudio, Inc. , Boston, MA, USA, URL http://www. rstudio. com).
MATERIALS AND METHODS Existing literature that contained design principles or guidelines for MPAs were collated using online search engines (Web of Science Core Collection, Scopus), Google Scholar and the internal search functions of conservation organisation websites. The search term ("marine protected area * " OR "marine reserve * " OR "no-take") AND (guideline * OR principle * OR criteria) was initially tested on 10 key documents (5 peer-reviewed and 5 "grey literature" reports), to ensure it was capable of detecting the relevant literature. Equal weight was given to grey literature in the literature search, in recognition that MPA principles or guidelines often appear in documents designed for use by management agencies, rather than for academic purposes. All results were uploaded to the online software Cadima (www.
cadima. info), through which we specified the research question in terms of Population/Outcome, and which automatically detects duplicates and assists with screening and data extraction  (O'Leary et al. , 2016a). After the initial literature search and duplicate exclusion, the resulting 795 documents were screened for relevance first by title, then by abstract and lastly by full-text articles, resulting, initially, in 264 articles included for data extraction (Table  1 , Supplementary Information 1). Of these, 177 documents contained information about design principles or guidelines for MPA design. The data extracted from ("marine protected area*" OR "marine reserve*" OR "no-take") AND ("design guideline*" OR "design principle*") Web of Science Core Collection 30 2020-08-22 ("marine protected area*" OR "marine reserve*" OR "no-take") AND ("design guideline*" OR "design principle*") Google Scholar 128 2020-08-23 marine protected area guidelines Various government and organisation websites 12 2020-08-26.
, 2011). In offshore environments, and especially in the deep sea, the difficulty of capturing species' ranges is compounded by the almost complete lack of data on larval duration and behaviour traits  (Hilário et al. , 2015). The dispersal of deep-sea organisms presents the added complexity of vertical swimming behaviour  (Afonso et al. , 2014) , which can influence modelled dispersal distances by up to an order of magnitude  (Gary et al. , 2020 ; see also Guideline 5). If it is impossible to contain a species' entire range within one MPA, MPA networks that comply with connectivity guidelines (Guideline 5), replication (Guideline 7) and minimum percentage guidelines (Guidelines 3 and 4) can be combined to protect as many of the species' critical areas as possible, thereby achieving the best possible outcome for a species or population.
Monitoring Although monitoring is an integral part of MPA management, many offshore environments are lacking in even the most basic baseline data. However, to meet global conservation targets, it is impractical to wait until these data are collected before proceeding with marine spatial planning and MPA establishment. The guidelines set out in this paper are designed to optimise the placement of offshore MPAs and MPA networks using existing data, and allowing for the incorporation of data collected in the future. To continue to improve the effectiveness of offshore networks of MPAs, especially within an adaptive management framework, effort must go into gathering and collating baseline data, followed by performance monitoring. This will include the use of remotely collected and centrally compiled biological and socio-economic data.
Global datasets on fisheries and oceanic habitats are being compiled with ever increasing levels of spatial accuracy  (Harris et al. , 2014; Pauly et al. , 2020; Global Fishing Watch, 2021). Regional or national datasets may be more available and appropriate to particular planning efforts and may inform baselines for monitoring; data from ABNJ may be less readily available. Information from monitoring can then feed into an adaptive management cycle for existing MPAs (e. g. ,  Dunn et al. , 2018)  and broader offshore marine resource management efforts as well as help refine and improve the design guidelines listed here for new offshore MPAs. Monitoring in the open ocean may rely more heavily on proxies or surrogates than in inshore areas, since data collection can be logistically challenging and expensive.
" Regions of the world where data have been or are being collected, and uncertainty is lower, can serve as testing grounds for MPA design. For example, a large portion of the southwest Pacific has defined bioregions, identified with a combination of empirical and modelled data and verified through participatory planning  (Beger et al. , 2020; Ceccarelli et al. , 2021). Bioregions have also been defined in offshore Australian (Department of the  Environment and Heritage, 2006) , UK  (Chaniotis et al. , 2020) , South African  (Livingstone et al. , 2018)  and Canadian waters  (Burt et al. , 2018). Multiple national and international seabed mapping projects are underway, including efforts to map the entire seafloor by 2030  (Wölfl et al. , 2019) ; technology innovations promise to deliver increasingly accurate biophysical data (e.
ACKNOWLEDGEMENTS This paper is adapted from a report prepared as part of the  Marine and Coastal Biodiversity Management in Pacific Island Countries (MACBIO)  project. MACBIO was funded by the  German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety's (BMUB) International Climate Initiative (IKI) , and implemented by the  Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ)  with the countries of  Fiji, Kiribati ,  Solomon Islands ,  Tonga , and Vanuatu. MACBIO enjoyed technical support from the  Oceania Regional Office of the International Union for the Conservation of Nature  (IUCN) and worked in close collaboration with the Secretariat of the  Pacific Regional Environment Program (SPREP). Permission from the copyright holder for the submission of this adaptation of the report Ceccarelli et al.
Web-based data analysis and visualization tools are mostly designed for specific purposes, such as the analysis of data from whole transcriptome RNA sequencing or single-cell RNA sequencing. However, generic tools designed for the analysis of common laboratory data for noncomputational scientists are also needed. The importance of such web-based tools is emphasized by the continuing increases in the sample capacity of conventional laboratory tools such as quantitative PCR, flow cytometry or ELISA instruments. We present a webbased application FaDA, developed with the R Shiny package that provides users with the ability to perform statistical group comparisons, including parametric and nonparametric tests, with multiple testing corrections suitable for most standard wet-laboratory analyses.
Introduction Increasing numbers of web-based data analysis and visualization tools have been developed using the R programming package Shiny  [1]  and made available to researchers. Despites other programming languages are suitable for web-based applications, these tools rely on the wellrecognized usefulness of R to analyze data from different perspectives, especially for statistical analysis, and provide interactive visualizations. Hence, Shiny tools are enabling wet-laboratory researchers the ability to take advantage of bioinformatics advancements  [2]. While they are free and save the user time in the analytic stages without requiring that the user have extensive computational skills, most of the current online Shiny applications are dedicated to specific objectives or technologies, such as shinyheatmap to generate heatmaps for large datasets  [3] , shinyCircos to build Circos plots from genomic data  [4] , iDEP for RNAseq data analysis  [5]  or shinyGEO to analyze gene expression datasets directly from the Gene Expression Omnibus (GEO) repository  [6].
Applications for data generated from common laboratory techniques such as quantitative PCR, flow cytometry or enzyme-linked immunosorbent assay (ELISA) are also needed. Technological advances in these methods have allowed researchers to generate significant data output. Flow cytometry technologies can run a large number of samples with a tenth of fluorochrome parameter combinations. In addition, multiplex ELISAs can produce readings for up to ten cytokines per well, and advances in quantitative PCR (qPCR) devices have allowed the analysis of samples in less than an hour. These highvolume data outputs leave laboratory researchers with a time-consuming data analysis process. First analysis steps often start with description of the dataset, group comparisons between groups of interest and analysis of correlated parameters.
Heatmap, principal component analysis (PCA) graph and correlogram allow to visualise the entire datasets and to identify potential subsets or outliers. In parallel, individual graphics, either in dots, whiskers or violins allow to explore individual parameters. For such analyses, researchers usually perform targeted parameter analysis with several hands-on processes, increasing the risks of information loss and human error. In order to give the possibility to explore research datasets, rapidly, with an easy-to-use tool, we created a free, user-friendly and interactive Shiny web application supporting regular laboratory analyses of a wide array of data, including flow cytometry and qPCR data. This multi-tool utility suite in R Shiny allows researchers to perform classical statistical group comparisons, including parametric and nonparametric tests with multiple testing correction and to produce heatmaps, PCA, receiver operating curves (ROCs) and correlogram visualizations.
FaDA application FaDA was developed in R (release 3. 6. 1, http://www. rproject. org) [7]  and implemented as a web application using the R Shiny package (version 1. 4. 0) from R Studio (http://shiny. rstudio. com). As an open-source application, the FaDA code is available through GitHub at https:// github. com/danger-r/FaDAapp. FaDA was dockerized using Docker software (https://www. docker. com/) and made available through ShinyProxy on a Linux server (CentOS 7 with 12 Go RAM allowed for FaDA) hosted at the BiRD bioinformatics core facility within the University of Nantes (https://pf-bird. univ-nantes. fr/). FaDA uses integrated work frames of R packages allowing an intuitive interface. A complete list of the packages used may be found in S1 Table. The interface layout is built using the shiny and shinythemes packages with a sidebar for user interaction and six main panels (About, Tutorial, Data Analysis, Heatmap & PCA, Correlation and ROC curves), with subtabs available within these panels (Fig  1).