dataesr/scientific-paragraphs-categorization huggingface dataset:
Study question: How can one design and implement a system that provides a comprehensive overview of research results in the field of epi-/genetics of male infertility and germ cells? Summary answer: Working at the interface of literature search engines and raw data repositories, the newly developed Male Fertility Gene Atlas (MFGA) provides a system that can represent aggregated results from scientific publications in a standardised way and perform advanced searches based on e. g. the conditions (phenotypes) and genes related to male infertility. What is known already: PubMed and Google Scholar are established search engines for research literature. Additionally, repositories like Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) provide access to raw data. Selected processed data can be accessed by visualisation tools like the ReproGenomics Viewer (RGV).
, 2018)  that was released in 2017. It provides information on genetic associations of 778 traits identified in genome-wide association studies  (Canela-Xandri et al. , 2018) , male infertility is, unfortunately, not one of them. Instead of being accessible through a specified tool, research on male infertility relies mainly on general search engines for scientific publications like PubMed or Google Scholar. These engines can be employed to search for information based on keywords of interest, e. g. , gene names in combination with specific conditions. However, if the required information can only be found in a supplementary table or figure, these search engines are unable to mark the corresponding publication as relevant. Other sources of information are raw data repositories like Gene Expression Omnibus (GEO) which provides access to the raw data files of microarray and genomic data from many publications  (Barrett et al.
1). Second, this data should be enriched and complemented by data from external databases, e. g. to provide general information on genes and enable standardisation of terms with ontologies. Third, data sets should be searchable and identifiable based on the occurrence of gene names or IDs in tables and figures of publications as well as on meta-information. Forth, maintenance and updating of the platform should be provided. Last, all data in the MFGA should be publically available. For technical details, please see sections Requirements engineering, Current state of requirements, IT architecture and Data model in the Extended Methods of the supplement, as well as Suppl. Fig. S1, S2  and S3.
, 2011). The number of available data sets is shown in brackets after each search term. Gene names and IDs can be specified in a text field as a comma-separated list. Two checkboxes enable including synonyms of the entered gene names and IDs into the search. Also, there A c c e p t e d M a n u s c r i p t are three search modalities: (1) users can perform a broad search to identify all data sets that are annotated with at least one of the specified search terms, (2) users can perform a more targeted search for all data sets that are annotated with at least one of the specified search terms per category and (3) users can perform a very specific search for all data sets that are annotated with each of the search terms (default option). As an example, Fig. 2  shows an extract of the output of a simple search request for data sets in the MFGA database containing information on the gene STAG3 (Suppl.
g. , supplementary table 3 of van der Bijl et al. (2019) which is shown partly in Fig. 3  (full version in Suppl. Fig. S7 ). For a deeper analysis of the underlying read data of individual data sets, the MFGA provides a link to the RGV, whenever a data set is available in both tools, such as, e. g. ,  Irie et al. (2015)  in Fig. 2. Data from external public databases has been integrated in order to enrich information on the publications shown in the MFGA. Throughout the application, gene names can be selected in table cells and plots to open up an overlay with further explanations (for an example, see.
Fig. S8a  and Suppl. Fig. 8b ). The main purpose of the MFGA is to enable researchers and clinicians to consider their own research results in the context of other relevant literature. To this end, the system enables a highly selective search for studies with comparable conditions and parameters and offers the means to quickly review their main analysis results. Additionally, searches for genes can be performed in order to identify data sets containing the corresponding genes in their analysis results. This functionality supports the validation of candidate genes. Further, supplementary information from various sources is embedded into the MFGA, e. g. , RefSeq (O'  Leary et al. , 2016) , HGNC  (Yates et al. , 2017; HGNC Database, 2018)  and GTEx project  (Lonsdale et al. , 2013; The Broad Institute of MIT and Harvard, 2019).
Compared to general search engines like Google Scholar and PubMed, with millions of records, the MFGA will always return smaller numbers of search results. However, in the domain of male infertility, the relevance of the individual results in relation to the corresponding search terms used in the MFGA is expected to be greater than when using A c c e p t e d M a n u s c r i p t those same search terms in a large search engine. Search engines for literature are usually restricted to mining textual information of publications and cannot consider information that is presented in tables, images, or supplementary material. The MFGA, however, is specifically designed for that task, which is enabled by its key features: A standardised data representation and disease-specific manual curation. Another problem occurs when relevant data sets are searched in raw data repositories like GEO or processed data resources like RGV  (Darde et al.
In the case where a publication provides, e. g. , only significant SNPs from a GWA study, the MFGA, too, reports only these. The only meta- Prospectively, the MFGA will be updated and enlarged based on the proposed content management process (Suppl. Fig. S1 ). Continuous technical maintenance will be provided by the Institute of Medical Informatics, University of Münster. In this context, a web form will be implemented to enable users to propose publications that are, from their point of view, still missing. Additionally, a closer integration of RGV and MFGA is planned, e. g. by coupling the publication submission forms, as well as further tools for meta-analyses. In conclusion, the MFGA is a valuable addition to available tools for research on the epi-/genetics of male infertility. It helps fill the gap between pure literature searches as provided by PubMed and raw data repositories like GEO or SRA.
The MFGA enables a more targeted search and interpretation of OMICS data on male infertility and germ cells in the context of relevant publications. Moreover, its capacity for aggregation allows for meta-analyses and data mining with the potential to reveal novel insights into male infertility based on available data. Ultimately, by combining RGV and MFGA with AI methods, we aim to develop a powerful gene prioritisation system dedicated to male infertility similar to the GPSy tool  (Britto et al. , 2012). represented in an interactive way and can directly be filtered and sorted. Additionally, histograms can be plotted for the table columns, here, e. g. , based on column "Testis Expression". For the full screenshot see Suppl. et al. , 2016) and HGNC (Yates et al. , 2017). Links for searching the gene in the MFGA database and opening the corresponding GeneCards  (Stelzer et al.
Acknowledgements We are grateful to all members of the  Clinical Research Unit (CRU) 'Male Germ Cells ' who contributed through either identifying relevant publications, supporting publication curation or providing feedback during the development of the MFGA:  Michael Storck ,  Marius Wöste ,  Nina Neuhaus ,  Sven Berres ,  Sandra Laurentino ,  Lina Franziska Lanuza Pérez , Corinna.
II. DATASET AND ANALYSIS In 2011, PHENIX collected data from Au+Au collisions at √ s N N = 200 GeV. After event selection and quality cuts, 4. 4 billion minimum-bias (MB) events were analyzed. These are combined with the previously reported 3. 9 billion MB Au+Au events from 2007 and 2. 9 billion from 2010  [6]. The high momentum photon triggered d+Au data set at √ s N N = 200 GeV was collected in 2008, and 3 billion events are analyzed. The p+p comparison data are from 2005 and 2006  [15]. The measurements in this paper use the PHENIX central spectrometers  [27]. Two particle correlations are constructed by pairing photons or π 0 s measured in the electromagnetic calorimeter (EMCal)  [28]  with charged hadrons reconstructed in the drift chambers and pad chambers  [29]. The acceptance in pseudorapidity is |η| < 0.
We expect this property to be translated into the dynamics, generating bursts with no privileged direction within the model. One can have a qualitative picture of this statement directly from Figure  5  and a more quantitative result can be obtained by producing a histogram of the bursts orientation angle θ. This angle is defined as the one between the x-axis (inlet-outlet direction, see Figure  5 ) and the major axis of an ellipse having the same second-moments as that particular burst. Figure  6B  shows the resulting histogram. We can see that, apart from fluctuations caused by the small size of the dataset, there isn't any particularly privileged orientation for the bursts. Indeed, such a distribution also indicates that viscous effects are much reduced in the experiment: if the dynamics were driven by viscous forces, one would observe bursts much more elongated in the inlet-outlet direction, following the average gradient of pressure in that direction.
But in a natural noisy signal, this approach is problematic due to high frequency fluctuations. One possible way to handle the issue is by employing a low-pass filter to remove the high frequency noise. This was our initial approach but it led to biases in the statistics, particularly in the region of small valley sizes. We decided to avoid the filter completely and perform the analysis in the raw signal employing a different technique to find the local extrema. We have employed Matlab's findpeaks routine to find all maxima in the noisy data and sort them out with respect to the peak prominence (a measure of the distance between the top of a particular peak and the surrounding signal). This can be used to remove most of the peaks mistakenly assigned to the fluctuations because they are very small.
ACKNOWLEDGMENTS We gratefully acknowledge the support from the  University of Oslo, University of Strasbourg , the  Research Council of Norway through its Centre of Excellence  funding scheme with project number  262644 , the  CNRS-INSU ALEAS program , the  LIA France-Norway D-FFRACT , and the  EU Marie Curie ITN FLOWTRANS network. We would also like to thank  Mihailo Jankov ,  James Campbell , and  Kristian Olsen  for many fruitful discussions. Finally, we would like to express our gratitude for the very constructive feedback obtained from the reviewers in the production of this work.
We then sought to identify the threshold with the best discriminatory power, using Cutoff Finder software. The baseline splenic volume threshold retained was 180 mL with a sensitivity of 67. 3% (61. 3-72. 9%) and a specificity of 50% (15-85%). Using this threshold in univariate Cox analysis, baseline splenic volume was identified as a prognostic factor for PFS (HR 1. 403; 95%CI [1. 073-1. 834]; log-rank p = 0. 01). By multivariate Cox analysis, the baseline splenic volume threshold of 180 mL was found to be significantly associated with PFS (HR 1. 362; 95%CI bootstrapped [1. 338-1. 370]; p = 0. 0249) (Table  3 ), and the stability of the model was verified using a bootstrap with 500 replications. Harrell's C-index for the baseline splenic volume threshold at 180 mL was 0. 84 (95%CI [0. 75-0.
However, a validation cohort in another prospective cohort of patients treated with FOLFIRI bevacizumab before using spleen volume as a prognostic marker in mCRC. In addition, further studies with different treatment strategies are warranted to determine the prognostic versus predictive role of this new marker. [25, 46]. From March 2010 to July 2013, 491 patients were included. Main eligible criteria were histologically proven, unresectable mCRC, WHO status ≤ 2, life-expectancy ≥ 3 months, and absence of previous chemotherapy or antiangiogenic therapy for metastatic disease  [25]. All patients provided written informed consent, and the study was approved by the Committee for the Protection of Persons Ile de France VIII. The trial was registered on ClinicalTrials. gov (NCT00952029). All patients were treated with FOLFIRI and bevacizumab for 6 months.
Flow Cytometry Myeloid subpopulations were identified using custom Duraclone tubes (Beckman Coulter), using anti-CD15-Pacific Blue, anti-CD33-FITC, anti-CD3-PC5. 5, anti-CD19-PC5. 5, anti-CD20-PC5. 5, anti-CD56-PC5. 5, anti-HLA-DR-AA750 and anti-CD14-Krome Orange dry antibodies (all obtained from Beckman Coulter). We added in liquid form anti-CD11b-BV605 antibody (Biolegend) and Draq7 viability dye (Beckman Coulter). Whole blood was removed to heparinized tubes (100 µL) and stained in Duraclone tubes for 15 min at room temperature. After surface staining, 2 mL of Versa Lyse/IOTest 3 solution (Beckman Coulter) was added for 10 min. Cells were then centrifuged at 350g for 5 min). All events were acquired on a BD CANTO2 cytometer equipped with BD FACSDiva software (BD Pharmingen, Le Pont De Claix, France), and data were analyzed using Kaluza software (Beckman Coulter).
The threshold was determined using the best cut-off method using Cutoff Finder software (Charité-Universitätsmedizin Berlin, Berlin Germany)  [47]  and analyzed in a multivariate model. Correlations between blood immune parameters and spleen volume were performed using the non-parametric Spearman correlation test. Tests were two-sided and a p-value < 0. 05 was considered significant. All analyses were performed using SAS software version 9. 4 (SAS Institute Inc. , Cary, NC, USA).
The resulting CE chromatograms can be subsequently analyzed using diverse software tools, such as QuShape  (12) , to obtain quantitative reactivity information from the longest readable region of the studied RNA. QuShape was designed to perform analyses of probing data in general, and at first demonstrated to work with the SHAPE experimental data  (12) , but it was also applied to analyze the reactivity patterns obtained with DMS or CMCT probing  (13, 14). Many tools for RNA structure prediction have been adapted to use chemical probing data, however only a few of them, e. g. , RNAfold from ViennaRNA  (15) , and Fold and ShapeKnots from RNAstructure  (16)  use the original reactivity patterns as an input rather than interpreted data such as predictions whether each of the residues is paired or unpaired.
The use of the experimental data is, however, not straightforward, as the data requires pre-processing to serve as an input. Proper normalization of probing results and integration of multiple online tools applied for data processing and visualization poses a technical challenge. Performing the analyses in a unified way and comparing results between consecutive experiments requires following a strict normalization protocol, some experience with bioinformatic software, and a significant amount of time spent on each analysis. Various computational tools have been developed for RNA structure prediction based on the chemical probing data, e. g. , RNAex  (17) , RNA Framework  (18)  or Structure-Fold  (19). However, these tools focus on high-throughput probing experiments (usually at the level of the whole transcriptomes) analyzed with next-generation sequencing.
Increasing popularity of RNA secondary structure analyses across whole transcriptomes has prompted the development of several databases serving as repositories for highthroughput probing data from various types of experiments, e. g. , Structure Surfer  (20) , FoldAtlas  (21)  and RSVdb  (22). However, these databases do not allow the user to analyze their own experimental data on the fly. Instead, they provide results of previous experiments, mostly done in a highthroughput fashion for entire transcriptomes of model organisms. To date, there has been no fully automated and simple tool dedicated to processing RNA chemical probing data from experiments focused on studying individual RNA molecules and analyzed with capillary electrophoresis. A few components of the workflow, which may facilitate the processing of the experimental data, are available.
Macro CE (https://github. com/afafbioinfo/Macro CE) is a standalone Python script for processing QuShape data with implemented normalization protocol. IPANEMAP (https: //github. com/afafbioinfo/IPANEMAP) is a set of Python scripts for predicting RNA secondary structures based on various chemical probing data (including SHAPE and DMS). It computes and outputs one or several secondary structures, corresponding to the conformers best supported by experimental data and thermodynamics. All these tools and programs may be useful at various stages of the RNA chemical probing data analysis, but they need to be installed and run locally from the command line, which is often a limiting factor for the inexperienced user. We have developed RNAProbe--a web server for normalization and analysis of RNA chemical probing data from low-throughput experiments focused on individual RNA molecules.
Inputs RNA sequence is required in all analysis modes. It can be uploaded either as a text file containing the RNA sequence or pasted into the 'direct input' window. The input sequence must be the same as it was used in the QuShape analysis (including the cassette); the sequence to be analyzed can be specified by selecting a region of interest (ROI), leaving out the cassette on 5 and 3 ends of the RNA molecule. The sequence should consist of only four letters corresponding to the four RNA nucleotides -A, C, G, U (either capital or lower case); should T be present in the sequence, it will be displayed as U in the output files. The QuShape file is a tab-separated text file, generated by the QuShape software  (12). The sequence is read from the 'seqRNA' column and compared to the input sequence file.
Experimental setup The add riboswitch aptamer domain is an allosterically regulated element, found in the 5 UTR of the adenosine deaminase mRNA of the Gram-negative bacterium Vibrio vulnificus, and it regulates the translation of a downstream gene in response to adenine in vivo  (33). Its structure and ligand binding mechanism has been studied and determined by a wide range of biochemical and biophysical techniques, including SHAPE probing  (34) , and X-ray crystallography  (35) , and also in combination with computational methods  (36 ). In the analysis conducted for the purpose of illustrating the functionality of RNAProbe, the sequence of this riboswitch was combined with the SHAPE cassette as described by Wilkinson et al. (10). Transcripts were produced by in vitro transcription with T7 RNA polymerase.
The negative control was treated with the same volume of DMSO. After incubation with the probing reagent, RNA was precipitated in the presence of ethanol. Resulting pellets were resuspended in 0. 5× TE buffer, and combined with fluorescent primers for the reverse transcription with the SuperScript III Reverse transcriptase kit, according to the manufacturer's instructions (different fluorophores are required for the positive/negative reactions and for the sequencing reaction). In addition to the standard reaction mix, sequencing reactions also contained the ddNTP of choice. Resulting cDNA samples were precipitated in ethanol, and recovered pellets were resuspended in Hi-Di formamide. After combining cDNA from positive/negative reactions with the sequencing ladder, samples were resolved by capillary electrophoresis, and the signal alignment to the corresponding sequence was performed in QuShape.
Example applications To demonstrate the performance of the RNAProbe server, we chose the add riboswitch aptamer domain from Vibrio vulnificus, for which a wealth of experimental data is already available. SHAPE probing was performed either in the presence or in the absence of the natural ligand (adenine). DMS and CMCT probing analyses were performed in the absence of the ligand. Experiments were conducted in triplicates for independently prepared RNA samples. The experimental protocols are described in the 'Materials and Methods' section of this manuscript. Figure  2  shows how the RNAProbe server visualizes the mapping of the probing data onto the secondary structure of the add riboswitch aptamer domain inferred from the crystal structure  (35) , consisting of three helices (P1, P2 and P3), with an additional pairing that forms a pseudoknot.
ACKNOWLEDGEMENTS The authors want to thank current and former members of the  Bujnicki group , in particular  Astha Joshi ,  Almudena Ponce-Salvatierra ,  Elżbieta Purta ,  Pritha Ghosh ,  Nithin Chandran , and  Małgorzata Kurkowska  for their helpful comments and testing the server, to  Marcin Magnus  for helping us to connect RNAProbe to the SimRNAweb server, and to all RNAProbe users who provided feedback.
Numerical and analytical predictions In this section we present our predictions for the PDFs, by computing them both with the numerical code described in the first part of sect. 6, and by evaluating the analytical formulae; these are always the matched ones. We compare these two classes of predictions, mutually validating them in the process. Unless explicitly indicated, all results are NLLaccurate with running α, and all have been obtained by setting µ 0 = m. We begin by plotting in fig. 5  the electron, photon, and positron PDFs, computed at µ = 100 GeV with the numerical code. The left panel shows these quantities in the full  z ∈ (0, 1) range, while the right panel is a zoom to the large-z region, where we consider ẑ ∈ (1, 15) (see eq. (7. 24)  for the definition of ẑ). Owing to the much faster growth of the electron PDFs in this region w.
Acknowledgments SF wishes to thank  M. Bonvini ,  S. Catani ,  M. Mangano ,  S. Marzani ,  D. Pagani ,  F. Piccinini ,  G. Ridolfi ,  H-S. Shao , and  B. Ward  for various conversations during the course of this work, and the  CERN TH division  for hospitality. V. B. acknowledges the support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 647981 ,  3DSPIN ). By putting everything back together, one sees that eq. (D. 12) coincides with eq. (5. 68).
It should provide a robust and unbiased extraction of the experimental parameters, such as signal-to-noise ratio or kernel shape, from the raw fluorescence datasets. It should finally offer a theoretically-grounded estimate of the inference performance for any given value of these parameters. In the present study, we build on a recently proposed non-negative sparse inference method to develop the socalled Blind Sparse Deconvolution (BSD) algorithm. This novel implementation features automatic estimation of the hyperparameters, enhanced speed, similar-to-better reconstruction performances and super-resolution capabilities. We additionally provide thresholding guidelines and theoretical bounds on its performance, in terms of inference efficiency and temporal accuracy as a function of the experimental parameters.