dataesr/scientific-paragraphs-categorization huggingface dataset:
The first formulas lead to an unbiased estimation of the direct and joint indirect effects, compliantly to  Shpitser and Sherman (2018). In addition, we propose a procedure based on the simulation of counterfactual distributions to estimate the indirect effects through individual mediators. As the proposed estimation method depends on the simulation of counterfactuals and not only on the observational data, we conduct an empirical study to show that the method result in unbiased estimates of the direct and indirect effects. Our methods is implemented in R (a documented R package is under preparation and will be soon posted on GitHub). Finally, we apply our method to a real dataset from a large cohort to assess the effect of hormone replacement treatment on breast cancer risk through three uncausally correlated mediators, namely dense mammographic area, nondense area and body mass index.
A Data simulation method Except for the LSEM framework, it is in general not straightforward to obtain true values of the mediation effects from a causal generative model, that is a set of causal structural equations. To overcome this difficulty, we start by simulating a large database of values for the treatment T and for all the counterfactual mediators M k (t), and outcomes Y (t, M 1 (t 1 ),. , M K (t K )), see Table  1  for an example. Then we simply calculate the indirect effects δ k (t) and δ Z (t) and the direct effect ζ (t) as means, according to the definitions given in the section 3. A. The large size of the dataset guarantees that these Monte-Carlo estimates can be taken as the true values. In this study we generate a dataset of 10 6 observations, so that the estimate error is as small as the 0.
Currently our program makes it possible to work with parametric models with continuous mediators and continuous or binary outcomes. A package will soon be published, possibly extending the current framework to other kind of models (e. g. for categorical mediators) and including methods for sensitivity analysis. We applied our R program to validate the proposed method empirically. This simulation study shows that our method provides an unbiased estimate of the direct effect, while, as expected, estimates obtained by running simple mediation analyses one mediator at the time are biased, even in the case of independent mediators. Moreover, when mediators share an unobserved common cause, we show that our multiple analysis provide estimates of the direct effects through individual mediators that are less biased than the ones obtained from simple analyses one mediator at the time.
The different steps in this strategy can be implemented using medflex, a recently introduced R package based on the natural effect model and imputation or weighting methods  (Steen, Loeys, Moerkerke, and Vansteelandt (2017) ). An alternative approach based on linear structural equations with varying coefficients was discussed by  Imai and Yamamoto (2013)  and implemented in the mediation package. Nguyen, Osypuk, Schmidt, Glymour, and Tchetgen Tchetgen (2015)  presented a method based on the Inverse Odds Ratio Weighting (IOWR) approach introduced by Tchetgen Tchetgen (2013). This method is very flexible as it accommodates generalized linear models, quantile regression and survival models for the outcome and multiple continuous or categorical mediators, however it does not allow to estimate the indirect effect through individual mediators, but only the joint indirect effect.
We conclude this brief overview of the literature around multiple mediation by underlining that our framework deals with natural direct and indirect effects. Vansteelandt and Daniel (2017)  recently introduced so-called interventional direct and path specific indirect effects that do add up to the total effect and are identifiable even when the mediators share unmeasured common causes or the causal dependence between mediators is unknown. As an illustration of our method, we conducted a multiple mediation analysis on a real dataset from a large cohort to assess the effect of hormone replacement treatment on breast cancer risk through three non-sequential mediators, namely dense mammographic area, nondense area and body mass index. The causal effects that we have estimated and reported can be interpreted as risk differences, that is differences in percentage points.
Results A total of 263 cytb sequences were obtained, aligned, and trimmed to 663 bp, with 63 distinct haplotypes identified (GenBank accession nos. MH545766 -MH545828) showing 99 polymorphic sites yielding 81 transitions and 26 transversions. For S7, 366 sequences (corresponding to 183 individuals) with 483 bp were analysed and 85 haplotypes were identified (GenBank accession nos. MH545829 -MH545911), with 90 polymorphic sites representing 44 transitions, 34 transversions and 16 sites showed insertion/deletion variation in a total of 7 indel events with an average indel length event of 2. 857. population structure. The haplotype network for the cytb of S. cantharus revealed four distinct groups (Fig. 1 ). The first group included mainly sequences from the North Eastern Atlantic (NEAT) areas (BG, EN, BI, GL, PN, AL and CN), with one haplotype shared among 61 individuals out of 263 and including three sampled in the Mediterranean Sea (MEDS), one from MU and two from VL.
Despite efforts to gather samples from multiple areas within the Gulf of Guinea only samples from one population, Angola, could be collected. Cytb results showed haplotypic diversity values similar to the NEAT and MEDS regions and higher nucleotide diversity despite the low number of individuals analysed. Expansion time for GLGN region dates from late Pleistocene, and these populations are likely to have maintained large number of individuals during glacial cycles since this area can function as tropical refugia where the mild conditions  23  allowed to maintain a high number of breeders, justifying the high diversity found in the small number of individuals analysed. No data for the S7 could be obtained, since this DNA marker could not be amplified for the samples in this population. Nuclear DNA experience lower mutation rates than mitochondrial DNA  24  and thus slower sorting among population is expected to occur, as discussed above for NEAT and MEDS regions.
conclusion The obtained results reveal a large scale structure for S. cantharus populations. The Cape Verde and Angola regions revealed two distinct clades and the Northeast Atlantic and the Mediterranean populations showed a clear separation but with incomplete lineage sorting from a common ancestor revealed by the nuclear marker. Similar diversity was estimated for all populations analysed with exception of Cape Verde. The shallow diversity, low Ne, and seemingly isolated nature may compromise the resilience of the Cape Verde population to increasing fishing pressure and environmental changes and this should be taken in account and considered for future studies in the area. Despite the evidence from the available phenotypic traits data of significant differences within NEAT populations, genetic data showed no differentiation among populations for both NEAT and MEDS regions.
DNA extraction, amplification, sequencing and alignment. Genomic DNA was extracted from fin samples with the REDExtract-N-Amp kit (Sigma-Aldrich) following the manufacturer's instructions. Mitochondrial DNA cytochrome b (cytb) and nuclear DNA first intron of the S7 ribosomal protein gene (S7) were amplified by polymerase chain reaction (PCR). For cytb, amplification of the region of interest was obtained with pair of primers purposely designed by the authors, based on 9 aligned cytb complete sequences of Spondyliosoma cantharus obtained from GenBank (http://www. ncbi. nlm. nih. gov/genbank) (accession numbers: DQ198007, EU036508/9, EF439602, EF439237, EF427607/8 and EU224083/4): BRBcytbF (forward) 5′-GCTGACTCATCCGAAATCTT-3′ and BRBcytbR (reverse) 5′-ATGTAGGGGTCTTCAACTGG-3′. S7 amplifications were performed with the following pair of primers: S7RPEX1F (forward) 5′-TGGCCTCTTCCTTGGCCGTC-3′ and S7RPEX2R (reverse) 5′-AACTCGTCTGGCTTTTCGCC-3′  39.
S7 haplotypes of length-variant heterozygotes were determined using Mixed Sequence Reader  43  , and manual adjustments were made whenever necessary. Allelic states of S7 sequences were estimated using the Bayesian programme PHASE 2. 1  [44] [45] [46]. Five runs with different seeds for the random number generator and 500 iterations as burn-in, 500 main iterations and a thinning interval = 1 were performed to check for consistency across results. All runs returned consistent allele identities. population structure. The relationship between haplotypes was analysed by haplotype networks built with the software PopART 47 using a TCS network  48. For cytb network some additional GenBank sequences from Madeira (MD) and Greece (GR) were added to extend the sampling area (GenBank accession nos. EF439237, EU036508/9).
Diversity analysis. Diversity indices (number of haplotypes, % of private haplotypes, haplotype and nucleotide diversities, mean pairwise differences and number of polymorphic loci)  54, 55  were estimated for all populations using ARLEQUIN V3. 5  50. Graphical presentation of diversity indices was obtained using ggplot2 package 56 for R  57. Demographic analysis. Demographic history for the region structure depicted for cytb by the haplotype networks and AMOVA analyses was investigated in ARLEQUIN V3. 5 with neutrality tests, Tajima's D 58 and Fu's F S 59 , the R 2  14  was also estimated using package pegas 60 for R  57. Mismatch distribution analysis was also performed for each region and compared to the distribution expected in populations affected by sudden expansion (1 × 10 5 replicates), under the assumption of selective neutrality, in which a unimodal distribution is expected  61.
Acknowledgements Authors are very grateful to the people who helped in the sample collection from the several studied areas, particularly to  Dr. M. Angeles Blanco  ( IEO, Spain ),  Dr. Eric Durieux  ( CNRS, France ),  Dr. Jim Ellis  ( CEFAS, United Kingdom ),  Dr. Virgílio Estêvão  ( INIP, Angola ),  Dr. Angel Fernandez  ( IEO, Spain ),  Dr. Rui Freitas  ( FECM/ UniCV, Cape Verde ),  Dr. José Antonio González  ( ULPGC, Spain ),  Dr. Carme Alomar Mascaró  ( IEO, Spain ),  Dr. Sanja Matic-Skoko  ( IZOR, Croacia ),  Dr. Kelle Moreau  ( ILVO, Belgium ),  Dr. Michele Salaun  (Ifremer, France),  Dr. Carmen van-Dunem Santos  ( UAN, Angola ) and  Dr. Antonio Solla  ( IEO, Spain ). We are also grateful to  Dr. Frederico Almada ,  Dr. Sara M. Francisco  and  Dr. Sofia Seabra  for their help in sample analyses.
Interconnecting results of previous OMICs studies is of major importance for identifying novel underlying causes of male infertility. To date, information can be accessed mainly through literature search engines and raw data repositories. However, both have limited capacity in identifying relevant publications based on aggregated research results e. g. genes mentioned in images and supplements. To address this gap, we present the Male Fertility Gene Atlas (MFGA), a web tool that enables standardised representation and search of aggregated result data of scientific publications. An advanced search function is provided for querying research results based on study conditions/phenotypes, meta information and genes returning the exact tables and figures from the publications fitting the search request as well as a list of most frequently investigated genes.
As basic prerequisite, a flexible data model that can accommodate and structure a very broad range of meta information, data tables and images was designed and implemented for the system. The first version of the system is published at the URL https://mfga. uni-muenster. de and contains a set of 46 representative publications. Currently, study data for 28 different tissue types, 32 different cell types and 20 conditions is available. Also, ~5,000 distinct genes have been found to be mentioned in at least ten of the publications. As a result, the MFGA is a valuable addition to available tools for research on the epi-/genetics of male infertility. The MFGA enables a more targeted search and interpretation of OMICs data on male infertility and germ cells in the context of relevant publications.
Introduction Male infertility is a prevalent and highly heterogeneous disease for which the underlying causes can currently be identified for only about 30% of male partners in infertile couples  (Tüttelmann et al. , 2018). In the last years, a growing number of studies have been published on the genetics and epigenetics of male infertility using a broad range of genomic technologies. For an overview see  Oud et al. (2019). Often, researchers give extensive insight into their findings by providing supplementary data or access to their raw data. Theoretically, this enables clinicians and other researchers to validate those findings or interpret their own results in a broader context; in practice, however, the findability and reusability of this vast set of information is limited. To date, there is no public resource for the field of male infertility that enables clinicians and researchers alike to easily access a comprehensive overview of recent findings, although tools for other diseases have long been established, such as, for example, the Gene ATLAS  (Canela-Xandri et al.
Requirements engineering The MFGA is designed to support researchers and clinicians in the field of male infertility in finding and evaluating the analysis results of relevant publications. To ensure that the MFGA Male Fertility Gene Atlas (MFGA) 5 offers a relevant scope and focusses on the central needs and interests of the targeted user group, an extensive requirements engineering process had to be employed. This process aimed at defining the most relevant parts of the system design; in case of the MFGA, this means answering the following five questions: 1. What kind of data should be available? 2. How should the database be maintained and kept up to date? 3. Who should be authorised to access the data on the website? 4. What interface should be provided to access the data? 5. How should the data be visualised? Since user-centric design is proven to be a critical factor of success for software projects  (Maguire and Bevan, 2002) , the requirements were acquired in close cooperation with a large group of prospective users of the MFGA.
These researchers and clinicians are part of the Münster-based clinical research unit (CRU326) (http://www. male-germ-cells. de) and provided input on a broad range of aspects of male infertility, germ cell development as well as sperm morphology, motility, and function. The development of the MFGA is based on the software development and requirements engineering paradigm Rapid Prototyping, which focusses on developing early testing versions of the product to enable informed user feedback and iterative improvements  (Budde et al. , 1992; Gordon and Bieman, 1995). The requirements analysis was based on a set of 39 representative and relevant publications/data sets that the MFGA should be able to host, provided by members of the CRU326. The publications were highly heterogeneous and covered a broad range of methodologies, from GWA studies to targeted genotyping, microarray, bulk or single-cell RNA sequencing as well as methylation.
Current state of requirements The requirements regarding the kind of data that should be available in the MFGA can be summarised in two points: First, the MFGA should host data representing the analysis results of a comprehensive set of publications on male infertility. Generally speaking, the MFGA needs to be able to deal with publications of arbitrary form and content. To account for the extremely high degree of heterogeneity between publications, a very flexible, modular data model is required. However, to enable the MFGA's central service of offering selective data retrieval operations, it is crucial to identify, standardise and tag recurrent relevant information items and to curate them manually for each publication. Whenever applicable, standardisation should be based on ontologies as provided by OBO Foundry  (Smith et al.
, 2007). Second, the MFGA should provide complementary data from external databases to increase the comprehensibility of information, e. g. , to explain the background of gene names and provide links to more details. However, the MFGA is not supposed to contain any raw data; instead the system should link to the corresponding repositories if provided by a publication. Also, the MFGA is not planned for processing raw data by itself; the system is only intended to show aggregated result data provided by publications. The database of the MFGA should be fully public and accessible to all researchers and clinicians in the field of male infertility. Maintenance and curation of selected publications should be provided by the development team. In order to keep the database up to date in a structured way, a recurring process for preparing new relevant publications will be implemented (Fig.
1 ). It comprises three main steps: selection of relevant publications, manual curation according to the requirements of the MFGA database and data upload. Prospectively, it should be triggered once per month and combine the proposed publications of four different sources into one prioritised list. The four information sources are (1) the list of not yet processed publications from the previous month, (2) newly published manuscripts from the CRU326, (3) other relevant new publications identified by the MFGA team, and, as an additional unbiased source, (4) recent publications queried from PubMed (query: "(male OR men) AND (fertility OR infertility) AND (gene OR genetic) AND ("YYYY/MM/01"[Date -All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted February 13, 2020.
IT architecture The MFGA has been implemented as a modern Java web application that is securely hosted on a server at the University of Münster, Germany, and can be accessed freely via the URL https://mfga. uni-muenster. de. Its architecture (Suppl. Fig. S1 ) is based on the specific requirements, identified during requirements engineering and explained in detail in the supplement. For a good user experience, the graphical user interface utilises the Bootstrap library which enables a smooth adjustment of the application to different browsers and devices  (Otto et al. , 2020)  and familiar design elements. On the server side, Spring Security framework is employed to provide secure authentication processes and manage data access privileges (Pivotal Software Inc. , 2019).
Data model The specific relational data model of the MFGA as well as the preparation process for publication data are a direct result of the requirements analysis and were developed based on the initial set of relevant publications. They are the main prerequisite for enabling complex search queries, aggregation functions and meta analyses on the MFGA's database. For representing the highly heterogeneous publications, a flexible and thus modular data model was designed (Suppl. Fig. S2 ). It is based on information items that were identified to be preserved over large subsets of publications on male infertility and standardised to seven classes of information items: publication meta information, data set meta information, processed tissue type, processed cell type, cohort's condition, image and table (Tab.
1). For each publication, they can freely be combined in any quantity including zero. In order to prevent inconsistencies in the database caused by misspelling or synonymous terms, structured data entry with drop-down lists and ontologies is implemented. Currently, the MFGA employs BRENDA Tissue Ontology (BTO)  (Gremse et al. , 2011)  for tissue type definition, Cell Ontology (CL)  (Diehl et al. , 2016)  for cell types and Human Phenotype Ontology (HPO)  (Köhler et al. , 2019)  for conditions. Further technical specification, especially representation and annotation of data tables and images, is explained in the extended methods (see supplement).
Functionality The MFGA provides a web interface for fast, simple, and straightforward access to the results of publications on the epi-/genetics of male infertility and germ cells. For this purpose, an advanced search form is provided on the Search tab of the atlas (Suppl. Fig. S3 ), enabling the specification of search requests for relevant data sets. This function supports search terms from seven categories: OMICS, data type (e. g. , single-cell RNA sequencing or targeted sequencing), condition, species, cell type, tissue type and gene/ID. For each of the first six categories, one can choose from a list of search terms equivalent to the information items stored in the database. Condition, cell type and tissue type are based on appropriate ontologies: HPO  (Köhler et al. , 2019) , CL  (Diehl et al.
, 2016)  and BTO  (Gremse et al. , 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. Also, there 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. 3  shows an extract of the output of a simple search request for data sets in the MFGA database containing information on the gene.
g. ,  Hammoud et al. (2015)  in Fig. 3. 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. 5 ). The overlay bundles textual information from RefSeq (O'  Leary et al. , 2016)  and HGNC  (Yates et al. , 2017; HGNC Database, 2018). Additionally, a link to the corresponding GeneCards page  (Stelzer et al. , 2016)  is provided, as well as a shortcut to the MFGA search for that gene. Data from the GTEx project  (Lonsdale et al. , 2013; The Broad Institute of MIT and Harvard, 2019)  is employed to show the gene's expression in different tissues scaled by the largest median.
Discussion The MFGA provides a public platform to support researchers and clinicians in obtaining an overview of the recent findings in the field of male infertility and germ cells. The web-based tool includes recent libraries and methods for an improved user experience and straightforward usability. In order to enable an advanced search for relevant publications, a relational data model has been developed and implemented for knowledge representation. It records the recurrent information items in a structured and consistent way and can accommodate arbitrary publications from the field of male infertility, regardless of the kind of data analysis and technology. The search form enables a broad range of simple to very complex search queries such as "What is known about the gene STAG3 in the context of male infertility?", "Which genes have been identified to be associated with azoospermia?" or "Which genes have been found to be expressed in Sertoli cells of human testis tissue using single cell RNA sequencing?" (Suppl.