dataesr/scientific-paragraphs-categorization huggingface dataset:
The presented approach is first used to identify 10 different types of cancer, and then, in a second experiment, the same technique is applied to separate tumor subtypes in breast cancer. The methodology not only is proven to be effective but also compares favorably to current state-of-the-art techniques. While a similar technique was presented in  [21, 22] , the approach we propose features several improvements and important innovations that set it apart from previous contributions: (i) previous works did not select for circulating miRNAs, and thus, resulting signatures could not be easily measured in clinical practice; (ii) previous techniques needed extra parameters to be defined by the user (for example, a desired number of features), while the novel approach we propose does not require users to arbitrarily set values for thresholds; and (iii) finally, the amount of data used in the experimental verification greatly increased, getting a total of 16 gene expression omnibus (GEO) datasets.
Materials and Methods First, we compiled a list of circulating miRNAs (mature sequence) based on 5 reviews of circulating miRNAs from cancer studies  [1, 2, [23] [24] [25]. Next, from this list, we consider only the miRNAs that appear in blood, serum, urine, plasma and saliva. To narrow it further, we focus on the miRNAs that can be detected by Affymetrix platforms Affy-1 (GPL8786), Affy-2 (GPL14613) and Affy-3 (GPL16384). Our choice of restricting to datasets from Affymetrix platforms GPL8786, GPL14613 and GPL16384 has the aim of avoiding the known issue of miRNA expression levels being platformand technology-dependent  [26] [27] [28]. After this selection, a total of 253 miRNAs remain. The detailed list is included in Appendix A.
Feature Selection As our objective is to select the most meaningful miRNAs to correctly classify the cancer types, we used a recursive ensemble feature selection algorithm where features in our datasets are expression values of different miRNAs. The idea behind recursive feature selection is to score each feature depending on its usefulness for the classification process, resorting to a classifier. Features with the lowest scores are then removed, and the process is iterated with the remaining features until the overall classification accuracy drops below a given threshold or when a user-defined number of features is reached. While this technique is effective, it still relies on a classification algorithm to score the features, and a single algorithm might be affected by bias when it assigns scores to the features.
Cancer Type Classification From the gene expression omnibus (GEO) repository  [33] , we selected 16 datasets for 10 different types of cancer based on clinical studies: Breast (BRCA), esophageal (ESCA), head and neck squamous cell (HNSC), liver hepatocellular (LIHC), prostate (PRCA), glioblastoma (GBM), colorectal (CRC), non-small-cell lung (NSCLC), gastric (GC) and ovarian (OVC), as summarized in Table  1. For each dataset, we downloaded the raw data and processed it using the function Affyrma() from the Matlab bioinformatics toolbox TM. This function processes the probe intensity values using RMA background adjustment, quantile normalization and summarizing procedures and then outputs expression (nondimensional). The resulting aggregated dataset for our multi-class classification problem presents 845 samples, 253 features and 10 different tumor classes.
Next, we applied Z-score normalization on the dataset to then run the feature selection algorithm in a 10-fold stratified cross-validation scheme. Then, we compared our results against two current state-of-the-art feature selection methodologies: a homogeneous ensemble classifier exploiting variations of SVC  [29]  and a feature selection tool based on genetic algorithms, called GALGO  [43]. Since each algorithm contains stochastic elements, we run each algorithm 10 times and keep the set of features with the best average accuracy. The homogeneous ensemble uses several runs of SVC to rank the features by weight and reduces the number of features by a given percentage at each step. In this case, we used the same parameters as for Algorithm 1: 20% step reduction and 90% accuracy as stop parameters.
Triple-Negative Breast Cancer Classification Cancer tumors are divided into tumor subtypes, which can be treated by different strategies depending on their classification. From the available data in the GEO repository, we were able to compile a dataset to assess the possibility of classifying tumor subtypes (luminal A, luminal B, HER2-enriched, triple-negative and normal  [47] ) in breast cancer (BRCA) using circulating miRNAs. Then, we selected datasets GSE86277, GSE86278, GSE86281 and GSE46823, which are BRCA studies with subtype information. From the BRCA subtypes, triple-negative has the worst prognosis, as it is resistant to hormone therapies  [48]. For this reason, we set the labels of the resulting dataset to separate the triple-negative subtype from the rest. Although making an analysis of all the subtypes would have been more interesting, the unbalance in the subtype samples found in the original data makes it impossible; thus, more precisely, the resulting dataset has 139 triple-negative samples and only 44 from the rest of the subtypes, for a total of 183 samples, 253 features and 2 classes (triple-negative/Other).
Next, we applied the function Affyrma from the Matlab bioinformatics toolbox TM. Then, we applied Z-score normalization on the dataset to run the feature selection algorithm in a 10-fold stratified cross-validation scheme. As in the previous experiment, the feature selection algorithm was set to identify the smallest miRNA subset sufficient to obtain a 90% accuracy. In addition, we compared our results with the 31-miRNA signature proposed by Romero et al. [35]  to separate Triple-Negative Breast Cancer (TNBC) from other subtypes of BRCA using miRNA-mRNA integrative analysis in TNBC tumors based on the differential expressed transcripts. It is important to take into consideration that this 31-miRNA list considers noncirculating miRNAs that are not included in our method and could potentially access more information.
Numerical Validation To further validate our methodology, we split the dataset described in Section 2. 2 into a training (90%) and a validation (10%) subsets. Then, we ran 10 instances of the recursive ensemble feature selection algorithm with 90% of the data in a 10-fold cross-validation, which yields the curve in Figure  5. Next, we selected the smallest signature that provided an accuracy of 90% or above, having as a result hsa-let-7a, hsa-mir-122, hsa-mir-200c, hsa-mir-708 and hsa-mir-23b, the same miRNAs identified in the previous experiment described in Section 3. 1. Then, we tested this signature on the 10% subset, comparing against signatures identified by other approaches: homogeneous ensemble feature selection, GALGO, K-best univariate feature selection (using f-score) and 3 random selected subsets.
Pathway Analysis Next, using the 5 candidate miRNAs identified by the proposed approach to separate the tumor type, we ran miRNet to identify the targeted genes, obtaining a total of 1732 genes. After we apply a 0. 05 betweenness filter, we reduced the list to 156 genes. From these genes, BCL2, CCNG1, COX1, TUBB2A, CELF1 and FOXJ3 are targeted by at least 3 of the 5 miRNAs (Figure  9 ). Finally, using the function explorer of miRNet, we performed a functional enrichment analysis with a hypergeometric test of the genes from the KEGG database and GO:BP. In Tables  6  and 7 , we show the results of the top 10 functional enrichment analyses for KEGG and GO:BP respectively. The first result in KEGG is the P53 signaling pathway. The P53 protein is a tumor suppressor protein, and it is involved in several anticancer mechanisms  [50].
Triple-Negative Breast Cancer Classification We ran the heterogeneous ensemble algorithm 10 times, identifying 5 meaningful miRNA features for separating triple-negative BRCA from the other subtypes (Figure  10 ). The resulting miRNAs are hsa-miR-378*, hsa-miR-221, hsa-miR-342-3p, hsa-miR-630 and hsa-miR-145. The corresponding expression levels for the identified miRNAs in TNBC and non-TNBC are reported in Figure  11. Next, we compared the accuracy between classifiers using all 253 miRNAs in the dataset, our 5-miRNA signature, and the 31-miRNA signature proposed by Romero et al. for distinguishing TNBC from other cancers (Table  8 ). From the results, our algorithm outperforms the 31-miRNA signature. In addition, the area under the curve (AUC) of the results (Figure  12 ) calculated with the gradient boosting classifier is above 90%.
This is considered outstanding results following the guidelines in  [52, 53]  for clinical use of algorithmic methodologies. Finally, the results of miRNet found 1294 genes targeted by the 5 miRNAs, with 79 having at least 2 miRNAs in common. From those 79, metastasis gene metadherin-positive (MTDH) is targeted by 4 miRNAs, while type 1 insulin-like growth factor receptor-positive (IGF1R) and cyclin-dependent kinase 6-positive (CDK6) are targeted by 3; see Figure  13. From the enrichment analysis, the most important functional pathway in the KEGG database (Table  9 ) is the p53 signaling pathway (the same identified in the previous experiments for separating cancer types), and in GO:BP (Table  10 ), the negative regulation of cell proliferation, with 12 of the 79 genes followed by regulation of cell proliferation and just cell proliferation.
The result with the smallest p-value from the enrichment analysis in the KEGG dataset identified a strong relationship between the P53 signaling pathway and hsa-miR-122, hsa-let-7a, hsa-miR-23b, hsa-miR-708 and hsa-miR-200c. P53 is an important tumor suppressor that regulates the expression of many genes and is one of the most common mutated genes in cancer. Many miRNAs work as direct and indirect mediators of the P53 activity and the components of its pathway  [78, 79]. Moreover, the normal function of this tumor suppressor helps the maturation of some miRNAs with growth-suppressing function  [80]. On the other hand, the first result in the enrichment analysis in the GO:BP dataset was cellular stress response. In normal cells, there is a balance between the activation of survival and cell death pathways, according to the type and duration of stress  [81].
1 Divide dataset in N folds, Select K classifiers, 2 while Accuracy > 0. 90 do Choose the number of features in the signature S; 3 for each fold n of N do Learn normalization on all folds minus n ; Normalize all folds on each of the F features ; 4 for each classifier k of K do Train classifier k n on all folds minus n, using all features; Test classifier k n on fold n; Obtain sorted list l kn of features from k n ; Assign weight w f nk to each f of the F features; 5 for each feature f of F do if f is among the top S features in l kn then w f nk = 1 else w f nk = 0 6 N c = N • K; 7 for each miRNA feature f do N t = ∑ N n ∑ K k w f nk ; s f = N t /N c ; 8 Select S-feature signature, from features with highest s f ; 9 for each fold n of N do 10 for each classifier k of K do Train classifier k n on all folds minus n, using signature; Test classifier k n on fold n; 11 Compare performance of classifiers using all features and signature; 12 S = 0.
Introduction Increases in facility deliveries in sub-Saharan Africa have not yielded expected declines in maternal mortality, raising concerns about the quality of care provided in facilities. The readiness of facilities at different health system levels to provide both emergency obstetric and newborn care (EmONC) as well as referral is unknown. We describe this combined readiness by facility level and region in Senegal. Methods For this cross-sectional study, we used data from nine Demographic and Health Surveys between 1992 and 2017 in Senegal to describe trends in location of births over time. We used data from the 2017 Service Provision Assessment to describe EmONC and emergency referral readiness across facility levels in the public system, where 94% of facility births occur. A national global positioning system facility census was used to map access from lowerlevel facilities to the nearest facility performing caesareans.
data sources We used data from nine DHS conducted in Senegal between 1992-1993 and 2017. The DHS are nationally representative, standardised surveys of women of reproductive age, collecting information on births and childbirth care. 37 The four annual continuous DHS between 2012-2013 and 2016 had smaller sample sizes 38 : we grouped the 2012-2013 and 2014 DHS, and the 2015 and 2016 DHS, to increase statistical power and generate regionally representative estimates. To assess facility EmONC and referral readiness in hospitals, health centres, health posts and health huts, we used data on infrastructure, staffing and equipment for childbirth collected in the most recent SPA, a nationally representative survey of facilities of all sectors (public, private not-for-profit, private for-profit and private religious).
Analyses Detailed indicator definitions are included in online supplementary appendix 1. For each DHS, we estimated the percentage (and 95% CI) of births by place and birth attendant among all live births in the 2 years prior to the survey, based on women's self-report. We also calculated the population caesarean section rate as a proportion of live births in the 2 years before the survey, as an indicator of CEmONC access. Our estimates are based on 31 108 live births to 29 938 women across the seven time points. We further tested differences in place of birth between the earliest (1992-1993) and most recent (2017) surveys using X 2 tests, and used United Nations Population Department data on population size and birth rate to estimate the average absolute number of births per year in the 2-year recall period of each time point.
DHS, Demographic and Health Survey. due to data quality concerns. Facilities were considered to provide CEmONC-1 if they provided BEmONC-1 and both CEmONC signal functions (caesarean sections and blood transfusion). We calculated the median number of each provider cadre employed and total SBAs (doctors, midwives and nurses) by facility level and urban/rural location within the public sector. Facilities were considered to have adequate referral readiness if they had a vehicle available for referrals, or had a telephone available and reported access to a vehicle elsewhere. We estimated the percentage of public sector births occurring in facilities with different levels of EmONC or referral readiness by multiplying the percentage of facilities in each readiness category in each facility level by the corresponding percentage of women reporting to deliver in each health facility level in the 2017 DHS.
We performed this calculation for all births in Senegal and by region. All DHS and SPA analyses took into account survey weights, as well as clustering and stratification where appropriate. We created maps showing the locations of public facilities with and without caesarean capacity using R. 48  Driving time to the nearest caesarean facility was estimated by calculating the driving time from facilities without caesarean capacity to each facility with caesarean capacity, and selecting the one with the shortest driving time. We used the OpenStreetMap-Based Routing Service (OSRM) package in R,  49 50  which allows for the computation of travel time between points based on assumptions relating to vehicle and road characteristics, such as average speed. 51 Eighteen facilities had long estimated driving times (>3 hours) most often due to lack of proximity to marked roads on OSRM; we replaced these with driving times reported by district medical officers in relevant districts (see online supplementary appendix 2, table  S1 ).
Acknowledgements We thank the women and health facility staff who participated in the DHS and SPA surveys, and the  DHS Programme  for making these data available. We are also grateful to the  Ministry of Health and Social Action  for helping us compile the list of facilities performing caesareans, to  MEASURE Evaluation  for sharing data from the  Urban Reproductive Health Initiative evaluation , and to the District Medical Officers who verified caesarean capacity and driving times to referral facilities in their districts. We are thankful to  Siri Suh  for the inspiration for the title of this article (see reference number  74 ). Contributors FLC, LB, MM-A and PS conceptualised the study, and designed the analyses with additional input from EHD and CR. KW, ER, LB and FLC prepared the databases.
FLC, AF, EHD and AD compiled the list of facilities performing caesareans, and EHD and AF vetted data sources. FLC and KW conducted the data analysis, all authors including ASM contributed to the interpretation of results. FLC drafted the full manuscript, all authors edited the manuscript. All authors approved the final version of the manuscript. Funding Some authors of this study were supported by funding from  MSD , through its  MSD for Mothers programme  (https:// merckformothers. com). Funding was used for general financial support, including staff salaries, travel and overhead. MSD had no role in the design, collection, analysis and interpretation of data, in writing of the manuscript, or in the decision to submit the manuscript for publication. MSD for Mothers is an initiative of  Merck & Co.
The frontier is sometimes hard to establish but the intention to deceive is the key aspect  (Bar-Ilan and Halevi 2018; Fanelli 2009). (Bar-Ilan and Halevi 2018) coined the term "scientific distortion" to describe a category of articles including intentional and unintentional errors, both being considered hurdles for the advancement of science. Although  (Ioannidis 2005)  claims that "most research findings are false for most research designs and most fields", retracted and corrected papers are very scarce. Data from the RetractionWatch 2 database show that the absolute number of annual retractions has grown over the past decade (from 100 annual cases before 2000 to nearly 1,000 in 2014) but even if the rate doubled from 2003 to 2009, it has remained stable since 2012 and only 4 of every 10,000 papers are now retracted  (Brainard 2018).
Although different from the ordinary process of self-correction of science (when new knowledge updates the old), retractions and errata are also a way to correct the scholarly record by changing the status of previously published works (Dougherty 2019). 1 Formalized procedure of debate dating from the Middle Ages. 2 RetractionWatch is a blog launched in 2010 by two science journalists and is dedicated to the tracking of retracted papers. In 2018, they released an online database of all the cases they have recorded: of all the cases they have recorded http://retractiondatabase. org. The identification of retracted and corrected papers is difficult and despite COPE guidelines 3  and the Crossmark initiative 4  , publishers and bibliographic databases do not adopt any standards and are not consistent in their reporting of cases.
Methods and data We introduce the idea to consider comments left on PubPeer as contradicting citations. Indeed, commenting on Pubpeer is the same principle as making a citation, with the consequence of creating a link between the comment and the target publication. However, what radically differentiates a comment on PubPeer from a citation in an article is that the former can be anonymous. In order to evaluate negative citations in articles and comments on PubPeer as mechanisms for correcting science, we have built up a corpus of articles associated with retraction or correction notices. It is a way of having a material that has already been officially contested and corrected. From a methodological point of view, this saves us from having to individuate false science, and, therefore, it allows us to build a corpus of contested articles in an objective manner.
We used Scopus and the Web of Science to constitute a corpus of 72,069 articles (metadata), focusing on a particular discipline, on some particular journals and even individuals. We used 3 different criteria (discipline, journal, author) and constituted 7 datasets for comparison (Table  2 ): -Discipline: we chose the Engineering field which seems to have not been the focus of as much attention as biomedical sciences. We retrieved the ISSN of all journals classified in the Engineering subject area according to the ASJC classification scheme 5  and made two queries to retrieve the corresponding articles from the Web of Science and Scopus databases, limiting the results to 2012-2015 publication years. -Journal: -we chose 2 journals in biomedical field, Cancer Research and Tumor Biology, which are journals with high rates of retractions according to the RetractionWatch database, -we chose a multidisciplinary high-impact journal, Science, because some studies hypothesized that articles in high-profile journals are more prone to retractions  (Fang and Casadevall 2011; Furman et al.
2012 ) and also because the most prestigious journals publish the least reliable science according to  (Brembs 2018 ). We used their ISSN to retrieve articles from the Web of Science and Scopus databases, limiting the results to 2012-2015 publication years. -Author: with 3 high-profile researchers whose substantial part of their work has been officially retracted or corrected: Sarkar, Schön and Voinnet. We made 3 queries and retrieved all published articles of these authors with no publication date limit. The corpus compilation is long and painstaking because retractions and errata are uncommon. It is therefore, necessary to have a very important initial database to obtain a sufficiently representative corpus. And they are also difficult to identify and locate online  (Hesselmann et al. 2017; Poworoznek 2003 ; Teixeira da Silva and Bornemann-Cimenti 2017) and in bibliographic databases.
That is the reason why we used both Scopus and Web of Science to identify retraction notices and cross-checked with RetractionWatch database. To be as exhaustive as possible, we retrieved the erratum notices for the corpus we delineated, extracted the DOI or the article title from each notice title (see example), and then tagged the original articles. When the same article is associated to one or several erratum notices and is also retracted, we tagged it as retracted. Example of a title erratum notice: "Erratum: Recruitment of RNA polymerase II by the pioneer transcription factor PHA4 (Science (2015) 350 (aad5928) DOI: 10. 1126/science. aad5928)" Example of a title retraction notice: "Retraction notice to "Preparation and characterization of vertically aligned ZnO microrods on glass substrate" [Mater.
Lett. 97 (2013) 71-73 ]" Then we used data from two websites to retrieve two kinds of critiques and enrich the corpus at article level: -Scite 6  to retrieve the number of contradicting citations, -PubPeer 7  to retrieve the number of comments. Scite was launched in 2019. According to their founders, it is a platform that allows finding if a scientific article has been supported, contradicted, or mentioned by subsequent studies. Scite automatically extracts citations from papers and classifies them using deep learning models and a network of experts. PubPeer was founded in 2012, it is a commenting website for centralized post-publication discussions; some of them have led to retractions or corrections. We assumed that the number of contradicting citations retrieved by Scite and the number of comments posted on PubPeer are indicators of controversy and attempts to make a correction.