dataesr/scientific-paragraphs-categorization huggingface dataset:
Results from pilot study We piloted the new terminology for inheritance, allelic requirement, and disease mechanism using the ACMG SF v2. 0, 59 genes (66 gene-disease pairs). 23  We generated curations for the purpose of piloting the terminology, and these curations are not intended for application to clinical practice. The list includes genes related to inherited cardiovascular disease, cancer phenotypes, and inborn errors of metabolism, and the gene-disease pairs are characterized by a range of inheritance types and different disease mechanisms. All 66 gene-disease pairs were successfully described using the new terminology (github. com/Imperi-alCardioGenetics/ACMGSF_pilot_curation/). Note that these are informal curations for the purpose of this study only; for official curations, please see the ClinGen website https://clinicalgenome.
This highlights the importance of standardized guidance setting out clear criteria for genedisease association and variant pathogenicity applied in formal curation efforts. 5, 34  Standardizing curation methods is beyond the scope of this project that instead focuses on providing a terminology framework for future work to be built upon. Our ontology has since been successfully applied for the curation of inherited cardiovascular condition gene-disease pairs, presented as a structured dataset and available publicly as the CardiacG2P. 35  For some gene-disease pairs, there are well established, often recurrent, pathogenic variants that are the only pathogenic variant in that gene, or that are rare and atypical causes of disease. For example, almost all evidence for KCNQ1 as a cause of short QT syndrome is derived from a single missense variant (NP_000209.
P. N. R. was supported by the  National Human Genome Research Institute  grant  U24HG011449. David Sant  was funded by  NIH :  T15LM007124. PanelApp Australia is funded by  Australian Genomics  ( NHMRC  grants  GNT1113531  and  GNT2000001 ). N. W. is supported by a  Sir Henry Dale Fellowship  jointly funded by the  Wellcome Trust  and the  Royal Society  ( 220134/Z/20/Z ) and the  Rosetrees Trust  ( PGL19-2/10025 ). Open Targets is supported by  Open Targets. The work performed by authors at  European Molecular Biology Laboratory-European Bioinformatics Institute  for the  Transforming Genomic Medicine Initiative  project was supported by the  Wellcome Trust  ( WT200990/Z/16/Z ). The views expressed in this work are those of the authors and not necessarily those of the funders.
Representative immunoblot images were chosen following at least 3 technical and 3 biological replicates. Statistics. All data are presented as the mean ± SD unless otherwise noted, and figure legends contain details about sample size. All analyses were carried out using GraphPad Prism 9 software by unpaired, 2-tailed Welch's t test (2 groups), ordinary 1-way ANOVA with Tukey's ad hoc test (more than 2 groups), or 2-way ANOVA with Tukey's ad hoc test for multiple comparisons unless noted differently. P ≤ 0. 05 was deemed statistically significant. Study approval. All experimental procedures involving animals in this study were approved by and conducted in strict accordance with relevant guidelines and regulations by the Institutional Animal Care and Use Committee at University of Florida and West Virginia University, the Association for Research in Vision and Ophthalmology Statement for the Use of Animals in Ophthalmic and Vision Research, and NIH regulations.
Acknowledgments This work was supported by  NIH  ( R01 EY030056 ,  EY030043 ),  West Virginia University  startup fund,  NIH National Institute of General Medical Sciences   P20GM144230  ( Visual Sciences Center of Biomedical Research Excellence) ,  BCM Families Foundation , and the  West Virginia Lions and Lions Club International Foundation. Address correspondence to:  Wen-Tao Deng ,  Department of Ophthalmology and Visual Sciences, Department of Biochemistry, West Virginia University , 108  Biomedical Road Room 222, Morgantown , West Virginia 26506, USA. Phone: 304. 293. 4622; Email: wen. deng@hsc. wvu. edu.
Dose control The number of ions used for each irradiation was controlled by the definition of an opening time of the microbeam on each target, as described previously  [44]. This opening time was calculated by performing periodical ion counting rate measurements with a PIPS (Passivated Implanted Planar Silicon) detector (PD50-12-100AM, Mirion Technologies, France) placed in front of the beam. A mean counting rate can then be obtained and used to define the beam opening time for a given number of ions. In addition, to ensure that the beam did not undergo major fluctuations during the irradiation, we measured the mean counting rate between each sample, and we observed no significant variation of the beam between two consecutive controls, as previously demonstrated in  [44]. During the experimental campaigns, for an opening time of 100 ms, we obtained a typical mean number of ions N = 412, with a standard deviation σ = 21 (data not shown), which is close to the expected value from a Poisson law, where σ = √N.
Irradiation parameters On the inverted wide-field epifluorescence microscope (AxioOb-server™ Z1, Carl Zeiss Microscopy GmbH, Germany) equipped with a 37 • C heating chamber at the end of the MIRCOM beamline, we selected relevant nuclei of GFP-expressing cells with a 20X objective (Zeiss LD Plan-NEOFLUAR 20x/0. 4 Korr) through automatic shape recognition by using ImageJ libraries  [51]  included in a custom-made control software called "MicroIrradiation Application" (MIA)"  [44]. We targeted the cells at the center of their nucleus, and we irradiated them with 4 MeV.
Image processing and kinetics analyses In order to quantify the fluorescence re-localization of GFP-tagged proteins observed with time-lapse imaging, we processed all images using the Fiji software  [52]. We selected and followed regions of interest (ROI) manually. We measured the mean intensity of ROIs in every image and plotted them against time. We corrected the resulting data for non-specific fluorescence bleaching and normalized them for the fluorescence intensity measured before irradiation. We analyzed more than 100 spots for each condition from at least 2 independent experiments and performed statistical analyses with Mann-Whitney test.
Image acquisition and analysis We acquired serial z-stack images either on an upright confocal LSM780NLO microscope (Carl Zeiss MicroImaging GmbH, Germany), equipped with a Plan-Apochromat 63x/1. 4 NA oil M27 objective and piloted with the Zen Black 2011 SP4 software (Carl Zeiss MicroImaging GmbH, Germany) or on a spinning disk microscope (GATACA, W1) using a PlanApo 60x/NA1,4 oil (Nikon, Europe B. V) for visualization of TG and 8-oxoG. We analyzed serial z-stack images by using ImageJ or Fiji softwares  [51, 52]. First, we identified as positive cells those showing a specific staining for the proteins of interest, corresponding to the irradiation pattern drawn, a cross of 5 spots (Fig. 1A ). Pearson correlation coefficients were calculated with the JacoP plugin (https://imagej. net/plugins/jacop)  [54].
At the indicated time-points post-irradiation we performed a sum or maximum intensity projection of Z-stacks, selected ROI manually and measured the mean fluorescence intensity in order to quantify protein recruitment or lesion induction inside each individual spot. We normalized the obtained data by the nuclear background fluorescence in an un-irradiated area. For some images, we also carried out fluorescence plot profile analysis on the image slice with the highest total intensity within cell nuclei to show the localization of DNA repair markers in relation to each other at the same damage spot. The presented data were normalized for the maximum signal intensity. In the case of OGG1 wt and mutated GFP-tagged constructs, at the indicated time-point post-irradiation, in order to quantify the intensities of GFP inside each individual spot, we performed a sum intensity projection of Z-stacks, selected ROI manually and measured the mean intensity of ROIs in every image for GFP signal and Alexa 594 signal corresponding to KU70/80.
e. , γH2AX, KU70/80 or NTH1, red lines). The presented data were normalized to the maximum of the intensity of each signal. (D) Levels of GFP-tagged proteins at irradiation sites. We measured the fluorescent signals at irradiated spots of cells from (A, B). The data shown in scatter dot plots represent the measurements of fluorescence intensities at IR spots normalized by the nuclear background fluorescence. The number of spots and cells (in brackets) analyzed from at least three independent experiments are indicated in the graph. To note, the number of spots for XRCC1-GFP and NTH1-GFP results from pooling data independently of the associated immunostaining allowing to localize damaged areas (i. e. , γH2AX, KU70/80). Bold bars represent the mean, error bars the standard error of the mean (SEM).
Conclusion In conclusion, our data shows for the first time that localized proton irradiation induces the oxidatively damaged bases, 7,8-dihydro-8-oxoguanine and thymine glycol within the cell nucleus. Consequently, the induction of these oxidative DNA base modifications 8-oxoG and TG leads to the fast recruitment of the DNA glycosylases OGG1 and NTH1 that respectively initiate the repair of those lesions by the BER pathway. Finally, the release of OGG1 and NTH1 from the damaged DNA is dependent on their ability to excise the oxidative base lesion. Expression of NTH1-GFP proteins. Western-Blots performed from protein extracts were analyzed using an antibody against NTH1 allowing to visualize both the endogenous NTH1 (indicated with an *) and the overexpressed NTH1-GFP fusion proteins. H3 was used as a loading control.
Acknowledgements The authors thank the  PSE-SANTE/SDOS/LMDN  team of the  IRSN  for their excellent technical expertise on the  MIRCOM facility ,  Valérie Buard  for technical support on the IRSN confocal microscopy facility, and  Marta Rodriguez-Alvarez  for participation in the validation of the NTH1 knockout cells. We are also grateful to  Dr. Stéphane Illiano  for his critical reading of the manuscript, and to  Dr Gaëtan Gruel  for his helpful suggestions.
Funding Source This work was supported by " IRSN-CEA Radiobiologie" program   2019. A. C. and A. K. received founding from the  ANR-DFG  financial agencies (i. e. Agence nationale de la recherche (ANR ,  French National Research Agency ) [ ANR-18-CE92-0013 "TG-TOX " to A. C. ] and the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  [grant number  KH263/4  to AK]); the  Deutsche Akademische Austausch Dienst (DAAD ,  German Academic Exchange Service)  [grant number  57388935 ] to A. K. and the  Campus France  though the collaborative program " Partenariats Hubert Curien (PHC)-PROCOPE " grant to A. C. E. R. was funded by  ANR-18-CE92-0013 "TG-TOX ". K. L. was funded by  IRSN Ph. D program.
Background: In recent years, human microbiome studies have received increasing attention as this field is considered a potential source for clinical applications. With the advancements in omics technologies and AI, research focused on the discovery for potential biomarkers in the human microbime using machine learning tools has produced positive outcomes. Despite the promising results, several issues can still be found in these studies such as datasets with small number of samples, inconsistent results, lack of uniform processing and methodologies, and other additional factors lead to lack of reproducibility in biomedical research. In this work, we propose a methodology that combines the DADA2 pipeline for 16s rRNA sequences processing and the Recursive Ensemble Feature Selection (REFS) in multiple datasets to increase reproducibility and obtain robust and reliable results in biomedical research.
Results: Three experiments were performed analysing microbiome data from patients/cases in Inflammatory Bowel Disease (IBD), Autism Spectrum Disorder (ASD), and Type 2 Diabetes (T2D). In each experiment, we found a biomarker signature in one dataset and applied to 2 other as further validation. The effectiveness of the proposed methodology was compared with other feature selection methods such as K-Best with F-score and random selection as a base line. The Area Under the Curve (AUC) was employed as a measure of diagnostic accuracy and used as a metric for comparing the results of the proposed methodology with other feature selection methods.
Background In literature, human microbiome studies have received increasing attention. This domain is considered a potential source for the diagnosis and development of new medical treatments  [1]. Several studies aim to identify variations in the gut microbiome and potential biomarkers to diagnose diseases and disorders such as inflammatory bowel disease (IBD)  [2, 3, 4, 5] , type 2 diabetes (T2D)  [6, 7, 8, 9] , autism spectrum disorder (ASD)  [10, 11, 12, 13] , and some types of cancer  [14, 15, 16, 17] , among others. Microbiome studies have also been used to develop medical treatments and to analyze the responses from patients  [18, 19, 20, 21]. Microbiome analysis consists in sequencing the gene encoding 16S ribosomal RNA (rRNA) and compare it with known bacteria sequence databases to identify bacterial members of a microbial population  [22].
Despite the promising results, several issues can still be found in these studies: • Datasets: high dimensional data with a small number of samples are common, usually because of the costs (time and money) associated with data collection from human participants. This causes machine learning models prone to overfitting and biased performance  [42]. • Inconsistent results: Most of the studies use Operational Taxonomy Units (OTUs) in their experiments, and due to the limitations and their inability to be used in independent studies  [43, 44] , may be the reason for obtaining inconsistent results  [29, 45, 46]. • Reproducibility: Several factors such as the lack of uniform processing methodologies, incomplete or erroneous descriptions of the simulations, incomplete or erroneous dataset documentation, which software version was used, incomplete documentation, or not having the code available for use are responsible for a lack of reproducibility in microbial research  [47, 45].
The main objective in this work, is to address the lack of reproducibility by providing a methodology, that considers more than one dataset, that combines a DADA2-based pipeline for 16s rRNA sequences processing and the Recursive Ensemble Feature Selection (REFS) algorithm, previously used in  [48]. This methodology also provides an approach to deal with high dimensional data with a small number of samples, inconsistent results, and the lack of uniform processing and analysis methodologies. The effectiveness of the proposed methodology was tested by comparing its results with different feature selection methods. Three experiments were performed analyzing microbiome data related to: Inflammatory Bowel Disease (IBD), Autism Spectrum Disorder (ASD), and Type 2 Diabetes (T2D). The results of these experiments provide valuable insights about the performance of the proposed methodology and its potential application in microbiome research.
Testing Phase We searched the 26 features selected by REFS in the testing datasets, the result was 22 out of 26 for PRJNA589343 and 20 out of 26 for PRJNA578223. We applied the validation module to the features found in both testing datasets. For PRJNA589343 we obtained an average AUC of 0. 748 and for PRJNA578223 we obtained an average AUC of 0. 74. Both average AUCs corresponds to a "good " diagnostic accuracy  [50]. In both cases, the classifier with the best performance was Extra Trees, Fig. 1c, d. For the comparative analysis, we searched for the 26 features selected by K-Best on each testing dataset, the result was 20 out of 26 for PRJNA589343 and 17 out of 26 for PRJNA578223. We applied the validation module to the features found in both testing datasets. The resulting average AUCs were 0.
Inflammatory Bowel Disease (IBD) Raw data processing The trimming parameters used for DADA2-based srcipt filtering process and the number of Amplicon Sequence Variants (ASVs) generated for each dataset were: • PRJEB21504 -parameter: trim = 20 and truncLen = c(160), 1793 ASVs generated. • DRA006094 -parameter: trim = 20 and truncLen = c(200), 375 ASVs generated. • PRJNA684584 -parameter: trim = 20, 1621 ASVs generated.
Testing Phase We searched the 53 features selected by REFS in each testing dataset, the result was 22 out of 53 for DRA006094 and 48 out of 53 for PRJNA684584. After applying the validation module, we obtained an average AUC of 0. 778 for DRA006094 and for PRJNA684584 we obtained an average AUC of 0. 71. Both average AUCs correspond to a "good " diagnostic accuracy  [50]. In this case, the classifier with the best performance was KNeighbors for DRA006094 and Extra Trees for PRJNA684584, Fig. 2c, d. For the comparative analysis, we searched for the 53 features selected by K-Best on the testing datasets. The result was 21 out of 53 for DRA006094 and 52 out of 53 for PRJNA684584. We applied the validation module to the features found in both testing datasets. The resulting average AUCs were 0.
Type 2 Diabetes (T2D) Raw data processing The trimming parameters used for DADA2-based srcipt filtering process and the number of Amplicon Sequence Variants (ASVs) generated for each dataset were: • PRJNA325931 -parameter: trimLeft = 15, 3316ASVs generated. • PRJNA554535 -parameter: truncLen = c(400), 3201 ASVs generated. • PRJEB53017 -no parameter used, 3672 ASVs generated.
Feature Selection Phase We selected PRJNA325931 for discovery according to the eligibility criteria. 9 out of the 3316 features resulted by using the Recursive Ensemble Feature Selection (REFS) algorithm. Thus, REFS achieved its highest accuracy (> 0. 90) with 9 features, Fig. 3a. The result of the validation module for the selected 9 features was an average AUC of 0. 79, which is considered "good " diagnostic accuracy  [50]. In this case, the Multilayer Perceptron (MLP) the algorithm had the best performance, Fig. 3b. In comparison, we applied the same validation module to the total 3316 features, the resulting average AUC was 0. 494. For feature selection using K-Best, with k=9, the average AUC was 0. 75. The detailed validation results are presented in Table  3.
Testing Phase We searched the 9 features selected by REFS in each testing dataset, the result was 5 out of 9 for both testing datasets. We applied the validation module to the features found in both testing datasets. For PRJNA554535 we obtained an average AUC of 0. 714 and for PRJEB53017 we obtained an average AUC of 0. 662. The average AUC for PRJNA554535 corresponds to a "good " diagnostic accuracy and for PRJEB53017 the average AUC corresponds to a "sufficient"  [50]. For both testing datasets, the classifier with the best performance was Extra Trees, Fig. 3c, d. For the comparative analysis, we searched for the 9 features selected by K-Best on each testing dataset, the result was 4 out of 9 for both testing datasets. We applied the validation module to the features found in both testing datasets.
Discussion In traditional analyses, groups of taxa called Operational Taxonomy Units (OTUs) are generated with sequences that are similar with a percentage of error, usually 3%  [43, 44]. Considering this error, it is possible to miss variations (possible mutations) making a specific taxa that could be important in medical applications unable to be analyzed. Using Amplicon Sequence Variants (ASVs) this potential loss can be avoided due to all their properties such as ASVs inferred independently from different studies or different samples can be comparable across studies, reduced need for computation power, and are not limited by incomplete reference databases to mention some of them  [43, 44]. ASVs allow individual experiment and the results could be tested and validated in separate datasets in contrast to merging datasets as in pooling analysis  [29].
Using our methodology, we are able to achieve a signature of taxa across different datasets. To the best of our knowledge, these type of experiments are not reported in the literature. Despite the promising results and findings, more research and experimentation should be done with microbiome sequencing because counterexamples can be found that make this methodology ineffective. Such is the case with datasets related to asthma: PRJEB44044  [51] , PRJNA601757  [52] , and PRJNA913468  [53] , where the feature selection and testing phase were inefficient. This was due to the lack of datasets with samples from the same source, the quality of the sequences, the lack of documentation, variations in the technical sequencing equipment used, also known as the batch effect  [54, 55]. Thus, this methodology is dependent of the batch effect.
Additionally, the experiments must be extended to study the relationship taxadisease or taxa-disorder for possible medical applications. Furthermore, from all experiments, it is easy to notice that the classification performance on the discovery dataset is considerably higher than those on the validation datasets. There are two possible explanations for this result. First of all, not all ASV features selected by the proposed methodology on the discovery dataset are found in the validation datasets: thus, the classifiers do not have access to all the information that led to the better performance on the discovery data, resulting in an decreased AUC. Secondly, the datasets could present differences due to the batch effect. We intentionally did not apply any batch correction methodology in this work, to better isolate and study the results of the proposed.
Conclusion We developed a methodology for reproducible biomarker discovery for 16s rRNA microbiome sequence analysis, addressing the issues related with high dimensional data with a small number of samples, inconsistent results, the lack of uniform processing and analysis methodologies, and to achieve validations in separate databases. The results from the three experiments show that the proposed methodology achieved higher accuracy compared to K-Best and 10-time random selection methods. This methodology is a first approach to increase reproducibility, to provide robust and reliable results. The approach to the individual study of ASVs makes possible to identify small variations that can have a positive impact on medical applications. This methodology provides results that allow pharmacologists, biologists, and health researchers to direct their efforts to the analysis of a list with a smaller number of individual taxa, instead of thousands of taxa grouped in clusters.
Methodology The proposed methodology consists of four phases: 1) dataset selection criteria, 2) raw data processing, 3) feature selection, and 4) testing. In contrast to other methodologies, such as pooling analysis  [29, 56] , we do not combine more than two datasets to produce a single one to be analyzed. The proposed methodology is oriented to work with Amplicon Sequence Variants (ASVs) because they can be used in independent studies  [43, 44]. Using ASVs provide a possible solution to avoid inconsistent results, at the same time, they can help achieve external validation in separate datasets which  [29]. For external validations, we recommend working with at least three datasets: one for discovery an the rest for testing. We address the issues of overfitting and biased performance associated with the datasets implementing a nested cross-validation scheme  [42].