dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017; Duchon et al. , 2021). These studies were pioneered and strongly driven by the use of the Ts(17 16 )65Dn/J (Ts65Dn) mouse line  (Davisson et al. , 1990 (Davisson et al. , , 1993;; Reeves et al. , 1995) , with more than 500 publications found in the PubMed database (November 2022). Originally, the Ts65Dn line was generated by the irradiation of DBA/2J males, which were then crossed with C57BL/6J females, with the progeny being checked for chromosomal abnormalities  (Davisson et al. , 1990 (Davisson et al. , , 1993)). Translocations for mouse chromosome 16 (Mmu16; 'Mmu' for Mus musculus) homologous to Hsa21 were then mated with the C57BL/6J×C3H/HeJ F1 hybrid line. The genetic background of B6C3HF1 was selected to obtain large litters and maintain higher transmission. The translocated minichromosome, Ts65Dn, encompasses 90 protein-coding genes (PGCs) from Mir155 to Zbtb21, linked to the centromeric part of mouse chromosome 17 (Mmu17), which contains a region non-homologous to Hsa21, from Pisd-ps2 to Pde10a, with approximately 46 PGCs  (Muñiz Moreno et al.
Structure of the Scaf8-Pde10a proximal region of Mmu17 Before creating the Ts66Yah model, we looked at the Scaf8-Pde10a region. We found it quite rearranged in several mouse lines, according to the Mouse Genome Informatics database. In particular, we closely investigated the corresponding segments in the DBA/2J line used to generate the first Ts65Dn chromosome, and also in the C57BL/6J and C3H/HeJ (as a proxy) mouse lines used to breed the Ts65Dn mouse carriers. Overall, the region was globally conserved in its organization but several loci were affected (Fig. S1A ). More precisely, the Snx9 locus was different in size in the three models and more perturbed in C3H/HeJ. Remarkably, the genetic interval encompassing Tulp4, Tmem181a and Sytl3 was not found in the DBA/2J genome, and other loci for Rpsh3 and Rnaset2 were duplicated differently in the three genetic backgrounds (Fig.
, 2021). This phenomenon should depend on genes located in Mmu17 either directly or indirectly, because Ts66Yah was not affected. According to the Mouse Genome Informatics database, mutants from five genes of the region -Arid1b, Cep43, Nox3, Pde10a and Serac1are linked to activity, motor and stereotypic behavior, and are strong candidates for which overdosage may enhance the activity of the Ts65Dn mouse model. Similarities were also observed in brain and craniofacial morphologies, with convergence found in both DS models. Nonetheless, the severities of the trisomy in these two features were slightly different: the Ts65Dn brain was larger than the Ts66Yah one in many different regions, while the shape of the cranium was affected differently, with more severe brachycephaly and increased mid-face shortening in Ts66Yah compared to Ts65Dn, a phenotype closer to human DS features.
Conversely, this region may change penetrance or expressivity of features in individual with DS. Indeed, SCAF8-PDE10A on human chromosome 6 is also subjected to copy number variation according to the DECIPHER database  (Firth et al. , 2009). In human, the region is rearranged in the 3′ part (Fig. S1B ) and contains the ARID1B, SERAC1, GTF2H5, RPSH3 and PDE10A disease genes. Thus, the additional trisomy of Scaf8-Pde10a has a strong effect on the expression of several DS-related features in models. This phenomenon of additive effect due to genetic interaction has been observed in other copy number variation diseases  (Pizzo et al. , 2019). Overall, it highlights the need for complete evaluation of the genetic background in individuals with DS, to define potential interaction with other candidate disease-associated variants and to get a better understanding of the DS complexity.
Statistical analysis For each dataset, we performed the Shapiro-Wilk test and quantile-quantile plots to analyze whether the data were normally distributed and the Brown-Forsythe test to ascertain the homogeneity of variances. If the P-value was greater than the significance level (0. 05), we assumed normality and equal variance. In this case, the statistical significance of differences between groups was inferred by an unpaired two-tailed t-test between genotype or repeated measures ANOVA (MWM, FC). The post hoc tests (Tukey test) were conducted only if F in repeated measures ANOVA achieved a 0. 05 level. In the case of datasets in which the assumptions of normality or homogeneity of variances were not fulfilled, we used the Kruskal-Wallis non-parametric test. We performed a one-sample two-tailed t-test for the DI of the NOR versus no discrimination (0%), or for the percentage of spontaneous alternation versus 50% (hazard) in the Y maze, or 25% (hazard) for the probe test in the MWM.
Identifying the explanatory phenotyping variables in the trisomic lines We used Gdaphen for 'genotype discrimination using phenotypic features'  (Chidiac et al. , 2021). Gdaphen is an R pipeline developed in our laboratory that allows the identification of the most important predictive qualitative and quantitative variables for genotype discrimination in phenotypic-based datasets without any prior hypothesis (available on github, https://github. com/YaH44/GDAPHEN). Gdaphen also allows the identification of the most important predictor qualitative and quantitative variables for genotype discrimination in animal models of different diseases. We used Gdaphen (https://github. com/YaH44/ GDAPHEN/releases/tag/Public) to identify the phenotypic explanatory variables recorded during the analysis more relevant to discriminate between mutant and wt genotypes from the Ts66Yah and Ts65Dn models.
To this end, the ten manually segmented in vitro magnetic resonance images from the MRM NeAt Mouse Brain Database (https://github. com/dama-lab/ mouse-brain-atlas/tree/master/NeAt) were considered  (Ma et al. , 2005). The image-processing pipeline consisted of the following steps: (1) a skullstripping step was first performed using the tissue brain segmentation method provided in SPMMouse (https://github. com/dama-lab/mousebrain-atlas/tree/master/NeAt) (Sawiak et al. , 2009) ; (2) each magnetic resonance image was then corrected for bias field in homogeneity using N4ITK  (Tustison et al. , 2010) ; (3) the ten anatomically annotated images from the MRM NeAt Mouse Brain Database were registered in a deformable way on each mouse image using the ANTs registration toolbox (http:// stnava. github.
io/ANTs/) (Avants et al. , 2008) ; and (4) anatomical labels were finally fused using the simultaneous truth and performance level estimation (STAPLE)  (Warfield et al. , 2004). By this way, the volumes of the 20 anatomical structures as well as the whole brain were computed for each image modality and each mouse. Finally, the volumes computed from the two image modalities were averaged out to obtain the final volume associated with each mouse. Morphological magnetic resonance images were compared across groups with a region-based analysis. The resulting region-based volume estimations were averaged out for each animal before the statistical analysis. For Ts65Dn, these data have been previously published by our group  (Duchon et al. , 2021)  and were reanalyzed with the same analysis pipeline for comprehensive comparison of both models.
Morphometrics Mice used for the craniofacial study were sacrificed at 18 weeks of age, and carcasses were skinned, eviscerated and stored in 96% ethanol. Cranium morphology was assessed using a Quantum µCT scanner (Perkin Elmer, Waltham, MA, USA). All scans were performed with an isotropic voxel size of 20 µm, 160 µA tube current and 90 kV tube voltage. We applied a common approach to shape analysis named geometric morphometrics (GM) using the Geomorph software package in the R statistical computing environment  (Adams and Otárola-Castillo, 2013). This approach used the coordinates of 39 relevant cranial landmarks that were recorded using Landmark software [Institute for Data Analysis and Visualization (IDAV) group at the University of California, Davis; Table  S6 ]. A generalized Procrustes analysis was then used to superimpose the specimens on a common coordinate system by holding their position, size and orientation constant.
Gene expression analyses The HIP and EC from male Ts65Dn mice (n=6) and control littermates (n=6), and male Ts66Yah mice (n=6) and control littermates (n=5), at the age of 5-6 months, were isolated and flash frozen in liquid nitrogen. Total RNA was prepared using an RNA extraction kit (Qiagen, Venlo, The Netherlands) according to the manufacturer's instructions. Sample quality was checked using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). The preparation of the libraries was done by the GenomEast platform, a member of the 'France Génomique' consortium (ANR-10-INBS-0009), using the TruSeq Stranded Total RNA Sample Preparation Guide -PN 15031048. Total RNA-Seq libraries were generated from a minimum of 150-300 ng total RNA using a TruSeq Stranded Total RNA LT Sample Prep Kit with Ribo-Zero Gold (Illumina, San Diego, CA, USA), according to the manufacturer's instructions.
The molecules extracted from the biological material were polyA RNA. Whole-genome expression sequencing was performed by the platform using Illumina Hiseq 4000 and generating single-end RNA-Seq reads of 50 bp length. The raw sequenced reads were aligned by Hisat2 against the GRCm38. v99 mouse assembly. In total, 55,385 ENSEMBL gene IDs were quantified aligning with the GRCm38. v99 assembly. HTSeq-count was used to generate the raw counts. The downstream analyses were continued with in-house bash scripts and R version 3. 6 scripts using FCROS  (Dembélé et al. , 2014)  and DESeq2  (Love et al. , 2014)  packages to identify the DEGs. Raw reads and normalized counts have been deposited in Gene Expression Omnibus (GEO) (GSE213500 for Ts65Dn and GSE213502 for Ts66Yah). We performed the differential functional analysis using GAGE pathway analysis  (Luo et al.
, 2009)  and grouped all the pathways into 25 functional categories (denoted meta-pathways). Then, to assess gene connectivity, we built a minimum fully connected PPI network (denoted MinPPINet) of genes known to be involved in the synaptic function as they were associated with synaptic pathways via the Gene Ontology (GO)  (Ashburner et al. , 2000)  and Kyoto Encyclopedia of Genes and Genomes (KEGG)  (Esling et al. , 2015)  databases. Regulatory information was also added to build the final RegPPINet. We used the betweenness centrality analysis to identify hubs, keys for maintaining the network communication flow. To further study the genotype-phenotype relationship in those models, we combined the behavioral results and the RNA-Seq data to identify central genes altered in the models and linked to the phenotypes observed using the genotype-phenotype databases GO, KEGG and DisGeNET.
First, we downloaded the list of experimentally validated genes known to be involved in hyperactivity or locomotion behavior from the human disease database DisGeNET (dataset: hyperactive behavior, C0424295, with 1263 genes) and annotated the genes with a high confidence ortholog in mouse. We added all the mouse genes involved in GO gene sets linked to locomotion or motor behavior (18 GO terms: GO:0007626, GO:0008344, GO:0031987, GO:0033058, GO:0035641, GO:0040011, GO:0040012, GO:0040013, GO:0040017, GO:0043056, GO:0045475, GO:0090325, GO:0090326, GO:0090327, GO:1904059, GO:1904060, GO:0036343, GO:0061744). Then, we queried our RNA-Seq data for these genes to identify those found deregulated in the datasets. Those variables were identified using a principal component analysis (PCA) after applying a multi-factor analysis of mixed data (MFA) implemented using MFAmix function from the PCAmixdata R package.
Iii) Interactions between proteins known to be part of complexes in violet. Iv) Predicted interactions were represented in grey including the PPI interactions identified by STRING DB  (Szklarczyk et al. 2017)  after merging both networks. * Represents those genes from the top 50 central genes list on the right side, known to be involved in synaptic pathways (they were linked to those functionalities in the GO/KEGG databases). C, Central Ts66Yah Hippocampi RegPPINet network colored i) in the upper panel highlighting the top 50 genes with the highest betweenness index value from red (higher index) to yellow (lower index) ii) in the lower panel highlighting the genes identified by gage to contribute to the alteration of synaptic related pathways. The list of the most central genes is included below the figure.
Acknowledgements We thank members of the research group, of the  IGBMC laboratory and of the ICS  for their help with brain morphometric analysis. We extend our thanks to the animal caretakers of the ICS, who are in charge of mouse wellbeing and who helped us to breed and maintain both mutant lines, and to the members of the  GENOMEAST Platform  for the RNA-Seq studies. We acknowledge the  Sisley-d'Ornano Foundation  and  Fondation Jeŕôme Lejeune , which awarded M. F. -C. a postdoctoral fellowship. This work was supported by  Fondation Jeŕôme Lejeune ,  Centre National de la Recherche Scientifique (CNRS) ,  Institut National de la Santéet de la Recherche Medicale (INSERM) , the  Universited e Strasbourg (Unistra) , and  French government funds through Agence Nationale de la Recherche  in the framework of the  Investissements d'Avenir program  by  IdEx Unistra  ( ANR-10-IDEX-0002 ), a  SFRI-STRAT ' US  project ( ANR 20-SFRI-0012 ), and  EUR IMCBio  ( ANR-17-EURE-0023 ) and  INBS PHENOMIN  ( ANR-10-IDEX-0002-02 ), and also provided by the  Joint Programming Initiative Neurodegenerative Diseases (JPND) multinational research projects HEROES  ( ANR-17-JPCD-0003 ) and  DYRK-DOWN  ( ANR-18-CE16-0020 ).
In contrast, the one-step methods model the data from binomial distributions directly such that the parameters are estimated by the ML method in one step. The one-step methods originate from the Sarmanov family of bivariate distributions and mixed-effects model for correlated binary outcomes. The widely-used models for one-step methods are the bivariate random-effects models, including the bivariate generalized mixed-effects model (BGLMM) and bivariate beta-binominal model (BBBM). 25, 26 In the metaanalysis, they model the events between the treatment and control groups with their potential correlations based on BGLMMs or BBBMs. 27 en the events are not rare and no extra corrections are required, the two-step methods perform similarly to one-step methods for the same dataset. 17 However, if the probability of the events is rare or if the study sample sizes are small, the normality assumptions in the twostep models might not hold.
Implementation In this article, all the two-step methods are implemented by the "meta" package (version 5. 2-0) in R (version 4. 1. 3). 32, 59 The bivariate random-effects models are implemented by SAS (version 9. 4) using PROC NLMIXED. 60 It uses an adaptive Gaussian quadrature to approximate the likelihood integrated over the random effects, and it maximizes the likelihood function by dual quasi-Newton optimization techniques for the BGLMM and BBBM.
Case studies In order to show and compare the empirical performance of various methods in estimating RDs, we apply the above methods to two real datasets, one without zero events and another with zero events, as shown in Tables  1  and 2. Detailed code for case studies could be found in the Supplementary Materials. the world, evidence on the effectiveness of prevention measures can be combined to inform decisions. Chu et al. 62  performed a meta-analysis on different prevention measures in preventing the transmission of COVID-19 and related diseases. We focus on the meta-analysis that compared the use of face masks with no face masks in healthcare settings. The meta-analysis had 26 studies in total, including 5 single-zero-event studies and 6 DZE studies. The total number of patients was 9,445; 6,003 were in the face mask group, and 3,442 were in the no face mask group.
Results Tables  3  and 4  present the estimates of the overall RD ( δ) and between-study standard deviation (τ ) from each model, respectively. All models did not encounter estimation difficulties in the risk of stunting in left-behind children dataset. Similarly, no estimation difficulty occurred in the risk of COVID-19 infection with face masks dataset after corrections for zero events. The implementations of the bivariate random-effects models took slightly longer time than the onestep methods. Table  3  shows that the estimated RDs were more consistent among different models (from 0. 008 to 0. 015) in the case study of stunting risk than the results in the case study of COVID-19 risk, where different models produced a range of RD estimates from -0. 197 to -0. 032, likely caused by DZE studies.
001. This suggested a high between-study heterogeneity so the RE model was suitable. Different heterogeneity estimators under the RE model gave consistent τ ; the largest difference between all τ estimates was 0. 007 (Table  4 ). Contrary to the small variation of τ in the case study of stunting risk, a wide range of estimated τ 2 was observed under various heterogeneity estimators in the case study of COVID-19 risk. Because I 2 in this dataset was larger than 80% and the p-value of the Q test was less than 0. 001 in the two-step methods, the RE model was also suitable. Among all the estimators for τ , the DL and HS estimators give smaller results than others, leading to substantial differences in δ. The considerable differences of the various methods in these case studies leave us to wonder which model provides the best estimates of RDs.
65  Under each setting in Table  5 , we generated 5,000 meta-analyses. We used the bivariate probit-normal model to generate meta-analyses with binary events, where event counts follow binomial distributions and probit-transformed probabilities follow normal distributions. To be comprehensive in our simulations, we also simulated data from the BBBM; see details in the Supplementary Materials. In real-world meta-analyses, event probabilities often have within-study correlations between the experimental and control groups. 66 We set the correlation coefficient ρ to be 0. 3 and 0. 6. Moreover, we set the number of studies K to be 10 and 30. The number of patients in each group for each study (n) was 100. The bivariate probit-normal model includes four other important parameters, i. e. , the means and variances of the event probabilities in the probit scale of the two groups.
Discussion This article comprehensively evaluates both the two-step and one-step models for estimating the RD in a meta-analysis with binary outcomes. While many existing studies have compared methods in estimating the odds ratio and the risk ratio under various situations, their conclusions may not easily extend to the RD because the RD can be defined in DZEs. In this study, we compared and evaluated multiple models to estimate the RD in meta-analyses with zero-event studies. Results from real-data case studies and simulations show that various models performed consistently in the absence of DZEs but had noticeable differences when DZEs appeared. For datasets with zero-event studies, we suggest using one-step methods, especially BGLMM (probit link) and BBBM. The CE model with the MH method is suitable when zero-event studies are not heterogeneous, in addition to one-step methods.
In order to avoid model singularity, the dataset should contain at least six studies such that n > p. We suggest researchers use two-step methods instead of bivariate random-effects models if there is a limited number of studies (e. g. , In addition, more parameters imply that estimation failures are more likely to arise with inappropriate starting values of parameters in bivariate random-effects models. 9] [70]  While they were not fully investigated in this article, these alternative CI constructions are expected to improve the CPs for two-step methods. Methods to improve the CPs for the bivariate one-step methods demand further research. Our work may spark future interests in estimating the RD from datasets of 2 × 2 tables, yet several limitations exist. Several RD estimators are not included in this article, such as Bayesian methods.
29, 71 Bayesian methods have become popular in meta-analyses as they could overcome some computational difficulties encountered by traditional methods and permit the use of pertinent external information for improving the estimation. Current software packages are available to implement our one-step models from Bayesian approaches, such as the PROC BGLIMM in SAS. 72, 73 Future studies may compare the performance of Bayesian methods with the methods discussed in this article. In addition, one may use other types of bivariate randomeffects models, such as the marginal beta-binomial model approach. 74 Finally, although we show that one-step models have better performance than two-step models, two-step models that apply artificial corrections currently dominate meta-analysis applications, partly because they are readily implemented by most meta-analysis software.
611. 1Riskof stunting in left-behind children Fellmeth et al. 61presented a meta-analysis investigating whether left-behind children and adolescents had different health conditions compared with children and adolescents with non-migrant parents. We focus on stunting as a binary outcome. This research identified 16 studies without zero events, including 38,779 children (17,066 left-behind children and adolescents and 21,713 children and adolescents with non-migrant parents). Because this dataset contains no zero events, we can use it to investigate whether each method gives a consistent estimate of RDs.
Acknowledgements Research reported in this publication was partially supported by the  National Center for Advancing Translational Sciences  grant  UL1 TR002494  (HC), the  National Library of Medicine  grant  R01 LM012982  (MX, LL, and HC), and the  National Institute of Mental Health  grant  R03 MH128727  (LL) of the  US National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
611. 1Riskof stunting in left-behind children Fellmeth et al. 61presented a meta-analysis investigating whether left-behind children and adolescents had different health conditions compared with children and adolescents with non-migrant parents. We focus on stunting as a binary outcome. This research identified 16 studies without zero events, including 38,779 children (17,066 left-behind children and adolescents and 21,713 children and adolescents with non-migrant parents). Because this dataset contains no zero events, we can use it to investigate whether each method gives a consistent estimate of RDs. 3. 1. 2 Risk of COVID-19 infection with face masks Due to the widespread of COVID-19 all over.
IMPLEMENTATION AND PRELIMINARY EVALUATION 3D video game engines are finding increasing use for constructing visualizations  (Buyuksalih et al. , 2017)  and simulations  (Cristie et al. , 2015)  in urbanism as well as other fields, and are particularly well suited for developing applications for immersive analytics  (Sicat et al. , 2018 , Cordeil et al. , 2019). For our development we used the Unity 3D engine, and its SDK for Mapbox, a platform providing cartographic data and tools to author, edit and stylize geospatial datasets. Furthermore, Unity 3D supports immersive technology such as the HTC Vive VR HMD used to test our prototype. Using the Mapbox SDK, we access geographical vector and raster data stored as Tilesets from Mapbox and use these to construct and deform textured or colored geometry in Unity at runtime.