dataesr/scientific-paragraphs-categorization huggingface dataset:
crispatus CIP104459 genome was then aligned with the sequences of the known human estrogens receptors listed in Table  2. A bacterial protein displaying high similarity and identity to human estrogens receptors was identified and subjected to molecular docking in silico with 17β-estradiol as potential ligand. We generated 3D models of L. crispatus proteins with RaptorX Structure 69 and visualized them with Python Molecular Viewer V1. 5. 6. Predictions were based on alignments with the crystalized structure of the corresponding eukaryotic ortholog. The potential binding of 17β-estradiol to the identified sensor protein was studied with AutoDock 4. 2  44. Binding values were generated with the Lamarkian Genetic Algorithm of AutoDock 4. 2. All calculations were performed with a DELL Precision T7610 computer equipped with four hard disks (4 Tb each, giving a total of 12 Tb under RAID5).
Acknowledgements The authors would like to thank the  Cosmetic Valley  world cluster and  CNRS GDR CNRS 3711 Cosm' Actifs  for their scientific and relational support. This work was supported by the  FUI program Gynceutics (BPI, Région Normandie ,  Région Centre ,  Région Aquitaine  and  European Union-FEDER). We thank  Julie Sappa  ( Alex Edelman associates ) for careful review of English.
In low-intensity counties, 871 zip codes were treated and had COVID-19 infection data, and 2,558 counties were controls and had COVID-19 infection data. The realized sample size was similar to the original sample size and did not lead to substantial loss in power. Summary statistics on the sample that was randomized are shown in Table  1  (Extended Data Figs. 1  and 2  in the Supplementary Appendix show their localization on the map). As is generally true in the United States, most counties lean Republican 8 ). Counties had, on average, 36% Democrats and 62% Republicans (based on election share in 2020), and 46% of zip codes were classified as urban. On 13 November 2020, the distance traveled was 8. 73% lower than during the benchmark month in the Facebook Movement Range data of February 2020.
We plan to conduct a more detailed spillover analysis in follow-up work, using the Social Connectedness Index constructed from the interactions on the Facebook platform  11, 12. This study had several limitations, in particular regarding generalizability. First, the study was conducted with Facebook subscribers, and mobility data were collected for Facebook users only. Although Facebook has an extensive reach (70% of Americans are users), this remains just one type of media. Second, it was an ad campaign. The messages might have been more or less effective if they had been relayed by celebrities or locally known figures, as we have tested in other work  13, 14. Third, we tested one kind of message, recorded by clinicians on smartphones. The results could be different if variables such as message content, identity of the messenger, length of message, production value of the videos or name recognition of the originating organization were varied.
Methods Trial oversight. The design was approved by the institutional review board (IRB) of the Massachusetts Institute of Technology (MIT) with Massachusetts General Hospital, Yale and Harvard ceding authority to the MIT IRB. Messages were produced by the research team and approved to run (without modification) after going through Facebook's internal policy review to ensure compliance with policies. The study was registered on ClinicalTrials. gov (NCT04644328) and the American Economic Association registry for randomized social experiments (AEARCTR-0006821). The IRB protocol and relevant amendments are available online (along with the data and code). Procedures that apply to this specific project are highlighted. There was no deviation from the IRB protocol. Further details on the design were registered in a statistical analysis plan and on the ClinicalTrials.
gov registration. The analysis in this paper focuses only on the primary outcomes registered on ClinicalTrials. gov. There was just one deviation from the pre-registration in ClinicalTrials. gov: we initially planned to construct zip code-level mobility outcomes from fine-grained data, but, owing to privacy concerns, those data are not available for this purpose. Because the publicly available mobility data are at the county level, we used only county-level mobility data (which we had always planned to use). The sample, the specific unit of randomization, the randomization methods and planned analyses were pre-registered before the Thanksgiving campaign in a publicly available statistical plan (https://doi. org/10. 1257/rct. 6821-1. 0). We followed the analysis plan for the primary outcomes outlined on 25 November for both the Christmas and Thanksgiving campaigns in the 13 states, with two deviations.
First, we were not able to obtain publicly available zip code-level mobility outcome from fine-grained data, so we used only county-level data on mobility. Second, we had not specified a functional form for the number of COVID-19 cases. We specify one below. In this paper, we focus on the direct effects of the intervention in the 13 original experimental states on the primary outcomes specified in the ClinicalTrials. gov registration. Although the statistical analysis plan also discusses indirect spillover effects, an extension of the campaign in ten new states 3 days before Thanksgiving and various supplementary analyses, these are left for follow-on work. The reasons given were one of the following: 'I have seen too much suffering in my hospital' , 'It is the safest way to celebrate' , 'I love you, Mom and Dad' , 'There is light at the end of the tunnel, and we just need to hang in there a little bit longer'.
By defining total value as more than simply the advertiser's bid, ads that are estimated to create more user engagement or that are of higher quality can beat ads with higher bids in the auction. In our study, the Facebook ad campaign algorithm and Ads Auction led to the delivery of campaign materials to 11,954,108 users at Thanksgiving and 23,302,290 users at Christmas. (More information about the Facebook Ads Auction is available at https://www. facebook. com/business/help/430291176997542?id=561906377587030. ) Trial design, eligibility, randomization and recruitment. Eligibility for the trial and the cluster randomization strategy were determined by data availability and power considerations. Movement range data computed by Facebook are publicly available at the county level. COVID-19-level data are available at the zip code level in the states where we conducted this experiment.
In L counties (blocking by county), zip codes were randomized into intervention with probability ¼ and control with probability ¾. Randomization was performed by the research team with Stata before each of the two interventions. This two-stage randomization ensures that there is randomized variation in the intensity of the campaign at both the county level and the zip code level. The county-level randomized variation allows us to measure the effect of a high-intensity versus low-intensity campaign on mobility using publicly available data. The zip code-level randomized variation allows for a direct comparison of treated and control zip codes. Because there was no individual randomization and recruitment, concealment was not necessary, and consent was not sought. No one in the team was blinded to the randomization.
For outcomes with zip code-level data, using intra-class correlations of 0. 2 (0. 475) assuming clusters of equal size, a sample of 6,998 zip codes would provide 80% power to detect effect sizes of 0. 057 (0. 072) standard deviations (using zip code-level data). For the zip code-level outcomes, power calculations were conducted via simulation. In both cases, our calculations were based on a 5% type I error rate in two-sided tests. Outcomes. Our primary outcomes are county-level mobility (distanced traveled and leave home metric) and zip code-level COVID-19 infections reported to state health authorities, which we regularly retrieved from state websites beginning on 12 November 2020 and ending on 11 February 2021. County-level mobility data. Our mobility outcomes come from the publicly available Facebook Movement Range dataset, which can be downloaded at https:// data.
Acknowledgements We thank the health team at  Facebook  for their in-kind financial support that allowed us to run the campaign and for their logistical help. In particular, we thank  N. Deolalikar. We also thank advisors  D. Bernard  and  S. Francis. We thank the team at AdGlow, in particular  C. Orellano  and  L. Novak , for running the campaign. We thank  A. Pompe  from  Facebook Data for Good  for helping us to understand the Facebook mobility data. We thank the team at Damage Control, in particular  P. Saha , for their tireless work in editing the videos. We thank  N. Shankar  and  M. J. Kim  for excellent research assistance. We are particularly grateful to all of the members of the ' COVID-19 Messaging Working Group ' with whom we developed and tested the original messages that led to this at-scale study.
ST1 was the most abundant (n = 60, 81. 1%), followed by ST2 (n = 10, 13. 5%) and ST5 (n = 4; 5. 4%) (Table  2 ). A phylogenetic analysis was generated to illustrate the relationship between the different subtypes obtained in our study, along with other Blastocystis ST1, ST2, ST3 and ST5 sequences recovered from human, chimpanzee and gorilla species, which are available in the GenBank database (Figure  1 ). This figure shows that sequences from each ST lineage were clustered together as a monophyletic group and supported by bootstrap values higher than 50%. However, paraphyletic and polyphyletic clades resulting from certain nucleotide variations between sequences (bootstrap values below 50%) were formed within each ST branch (Figure  1 ).
codoncode. com/aligner (accessed on 10 January 2021)) and compared with those available in the GenBank database by using the BLASTn programme (www. ncbi. nlm. nih. gov/BLAST (accessed on 10 January 2021)). Subtypes (STs) were then determined by selecting the best match (exact/closest similarity) with all Blastocystis sp. subtype sequences available in the GenBank database. Phylogenetic analyses were performed using the maximum likelihood method implemented in MEGA X software (https://www. megasoftware. net/ (accessed on 10 January 2021)). The maximum likelihood analysis was based on the Tamura-Nei model. Bootstrap values were calculated from 1000 replicates.
General procedure Each participant was tested in two separated experimental sessions (mean inter-session interval = 21. 5 days, SD = 17. 4). In the first session, structural and resting-state functional MRI (rs-fMRI) data were acquired along with some taskrelated fMRI data collected for different aims and published elsewhere (e. g. ,  Visalli et al. 2019). The order of acquisition was: (i) rs-fMRI, (ii) task-related fMRI, and (iii) structural MRI. In the second session, participants performed the color-shape task-switching paradigm, which is the focus of the present study.
The color-shape task-switching paradigm Participants performed a color-shape task-switching paradigm  (Rubin and Meiran 2005; Ambrosini and Vallesi 2016; Vallesi et al. 2016) , which was implemented in MATLAB (The MathWorks, Inc. , Natick, Massachusetts, United States) using the Psychophysics Toolbox 3  (Kleiner et al. 2007). A graphical representation of the paradigm is presented in Fig. 1. Participants sat in a dimly lit soundproof cabin at a viewing distance of approximately 60 cm from the computer screen. Each trial started with a black fixation cross (visual angle: 0. 28° by 0. 28°) displayed at the center of the screen. After 1500 ms, a cue stimulus (visual angle: 5. 5° by 1. 7°) signaling the task to be performed was presented 4. 6° above the fixation cross. The color task cue consisted of a row of three colored rectangles (purple, orange, and yellow), while the shape task cue consisted of a row of three small black shapes (a triangle, a circle, and a square).
Behavioral scores Data from three participants were discarded since they were excluded for imaging issues (see below). The final sample for all the reported analyses, hence, included 44 participants. A sensitivity power analysis (G*Power 3 software;  Faul et al. 2007 ) revealed that our sample size was large enough to detect significant (α = 0. 05) mean differences between two dependent means (e. g. , the main effect or the interaction effect of a 2-by-2 repeated measures ANOVA) with a medium effect size d = 0. 43  (Cohen 1988 ) with a statistical power of 0. 80. Data from the practice phase, first trial in a block, error (incorrect or no response) and post-error trials were excluded from analysis. For each trial type (i. e. , single-task, repeat and switch trials) at each CTI, response time (RT) values more extreme than one and a half times the interquartile range (i.
MRI acquisition Structural and functional images were acquired with a 3 T Ingenia Philips scanner (Philips Medical Systems, Best, The Netherlands) at the Neuroradiology Unit of the Padova University Hospital. The system was equipped with a 32-channel head-coil. Functional data consisted of 250 T2*-weighted echo-planar image (EPI) volumes (repetition time, TR: 2000 ms; echo time, TE: 30 ms; 39 axial-slices with ascending acquisition; voxel size: 3 × 3 × 3 mm; flip angle, FA: 76°; acquisition matrix: 84 × 84). After the functional session, high resolution T1-weighted anatomical images were acquired (TR/TE: 8. 10/3. 72 ms; 180 sagittal slices; voxel size: 1 × 1 × 1 mm; FA: 8; acquisition matrix: 256 × 256). To reduce head movements during data acquisition, small foam cushions and sponge pads were placed around the participant's head.
Resting-state fMRI preprocessing Two participants were excluded after visual quality check, reporting enlarged ventricles which could affect normalization steps, while one participant was excluded due to an incomplete rs-fMRI exam, thus leaving a sample of 44 participants for the analysis. Functional data were preprocessed using the FMRIB Software Library, version 6. 0. 0) (Smith et al. 2004 ), according to a previously described pipeline  (Pini et al. 2020). Functional data were motion corrected, brain extracted and registered to the corresponding structural image with rigid-body transformation and nonlinearly registered to a symmetric brain template (ICBM 2009a Nonlinear symmetric template). Functional data were high-pass filtered (100 s) and for each functional volume, the framewise displacement (FD) was estimated and volumes with more than 0.
Fronto-parietal network identification For an overview of the methodology see Fig. 3. First, we identify brain regions organized within the FPN through Fig. 3  Workflow of the functional connectivity and homotopy analysis. From the rs-fMRI preprocessed data we extracted the frontoparietal network (FPN) through an independent component analysis approach, and voxel-mirror homotopy properties. Functional maps were then correlated with behavioral performance (mixing and switching costs) through a permutation strategy an independent component analysis (ICA). In this step, we further included 15 participants with the same rs-fMRI data who did not come back to complete the second (behavioral) session and an independent dataset of 21 healthy young individuals with MRI sequences with equivalent acquisition parameters collected at a different site (see Supplementary Methods), for a total of 83 healthy participants (45 females; mean age = 24.
2020). For simplicity, results are reported in the left hemisphere. Finally, we included the language network, identified through a spatial correlation procedure with the fslcc utility from FSL compared with the Shirer's language template  (Shirer et al. 2012). This network was included as a control network. We assumed that homotopy of regions overlapping with language network hubs would not exhibit a significant association with executive tasks, since these regions converge on a left-lateralized network active during speech reception and production  (Braga et al. 2020). Finally, for each participant we computed the correlation between cognitive performance and FPN functional connectivity strength. Specifically, for each participant, the GIFT FPN maps were inserted into a voxel-wise correlation analysis with the switching and mixing tasks.
Voxel-mirror homotopy computation For each participant, we computed homotopy brain property as the voxel-mirrored homotopic functional connectivity between every pair of mirror voxels through in-house scripts based on R using unsmoothed data as previously reported  (Gracia-Tabuenca et al. 2018; Zuo et al. 2010). Briefly, in the symmetrical brain space we computed the Pearson's correlation coefficient between each voxel time-series residuals (after pre-processing steps) with the hemispheric counterpart. Voxels belonging to the three sagittal midline slices (9 mm) were excluded, to avoid inter-hemispheric partial volume effect. Then correlation values were Fisher Z-transformed. Finally, homotopy maps of each individual were smoothed with a gaussian kernel of 6 mm. We included in the analysis both whole-brain homotopy maps (whole voxel values) and homotopy connectivity within the FPN, computed masking the whole-brain homotopy maps with the group ROI-FPN binarized map (see previous section).
Reliability analysis To ensure that the mean homotopy map used in the correlational analysis exhibits a reliable profile, we computed, through the same procedure, voxel-mirrored homotopic functional connectivity from a larger open-source available dataset. To this aim, we retrieved participants from the Population Imaging of Psychology dataset (PIOP2), which is released within the Amsterdam Open MRI Collection (AOMIC). This dataset was selected according to the number of scans (250 scans) and TR (2000 ms), which were comparable with the dataset used in the present study (240 scans and TR = 2000 ms). For further details about PIOP2 dataset, see  Snoek et al. (2021). From the PIOP2 dataset, we retrieved 224 participants with both structural MRI and rs-fMRI data available. The list of PIOP2 participants included in this analysis is reported in Supplementary Table  S1.
From the PIOP2 dataset, we computed both mean Z-Fisher homotopy maps and one-sample homotopy t-maps. We then ran spatial cross-correlation between both mean and t-maps from PIOP2 and the study dataset through the FSL utility fslcc (FSL v. 6. 0. 0; https:// fsl. fmrib. ox. ac. uk/ fsl/). Reliability of behavioral measures-i. e. , mean RT for each trial type (pure, switch, and repeat trials) at each CTI (300 and 1200 ms) and the resulting mixing and switching costs-was evaluated by means of split-half correlations corrected with Spearman-Brown formula. Reliability coefficients were calculated as follows: (i) trials of each participant for each trial type-by-CTI combination were randomly splitted into two subsets of equal size; (ii) mean RTs was computed separately for the two subsets; (iii) switching and mixing costs were also calculated for the two subsets; (iv) correlations of participant's mean RTs and costs between the two subsets were calculated and corrected.
Functional and behavioral reliability As shown in Fig. 10 (panel A ), the homotopy maps from the PIOP2 cohort and our study dataset were similar. The spatial cross-correlation between mean (Z-Fisher) maps was r = 0. 89 (Fig. 10 ; panel B). A similar cross-correlation value was reported when comparing one-sample t-maps, with r = 0. 92, suggesting that homotopy features are highly reproducible among independent datasets. For the behavioral measures, reliability of mean RTs for the six trial type-by-CTI combinations was: 0. 96 (range of Pearson's correlation coefficients: 0. 91-98) for pure trials at short CTI; 0. 96 (range: 0. 92-0. 99) for pure trials at long CTI; 0. 92 (range: 0. 80-0. 97) for repeat trials at short CTI; 0. 93 (range: 0. 79-0. 98) for repeat trials at long CTI; 0. 91 (range: 0.
Funding Open access funding provided by  Università degli Studi di Padova  within the  CRUI-CARE Agreement. This work was funded by the  European Research Council Starting  Grant  LEX-MEA GA no. 313692 (FP7/2007-2013 ) to AV, and was also partially supported by the " Department of Excellence 2018-2022 " initiative of the  Italian Ministry of University and Research (MIUR)  awarded to the  Department of Neuroscience-University of Padua, Italy.
Statistical analysis For bacterial taxonomy analysis we used the microbiome analyst software to determine community properties. PCoA plots using unweighted UniFrac distances were used to analyze the segregation between WT fecal microbiota (FM)àGF and Nod1 -/-FMàGF mice using analysis of group similarities (ANOSIM statistical analysis). Alpha diversity, calculated as the Channon index, was analyzed using Mann Whitney U test. The p values below 0. 05 were considered to be statistically significant. For animal and cell analyses, data were analyzed using Prism 8. 0 (GraphPad Software). For normally distributed data, significance of differences between groups was evaluated by one-way analysis of variance (ANOVA) with Bonferroni's post hoc test for multiple comparisons. For pairwise comparisons the two-tailed Student's t-test was used.
The work was supported by grants from  PTR from Institut Pasteur  (to AT, MC and IGB), by  ANR   18-CE14-0020  (to AT, MC and IGB), and by  CNRS of Orléans (France)  and  European funding in Region Centre-Val de Loire  ( FEDER N° 2016-00110366 BIO-TARGET  and  EX005756   BIO-TARGET II ). AR was supported by  Investissement d'Avenir program ,  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  ( ANR-10-LABX-62-IBEID ). authors declare that they have no relevant conflict of interest.
Ethics The GEN-COVID study was approved by the University Hospital of Siena Ethics Review Board (Protocol n. 16917, dated  March 16, 2020). This observational study has been inserted in www. clinicaltrial. org (NCT04549831). The Spanish Covid HGE cohort is under IRB approval PR127/20 from Bellvitge University Hospital, Barcelona Spain. Written informed consent was obtained from all individuals who contributed samples and data.
Statistical analysis Since serum and plasma T values were not normally distributed, the statistical analyses were performed using non-parametric tests. When appropriate, transformation was used for skewed data in regression models. We used the Mann-Whitney U test to compare T levels in males with AR long-polyQ (  23 ) versus males with short-polyQ repeat  (22). Logistic regression analysis was performed to test the contribution of age, T, and the number of polyglutamine repetitions on COVID-19 outcome. The only prespecified interaction tested was the T by polyQ (categorical). Box-Tidwell procedure was used to assess linearity and the Hosmer and Lemeshow to assess goodness of fit test. Multicollinearity was assessed by variance inflation factor, and dealt with by dropping the offending variables from the analysis on the basis of clinical grounds.
Role of funders The work was financially supported by MIUR project "Dipartimenti di Eccellenza 2018-2020" to Department of Medical Biotechnologies University of Siena, Italy (Italian D. L. n. 18  March 17, 2020)  and by "Bando Ricerca COVID-19 Toscana" project to Azienda Ospedaliero-Universitaria Senese. It was also funded by private donors for COVID-19 research and charity funds from Intesa San Paolo "Fondo di Beneficenza n. b/2020/0119". The sponsors of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript. The authors collected the data, and had full access to all of the data in the study. They also had the final decision and responsibility to submit the study results for publication.