dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical methodology Statistical analysis was conducted in Matlab 2021b (R2021b, The MathWorks Inc. , Natick, MA, USA). Wilcoxon rank sum test and χ 2 test were used for between-group comparisons of demographic factors. The ComBat harmonization algorithm was used for parametric adjustment of the structural measures to account for inter-site differences  [39]. This method has been shown to remove site-differences when preserving the relevance of covariates of interest  [39, 40]. For the present implementation, we explicitly included as modulators in ComBat: APOE4, FHD, years of education, age and sex. Data from 6 participants were excluded due to missing information (5 APOE; 1 education). Linear regression models were used to investigate independently the association of structural measures with FHD and APOE4 genotype.
Results Demographic specifications for the whole cohort with analysable MRI data can be found in Table  1. In addition to the excluded datasets due to imaging issues and missing information, a further 14 datasets were excluded due to issues with the automated pipeline. Participants without FHD (FHD-) were slightly younger than participants with FHD (FHD+) and APOE4 carriers (APOE4+) were younger than non-carriers (APOE4-). As would be expected, the FHD+ group had significantly more APOE4 carriers.
Funding This work was funded by grants for the  PREVENT-Dementia program  from the  UK Alzheimer's Society  (Grant nos. 178 ,  264  and  397 ). The  PREVENT-Dementia study  is also supported by the  US Alzheimer's Association  (Grant no. TriBEKa-17-519007 ) and philanthropic donations. LS is supported by the  Cambridge NIHR Biomedical Research Center (BRC)  and  Alzheimer's Research UK  ( ARUK-SRF2017B-1 ). JOB receives infrastructural support from the  Cambridge NIHR BRC.
worldwide data sources on COVID-19 for every country reporting COVID-19 cases. On the basis of data, we performed random and fixed meta-analyses for CFR of COVID-19 by continents and income according to each individual calendar date. CFR was estimated based on the different geographical regions and levels of income using three models: pooled estimates, fixed-and random-model. In Asia, all three types of CFR initially remained approximately between 2. 0% and 3. 0%. In the case of pooled estimates and the fixed model results, CFR increased to 4. 0%, by then gradually decreasing, while in the case of random-model, CFR remained under 2. 0%. Similarly, in Europe, initially, the two types of CFR peaked at 9. 0% and 10. 0%, respectively. The random-model results showed an increase near 5. 0%. In highincome countries, pooled estimates and fixed-model showed gradually increasing trends with a final pooled estimates and random-model reached about 8.
| METHODS There were several data sources collecting worldwide data reports of COVID-19 (https://www. worldometers. info/coronavirus/, https: //www. ecdc. europa. eu/en/geographical-distribution-2019-ncovcases, https://coronavirus. jhu. edu/data/mortality, https://covid19. who. int/). We chose sources updating data of the cumulative cases and mortality data on a daily basis, allowing us to view past data in a downloadable file. We extracted the country, calendar date, the country's cumulative confirmed cases of COVID-19 of that date, and the country's cumulative deaths with COVID-19 of that date. Subsequently, proportion meta-analyses were performed to obtain the average CFR for each calendar day. We collected global data of COVID-19 confirmed cases and deaths from the European Centre for Disease Prevention and Control website (https://www.
ecdc. europa. eu/en/geographical-distribution-2019-ncov-cases). These data revealed each country's information from December 31, 2019 to October 30, 2020. To offset the effect of the vaccine on the disease, the endpoint was determined before the date on which the vaccination began. The CFR was defined using the following mathematical equation: Since the number of confirmed cases and deaths are not reported on a daily basis, we encountered missing data. These referred to the reported numbers from countries that contained "blanks", and existed from almost every country, mostly during the early phases of the pandemic. We decided to fill missing data by processing the data as the number of cases in the most recent report before the blank rather than dividing the number of cases equally among the missing days.
29 However, this approach is not without flaws. It has been reported that even adjusting the calculations temporally does not guarantee the preciseness of the dates of the actual infected patients. 17 Other challenges to accurate CFRs estimation include laboratory positivity despite clinical recovery and time delays between testing and reporting of the results. 13, 17 In this manuscript, we resorted to using the conventional methods of calculating CFR estimates. In the current study, we observed diverse CFRs estimations resulting from our meta-analysis of the COVID-19 pandemic when analyzed based on continents and levels of income. One possible explanation is that a statistical bias has occurred because our model included countries and groups without normalizing their numbers. Therefore, a more standardized and homogeneous analysis of the data is warranted in future studies.
Method Participants Thirty German-speaking participants took part in the study. The data of two participants were excluded from the analysis for having poor accuracy in one block (42%) and slow response times (mean RTs = 1276 ms; sample mean RTs ranging from 447 to 871 ms), respectively. The participant selection procedure is reported in the Supplementary Materials. Therefore, we analyzed the data of twenty-eight participants (20 female, 7 male, 1 reported being not represented by these two categories; mean age (SD) = 25. 1 (4. 6), range = 19-32). All participants had normal or corrected-to-normal vision and gave informed consent to participate for course credits or 8€. The study was approved by the Ethics Committee at the Department of Psychology of Humboldt-Universität zu  Berlin (Nr. 2019-42).
Procedure Participants sat at about 60 cm from the monitor (refresh rate 100 Hz). Stimulus presentation and response collection were implemented in Matlab, using the Psychophysics Toolbox  (Brainard, 1997; Kleiner, Brainard, & Pelli, 2007; Pelli, 1997). The experiment started with ten practice trials in which one of the operands was either 2 or 9. Stimuli were sequentially presented at the center of the monitor. Each trial started with a fixation point (''#'') presented for 1000 ms, followed by the first operand, the sign (''X'') and the second operand, presented for 300 ms each. The second operand was followed by a forward mask ("##") for 170 ms, the prime for 30 ms, a backward mask ("##") for 100 ms, and then the probe remained on the screen until the participant answered or for 2000 ms.
Results Analysis was performed in R (R Core Team, 2021) and RStudio (RStudio Team, 2021), using the following open source packages: BayesFactor  (Morey & Rouder, 2018) , ggpubr  (Kassambara, 2020) , ggridges  (Wilke, 2021) , here  (Müller, 2020) , janitor  (Firke, 2021) , knitr  (Xie, 2014 (Xie, , 2015 (Xie, , 2021)) , kableExtra  (Zhu, 2021) , plotly  (Sievert, 2020) , tidyverse  (Wickham et al. , 2019). The raincloud plots were generated with the code from  Allen et al. (2021). Analyses were performed only on correct equation trials and non-filler trials (see Table  A1 ). Trials with incorrect (311 trials, 5. 78%) or omitted responses (22 trials, 0. 41%), RTs faster than 200 ms (1 trial, 0. 02%), or wrong timing (1 trial, 0. 02%, i. e. , trials in which the onset of the stimulus on the screen was not correct) were excluded from the analysis.
For each participant (only considering correct equation trials), trials with RTs more than 2. 5 SD from the mean were considered outliers and excluded from the analysis (166 trials, 3. 3%). Effect sizes are reported following the recommendation of  Lakens (2013). The data and the R code used for the analysis are available at the Open Science Framework (see Supplementary Materials). For each participant, mean RTs were calculated across the factors prime and problem size. There were six prime conditions (neutral, identity, neigh-con, neigh-inc, unrel-con and unrel-inc) and two problem size conditions (small, product ≤ 30, and large, product > 30). The neutral prime was used as a baseline to test the effect of the other prime conditions, separately for small and large problems. Mean RTs are reported in Table  1  and Figure 1.
The effect of the prime was tested with Bayes factors (Table  1 ; see Supplementary Materials for frequentist analysis). For each non-neutral prime condition, we tested the null hypothesis that there was no difference compared to the neutral condition (H 0 : δ = 0). The alternative hypothesis was two-sided, H 1 : δ ≠ 0. The prior distribution for δ was specified as a Cauchy distribution with scale r = 0. 707. The Bayes factors were computed with the BayesFactor package  (Morey & Rouder, 2018). For small problems, we found strong evidence that unrelated primes (unrel-con and unrel-inc) produced interference, whereas there was moderate (neigh-inc) or inconclusive (neigh-con) evidence for the interference of neighbor primes. This pattern was interpreted as a reverse relatedness effect, inasmuch only unrelated primes produced strong interfer ence.
In Experiment 1, the SOA between prime and probe was constant. The following experiments will use different SOAs to test the predictions of our theoretical framework. How different SOAs are expected to influence the retrieval process is graphically presented in Figure  2. Since no previous studies used a masked prime in a verification task, no data was available to select the SOAs. The SOAs used in Experiments 2 and 3 aimed to explore how the interference of the prime changes as a function of the SOA. Therefore, our predictions only approximately described the hypotheses on the temporal dynamic of the retrieval process and we aimed to refine them empirically with the results of Experiments 2 and 3.
Method Participants Thirty-four German-speaking participants took part in the study. The data of six participants were excluded from the analysis for having poor accuracy in one block (see Supplementary Materials). Therefore, we analyzed the data of twenty-eight participants (17 female, 11 male; mean age (SD) = 30. 6 (6. 2), range = 21-40). All participants had normal or corrected-to-normal vision and gave informed consent to participate for course credits or 8€. The study was approved by the Ethics Committee at the Department of Psychology of Humboldt-Universität zu Berlin (Nr. 2019-42).
Result Verification Task Analyses were performed only on correct equation trials and non-filler trials (see Table  A2 ). Trials with incorrect (863 trials, 6. 85%), omitted responses (49 trials, 0. 39%), RTs faster than 200 ms (4 trials, 0. 03%), or wrong timing (5 trials, 0. 04%) were excluded from the analysis. For each participant (only considering correct equation trials), trials with RTs more than 2. 5 SD from the mean were considered outliers and excluded from the analysis (391 trials, 3. 3%). The data and the R code used for the analysis are available at the Open Science Framework (see Supplementary Materials). For each participant, mean RTs were calculated across the factors prime (neutral, neigh-con, neigh-inc, unrel-con, and unrel-inc), problem size (small and large), and SOA (SOA 070 , SOA 170 , and SOA 120 ).
Method Participants Thirty German-speaking participants took part in the study. The data of one participant was excluded from the analysis for having poor accuracy in one block (see Supplementary Materials). Therefore, we analyzed the data of twenty-nine participants (18 female, 11 male; mean age (SD) = 27. 6 (6. 3), range = 18-39). All participants had normal or corrected-to-normal vision and gave informed consent to participate for course credits or 8€. The study was approved by the Ethics Committee at the Department of Psychology of Humboldt-Universität zu Berlin (Nr. 2019-42).
Results Verification Task Analyses were performed only on correct equation trials and non-filler trials (see Table  A2 ). Trials with incorrect (728 trials, 5. 58%), omitted responses (80 trials, 0. 61%), RTs faster than 200 ms (11 trials, 0. 08%), or wrong timing (3 trials, 0. 02%) were excluded from the analysis. For each participant (only considering correct equation trials), trials with RTs more than 2. 5 SD from the mean were considered outliers and excluded from the analysis (399 trials, 3. 2%). The data and the R code used for the analysis are available at the Open Science Framework (see Supplementary Materials). For each participant, mean RTs were calculated across the factors prime (neutral, neigh-con, neigh-inc, unrel-con, and unrel-inc), problem size (small and large), and SOA (SOA 050 , SOA 170 , and SOA 220 ).
, C N are the isolated exceptional P 1 curves with normal bundle O(-1) ⊕ O(-1) contracted in the flop  [15, 16]. The Kähler cones K(X) and K(X ) share a common wall. The change in the cup product cubic form corresponds in topological string theory to the statement that the A-model 3-point correlation function on K(X) may be analytically continued to give the A-model 3-point correlation function on K(X )  [17]. III. THE MANIFOLD 7887. In this example X is a generic Calabi-Yau hypersurface in the ambient space A = P 1 × P 3 defined by the configuration matrix P 1 P 3 2 4 2,86 (III. 1) with identification number 7887 in the CICY list  [1, 2]. If L is a line bundle over X, we write its first Chern class as c 1 (L) = k 1 D 1 + k 2 D 2. Line bundle cohomology dimensions, computed algorithmically using the CICY package  [18]  for -3 ≤ k 1 ≤ 4, -1 ≤ k 2 ≤ 9 are shown in the chart below.
Data and methods The analyses presented in this article are based on the novel Sub-National COVID-19 Incidence and Determinants Dataset, which includes 502 sub-national areas of 45 countries in five continents (Europe, Asia, North America, South America and Oceania). The information used to build this dataset was drawn from a variety of sources in order to cover four major areas of interest: health outcomes of the pandemic (COVID-19 cases and deaths), air travel variables (number of incoming air passengers, centrality of local airports in the global airline network, air travel limitation policies), population mixing and non-pharmaceutical interventions (NPIs, based on the Oxford-tracker stringency index), and pre-pandemic area characteristics (demographics, public health and prior mortality rates).
Details on the sources and the variables can be found in the accompanying document of the dataset. All data come from public sources, with the exception of information on air passenger volume, which we were authorized to draw from Sabre Travel Data, a private company dataset on airport traffic collected directly from the airline industry (https:// www. sabre. com). These data were further processed and aggregated by areas in our dataset. In this article we model the precocity (i. e. , the calendar week of the 1st and 10th COVID-19 deaths) and severity (i. e. , the weekly COVID-19 number of deaths) of COVID-19 through ordinary least squares (OLS) and negative binomial mixed (i. e. , GLMM with negative binomial link function) models. We draw on a plethora of model specifications adjusting for variables that were found to affect the outcome significantly in previous studies (Table  1 ).
Our dataset includes information on the number of incoming passengers along all flight routes. The bulk of them diminished drastically after March 2020. Radical drops-with full cancellation of routesaffected Asia particularly (Fig. 1A  [April 2019] and B [April 2020]). Globally, the decline in the volume of passengers was dramatic (Fig. 1C ). At its lowest, in May 2020, the number of air passengers was 12 percent of the corresponding 2019 figure and, until October 2020, it had recovered to no more than 39 percent. In May 2020, only 3 percent of routes had not experienced any decline in air passengers compared to the same month one year earlier (Fig. 1D ). However, air traffic was not entirely cancelled, as Fig. 1B  shows. Some passengers continued to move between most parts of the globe. Potentially, this might have led to a higher incidence of the disease where larger numbers of incoming travelers continued to arrive, especially if their origin was from highly infected zones.
The impact of late 2019 air passenger traffic on the precocity of COVID-19 outbreaks (calendar week of occurrence of 1st and 10th death) [2A: traffic from Chinese airports; 2B: traffic from other airports]. Source: Sub-National COVID-19 Incidence and Determinants Dataset. OLS regressions without and with continent fixed effects. Standard errors in parentheses. *p < 0. 05, **p < 0. 01, ***p < 0. 001.
The impact of air passenger traffic on the severity of COVID-19 deaths (number of deaths in the first week of April-October 2020), controlling for population mixing (NPI), structural predispositions and recursive effects. Negative binomial regressions with continent fixed effects. Risk ratios and confidence intervals, For full model specifications with coefficients, see TableC2in Supplementary Information. Source: Sub-National COVID-19 Incidence and Determinants Dataset.
The impact of air passenger traffic on the severity of COVID-19 deaths in the first 30 epidemiological weeks (week1 = first COVID-19 death) controlling for population mixing (NPI), structural predispositions and recursive effects. Negative binomial regressions with fixed effects by continent. Risk ratios and confidence intervals. For full model specifications with coefficients, see TableC4in Supplementary Information. Source: Sub-National COVID-19 Incidence and Determinants Dataset. but without a clear trend. The effects of structural factors are relatively stable, with the exception of the cardiovascular mortality rate, which boosts COVID-related deaths strongly in the first 14 weeks of the epidemic but declines markedly and gradually in the following. All other factors have an influence similar to the previous analysis and do not show any regular dynamic over time.
The impact of air passenger traffic on the severity of COVID-19 deaths (number of deaths in the first week of April-October 2020), controlling for population mixing (NPI), structural predispositions and recursive effects. Negative binomial regressions with continent fixed effects. Risk ratios and confidence intervals, For full model specifications with coefficients, see TableC2in Supplementary Information. Source: Sub-National COVID-19 Incidence and Determinants Dataset. International mobility Travel restrictions (Ref: tot ban) Local factors Population size No airport in region Stringency index (lag 3 wks) None/minimal Real GDP pc Passengers (lag 3 wks) PPP (lag 1 mnt) Population density (pop/km2) Hospital beds Importation per 1000 risk (lag 1 mnt) Share of 65+ Cardiovascular Importation death rate risk x passengers Partial ban Cancer death (lag 3 wks) rate Prevalence of Airport adult obesity centrality (lag 1 mnt) Week of first COVID death 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 IRR, weekly deaths IRR, weekly deaths IRR, weekly deaths Deaths in first week of the month 40 45 50 55 60 65 Apr May Apr May Jun Jun Jul Aug Jul Sep Aug Oct Sept Oct Month Figure 3.
METHODS Empirical patterns of contacts. We used empirical data describing time-resolved face-to-face proximity contacts between individuals in two educational settings, collected in France using wearable RFID sensors in a prepandemic period. The Primary school dataset describes the contacts among 232 students (6-11 years old) and 10 teachers in a primary school in Lyon, composed of 5 grades, each of two classes  15. The Secondary school dataset describes the contacts between 325 students (17-18 years old) of 9 classes in a secondary school in Marseille  16. Classes belong to the second year of "classes préparatoires", specific to the French schooling system for preparation to University entry, and are divided in three groups, based on the specialization. We built temporal contact networks, composed of nodes representing individuals (classified by class and student/teacher), and links representing empirically measured proximity contacts occurring at a given time (Figure  1b, c ).
As each dataset covers only a few days, we developed an approach to temporally extend the datasets by generating synthetic networks of contacts that reproduce the main features observed empirically (class structure, within-vs. between-classes links, contact duration heterogeneity, and similarity across days; Appendix, pp. 14-18). The secondary school synthetic network was further extended to generate a synthetic first year (to consider the full curriculum of the "classes préparatoires") including teachers whose contacts were inferred from an additional dataset for the same school. The resulting network for the secondary school was composed of 650 students and 18 teachers. Field screening data in schools during the spring 2021 wave in France. In response to a rising third wave in France in the spring 2021 due to the Alpha variant, local authorities in the Auvergne-Rhône-Alpes region proposed pilot screenings at schools on a voluntary basis to detect cases.
In the protocols' analysis, we performed 1,000 stochastic runs for the primary and 2,000 for the secondary school for each parameter set, over the course of a trimester (90 days). We computed medians and 95% bootstrap confidence intervals from simulation outputs to compare protocols with a Mood's median test. Interquartile ranges (IQR) were used to describe observed adherence. Role of the funding source. The funders had no role in study design, data collection, data analysis, data interpretation, writing of the manuscript, and decision to submit. The first author, the second author, and the corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
009) compared to teachers (Figure  1e, f ). Using the empirical contact patterns, we inferred the school-specific transmissibility from screening data in primary schools satisfying the inclusion criteria: 71 primary schools and 12,146 tested students with the reference inclusion criteria; 103 primary schools and 15,916 tested students with the sensitivity inclusion criteria. Secondary schools were excluded because of limited participation. We estimated a school-specific R Alpha during the Alpha 2021 spring wave in France between 1. 40 (1. 35 -1. 45) and 1. 44 (1. 40-1. 48) in the primary school, and 1. 46 (1. 41 -1. 51) and 1. 50 (1. 46-1. 54) in the secondary school (for the reference and sensitivity inclusion criteria, respectively), with the reactive class closure protocol and mask mandate in place (Figure  2a ).
(f) Daily average time that a teacher or student spends in interaction. In panels d-e-f, histogram bars refer to the empirical networks; points and error bars (95% bootstrap confidence intervals) refer to the synthetic networks. In panels d-e, the increase of average number of contacts and duration in the synthetic secondary school networks compared to their empirical counterparts is due to the ad hoc addition of contacts between school years. In panel f, no empirical data is shown for teachers, as they did not participate to the data collection, and their contact behavior was inferred from another dataset. (g) Number of schools participating to the pilot screenings during the spring 2021 wave in the Ain, Loire, and Rhône departments. (h) Observed adherence to screening. Boxplots represent the median (middle line), interquartile range (box limits) and 2.