dataesr/scientific-paragraphs-categorization huggingface dataset:
Daily relative change is estimated as the percentage change with respect to the corresponding baseline weekday for any given report date. To protect users' privacy, absolute mobility values are not available. We obtain mobility data for each category of the GCMR for 108 sub-national regions (the GCMR's sub region 1 variable), covering a geographic area inhabited by 99% of the population of England and Wales, from February 15 (the first available date in the dataset) to August 14, 2020. We aggregate the GCMR data by week (from Saturday to Friday, for consistency with mortality data) and region (taking the weighted average across all counties belonging to a given region, with weights equal to their population sizes) to make it comparable to the mortality data. Relying on a fixed baseline period ignores yearly seasonality of movement, which may be affected by weather patterns, national holidays, vacation periods, etc.
Important bank holidays, extreme weather events, or other major events during the 5-week long baseline period can affect the estimates of future relative changes in visits to grocery stores. We have no access to the raw mobility data used to produce the GCMR, but we find no evidence of any major events in England or Wales that could have systematically biased the Google mobility data during the baseline period. Furthermore, the baseline week is based on the median value, which would be largely unaffected by short-lived temporary fluctuations in absolute mobility values. Finally, the baseline period is not affected by restrictions on movement, which where first introduced on March 12, 2020.
e. α 1 = 1). Exposures over weeks and years are obtained by linear interpolation of mid-year populations in years t = 2015,. , 2019 and linear extrapolation for the year 2020. Whereas the coefficients α w capture the typical pattern of mortality in registered week w (including cyclical behavior of weekly mortality data, bank holidays effects, etc. ), the smooth time component aims to describe long-term mortality trends. Given that mortality data are recorded by registration rather than occurrence, such a specification provides a better description of the data than including trigonometric functions for the seasonal pattern of deaths. Estimation of the model described in Equation (  1 ) is performed in R (R Development Core Team, 2020) using the mgcv package  (Wood, 2019). For each region independently, we model data for the pre-pandemic period (from week 2015-01 to week 2020-06, corresponding to a total of m = 267 observations) and predict the expected number of deaths dw for the COVID-19 period (from week 2020-07 to week 2020-33).
To account for uncertainty in the estimates, related to both the computation of excess mortality and the mixed-effects regression in our estimates, we employ a bootstrap approach to derive 95% pointwise confidence intervals for the model results. Specifically, we generate 1,000 simulated death counts from the deviance residuals of the GAM; for each simulation, we compute the regression in Equation (  2 ) and derive fitted excess mortality rates. We fit the model in R using the lme4 package  (Bates et al. , 2015) , and we compute B-spline bases using the splines package (R Development Core Team, 2020). Finally, all data and source codes necessary for the reproducibility of our results are available at the following [blind for peer-review] repository: https://osf. io/4pfb7/?view_only= 3155acef790e4e5f8ac96f4c2c6ede71 3 Results.
Discussion Local and national governments around the world have implemented a variety of policies aimed at reducing social contacts to curb the transmission of and deaths from COVID-19. We investigated the relationship between excess mortality and changes in human mobility to assess the effectiveness of non-pharmaceutical interventions. Specifically, we leveraged digital data derived from the Google Table  2. Population size, estimated number of excess deaths, and estimated number of deaths averted by the mobility reductions (counterfactual analysis) with 95% confidence intervals by region in England and Wales during weeks 13-33 of 2020. Estimates have been rounded to the nearest hundredth to avoid giving a false sense of precision in the presence of uncertainty (as in  Kontis et al. , 2020) ; as such, figures for the Total row may differ from the sum of the regions.
Source: Authors' own elaboration based on data from Office for National  Statistics (2020a Statistics ( , 2021) )    COVID-19 Community Mobility Reports (Google LLC, 2021) to explore the association between mobility and excess mortality at the regional level in England and Wales. We found a strong positive relationship between the mobility of Google Maps users and population-level excess mortality, which is considered to be the best indicator of the impact of the pandemic on mortality (National Academies of Sciences, Engineering, and Medicine, 2020). Our analysis determined that a time lag of at least five weeks is needed to reveal a positive association between mobility and mortality, while smaller lags display a negative relationship. A five-week time period is consistent with preliminary estimates of the disease duration from infection to death, with the incubation period (i.
Back-engineering the underlying absolute measurements does not seem possible or desirable, given privacy concerns in sparsely populated areas. Moreover, no information is provided on the population composition of Google Maps users, such as age-group or sex breakdowns, thus limiting our ability to assess the representativeness of the data. This lack of detail makes the data less informative than it could possibly be, if all raw measures were made available together with the description of the algorithms used to produce them. Nonetheless, we believe that the Google mobility data provide a first and valuable approximation to the changes in human mobility occurred during the COVID-19 pandemic. Such data are necessarily affected by biases related to population sampling, which depends on the market share of the operator providing the data and the different usage across socio-demographic groups.
, 2020). Finally, we are reassured by the similar general patterns of the Google mobility data as compared to other digital sources, such as Apple and Facebook (which only report data for England and Wales as a whole and not at the sub-national level, see Figure A. 8 in Appendix A). Thus, we are confident that this data source approximates well the overall mobility trends at the regional level. Our study provides evidence on the positive impact of NPIs to mitigate the mortality burden of COVID-19. We conclude by encouraging private companies such as Google to continue sharing data to foster academic research in areas of public interest. Further improvements include more transparency about the data-generating process and the sharing of raw data, keeping as a priority the privacy of users. An increasing number of studies are showing the great potential of digital data for public health research  (Coppersmith et al.
MWE promotes spatial proximity on the cortical mantel while coping with the varying noise levels across subjects. On realistic simulations, MWE decreases the localization error by up to 4 mm per source compared to individual solutions. Experiments on the Cam-CAN dataset show a considerable improvement in spatial specificity in population imaging. Our analysis of a multimodal dataset shows how multi-subject source localization closes the gap between MEG and fMRI for brain mapping.
If other imaging data are available such as fMRI  [50, 70]  or diffusion MRI  [12] , they can be used as prior information for example in hierarchical Bayesian models  [55]. While such techniques have had some success, source estimation in the presence of complex multi-dipole configurations remains a challenge. To address it, one idea is to leverage the anatomical and functional diversity of multi-subject datasets to improve localization results. This idea of using multi-subject information to improve the spatial accuracy of M/EEG source imaging has been proposed before in the neuroimaging literature. Larson et al. [38]  hypothesized that different anatomies across subjects allow for point spread functions that agree on a main activation source but differ elsewhere. Averaging across subjects thereby increases the accuracy of source localization.
The paper is organized as follows. In Section 2, one explains how multi-subject M/EEG source imaging can be cast as a multi-task regression problem. Methods from the literature that adopt this approach are briefly recalled. Next some background on UOT metrics are presented. Then the reweighted minimum Wasserstein estimates (MWE 0. 5 ) method is presented. UOT is proposed to cope with inter-subject spatial variability, 0. 5 pseudo-norm is used to limit amplitude estimation bias and concomitant estimation is exploited to handle subjects affected by different noise levels. Finally experimental results on both simulations and two public MEG datasets  [60, 66]  are reported. A preliminary version of this work was presented at the international conference on Information Processing in Medical Imaging (IPMI)  [31].
For the inverse problem to be cast as a least squares problem, we perform a whitening transformation of the data. We estimate Σ during the baseline (before stimulus) using a cross-validation estimator provided in the MNE software  [14, 21, 22]. Given a noise covariance matrix estimate Σ, the whitening step amounts to computing the transformed data Σ-1 2 y and Σ-1 2 L. In the rest of this paper, we assume that the data are whitened. Source localization. Source localization consists in solving in x the inverse problem (1) which can be cast as a least squares problem: x = arg min x∈R p y -Lx 2 2. (2) Since n p, problem (2) is ill-posed and additional constraints on the solution x are necessary. These constraints materialize the type of prior knowledge one has on the source estimates and are commonly applied through p norms  [28, 49, 56].
Simulations with semi-real data Benchmarks. As discussed in introduction, standard sparse source localization solvers are based on an 1 norm regularization, applied to the data of each subject independently. We use the independent Lasso estimator as a baseline. To illustrate the effect of reweighting separately, we also study the performance of a reweighted Lasso, i. e an independent regression with a 0. 5 penalty. Note that reweighted Lasso was coined iterative reweighted mixed-norm (irMxNE) in the context of M/EEG source imaging in  [56]. We compare MWE 0. 5 to the Group-Lasso estimator (6)  [3, 69]  which was proposed from multi-subject source imaging to promote functional consistency across subjects  [39]. We also evaluate the performance of a more flexible block sparse model where only a fraction of the source estimates are shared across all tasks: Dirty models  [30].
Hyperparameters of W. The parameters defining the Wasserstein distance W are ε (entropy regularization) and γ (marginal relaxation). Large values of ε accelerate the convergence of the Sinkhorn algorithm but induce an undesired blurring of the source estimates. Very Low values however lead to numerical instability. We set ε to 0. 002 divided by the median of the ground metric M which provides a good trade-off between computation speed and sharpness of the barycenter. With the same reasoning, low values of γ allow for a "free" transport, thus the barycenter converges towards a blurred uniform distribution. We set γ to a lower bound γ M = -max M 2 log 0. 8 ≈ 1 that guarantees a minimal transport of mass using a strategy proposed in  [32]. Simulation data and MEG/fMRI datasets. In our simulations, we use semi-real data, i.
e. we simulate MEG data y with real leadfield matrices L. To do so, we rely on the public Cam-CAN dataset  [60]. We use the MRI scan of each subject to compute a source space and its associated leadfield comprising 2562 sources per hemisphere  [22]. Keeping only MEG gradiometer channels, we have n = 204 observations per subject. To keep the simulation settings simple, we restrict all leadfields to the left hemisphere. We thus have S = 32 leadfields with p = 2562. We simulate an inverse solution x s with 5 sources (5-sparse vector) by randomly selecting one source per label (a. k. a. region of interest) among 5 pre-defined labels using the aparc. a2009s parcellation of the Destrieux atlas  [13]. To model functional consistency, 50% of the subjects share sources at the same locations, the remaining 50% have sources randomly generated in the same labels (see Figure  3  for an example).
Experiments on MEG data Datasets description. The different strategies were evaluated on two publicly available MEG datasets: DS117  [66]  and Cam-CAN  [60]. DS117 provides MRI, MEG, EEG and fMRI data of 16 healthy subjects to whom were presented images of famous, unfamiliar and scrambled faces. The fusiform face area (FFA) which specializes in facial recognition activates around 170ms after stimulus  [29, 34]. We pick the time point in the contrast response famous vs scrambled with the peak response for each subject within the interval 150-200ms after stimulus. Similarly, Cam-CAN provides MEG, EEG and MRI data of around 650 healthy subjects with several types of tasks. We select the youngest 32 subjects (aged between 18 years and 29 years) and use their MEG recordings to study the auditory N100 response.
We average the responses of 3 stimuli: 300Hz, 600Hz and 1200Hz with a total of 60 trials. We pick the time point with the peak response within 80-120 ms after stimulus. For both datasets, the leadfield operator of each subject was obtained from their T1 MRI scan using a cortically constrained source space formed by about 2500 candidate dipoles per hemisphere. Model selection. For all lasso-type models, there exists λ max such that for λ ≥ λ max the inverse solution is 0 everywhere. For instance, with 1 and 0. 5 we have λ max = L y ∞ n  [54]. This allows to set λ in a relative scale between 0 and 1, making this choice less sensitive to the data. In practice, one can pick a certain value in [0, 1] based on the number of active sources, which is the heuristic used in the following experiments with real data.
Even though the choice of λ max does not theoretically guarantee null source estimates with MWE 0. 5 , we observe experimentally that reweighting and the OT regularizer promote even more sparsity With reweighted MWE, a similar phase transition occurs for both datasets after a certain µ max. with a lower λ compared to Lasso models. We use the same relative scaling to set λ for MWE 0. 5. The OT regularization parameter µ controls the level of consistency across subjects. Figure  5  shows that for the reweighted MWE 0. 5 , there exists a phase transition at a certain value µ max , after which the source estimates lose all sparsity and cover the entire cortical mantle uniformly. MWE 1 however shrinks the source estimates towards 0 but fails to produce sparse solutions. In practice, based on the complexity of the topographic maps of the MEG data, we select λ and µ that lead to -on average -a 2-sparse solution with Cam-CAN (λ = 30%, µ = 3) and a 6-sparse solution with DS117 (λ = 20%, µ = 0.
Thanks to their ability to model spatial proximity between source estimates, the MWE model allows to promote functional similarities across subjects using the geometry of the cortical mantle. Fortunately, the computation of the Wasserstein barycenter does not lead to a computational bottleneck. In our experiments, 40% to 60% of time is spent on optimal transport versus proximal coordinate descent. Thanks to careful optimization procedures based on Sinkhorn iterations and block coordinate descent algorithms, the model proposed here runs in a few minutes on empirical M/EEG datasets. Beyond the use of Wasserstein metrics to cope with spatial misalignments, the proposed MWE q model brings in two important ingredients from the statistics literature employing sparsity promoting regularizations: concomitant estimation and convex reweighted schemes.
By using concomitant estimation, the MWE q model can cope with the different noise levels and signal-to-noise ratios for the different subjects. This is particularly critical to have the number of hyperparameters of the model that is fixed and does not scale with the number of subjects. In theory, for source imaging with a solver such as dSPM or sLORETA, that is applied independently for all subjects, the regularization parameters could be tuned for each dataset. The MWE q model has a list of regularization parameters that does not depend on the number of subjects. Besides, results from Figures  4  and 5  demonstrate the benefit of MWE 0. 5 vs. MWE 1. Employing a more aggressive sparsity promoting regularization improves in particular the source amplitude estimation as shown by the MSE metric.
Also it greatly simplifies the setting of the regularization parameter µ as solutions become suddenly much less sensitive to this choice of parameter. From a more neuroscientific perspective, the model presented here has potentially interesting consequences. Results on Cam-CAN demonstrate that the barycenters obtained with MWE 0. 5 have a higher spatial specificity. As seen in Figure  7 , activation foci in x are well limited to primary auditory cortices while solvers that are not based on a group-level multi-task regression model lead to spurious activations next to secondary somatosensory cortices and on middle temporal gyrus. On DS117 dataset, the cognitive task performed by the subjects is more advanced, complicating the discussion of the results in terms of localization. Yet, the availability of the fMRI data allows for a quantification of the activation foci between MEG and fMRI.
DNA replication must be faithful and follow a welldefined spatiotemporal program closely linked to transcriptional activity, epigenomic marks, intranuclear structures, mutation rate and cell fate determination. Among the readouts of the spatiotemporal program of DNA replication, replication timing analyses require not only complex and time-consuming experimental procedures, but also skills in bioinformatics. We developed a dedicated Shiny interactive web application, the START-R (Simple Tool for the Analysis of the Replication Timing based on R) suite, which analyzes DNA replication timing in a given organism with high-throughput data. It reduces the time required for generating and analyzing simultaneously data from several samples. It automatically detects different types of timing regions and identifies significant differences between two experimental conditions in ∼15 min.
INTRODUCTION DNA replication is a highly regulated process involved in the maintenance of genome stability  (1) (2) (3). Its accuracy relies partly on a spatio-temporal program that regulates timing and location of origin firing  (4, 5). Based on this program, replication is organized into large-scale domains that replicate at different times in S phase  (6) (7) (8). Protocols developed to study the replication timing (RT) in specific cell lines have been established in different laboratories  (9) (10) (11) (12) (13). A script dedicated to RT analysis was previously developed by David Gilbert's laboratory  (10, 12) , but it required skills in bioinformatics and R language. In order to make the analysis of experimental results easier for biologists, we implemented an interactive suite, START-R (Simple Tool for the Analysis of the Replication Timing based on R) Analyzer and START-R Viewer, showing user-friendly interfaces.
This START-R suite is dealing with RT experiments made with microarrays or with Repli-seq data (either Early/Late or S/G1 ratios) from different organisms. These web applications would make easier differential analyses of RT, by comparing conditions such as treated/untreated cells or mutated/normal cells. They are free and may be improved by developers, according to specific needs. In addition, RT profiles correlate with A/B compartment profiles predicted by chromosome conformation methods. Regions of the genome defined by Hi-C profiles as A compartments are also identified as Early replicated domains, whereas regions defined as B compartments are Late replicating domains  (14). Furthermore, some replicating domains coincide with a subset of topologically associating domains and more closely with the ones located at compartment boundaries  (15).
START-R Analyzer and START-R Viewer (doi:10. 5281/zenodo. 3251905) were developed using the Shiny R package (W. Chang, J. Cheng, J. Allaire, * To whom correspondence should be addressed. Tel: +33 1 57 27 80 74; Email: jean-charles. cadoret@ijm. fr † The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors. ‡ The authors wish it to be known that, in their opinion, the last two authors should be regarded as Joint Last Authors. Y. Xie and J. McPherson, Shiny: web application framework for R, R package version 1. 2. 0, 2018, https: //CRANR-project. org/package=shiny). The source code and the installation procedure are available on GitHub (https://github. com/thomasdenecker/START-R). All R packages used in the START-R suite are listed in the Readme file on GitHub.
Users should install Docker and then follow installation procedures described for each operating system (Windows, Linux and Mac OS) in the Readme file (Figure  1 ). Although the installation of these web-based applications has been simplified as much as possible, it may even so require some computer knowledge (especially on Linux). Once installed, the START-R suite is an easy-to-use tool requiring no computer skills (see Supplementary Figure  S1  and the Wiki section on GitHub for more details).
Validation of START-R suite using microarray data from other laboratories We analyzed with the START-R suite the microarray data obtained by Hiratani et al. (16)  of D3esc and D3npc9 cell lines during mouse cells' differentiation (GEO accession numbers: GSM450273 and GSM450285, respectively). As data extracted from the Nimblegen platform are in PAIR format, we used a script to convert data into a valid format for START-R Analyzer (convertPair. R, available on GitHub in 'supplement script' file, https://github. com/ thomasdenecker/START-R).
Early/Late-seq data and conversion In order to validate our software, we used data corresponding to Repli-seq 46C mouse cells--Early S fraction or Late S fraction, respectively (GSM2496038 and GSM2496039). Read mapping was obtained using Bowtie2 (2. 3. 4. 2 version) with the very sensitive end-to-end option. Then, PCR duplicates were removed by RmDUP from SAMTools (2. 0. 1 version). BamCoverage parameters were fixed (3. 1. 2. 0. 0 version with default parameters) to a bin size of 10 kb (corresponding to the genomic distance between microarray probes) and reads per kilobase million (RPKM) normalization to generate a bedGraph file. A headline was added to the file to name the four columns (chr, start, end and gProcessedSignal for Early file or rProcessedSignal for Late file, respectively). Then, a script to convert and merge the bedGraph files from Early and Late samples to a format compatible with START-R Analyzer was developed.
Validation of START-R suite using S/G1 data from multiple species Different laboratories analyze variations of DNA copy number between G1 and S phase cells (S/G1 ratio) to study the RT program with Repli-seq. We used data obtained from different organisms such as Drosophila, zebrafish and humans  (17) (18) (19) , to validate the START-R suite (GSM3154888, GSM3154890, GSM2282090, SRX3413939-40). As previously, BAM coverage files from G1 and S fractions are converted to a format compatible with START-R Analyzer with the aforementioned 'convert bamcoverage file. R. ' script.