dataesr/scientific-paragraphs-categorization huggingface dataset:
0%) compared to Once (M = 99. 5%, p <. 0001) and UM (M = 99. 5%, p <. 0001). The average accuracy was significantly higher (F 1,11 = 8. 28, p =. 015, η 2 = 0. 05) on Tablet Manuscript submitted to ACM (M = 98. 8%) than on the Phone (M = 97. 1%). We did not find any significant main effect of Orientation (p =. 85) on accuracy. We also observed a significant Device × Input Method interaction (F 2,22 = 12. 36, p <. 001, η 2 = 0. 08) which is due to the increase of performance for Swipe between the Phone (M = 92. 6%) and Tablet configurations (M = 97. 3%). Subjective Preferences. After each individual condition of our experiment, we gathered subjective preferences of our users. We originally analysed the data for each Device × Orientation combination, and found out that the results are very similar for each individual condition, and thus decided to aggregate the results (Table  2 ).
Manuscript submitted to ACM Advantages over physical hotkeys. In addition, SoftCuts make good use of the space used on a soft keyboard. Compared to physical hotkeys, where users need to memorize the mapping between hotkeys and commands, SoftCuts display the name of the command with an icon, making it easier to do command selection even with low familiarity with the mapping of the current application. Advantages over other command selection techniques. Compared to other command selection techniques, SoftCuts also potentially leverage users' prior knowledge of hotkeys on desktop computers and can be implemented on any applications, making it a generic and consistent command selection technique across devices. Implementing SoftCuts comes at the small cost of adding a modifier key (either Control or Command) at the bottom left part of the soft keyboard.
The visual stimulus consisted in six blocks of a blue light (20s, 2Hz, 10ms pulse duration) alternating with darkness rest periods (40s) using a light emitting diode (LED) and Arduino programming board (ArduinoCham, Switzerland). The Arduino programming board synchronized the trigger from the Bruker scanner during the fMRI scanning. BOLD fMRI data were acquired twice, DfMRI three times with b = 1000, and six times with b = 1800 in each session (before and after TGN-020 administration). TGN-020 (200 mg/kg i. p. , Merck KGaA, Darmstadt, Germany), an AQP4 inhibitor  [22] , was administrated after an initial set of baseline BOLD fMRI and DfMRI measurements. Functional data were collected again after 15 minutes from TGN-020 administration. TGN-020 was administered to a group of nine mice, while the other nine (control group) received a saline injection.
MRI data analysis As previously described  [25] , SPM12 software (Welcome Trust Center for Neuroimaging, UK) and a tailor-made program (Matlab, The MathWorks, Inc. , USA) were used for fMRI analysis. Image processing, consisting of slice timing correction, motion correction, normalization of brain coordinates, and smoothing (Gaussian kernel with FWHM of 0. 6 mm), was performed for all fMRI data before statistical analysis. Statistical t-maps were calculated using a generalized linear model. Activation was detected using a statistical threshold of p < 0. 05 (false discovery rate (FDR) corrected for multiple comparisons). Activities before and after administration were compared using paired t-test (p<0. 05 FDR corrected). Region of interest (ROI) of primary visual cortex (V1) for the time course analysis were defined using the Allen Mouse Brain Atlas  [26, 27].
0. 1 mg/kg bolus, 0. 2 mg/kg/h continuous infusion, Orion Pharma, Espoo, Finland) as in the fMRI protocol. Electrodes were connected to a differential AC amplifier Model 1700 (AM systems, Sequim, WA, USA), via a Model 1700 head stage (AM systems, Sequim, WA, USA). The LFP and MUA signals were acquired at 10 kHz sampling rate using dedicated data acquisition software (Power Lab, AD Instruments, Dunedin, New Zealand). The reference electrode was positioned on the scalp. The visual stimulation paradigm was the same as for fMRI, with six blocks of alternating blue light stimulation (20s, 2Hz, 10ms pulse duration) and rest (40s) periods in a dark room.
Methods: The study was based on data from EPIPAGE-2, a French national prospective cohort study of preterm births during 2011 that included 2593 children born between 24 and 31 weeks' gestation. The primary outcome was NNSHs at 2 years. Multivariable loglinear regression models with generalized estimation equations were used to study the association between the characteristics studied and NNSHs. Multiple imputations were used to take into account missing data.
Data collection Perinatal, maternal and neonatal data were collected from medical records by local obstetric and neonatal teams in both maternity and neonatology units. Maternal data included demographic and social information and delivery data. Neonatal data included the infant's condition at birth, neonatal complications and care received in neonatal intensive care units. Information related to family circumstances as well as child health and development at 2 years' CA was collected by using a self-administered questionnaire completed by the parents. The questionnaire was sent by post for completion manually or accessed online for completion through secure servers.
Missing data 0] [31]  For the outcome variable, 20% of data were missing. For other co-variables, missing data were minimal (<1%) for perinatal factors (gestational age, small-for gestational-age and parity) and between 3 and 11% for neonatal care factors (NIDCAP, oral stimulation and feeding by nasogastric tube). In total, 50 independent imputed datasets were generated. Estimates were pooled according to Rubin's rule. 32 Imputation details are in eTable 1. Perinatal and sociodemographic characteristics of the population studied, of those lost to follow-up and of those with missing data on the outcome were described. The distribution of NNSHs was described according to all studied factors. All percentages and crude risk ratios (RRs) were weighted to account for differences in the sampling process between gestational age groups.
Limitations of the data In total, 660 children were missing (lost to follow-up and with missing data on the outcome). Their characteristics differed from those of children included and were associated with a lower frequency of NNSHs; because children lost to follow-up were probably less prone to have NNSHs, the frequency may have been slightly overestimated. However, the estimated frequency of NNSHs by imputations and in complete cases was the same. The associations found between the studied factors and NNSHs were also similar in the two analyses. We used a binary outcome variable, NNSHs (No/Yes), based on three questions referring to sucking a thumb, finger or pacifier. In our study, among children who were considered to have NNSHs, 86% used pacifiers and 13% thumbs and fingers. Among children who used a pacifier, 78% of parents responded to the modalities concerning frequency "often" or "all the time or almost", and in cases involving those who used the thumbs and fingers, 38% of parents responded "often" and "all the time or almost".
ACKNOWLEDGEMENTS We are grateful to the families of preterm infants for their participation in the  EPIPAGE-2 cohort study and for the cooperation of all maternity and neonatal units in France. We thank the  EPIPAGE-2 Study Group  for its substantial contribution to the conception, design, and acquisition of data. We would also like to acknowledge  Laetitia Marchand-Martin  and  Dr Andrei S Morgan  for their collaborative help in statistical methods.
FUNDING This project was funded by the following organizations: The  French Institute of Public Health Research/Institute of Public Health  and its partners: the  French Health Ministry , the  National Institute of Health and Medical Research (INSERM) , the  National Institute of Cancer , and the  National Solidarity Fund for Autonomy (CNSA) ; The  National Research Agency  through the  French EQUIPEX program  of investments in the future (reference  ANR-11-EQPX-0038 ); the  PREMUP Foundation ; and the  Foundation de France  (reference  00050329 ). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Materials and Methods The direct survey of consumers was concerned with the perception of natural preservatives as an element for both improving the shelf life of food products and replacing synthetic preservatives with natural preservatives, with the aim of understanding the type of message communicated to the consumer. The questionnaire was distributed throughout Italy by ADICONSUM (Consumers and Environment Defence Association), who administered the questionnaire via email to its members and through Facebook, collecting 479 complete answers. Participation took place in an absolutely anonymous form, and the participants were informed in advance that the data collected would have been treated in an extremely confidential manner and used only for scientific research purposes. However, respondents were recruited from diverse community centres.
According to Dickson-Spillmann et al. [43] , consumers consider organic foods to be healthier, uncontaminated, and purer than conventional foods, and not altered or polluted by synthetic additives or by excessive human interference. Finally, we also asked about information relating to personal data (gender, age, profession, level of education, etc. ). A Principal Component Analysis (PCA) was applied (computed using IBM SPSS Statistics-version 20. 0. 0) on the original data to reduce the initial diversity of a certain number of variables  [44]. The factorial scores of the components obtained from PCA were used for a Cluster Analysis related to consumers' perceptions about biopreservatives.
We wondered whether GWAS-based AD risk variants would be equally responsive to Ab or TAU pathology. We included established AD risk variants, i. e. , loci with P < 5 × 10e-8 in various GWAS studies, as well as subthreshold AD risk variants as these contribute significantly to AD risk predictions through polygenic inheritance  (Escott-Price et al, 2015 , 2017). We examined multiple sets of such genes taken from  Marioni et al (2018) , which combines UK Biobank AD-by-proxy data with the IGAP database and which confers risk loci onto genes based on proximity (thus from here on, AD risk variants are referred to as AD risk genes, noticing that this is based on these assumptions). Using arbitrary Bonferroni-adjusted P-value (P mar ) cut-offs with decreasing significance for AD association, gene sets of increasing size were created (see Fig  1D  and Appendix Table  S1 ).
AD risk genes are co-regulated in a specific functional gene expression module Next, we performed unbiased weighted gene co-expression network analysis (WGCNA;  Zhang & Horvath, 2005; Langfelder & Horvath, 2008)  on each mouse model separately to investigate whether AD risk genes would cluster into functional modules. We obtained in total 63 modules (Appendix Figs  S2  and S3 ). GSEA with the GWAS gene set generated from  Marioni et al (2018) , at different cut-offs for statistical significance as explained above  (Fig 3A and Appendix Table S1) , demonstrated that the largest set of risk genes (e. g. , n = 1,799 genes at P mar < 0. 05) enrich among 4 APPtg-and TAUtg-based modules  (Turquoise, Blue, Fig 3A and Appendix Figs S2 and S3). However, when taking gene sets defined by increasing statistical significance (P mar < 0.
We functionally characterized this Ab-induced transcriptional response. The APPtg-Blue module shows a highly significant overlap with the microgliaspecific gene set defined by  Zeisel et al (2015)  (LOR: 1. 90, P adj = 1. 74e-77; Fig 4B and Appendix Table  S2 ) and to a lesser extent the astrocyte gene set (LOR: 0. 54, P adj : 0. 014). Moreover, this module is highly enriched for GO categories involving immune response, cytokine production, and inflammation  (Fig 3D and Datasets EV2 and EV3). It furthermore shows a highly significant overlap with a recently published Ab-response network  (Salih et al, 2019 ; LOR: 2. 0, P = 2. 2e-16) as well as with the microglia-immune module derived from the brains of late-onset AD patients  (Zhang et al, 2013 ; LOR: 2. 14, P = 8. 0e-73), demonstrating that this transcriptional response to increasing Ab is similar in this Ab mouse model and AD patients.
The latter criterion is restrictive because we require that the gene is not only part of the APPtg-Blue response module but is also significantly altered in expression, thus part of the core response that defines the module. These three criteria show a highly significant intersection of 18 genes which is markedly better than expected by chance alone (expected 2. 2 genes, P adj = 9. 0e-11, SuperExact-Test;  Wang et al, 2015, Fig 3E and  Appendix Table  S3 ). These 18 genes are APOE, CLU, GPC2, INPP5D, CD33, PLCG2, TREML2, SPI1, FCER1G, SYK, GRN, SLC2A5, SAMSN1, PYDC1, HEXB, RRBP1, LYN, and BLNK (see Fig  5 ). The full set of GWAS genes with P mar < 0. 5 and significantly differentially expressed in the APPtg-Blue module (n = 263 genes) are listed in Dataset EV4. Among the 18 genes, we find 7 established AD GWAS variants, i.
When examining the longer list of priority GWAS genes (see Dataset EV4), we find more members of this pathway, including TREM2, ITGAM, and VAV1. Moreover, the same genes, as well as others of the top 18 list, i. e. , PYDC1/PYCARD (P mar : 4. 69e-04), GRN (P mar : 1. 56e-04), SLC2A5 (P mar : 2. 07e-04), and SAMSN1 (P mar : 4. 66e-04), are all part of the "immune and microglia" module regulated by TYROBP inferred by Zhang et al from RNA-seq data derived from late-onset AD patients  (Zhang et al, 2013). Other microglia-and lysosome-related genes are TREML2 (P mar : 4. 90e-06) and HEXB (P mar : 4. 87e-04). RRBP1 (P mar : 7. 10e-04) is an ER-associated ribosomal protein  (Savitz & Meyer, 1993) , with high expression in glial cells (Dataset EV4). All of the 18 AD risk genes are expressed in microglia according to the expression data from  Zeisel et al (see Fig 4A; Zeisel et al, 2015)  and are predicted SPI1 targets according to i-cisTarget (see Fig 4C ), and 11 out of these (APOE, BLNK, HEXB, INPP5D, LYN, PLCG2, RRBP1, SAMSN1, SLC2A5, SPI1, SYK) are demonstrated SPI1 targets in a ChIPseq experiment in the BV2 microglia cell line  (Satoh et al, 2014).
Single microglia sequencing confirms high proportions of activated microglia in APPtg mice Given that the neuroinflammatory and microglial response appears markedly different in APPtg and TAUtg mice, we performed single cell sequencing on FACS-sorted CD11b + /CD45 + hippocampal microglia on all 8 experimental groups, with the minor modification that the oldest mice were 11M instead of 10M. After filtering and doublet removal, 15,599 single cells remained, which were subjected to dimensional reduction, batch effect removal, and clustering with Seurat  (Butler et al, 2018; Stuart et al, 2019). Nine different clusters were obtained (see Fig  6A ) and, after matching the expression of their marker genes with publicly available gene sets (see Appendix Fig S4;  Friedman et al, 2018; Mancuso et al, 2019a; Sala Frigerio et al, 2019) , were termed activated response microglia (ARM, n = 2,841 cells), CNS-associated macrophages (CAMs, n = 720), cycling and proliferating microglia (CPM, n = 285, see Dataset EV6 for a list of marker genes), homeostatic microglia cluster 1 (HM.
1, n = 7,111), homeostatic microglia cluster 2 (HM. 2, n = 2,895), interferon-response microglia (IRM, n = 387), high major histocompatibility complex-expressing microglia (MHC. high, n = 144, see Dataset EV6 for a list of marker genes), monocytes (Mnc, n = 100), and transitioning response microglia (TRM, n = 1,115). As described in Sala Frigerio et al (2019), HM. 2 cells appear to be in a slightly more metabolically and transcriptionally active state than HM. 1 cells, with mild upregulation (LFC: +0. 1 to +0. 6) of Apoe, Lyz2, H2-D1, and Cd52 and many ribosomal and mitochondrial genes (see Dataset EV5). When assessing the distribution of cells of each experimental group over the different clusters, it becomes apparent that the ratio of HM (HM. 1 and HM. 2) versus ARM is vastly different among the experimental groups  (Fig 6B and C, Appendix Fig S5).
In TAUtg mice, however, such an extreme phenotypic shift is not observed, with few ARM cells reaching the final stage of activation in TAUtg-11M mice  (Fig 6D). When comparing all ARM cells to all HM cells within the APPtg and the TAUtg mice, and using Spearman correlation with the results from the differential expression (Fig 6E ), it is clear that the overall ARM phenotype between APPtg and TAUtg mice is not different (Spearman correlation R = 0. 91, P = 2. 2e-16; see also Dataset EV7), with increased expression of Apoe, Ccl4, Ccl3, Clec7a, and Lyz2, and downregulation of homeostatic markers P2ry12, Tmem119, and Nav2. Thus, whereas the ARM profile in TAUtg or APPtg mice is not intrinsically different, Ab exposure appears to induce a more rapid and severe ARM response. Despite many early changes in transcription, the inflammatory response in the TAUtg mice appears late and as a relatively mild part of the pathology.
Microgliosis mainly contributes to the expression of top 18 genes When assessing the top 18 genes within the single microglia dataset, we can observe very low expression of Clu, Gpc2, and Treml2 and modest expression of Syk and Plcg2 (see Fig  EV2 ). With cut-offs of P adj < 0. 05 and À0. 1 < LFC >+0. 1, we demonstrate that Slc2a5, Siglech, Inpp5d, Pycard, Hexb, Lyn, Spi1, and Rrbp1 are all significantly higher expressed in HM compared to ARM, whereas Fcer1g and Apoe are significantly higher expressed in ARM compared to HM. The enrichment for the top 18 genes among genes upregulated in HM cells compared to ARM cells was found significant (Fisher's exact test, P = 7. 0e-4). Publicly available data (http://research-pub. We aimed to further understand for each of the top 18 genes what the microglial contribution was to the observed increased expression in the bulk RNA-seq data.
Given the low yield, it was decided to run another n = 1 for the TAUtg and TAUwt samples, which again gave a low recovery. These mouse microglia were subsequently mixed with human H9 embryonic stem cell-derived monocytes to ensure the needed cell density for 10× Genomics. Next, isolated microglia were prepared for single cell sequencing using the 10X Genomics single cell gene expression profiling kit. cDNA libraries were prepared following manufacturer's instructions, and all samples were sequenced in a single run on the Illumina HiSeq 4000 platform with the 10X Genomics recommended sequencing specifications. The raw BCL files were demultiplexed and aligned with Cellranger (v3. 1. 0) against a joint database with the mouse genome (mm10, GRCm38), the rat genome (Rnor_6. 0), and the human genome (GRCh38).
Cells from each experimental group were clustered using  Scanpy (v1. 4. 4; Wolf et al, 2018). Cells were separated, as expected, in clusters per organism. For each cluster with a clear mouse origin, the cell ids were stored. For consistency, we performed this step also on samples with no rat or human cells added. Subsequently, all cells were remapped against a mouse database (mm10, GRCm38) and only cells identified as mouse cells in the previous step were kept for analysis. Cells with < 750 reads or with more than 5% of reads mapping to mitochondrial genes were filtered out. Cells with more than 30,000 genes were considered doublets and removed. Genes detected in < 100 cells were discarded. Per experimental group, we obtained the following number of cells: APPwt-4M: 700; APPwt-11M: 1,799; APPtg-4M: 991; APPtg-11M: 2,275; TAUwt-4M: 3,856; TAUwt-11M: 761; TAUtg-4M: 4,224; and TAUtg-11M: 1,503.
The mean sequencing depth was 6,580 reads/cell, and on average, 2,335 genes were detected per cell. Cells passing QC were analyzed using the Seurat (v. 3. 1. 1) R package. The combined object (including all APP and TAU experimental groups) was split into a list, where every element is a batch (as referred to sorting date, corresponding to the 4 age*genotype groups +1 additional TAU-11M sorting run). Standard pre-processing (log normalization) was performed individually for each of the five batches, and variable features (nfeatures = 2,000) were identified based on a variance stabilizing transformation (selection. method = "vst"). Next, we identified anchors using the "FindIntegrationAnchors" function, giving the list of Seurat objects as input. We used all default parameters, including the dimensionality of the dataset (dims = 1:30).
We passed these anchors to the "IntegrateData" function, to get an integrated (or "batch-corrected") expression matrix for all cells, enabling them to be jointly analyzed. Scaling was performed on all genes from the filtered, unintegrated matrix. We used the integrated matrix for downstream analysis and visualization using the standard Seurat workflow. Unbiased pseudotime inference of microglia progression from HM to ARM/DAM was performed using the Scorpius (v1. 0. 4. 1) R package using default parameters.
Gene sets for determination of microglial cell state We extracted publicly available marker sets of different microglial cell states: activated response microglia, cycling and proliferating microglia, homeostatic microglia, interferon-response microglia, and transitioning response microglia from Sala Frigerio et al (2019); cytokine response microglia from Mancuso et al (2019a); and neutrophil-monocytes and macrophages from  Friedman et al (2018) ; and a list of MHC markers can be found in Dataset EV6. For each microglial cell, we calculated the mean abundance levels of each gene in a marker set against the aggregated abundance of random control gene sets, using Seurat's "AddModuleScore" function. These data were visualized on UMAP embeddings to determine cellular states within the single cell clustering.
Cell-specific datasets Cell type-specific genes sets for pyramidal neurons (n = 701 genes), interneurons (n = 364), astrocytes (n = 239), microglia (n = 435), oligodendrocytes (n = 452), endothelial (n = 352), and ependymal cells (n = 483) were derived from  Zeisel et al (2015)  and for homeostatic and activated response microglia from Sala  Frigerio et al (2019). Using these gene sets and a count matrix that was z-score normalized across samples, we calculated for each cell type (t) the average z-score (Z tg ) for each experimental group (g) and compared this to the respective 4M WT group. We assessed significance using empirically derived P-values  (Appendix Fig S6).
GWAS gene set enrichment analysis Human GWAS genes derived from  Marioni et al (2018)  were converted to mouse orthologues using the Ensemble Biomart Release 94  (Zerbino et al, 2018). Using the gene set enrichment analysis preranked module (Broad Institute  (Subramanian et al, 2005; Mootha et al, 2003) , two-sided testing), enrichment for GWAS genes was tested among all genes, sorted from up-to downregulated genes based on signed log10(P-value) based on the different statistical comparisons (age, genotype, and age*genotype interaction), for APPtg and TAUtg separately, and performing BY P-value adjustment. Thus, GWAS gene enrichment is assessed based on gene rank and therefore impervious to varying numbers of significantly differentially expressed genes.
Weighted gene co-expression network analysis (WGCNA) The WGCNA package in R  (Langfelder & Horvath, 2008)  was used to build unsigned mRNA co-expression networks for APPtg and TAUtg mice separately using all 15824 expressed genes. To generate an adjacency matrix with is the smallest threshold that satisfies the scale-free topology fit at R 2 = 0. 9, soft power 3 is used for APPtg and 4 for TAUtg mice, respectively. The topology overlap (TO) was calculated based on the adjacency matrix which measures the network interconnectedness. The topology overlap dissimilarity was then calculated by 1-TO and used as input for average linkage hierarchical clustering. Branches of the hierarchical clustering tree were then assigned into modules using cutreeHybrid from the dynamic-TreeCut package (deepSplit = 2, minModuleSize = 30,  Langfelder et al, 2008).