dataesr/scientific-paragraphs-categorization huggingface dataset:
14% of errors and one the reaction time threshold for pro-as well as anti-saccades with average reaction times of 419 and 335 ms, respectively). In the adolescents group, two participants were removed (one exceeded the error rate threshold for pro-saccades committing 9. 91% of errors  and one the reaction time threshold for pro-saccades with an average reaction time of 308 ms). In the young adults group, four participants were removed (one exceeded the error rate threshold for anti-saccades committing 60. 00% of errors and one the error rate threshold for pro-saccades committing 8. 24% of errors and two the reaction time threshold for pro-saccades with average reaction times of 233 and 234 ms each). Thus, the data from 31 children, 29 young adolescents, 22 adolescents and 31 young adults remained in the final analysis.
Acknowledgements This research was supported by grants from the  Agence Nationale pour la Recherche (France ,  ANR-14-ACHN-0023 ) and the foundation AMIDEX (France,  ANR-11-IDEX-0001-02 ) and Excellence and the  Excellence Initiative of Aix-Marseille University  (A*MIDEX) to MHG, as well as by the  Institute for Language Communication and the Brain (ILCB).
Fluorimetric detection of intracellular sinapine The sinapine entry in the cardiomyocytes was measured using the sinapine autofluorescence. The cardiomyocytes were maintained in a laminin-coated Petri dish on a microscope stage (Axiovert, Zeiss, Germany; 20× objective). Cells were illuminated at 340 nm, using a lambda DG-4 excitation system (Sutter Instrument Company, CA, USA). Images were then captured digitally every 0. 35 s with a cooled CCD camera (Photometrics, Roper scientific, France) programmed for 450 nm emission. Changes in fluorescence after correction for background were analyzed with the Metafluor software (Universal Imaging Corporation, USA).
To do so, mitochondria was extracted using the same procedure as that used for plasma. The cytosol (50 μL) was pipetted into a 10 mL volumetric flask then spiked successively with an internal standard solution (200 μL at 250 ng/mL in methanol) and with 5 mL of methanol-water (50/50 v: v). The flask was ultrasonicated for 5 min and, once cooled down, completed up to 10 mL with methanol-water (50/50 v: v). Both the mitochondrial and the cytosolic fractions were analyzed with the LC-MS/MS procedure described above. Statistical analysis Statistics were performed using the StatView 5. 0 program (SAS Institute, Cary, NC). Data were expressed using the mean ± SEM. Differences were assessed with the one-way ANOVA, whenever appropriate. When significant interactions were found, a Bonferroni post hoc test was applied with p < 0.
Acknowledgements This work was supported by a PhD grant to D. B from the  SFR Tersys  and by a PhD mobility grant to D. B from the  Groupe de Réflexion sur la Recherche Cardiovasculaire. We thank  Dr. Anne Virsolvy  for the technical help with the fluorimetric detection of intracellular sinapine,  Ms. Céline Gérin  for LogP measurements, and  Ms. Pascale Bellenger  for HPLC determination of sinapine and sinapic acid in biological samples.
Cell filtering To retain only high quality cells, we applied filters on unique molecular identifier (UMI), gene and mitochondrial counts per cell. We first filtered cells based on the percentage of UMI associated with mitochondrial transcripts and number of detected genes and retained cells within a 3 median absolute deviation (MAD) around the population median for these metrics. We further excluded cells having a number of UMI above 3 MADs of the population median. Potential doublets were removed using Scrublet  (Wolock et al. , 2019). Finally, we excluded genes detected in less than 3 cells. Dimensionality reduction was performed using the SPRING tool, a 2D projection using forcedirected layout of the k-nearest-neighbor  (Weinreb et al. , 2018). After dimensionality reduction, 11 cells sharing neighbours between excitatory and inhibitory neurons, and therefore likely corresponding to undetected doublets, were removed manually.
Cell state score computation We reasoned that clustering procedure gives meaningful results only if applied to cell populations in the same broad transcriptional state (e. g. apical progenitors or late neurons). To select cells in the same transcriptional state we computed apical progenitors (AP), basal progenitors (BP), early neurons (EN) and late neurons (LN) signature scores using lists of published and manually selected genes  (Telley et al. , 2016 ; the detailed list is available in the annotated codes, see Data availability below) as input to the Seurat function 'addModuleScore'. The scores were differentially combined to define and extract the populations of interest by K-means clustering prior to downstream analysis.
Neuronal populations clustering To explore neuronal transcriptional diversity, we first applied iterative graph-based clustering using scrattch. hicat (v0. 0. 16)  (Tasic et al. , 2016)  on glutamatergic and GABAergic neurons separately after removing cell cycle, ribosomal or mitochondrial associated genes form the input count matrix. At each clustering iteration step we further excluded principal components (PCs) highly correlating with sequencing depth, percentage of ribosomal or mitochondrial transcripts detected per cells. Because variation among some glutamatergic neuron clusters were obviously associated with different maturation states we further excluded those with a LN score < 1. Hierarchical grouping of the remaining clusters was performed with the scrattch. hicat function 'build_dend' using the mean gene expression profile across clusters as an input.
To defined clusters specific transcriptional signatures, we further subset the clusters core cells as assigned more than 95/100 trials to the same cluster using the centroid classifier of the 'map_sampling' function. Top cluster marker genes were identified using a receiver operating characteristic (ROC) analysis implemented in the Seurat function 'FindMarkers'. Barplot representation of gene expression was built using the scrattch. vis package (v0. 0. 210).
Apical progenitors analysis We first aimed at ordering APs along the main axis of variation by performing a new SPRING dimensionality reduction while excluding cell cycle associated genes. Cells were projected on the principal curve using princurve (v2. 1. 4) as described previously  (Mayer et al. , 2018). Genes differentially expressed along this inferred DV axis were identified using the 'differentialGeneTest' function of the Monocle package (v2. 14) (Trapnell et al. , 2014). Progenitor cells were grouped in 7 clusters using partition around medoids on the smoothed expression profile of the significantly differentially expressed genes. Genes with similar expression dynamics were grouped by hierarchical clustering. To investigate sources of variation associated with the RC axis among pallial progenitors, we performed a principal component analysis (PCA) dimensionality reduction using the Seurat function 'RunPCA'.
After excluding the first 3 PCs correlated (absolute Pearson's cor. > 0. 25) with the cell cycle or the inferred DV axis, we projected cells along a principal curve spanning the remaining 7 PCs and identified significantly variable genes along this new axis using the same approach as described above for the DV axis. Among these 159 genes we identified those with similar graded expression by fitting a generalized additive model using the 'gam' function from the mgcv package (v1. 8-33) over both RC and DV axes and performed PCA on their smoothed expression landscape. To predict temporal maturation scores for apical progenitors we took advantage of the  Telley et al. , (2019)  dataset. After retrieving the raw count matrix from the GEO repository (GSE118953) we extracted cells sorted 1h after FlashTag injection.
Based on their expression profile, we excluded medial pallium APs as well as GABAergic interneurons to retain 725 high quality cells (E12: 178 cells, E13: 202 cells, E14: 124 cells, E15: 221 cells). We ran a PCA over all differentially expressed genes between the distinct sampling groups identified with the Wilcoxon rank sum test implemented in the 'FindAllMarkers' function. Cells were projected onto a pseudo-maturation axis by fitting a principal curve over the first 6 PCs. We trained a random-forest regression model implemented in the Caret package (v6. 0-84) to predict the cells' pseudo-maturation score (position along the axis) using the scaled expression value of the top 1129 variable genes selected with the 'FindVariableGenes' function. The 100 most important features were set as an input to re-optimise the regression model which was used to predict the temporal maturation for APs in our dataset.
To investigate the spatial and temporal components, we first identified genes with variable expression in pallial apical progenitors along the DV axis using the 'differentialGeneTest' function as described above, and those differentially expressed between E12 and E15 1h after FlashTag injection in the  Telley et al. (2019)  dataset. The "temporal module" was defined as the intersection between the two sets of differentially expressed genes. We then computed the Pearson's correlation coefficient between the smoothed expression matrices over 200 points of temporal genes in our dataset and that of Telley et al. Genes of the temporal module showing coordinated changes in expression in the two datasets were grouped using hierarchical clustering. The 'spatial module' was defined as genes differentially expressed along the DV axis at E12 but which do not show variation between E12 and E15 in the  Telley et al.
(2019)  dataset. Embryonic brain atlas (La  Manno et al. , 2020)  expression and metadata were retrieved from http://mousebrain. org/. We used LoomR 0. 2. 1. 9000 (https://github. com/mojaveazure/loomR) to extract cells annotated as "Radial glia" from the 9 sampling points ranging from E11 to E15. 5. We further retained only those annotated as coming from "Forebrain" tissue. The raw count values were normalized to the library size and scaled using Seurat (v 2. 3. 4) and UMAP dimensionality reduction was performed using the Seurat function 'RunUMAP' on the 30 first PCs. Spatial VP and Temporal VP signatures were computed with the Seurat function' 'addModuleScore', using genes expressed at the VP and belonging to the "spatial" and "temporal" modules described above.
Immunostaining The following primary antibodies were used: chick anti-GFP (Aves Labs GFP-1020, 1:1000), chick anti-βGal (Abcam ab9361, 1:1000), rabbit anti-Nfib (Atlas Antibodies HPA003956, 1:1000), rabbit anti-Foxp2 (Abcam ab16046, 1:1000), rabbit anti-Tbr2 (Abcam ab216870, 1:300), rabbit anti-Tbr1 (Abcam ab31940, 1:500), rabbit anti-Ki67 (Abcam ab15580, 1:500), goat anti-Nurr1 (R&D systems AF2156, 1:200), mouse anti-Meis2 (Sigma WH0004212M1, 1:1000) and guinea pig anti-Bhlhb5  (Skaggs et al. , 2011, 1:100). The following secondary antibodies were obtained from Jackson ImmunoResearch: donkey anti-chick Alexa-488 (1:1000), donkey anti-mouse Cy5 (1:500), donkey anti-guinea pig Cy5 (1:500), donkey anti-rabbit Cy3 (1:700), donkey anti-rabbit Cy5 (1:500), donkey anti-goat Cy3 (1:700) and donkey anti-goat Cy5 (1:500).
Image acquisition In situ hybridisation images were obtained using a Hamamatsu Nanozoomer 2. 0 slide scanner with a 20X objective. Immunofluorescence images were acquired using a Leica SP8 confocal microscope with a 40X objective. Quantifications were achieved by counting the proportion of GFP + cells among the marker-positive population using the ImageJ software. Data are represented as mean ± sd, individual values are plotted.
Sampling cellular diversity around the developing ventral pallium Using a droplet-based approach (10x Genomics Chromium V2), we performed unbiased scRNAseq on dissected explants containing the PSB and adjacent tissue throughout the entire rostro-caudal (RC) extent of the telencephalon, obtained from wild-type E12. 5 embryos (Fig. 1A ). After quality control and filtering, we obtained 4225 cells sequenced at a median depth of 11,665 UMI and a median detection of 3,571 genes per cell. Visualisation of transcriptional heterogeneity on a SPRING plot revealed that the dataset is organised in a "crab-shaped" topology consisting in a large group of cycling progenitors (Sox2 and Nes/Nestin) from which stemmed two branches of postmitotic cells corresponding to pallial  glutamatergic (Tbr1  and Slc17a6/Vglut2)  and subpallial GABAergic (Dlx5  and Gad2/GAD65 ) differentiation trajectories, respectively (Fig.
S1B ), we noticed that one group expressing genes associated with proliferation such as Top2a, Mki67, Cdk1 or Aurka, and annotated as S or G2/M phase cells, appears to diverge from another group of aligned G0/G1 cells (Fig. 1B, S1B ). The latter most likely corresponds to the trajectory followed by cells transitioning from AP to neurons without achieving a last division as BP, hence representing a nonproliferative intermediate state. This observation suggests that at E12, direct neurogenesis involves a transition through a "BP-like" transcriptomic state Eomes + (Tbr2), Btg2 + (Tis21) and Neurog2 + without replicating their DNA or dividing. Taken together, these results indicate that the two main axes in the low-dimensional manifold of our dataset reflect (i) the differentiation status, from cycling progenitor to postmitotic mature neuron, and (ii) the pallial or subpallial origin of the cells (Fig.
Conversely, Nurr1 + cells are more often Dbx1-derived (43±14%) than Emx1-derived (18±9%). We concluded that Nr4a2 + neurons are mostly VP-derived whereas Foxp2 + are mostly LP-derived although care should be taken in the interpretation of these data as (i) β-Galactosidase expression is not permanent in Dbx1 LacZ/+ animals and (ii) a certain extent of recombination in the VP is evident in the Emx1 Cre line (sometimes even in subpallium, see Fig. 4D, E ) and we cannot formally exclude postmitotic recombination in VP derivatives. Nevertheless, Nr4a2 + neurons clearly appear as the most ventral pallial glutamatergic type in our dataset. Our data therefore indicate that neuronal identities in the LP and VP are not sharply distinguished by tracing from Emx1-or Dbx1-expressing progenitor.
Early spatial identity within pallial APs reflects temporal dorsal-pallial maturation The cerebral cortex displays a well-known neurogenic gradient in which neurogenesis is initiated in ventral regions and progressively extends to dorsal territories  (Bayer and Altman, 1991). In addition, it was recently shown that neuronal diversity in the developing neocortex arises mostly from the temporal progression of APs  (Okamoto et al. , 2016; Telley et al. , 2019). We therefore queried in which extent the distinction between LP, VP and DP progenitors could mirror that of young vs old dorsal cortex APs. To test such a possibility, we used the time-series dataset from  Telley et al. (2019) , which describes the transcriptomic signature of E12 to E15 APs from the DP, and trained a regression model to predict a maturation score for each AP present in our dataset (Fig.
6A ). We found a clear difference in the maturation estimate between clusters as the VP displayed the highest predicted score (Fig. 6B ). In other words, VP progenitors display a more mature transcriptomic profile than their DP counterparts. This also indicates that part of the DV axis transcriptional differences can be explained by the expression of genes responsible for the temporal maturation of dorsal cortex APs. We found that among the 393 pallial variable genes in our dataset, 102 also vary during dorsal cortex AP temporal maturation, therefore representing a "temporal module" of pallial AP identity. Conversely, the remaining 291 genes form a "spatial module" expected not to show such variation in time (Fig. 6C , Tables  S8, S9 ). We used genes of the temporal module to investigate whether the temporal-associated signature changes continuously along the spatial and temporal axes.
To this aim, we performed correlation between the smoothed gene expression profiles computed in the two datasets and found two blocks of strong correlation between VP/LP. 1 and E15 APs on the one hand, and LP. 2/DP and E12 APs on the other hand (Fig. 6D ). The distinction between LP. 1 and LP. 2 appears very sharp when considering genes from the temporal module, i. e. genes whose expression is turned on or off when dorsal cortex APs switch from the production of deeper layer to upper layer neurons. We found that within the temporal module, 66 genes are enriched in the E12. 5 VP and upregulated at E15 in the DP, as exemplified by the transcription factor Zbtb20 (Figs  6E, F, S6D ). They can be pictured as a wave of genes expanding dorsally with time. 28 genes, such as Lrrn1, displayed the opposite dynamics, highly expressed in the DP at E12 and regressing at E15 (Figs  6E, F, S6D ).
The remaining 9 genes displayed more complex patterns (Fig. 6E , Table  S8 ). When focussing on the spatial module (i. e. genes whose expression is constant from E12 to E15), we found far more genes upregulated in the VP than in the DP (225 vs 41, respectively), indicating that the spatial signature of the VP is more pronounced than that of any other pallial domain (Fig. 6G, H , Table  S9 ). Celf4 and Mpped2 exemplify such time-invariant genes enriched either in the VP or the DP, respectively (Fig. 6H ). By contrast, only 25 genes were found enriched in the LP. 1/LP. 2, none of which displayed strong specificity or sharp pattern (not illustrated), indicating that APs in these domains are best defined by the combined low expression of VP-or DP-enriched genes (Fig. 6G ). To further investigate spatio-temporal relationships over different embryonic stages, we took advantage of a recently published dataset (La  Manno et al.
, 2016a; Watson and Puelles, 2017) , to which the Dbx1 lineage is not supposed to contribute  (Puelles et al. , 2016b). By contrast, the Fezf1 + population we characterised is most likely derived from the LP according to its position in an Emx1-derived domain dorsal to Nr4a2 cells, and possibly corresponds to neurons that will eventually settle in the lateral amygdala, as expression of Fezf1 + has been reported in this region  (Hirata et al. , 2006; Kurrasch et al. , 2007). This population would represent an early-born LP contribution to the amygdala, as previously postulated  (Cocas et al. , 2009; Gorski et al. , 2002). Our findings therefore point to the idea that LP and VP derived structures are perhaps not as sharply distinguished as initially anticipated  (Puelles et al. , 2000). We captured distinct CR subtypes in our dataset.
By contrast, APs localised at intermediate positions along the DV axis do not show such a strong transcriptional identity that could be transmitted to BPs, explaining why our lineages reconstruction were only effective when comparing the most distant terminal fates in our dataset. However, the near-perfect tangential segregation of Ppp1r14c + and Fezf1 + neurons argues in favor of an equivalent spatial segregation of their respective progenitors in the LP. Therefore, we propose that at the cell population level, bias towards a specific lineage progressively decreases, as progenitors are located closer to a transition zone between two domains while the opposite probability towards generating the neighbouring cell type would increase (Fig. 8A ). Such a model is supported by the observation that genetic tracing using sharp domain-restricted enhancers results in fuzzy fate maps with graded contribution to adjacent cortical structures  (Pattabiraman et al.
, 2019). In our dataset, the bias for Bhlhe22 + and Nr4a2 + lineages is fairly constant from AP to LN, supporting an early commitment from the AP state. By contrast, the bias for other lineages only emerges at postmitotic stages, suggesting that fate restriction occurs at distinct steps in each lineage, although we cannot formally exclude that commitment indeed occurs at the AP level but remains hidden at the transcriptomic level, as shown in other developmental systems  (Weinreb et al. , 2020; Ziffra et al. , 2020). In an attempt to graphically represent our findings we propose to adapt the classical Waddington metaphor to integrate the influence of both space and time on the same landscape (Fig. 8B ). In this representation, AP identity is encoded in its coordinates on a spatio-temporal map.
G. Heatmap showing the smoothed expression of the 291 genes belonging to the spatial module along the pseudo DV axis. H. Representative genes expression profile according to the pseudo DV score in our dataset (top) and maturation score in Telley's dataset (bottom) illustrating time-invariant genes enriched either in the VP or the DP. I. UMAP plot of E11 to E15. 5 forebrain APs extracted from the dataset of La Manno et al. (2020). J. Signature scores for VP genes belonging to the spatial or the temporal module.
We are grateful to  Anne Teissier  and  Oriane Blanquie  for critical reading of the manuscript. MXM is the recipient of an  Allocation Spécifique PhD fellowship  from the  École Normale Supérieure , FC is an Inserm researcher, AP is a CNRS investigator. This work was supported by grants from  Agence Nationale de la Recherche  ( ANR-15-CE16-0003-01  and  ANR-19-CE16-0017-03 ) and  Fondation pour la recherche médicale (Équipe FRM   DEQ20130326521  and  EQU201903007836 ) to AP and State funding from the  Agence Nationale de la Recherche  under " Investissements d'avenir" program  ( ANR-10-IAHU-01 ) to the  Imagine Institute.
Our second study evaluates the usage of the same three input methods across varied mobility conditions like sitting, standing and walking. Our results suggest that while the input method Once (based on two Manuscript submitted to ACM successive taps on soft keys) overall performed best, some users also like using Swipe for one-handed interaction as well as User Maintained for Tablet. Altogether, these results confirm that SoftCuts could be generalized as an efficient command selection mechanism for touch-based devices.
Existing Implementations Latest Microsoft Surface  [38]  and Samsung Galaxy S  [41]  tablets support the selection of a subset of available commands using keyboard shortcuts (Figure  2 ), by respectively hitting or holding a modifier key (Ctrl) and then hitting a key associated with a command. However, commands are poorly suggested to the user: in Microsoft implementations, the command is scarcely displayed using only text over the associated key, where on Samsung tablets, keys associated with commands are only highlighted without describing which command would be selected. As a result, these commercial solutions do not fully leverage the dynamic nature of soft keyboards that may make the discovery and learning of hotkeys Manuscript submitted to ACM even easier than on desktop systems, as the keyboard layout could be changed at will.
Participants and Apparatus Twelve participants (all right-handed, 5 female), aged 18 to 30 (M = 25. 1, SD = 3. 29) were recruited from the university community. They received the equivalent of 7. 5 USD for their participation. One participant used a tablet every day while three used it at least once per month. Only one participant was familiar with swipe-based keyboards. The experimental software was written in Java using Android Studio as an IDE and ran on version 28 of Android SDK. We used a Samsung A10 phone (6. 2", 168 grams) running Android 9 for the Phone condition and a Samsung Galaxy Tab 4 (10. 5" screen, 483 grams) running Android 9 for the Tablet condition.