dataesr/scientific-paragraphs-categorization huggingface dataset:
Epigenetic landscape reconstruction To visualize the 3D transcriptional uncertainty landscape, we apply dimensional reduction techniques such as Principal Component Analysis (PCA) or t-SNE on the z-scored expression data, to project the gene expression of each individual cell on two dimensional axis, which gives the x-y axis of the landscape plot. For the z axis, we plot the NLL values. The transcriptional uncertainty landscape surface is reconstructed by estimating local approximation of individual cell 3D coordinates on a regular 30x30 grid by using a publicly available Matlab surface fitting code called GRIDFIT  67.
Bargaje et al. scRT-qPCR dataset. The dataset includes the expression profiles of 96 genes from 1896 single cells at 8 different time points (day 0, during the differentiation of human pluripotent stem cells (iPSCs) into either mesodermal (M) or endodermal (En) fate  35. By employing CALISTA, we obtained five cell clusters and detected a bifurcation event, which gives rise to the two final cell fates. After lineage inference, we pseudotemporally ordered cells along the inferred differentiation paths (for more details, see  40  ).
Treutlein et al. scRNA-sequencing dataset. The dataset includes the gene expression profiles of 405 cells during reprogramming of mouse embryonic fibroblast (MEF) into a desired induced neural (iN) and an alternative myogenic (M) cell fates  38. We preprocessed the data using CALISTA to select the 40 most variable genes (10% of the number of cells) for the transcriptional uncertainty analysis. CALISTA identified four different subpopulations and successfully recovered the bifurcation event (for more details, see  40  ).
Richard et al. scRT-qPCR dataset. The dataset contains the expression profile of 91 genes measured from 389 cells at 6 distinct time points (0, 8, 24, 33, 48, 72 h) during the differentiation of primary chicken erythrocytic progenitor cells (T2EC)  8. Following CALISTA pre-processing step, we removed cells in which less than 75% of the genes are expressed. Then, we selected the subset of genes with at least one non-zero expression values. A total of 354 cells and 88 genes were considered in the transcriptional uncertainty analysis. Based on eigengap heuristics  40, 47  , we grouped cells into 6 optimal clusters and ordered cells along the inferred linear trajectory (see Supplementary Figure  S7 ).
Stumpf et al. scRT-qPCR dataset. The dataset comprises the single-cell expression of 97 genes at 7 time points (0, 24, 48, 72, 96, 120, 168 h) during neural differentiation of mouse embryonic stem cells (E14 cell line)  10. In the data pre-processing, we excluded cells in which less than 70% of genes are expressed. Then, we selected genes with at least one non-zero expression values. A total of 276 cells and 93 genes were considered for for the transcriptional uncertainty analysis. Based on eigengap heuristics  40  , we grouped cells into five optimal clusters and ordered cells along the inferred linear trajectory (Supplementary Figure  S8 ).
Moussy et al. scRT-qPCR dataset. The single-cell expression dataset includes normalized Ct values for 91 genes in 435 cells captured at 5 distinct time points (0, 24, 48, 72, 96 h) during human cord blood-derived CD34+ differentiation  12. We employed CALISTA to group cells into 7 clusters, reconstruct the developmental trajectory and calculate pseudotimes (Supplementary Figure  S9 ).
Guo et al. scRT-qPCR dataset. The dataset comprises the single-cell expression values of 48 genes from 387 individual cells isolated at 4 distinct developmental cell stages, from 8-cell stage mouse embryos to 64-blastocyst  36. By applying CALISTA, we identified seven different subpopulations along the differentiation process, and the inferred lineage hierarchy pinpointed two bifurcations events at 32-and 64-cell stage (Supplementary Figure  S10 ). The timing of the lineage bifurcations coincides with two well-known branching points: one at 32-cell stage when totipotent cells differentiate into trophectoderm (TE) and inner cell mass (ICM), and another at 64-cell stage when ICM cells differentiate into primitive endoderm (PE) and epiblast (E).
Nestorowa et al. scRNA-sequencing dataset. The dataset comprises single-cell gene expression of 1656 cells from mouse hematopoietic stem cell differentiation  39. We pre-processed the data by removing genes with non-zero values in less than 10% of the cells. Then, we selected 433 most variable genes, which is 10% of the number of genes after the previous preprocessing step, for the transcriptional uncertainty analysis  40. We set the optimal number of clusters based on the original study  39  , which reported six different subpopulations and two bifurcation events: the first one producing common myeloid progenitor (CMP) from lymphoid-primed multipotent progenitors (LMPP), and the second one generating granulocyte-monocyte progenitors (GMP) from megakaryocyte-erythroid progenitors (MEP) (Supplementary Figure  S11 ).
Moignard et al. scRT-qPCR dataset. The dataset contains the single-cell expression level of 18 transcription factors measured in a total of 597 mouse bone marrow cells during hematopoietic differentiation. By applying CALISTA, we successfully identified the five subpopulations and the two branching points detected in the original study 37 : long-term hematopoietic stem cells (HSC) differentiating into megakaryocyte-erythroid progenitors (PreM) or lymphoid-primed multipotent progenitors (LMPP); LMPP cells differentiating into granulocyte-monocyte progenitors (GMP) and common lymphoid progenitors (CLP) (for details see  40  ).
RNA VELOCITY ANALYSIS Cells and genes were first filtered based on the pre-processing strategy in the original publication by La Manno and colleagues  42  , which resulted in a total of 1720 cells and 1448 genes from human glutamatergic neurogenesis, and a total of 18140 cells and 2141 genes from mouse hippocampus dataset. We further reduced the number of genes to only the top 500 highly variable genes for the transcriptional uncertainty analysis. The cell cluster assignments generated by Velocyto -the algorithm for computing RNA velocity from the original publicationwere considered, instead of using CALISTA. Based on the clustering, we employed CALISTA to generate the lineage progression and cell pseudotimes (Supplementary Figure  S12 ). The RNA velocity and transcriptional uncertainty values for the top 500 genes were calculated by employing Velocyto and CALISTA, respectively.
3. Questionnaire: when the mission is finished, the participants are asked to fill the NASA-TLX survey  [36]  to get their subjective feedback concerning the effort made to carry out the mission (see the paragraph "Subjective Feedback" Section 2. 5. 1). 4. Break: a 2 min break is proposed to the participants. After the end of the four missions, the participants are asked to fill again the KSS survey. The experiment was finalized by checking data records and removing the ECG equipment from the participant. The experimental protocol is summarized in Figure  2. As the participants have to perform four missions, the experiment lasted about 1h30 including training, device set up, surveys, and scenarios.
Data Collection All robotic and mission state variables, as well as human operators' actions on the ground station (keyboard inputs and mouse clicks), eye-tracking, and ECG data were recorded using Lab Streaming Layer (LSL) (https://github. com/sccn/labstreaminglayer). LSL is a middleware developed by the Swartz Center for Computational Neuroscience (UCSD), for unified collection of experimental measurements. This framework allows time-synchronization and online access to data while avoiding networking issues. LSL also provides a software, called LabRecorder, that allows to select and record all data of interest. The robot position, battery level, amount of water available on board, temperature and automation level as well as the amount of water in the external tank, the state of the forest, elapsed time, leak position and participant actions on the interface were streamed via LSL each second  [34].
Data Processing Performance Scores The global mission performance was assessed using the total number of extinguished fires (see Section 2. 2). Note that the human operator must properly manage the filling of the external tank, monitor the robot's parameters (water reserve, battery, temperature, and automation level) and ensure manual control when necessary in order to reach a high mission score. In summary, the global mission score is intrinsically dependent on all of these tasks. However, to evaluate the impact of the robot's automation level, we also computed the total number of extinguished fires effectively done by the robot in autonomous mode and the total number of extinguished fires effectively performed by the human operator in manual control. In other words, for a given mission and a given automation level, the total number of fires is computed as the sum of fires extinguished during all 10-s time windows.
Behavioral Data The behavioral data concerns the human operator's actions (keystrokes and mouse clicks) on the interface recorded every second. Only keystrokes and mouse clicks related to the different sub-tasks were considered (e. g. , typing error was filtered). Thus, to assess on which tasks the participant's actions were focused, we were interested in the average number of keystrokes/clicks performed and related with navigation and external tank sub-tasks given the robot's level of automation, mission, and participant. Additional metrics could also be computed based on the behavioral data. As stated before, the global mission score depends on a correct water level management of the external tank because the robot needs to recharge in water from it to extinguish fires. Therefore, given the fact that this sub-task needs to be performed by the human operator during the whole mission, we are also interested in the average variation of the water level of the external tank given the robot's automation level, mission and participant.
ECG data An ECG was used to collect participants' cardiac activity at a sampling rate of 500Hz with the Faros Emotion 360 system. This ECG device filters the data and automatically extracts and streams the inter-beat intervals (IBI) or R-R intervals. Heart Rate (HR) was computed in beats per minute (bpm) using the serie of R-R intervals  [37, 38]. Given that IBI n = R n -R n-1 , with R n as the timestamp of the nth R peak (i. e. , highest positive peak), and given time window w = [w a , w b ] with (w a , w b ) ∈ R 2 with w a < w b , we can define the set of considered indices W = {n ∈ N|R n-1 ∈ w and R n ∈ w} ⊂ N. Thus, the mean HR can then be computed as follows, HR(W ) = 60 • #W ∑ n∈W IBI n , (2) where, #W states for the cardinal of the set W. The Heart Rate Variability (HRV) can also be computed in the time domain as the variability of the Inter-Beat Intervals (IBI): HRV(W ) = 1 #W -1 ∑ n∈W IBI n - 60 HR(W ) 2.
Classification Finally, two-dimensional classifiers were built to evaluate the performance predictive power of the behavioral and physiological features. More precisely, inter-subject single-trial classification models were studied exploiting the dataset composed by the ECG, the ET, and the keystrokes/clicks features, computed for 10-s time windows of a given automation level (see the paragraphs "Behavioral Data", "Eye-tracking" and "ECG data" respectively in Section 2. 5. 1). Each sample was labeled by the related mission performance (high or low score). The total dataset is composed by 3591 samples, with 1459 samples for the low-scoring group and 2132 for the high-scoring one. Note that this unbalanced dataset can be explained by the fact that some low-scoring missions ended prematurely.
Context. Recent high-contrast imaging surveys using the Spectro-Polarimetic High contrast imager for Exoplanets REsearch (SPHERE) or the Gemini Planet Imager (GPI), looking for planets in young, nearby systems showed evidence of a small number of giant planets at relatively large separation beyond typically 10 to 30 au where those surveys are the most sensitive. Access to smaller physical separations between 5 and 30 au is the next step for future planet imagers on 10 m telescopes and the next generation of extremely large telescopes in order to bridge the gap with indirect techniques such as radial velocity, transit, and soon, astrometry with Gaia. In addition to new technologies and instruments, the development of innovative observing strategies combined with optimized data processing tools is participating to the improvement of detection capabilities at very close angular separation.
In that context, we recently proposed a new algorithm, Keplerian-Stacker, combining multiple observations acquired at different epochs and taking into account the orbital motion of a potential planet present in the images to boost the ultimate detection limit. We showed that this algorithm is able to find planets in time series of simulated images of the SPHERE InfraRed Dual-band Imager and Spectrograph (IRDIS) even when a planet remains undetected at one epoch. Aims. Our goal is to test and validate the K-Stacker algorithm performances on real SPHERE datasets, to demonstrate its resilience to instrumental speckles and the gain offered in terms of true detection. This will motivate future dedicated multi-epoch observation campaigns of well-chosen, young, nearby systems and very nearby stars to search for planets in emitted and reflected light, respectively, in order to open a new path concerning the observing strategy used with current and future planet imagers.
Methods. To test K-Stacker, we injected fake planets, and scanned the low S/N regime in a series of raw observations obtained by the SPHERE /IRDIS instrument in the course of the SHINE survey. We also considered the cases of two specific targets intensively monitored during this campaign: β Pictoris and HD 95086. For each target and epoch, the data were reduced using standard angular differential imaging processing techniques, then recombined with K-Stacker to recover the fake planetary signals. In addition, the known exoplanets β Pictoris b and HD 95086 b previously identified at lower S/N in single epochs have also been recovered by K-Stacker. Results. We show that K-Stacker achieves high success rate of ≈ 100% when the S/N of the planet in the stacked image reaches ≈ 9. The improvement of the S /N ratio goes as the square root of the total exposure time contained in the data being combined.
In Paper I  (Nowak et al. 2018) , by using simulated VLT-SPHERE observations, we have shown that when the total number n of available images is large enough to get √ n × (S/N) ≥ 7 (where (S/N) is the Signal to Noise levels in individual frames), the K-Stacker algorithm is able to detect the planet with a high level of reliability > 90%. The number of false positives were low but the simulated images did not reproduce instrumental speckle noise and angular spectral differential imaging (ASDI) reductions. The main goal of this paper is to validate the K-Stacker algorithm on real data obtained on sky reduced by the most recent algorithms (PCA ADI and ASDI). In Section 2, we describe the observations used in this paper, which come from the SPHERE SHINE survey  (Chauvin et al. 2017a). In Section 3, we present the results of a blind test where K-Stacker was used to search for fake planets hidden in real IRDIS observations reduced by a PCA ADI algorithm.
Description of the observations used in this paper All the observations used in this paper come from the SHINE survey done with the Spectro-Polarimetic High contrast imager for Exoplanets REsearch (SPHERE) instrument at the focal plane of the VLT-UT3  (Beuzit et al. 2019). The SHINE program  (Chauvin et al. 2017b ) is a very high-contrast near-infrared survey of more than 600 young, nearby stars aimed at searching for and characterizing new planetary systems. The goal of this project is also to find statistical constraints on the rate, mass and orbital distributions of the giant planet population at large orbits. Even if the SHINE observations have not been organized for the K-Stacker 'philosophy' where we plan to observe less stars but over more epochs, the number of observed targets reduced homogeneously by the SPHERE Data Center  (Delorme et al.
Set-up of the blind test In order to extend the demonstration of the K-Stacker algorithm on simulated IRDIS datasets presented in Paper I, we performed a new analysis on real IRDIS observations obtained during the SHINE survey. The methodology followed during this new blind experiment is similar to the one discussed in Paper I, with the exception of an additional PCA ADI reduction step. To stay as close as possible to the conditions of Paper I, and to demonstrate the true potential of K-Stacker, we injected planets in fake "K-Stacker runs", each made of 10 observations taken from the SHINE survey. As most stars have not been observed that many times during the survey, we created the K-Stacker runs by combining observations acquired on different but similar targets. In particular, we were careful in selecting observations of stars with similar magnitudes, and acquired in similar conditions (seeing, AO performances, etc.
e. in each observation, a parallactic rotation of at least one PSF FWHM at the position of the planet must be reached to subtract efficiently the instrumental speckles with ADI). The trade-off comes from the difficulty in setting up the observation strategy, the telescope overheads, and the star declination (to adjust the minimum exposure of individual observations). For future imaging surveys with K-Stacker on SPHERE+  (Boccaletti et al. 2020 ) and/or the ELTs instruments, a software to optimize observing schedules will have to be developed. Inspired from the algorithm used in the SHINE survey  (Lagrange et al. 2016) , the goal of this K-Stacker scheduler will be to compute the minimum exposure time required at each epoch for each star to have an efficient ADI subtraction, but at the same time to.
Acknowledgements. SPHERE is an instrument designed and built by a consortium consisting of  IPAG (Grenoble, France ),  MPIA (Heidelberg, Germany) ,  LAM (Marseille, France), LESIA (Paris, France), Laboratoire Lagrange (Nice, France) ,  INAF-Osservatorio di Padova (Italy) ,  Observatoire de Genève (Switzerland) ,  ETH Zurich (Switzerland) ,  NOVA (Netherlands) ,  ONERA (France)  and  ASTRON (Netherlands)  in collaboration with  ESO. SPHERE was funded by  ESO , with additional contributions from  CNRS (France) ,  MPIA (Germany) ,  INAF (Italy) ,  FINES (Switzerland)  and  NOVA (Netherlands). SPHERE also received funding from the  European Commission Sixth and Seventh Framework Programmes as part of the Optical Infrared Coordination Network for Astronomy (OPTICON)  under grant number  RII3-Ct-2004-001566  for FP6 (2004)(2005)(2006)(2007)(2008), grant number  226604  for FP7 (2009)(2010)(2011)(2012) and grant number  312430  for FP7 (2013)(2014)(2015)(2016).
We also acknowledge financial support from the  Programme National de Planétologie (PNP)  and the  Programme National de Physique Stellaire (PNPS)  of  CNRS-INSU in France. This work has also been supported by a grant from the  French Labex OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ). The project is supported by  CNRS , by the  Agence Nationale de la Recherche  ( ANR-14-CE33-0018 ). It has also been carried out within the frame of the  National Centre for Competence in Research PlanetS  supported by the  Swiss National Science Foundation  (SNSF). MRM, HMS, and SD are pleased to acknowledge this financial support of the  SNSF. Finally , this work has made use of the the SPHERE Data Centre, jointly operated by  OSUG/IPAG (Grenoble), PYTHEAS/LAM/CESAM (Marseille) ,  OCA/Lagrange (Nice) ,  Observatoire de Paris/LESIA (Paris) , and  Observatoire de Lyon , also supported by a grant from  Labex OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ).
Indeed, for all our datasets, random associations occurred on less than 2% of all events while we observed at least 11% for synaptic hub related associations (see Table  1 ). We further tested whether the reverse FASS experiment, sorting VGLUT1 venus cortico-striatal synaptosomes would co-purify TH or VGLUT2-labelled terminals (see Figure  S3AI ). As expected, TH varicosities were found massively associated to the striatal VGLUT1 venus synaptosomes (37. 9% VGLUT1+/TH+ in sorted samples; Figure  S3G  upper right quadrant) but VGLUT2 synaptosomes were mostly segregated from VGLUT1 venus positive synaptosomes (Figure  S3HI ). Decisively, VGLUT2+/VGLUT1 venus + particles were not coenriched through fluorescence sorting (8. 71% in unsorted sample vs 5. 45% in sorted synaptosomes; Figure  S3HI  upper right quadrants).
Cellular organisation of dopaminergic projections to the striatum. The nature of dopaminergic synaptic structures is the topic of a long-standing debate. Previous anatomical investigations in the field identified that the distribution of dopamine varicosities in the neuropil is biased toward a proximity to effector synapses but only a minority makes synapses with a target structure in the striatum  (Descarries et al. , 1996; Moss and Bolam, 2008). However, other authors reported the frequent occurrence of symmetrical synaptic contacts of dopaminergic thin axonal portions with MSNs spines or dendritic shafts  (Freund et al. , 1984; Gaugler et al. , 2012; Groves et al. , 1994). Our current dataset strongly advocates for a specific and frequent adhesion of dopaminergic axonal varicosities of rather small diameter with target structures (Figures  1  and 2IP ).
Functional consequences of the existence of Dopaminergic synaptic hubs. Our dataset suggest that nearly all dopaminergic terminals adhere to a post-synaptic element populated with cognate receptors. Yet, around 2/3 of the dopaminergic varicosities are thought to be "silent" at a given time in the striatum (C. Liu et al. , 2018; Pereira et al. , 2016). It thus seems like the formation of a synaptic contact at dopaminergic varicosity is not sufficient to establish a functional release site but rather additional plasticity may be required to engage silent dopaminergic synapses into dopamine transmission. The discovery of synaptic hubs linking dopaminergic and effector synapses provides a unique ex-vivo paradigm to study the complex interactions of receptors -through signalling crosstalk or heteromeric interactions -identified in the past decades  (Cahill et al.
1% formic acid in 5% ACN, and solvent B was 0. 1% formic acid in 80% ACN). Separation flow rate was 300 nl/min and the mass spectrometer operated in positive ion mode at a 1. 8-kV needle voltage. Data were acquired using Xcalibur 2. 2 software in a data-dependent mode. MS scans (m/z 300-2000) were recorded at a resolution of R = 70000 (@ m/z 200) and an AGC target of 10 6 ions collected within 100 ms. Dynamic exclusion was set to 30 s and top 15 ions were selected from fragmentation in HCD mode. MS/MS scans with a target value of 1 x 10 5 ions were collected with a maximum fill time of 120 ms and a resolution of R = 35000. Additionally, only +2 and +3 charged ions were selected for fragmentation. Other settings were as follows: no sheath nor auxiliary gas flow, heated capillary temperature, 250 °C; normalized HCD collision energy of 25% and an isolation width of 3 m/z.
Data were searched by SEQUEST through Proteome Discoverer 1. 4 (Thermo Fisher Scientific Inc. ) against Mus musculus reference Proteome Set (Uniprot 2017-05; 50885 entries). Spectra from peptides higher than 5000 Da or lower than 350 Da were rejected. The search parameters were as follows: mass accuracy of the monoisotopic peptide precursor and peptide fragments was set to 10 ppm and 0. 02 Da respectively. Only b-and y-ions were considered for mass calculation. Oxidation of methionines (+16 Da) and carbamidomethylation (+57 Da) and propionamidation of cysteines (+71 Da) were considered as variable modification. Two missed trypsin cleavages were allowed. Peptide validation was performed using Percolator algorithm[46] and only "high confidence" peptides were retained corresponding to a 1% False Positive Rate at peptide level.