dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank the  URDR team members of the Jules-Gonin eye hospital  for discussion on the work: in particular,  Amélie Clement ,  Florian Udry , and  Roger Moser  for their technical support. We are also thankful to  Botond Roska  at the  Friedrich Mischer Institute  for providing the Chrnb4-EGFP mice,  Cathrin Brisken  at the  EPFL  for providing the DsRed mice, for the technical assistance provided by the FACS sorting facility at the  Ludwig Cancer Research Institute  and at the  CHUV in Lausanne , for the technical assistance of the confocal facility at the  EPFL in Lausanne , and to our technician  Dana Wanner. This work has been supported by the  Fondation Provisu , the  Bayer GOAP program , the  Novartis Foundation , the  Fondation asile des aveugles , the  Fondation Open Eyes , and the  Foundation Bertarelli.
On the contrary, dFab continuously accumulated in large vesicles in a time-dependent manner, and it showed a 3-fold increase in mean intensity compared to sFab at 60 min (Figures  1D  and 1E ). Such intracellular accumulation of dFab could result from changes in endocytosis, recycling, and/or degradation of the construct. To identify the specific trafficking differences between dFab and sFab, we used mathematical modeling to analyze the kinetic data generated through time course experiments (Figure  S2 ). This model is formulated as a set of ordinary differential equations that describe the amount of TfR at the plasma membrane and within intracellular vesicles. We considered that the amount and distribution of TfR was determined by the processes of internalization, recycling, and degradation (see the Supplemental Experimental Procedures).
To test whether transport of dFab into sorting tubules is impaired, we performed live-cell imaging of Brain Shuttle trafficking in bEnd. 3 cells, and we detected constructs with a fluorescently labeled anti-human Fab fragment (Figure  S3 ). In living cells, both sFab and dFab localized to vesicles (Figure  2C ; Movies S1 and S2). However, only sFab was localized to narrow motile tubules (Figure  2C ), which we also observed for the endogenous ligand of TfR (data not shown). In the case of dFab, the number of tubules was 3-fold lower (Figure  2D ). sFab and Tf were localized in the same tubular structures (Figure  2E ), thus confirming these tubules form part of the early/recycling endosomal network. Together, our data show that dFab transport into tubules is impaired. Overexpression of Small GTPase Rab17 Enhances Endosome Tubulation and Redistributes dFab to Sorting Tubules Together, the results from mathematical modeling and live-cell imaging showed that a key difference between sFab and dFab is their differential intracellular transport along sorting tubules.
To reduce potential artifacts of Rab protein overexpression, we (1) sorted cells based on GFP expression using fluorescence-activated cell sorting (FACS), (2) verified by confocal microscopy that the intracellular localization of mEGFP-tagged Rab proteins reproduced previous data on Rab localization, and (3) confirmed that overexpression did not significantly perturb the transport kinetics of Tf. For example, GFP-Rab11a localized to small tubules at the perinuclear region, and its expression did not alter Tf-trafficking kinetics (Figure  S4A ). Next, we measured the steady-state colocalization of Rab proteins that localized to vesicles or tubules with Tf. We observed the highest degree of colocalization (60%) with Rab4a, a regulator of TfR recycling  (Stenmark, 2009) , and with Rab17, a regulator of TfR transcytosis in epithelial cells  (Zacchi et al.
, 1998)  (Figure  3A ). Although Tf colocalized with multiple Rab proteins, only Rab17 colocalized extensively with Tf-positive tubules, whereas other proteins, such as Rab4a, colocalized mostly with vesicles (Figure  3B ). Endogenous Rab17 was also localized to tubules in epithelial cells in vivo  (L€ utcke et al. , 1993). Interestingly, we found that Rab17, as well as Rab25, another well-known regulator of transcytosis in epithelial cells  (Tzaban et al. , 2009) , was not expressed in bEnd. 3 cells (Figures  3C  and S4B ). This observation is in agreement with in vivo expression data from mouse BECs  (Zhang et al. , 2014). Therefore, based on the specific Rab17 localization to Tf-positive tubules upon overexpression in bEnd. 3 cells, we decided to investigate the effects of Rab17 overexpression on the formation of sorting tubules in BECs.
Rab17 showed a low degree of colocalization (10%) with the early-endosome marker EEA1 (Figure  3D ). Interestingly, EEA1 colocalization occurred only in vesicular structures, but not in tubules, suggesting that Rab17-positive tubules originate from early endosomes. On the other hand, colocalization between Rab17 and the recycling endosome marker Rab11 was higher (20%) (Figure  3D ), but it occurred only partially with a subpopulation of tubules. These data suggest that Rab17-positive tubules constitute a distinct compartment from recycling endosomes. Next, we assessed the effects of Rab17 overexpression on tubules by (D) Quantification of the number of intracellular tubules per cell area. The graph shows boxplots with interquartile ranges and median. The mean is shown as a cross and error bars show the 5th and 95th percentiles.
Immunofluorescence experiments also confirmed a significant increase in TfR at the plasma membrane (Figure  S4D ) in Rab17-expressing cells compared to control cells. Together, these data suggest that overexpressing Rab17 in BECs specifically enhances tubule formation but does not impact TfR turnover or sorting. 5 μm 5 μm A R a b 4 a R a b 5 a R a b 7 R a b 1 1 a R a b 1 4 R a b 1 7 R a b 2 2 a R a b 2 5 Tf -Rab colocalization B Normalized mRNA expression R a b 4 a R a b 5 a R a b 7 R a b 1 1 a R a b 1 4 R a b 1 7 R a b 2 2 a R Next, we used Rab17 overexpression as a tool to assess how enhancing tubule formation affects dFab localization. As observed for Tf, sFab-positive tubules colocalized with Rab17 (Figure  4A ; Movie S3), but they were negative for Rab4a, Rab5a, and Rab11a (data not shown).
Strikingly, while dFab was localized almost exclusively to vesicles in bEnd. 3 cells, in Rab17-expressing cells dFab was prominently localized to tubules (Figure  4B ; Movie S4). We found that the number of tubules was similar among Tf, sFab, and dFab in Rab17 cells (Figure  4C ). Within these tubules, dFab colocalized with TfR, suggesting that the construct segregates together with the receptor during transport (Figure  4D ). Relocalization of dFab into tubules required a functional Rab17 guanosine triphosphate (GTP)-guanosine diphosphate (GDP) cycle (Figures  S5A  and S5B ). These data show that overexpression of Rab17 is sufficient to promote the transport of dFab to tubules. Promoting dFab Transport into Sorting Tubules Is Sufficient to Reduce Its Degradation and Induce Its Transcytosis Since Rab17 promotes dFab transport into tubules without perturbing the endogenous turnover of TfR in bEnd.
Remarkably, we detected a (C) Expression analysis of the same Rab proteins examined in (A) by RT-PCR. Bars show the mean expression and SD of technical triplicates from a single experiment. (D) Representative images of cells expressing GFP-Rab17 (green) and stained with either EEA1 or Rab11 (magenta). Scale bars, 10 mm. The panels to the right show higher magnification images of the boxed regions. Arrows point to vesicles where Rab17 and EEA1 colocalize. Arrowheads point to individual Rab17 tubules with partial colocalization of Rab11 (lower panel). Grayscale images show the individual signal from Rab17 and EEA1 or Rab11. Scale bars, 5 mm. The graph shows the quantification of the fraction of Rab17-positive structures colocalized with either EEA1 or Rab11. Data are plotted as boxplots with interquartile ranges and median for at least 30 cells from 3 independent experiments.
The intensity values were normalized to the Tf intensity in bEnd. 3 cells at 10 min and are shown as arbitrary intensity units. Points show the average and error bars show the SEM from 2 independent experiments. Lines show the result of mathematical modeling simulation parameters that generated the best fit for the experimental data. The inset in (H) shows the Rab17 data normalized by its intensity value at time point 0 to highlight that there are no substantial changes to Tf trafficking upon Rab17 overexpression. For all images, DAPI-stained nuclei are shown in blue. See also Figure  S4  and Tables  S1  and S2. significant nearly 3-fold increase of dFab in the basolateral compartment, but not in the apical compartment, in Rab17 cells compared to control cells (Figure  4I ). These measurements show that Rab17 overexpression specifically increases dFab transcytosis across bEnd.
The intensity values were normalized to the dFab intensity in bEnd. 3 cells at 10 min and are shown as arbitrary intensity units. Points show the average and error bars show the SEM from 2 independent experiments. Lines show the result of mathematical modeling simulations. The inset in (F) shows the Rab17 data normalized by its intensity value at time point 0 to highlight the change in clearance rate of dFab upon Rab17 overexpression. (G) Representative images of bEnd. 3 cells infected with adenoviruses driving the expression of mCherry and Rab17 (Rab17) or mCherry alone (control) showing the presence of tight junctions labeled by both Claudin-5 and ZO-1. mCherry is shown in magenta, tight junction markers are in green, and DAPI-stained nuclei are in blue. Scale bars, 20 mm. (H) TEER values in U , cm 2 comparing Rab17 and control cells.
Statistics Statistical comparison of data were performed using GraphPad Prism 6. Parametric tests (two-tailed Student's t test for pairwise comparisons or ANOVA for multiple comparisons) were used when datasets were symmetric when displayed using boxplots. Otherwise, data were compared using non-parametric tests (Mann-Whitney test for unpaired pairwise comparisons, Wilcoxon test for paired pairwise comparisons, and Kruskal-Wallis test followed by Dunn's post hoc test for multiple comparisons). The p values and n values for all comparisons are included in each figure legend. For further details see the Supplemental Experimental Procedures.
ACKNOWLEDGMENTS We thank  Per-Ola Freskga  ˚rd for his valuable discussion and guidance for the project;  Laurence Ozmen  and  Julie Lambotte  for their support with the in vivo experiments;  Fiona Gr€  uninger and  Ulrich Goepfert  for providing the Brain Shuttle constructs;  Jens Niewoehner ,  Olaf Mundigl , and  Monika Heidrich  for advice for primary cell culture; and  Irene Knuesel ,  Annie Moisan , and  Laurence Ozmen  for critical reading of the manuscript. We thank  Jean-Luc Vonesch ,  Basile Gurchenkov , and the  Imaging Centre at the IGBMC  for discussions and support with the live-cell imaging experiments. R. V. was supported by the  Roche Postdoctoral Fellowship (RPF) program  ( 2014-2016 RPF-ID 301 ). R. V. , M. S. , J. S. , and L. C. are employees of  Hoffmann La Roche.
| Bioinformatic analysis To improve the efficiency of the Roche pipeline, composed of the Roche 454 GS Reference Mapper for mapping (on UCSC human reference genome hg19) and Amplicon Variant  Analyzer (v2. 5p1)  for variant calling, an additional in-house pipeline was used. Sequencing reads were aligned to the reference genome (UCSC hg19) using Roche 454 GS Reference Mapper and Amplicon Variant Analyzer (version 2. 5p1). BAM files were converted to FASTQ and realigned to hg19 using Bowtie2 V. 2. 2. 6  (Langmead & Salzberg, 2012). Samtools V. 0. 1. 19 and Python V. 2. 7. 11 were used for additional file handling. Variant calling was performed combining GATK Unified Genotyper  (Genome Analysis Tool Kit v. 3. 4; McKenna et al. , 2010)  and  Platypus (v. 0. 8. 1; Rimmer et al. , 2014). The annotation and the prioritization steps were executed with ANNOVAR (15.
06. 07; Wang, Li, & Hakonarson, 2010). Finally, the target region reads depth was controlled using  BEDTools (v. 2. 25. 0; Quinlan & Hall, 2010)  and the alternative allele frequency was computed using pysamstats (v. 0. 24. 2; https://github. com). Candidate variants were filtered according to read depth, allele frequency, and prevalence in genomic variant databases such as ExAc (v. 0. 3), ClinVar, Kaviar (including 1,000 g-phase3, dbSNP146;  Glusman, Caballero, Mauldin, Hood, & Roach, 2011; Landrum et al. , 2016). Variant prioritization tools (PolyPhen2, SIFT, LRT, MutationTaster, Mutation Assessor) were used to predict the functional impact and then focused on variants with predicted deleterious consequences (nonsense SNVs, frameshift indels, essential splice variants, and complex indels).
ACKNOWLEDGMENTS Funding of the study reported in the paper was provided by the  Hungarian Brain Research Program  (Grant No. KTIA_13_NAP-A-II/15 ,  TÁMOP-4. 2. 2. A-11/1/KONV-2012-0052  and  TÁMOP-4. 2. 2. A-11/1/ KONV-2012-0035 ).
We reasoned that by providing sleepers with sensory information that can be learnt passively, we could probe neural plasticity during sleep. Indeed, so far, only conditioning had been evidenced in sleeping animals  6, 32  or humans  3, 7, 23  while declarative forms of memory tend to produce null results  9, 33. Here, we show that other forms of implicit memory can be acquired during sleep. Performance upon awakening is improved, providing evidence for perceptual learning during REM sleep. EEG markers of learning computed overnight confirm sleepers' ability to learn in both REM sleep and light NREM sleep. The link between this form of perceptual learning and more standard instances of nondeclarative memory remains to be specified, but previous data suggests that it contains both shorter-term (hundreds of millisecond) and longer-term (from tens of seconds to weeks) components  30.
2c ), which may reflect MEPs or, in addition, modulations of ongoing neural oscillations  30, 31. We observed an increase in ITPC for RefRN targets compared to RN targets (Fig. 2d , two-tailed paired t-test: t(19) = 2. 18, P = 0. 042, Hedges' g = 0. 46). The ITPC increase was observed around the target presentation rate (2 Hz, Supplementary Fig. 3a ) and was maximal at central electrodes (Supplementary Fig. 3b ). The same MEP analyses were conducted on data collected during sleep; data from each distinct sleep phase was analyzed separately (Fig. 4a ). During REM sleep, MEPs for RefRN trials differed from N trials (P cluster < 0. 05). However, the waveform of the potentials evoked by each target was different from typical MEPs (Fig. 2b ). Rather, this waveform was consistent with the well-documented transformations of auditory-evoked potentials during REM sleep, which includes a decrease of N1 amplitude in favor of larger P1 and P2 potentials  36  (Supplementary Fig.
The white-noise learning paradigms provide therefore a unique opportunity to investigate the learning of novel sensory information. Stimuli were presented to participants using the PyschToolbox extension  70  for Matlab (Mathworks Inc. , Natick, MA, USA) and were played at 50 dB (soundcard: Echo Indigo, Echo Digital Sound Corp. , Santa Barbara, CA, USA) through a loudspeaker placed near the bed to ensure comfortable listening conditions while minimally disturbing sleep. Contrasts of interest and expected results. As thoroughly discussed recently  30  , the noise-memory paradigm allows exploring the rapid formation of memory to noise at different time scales. The fact that listeners could discriminate between RN and N trials demonstrates their ability to detect the reoccurrence of a nondescript noise segment embedded in running noise after only few presentations (max: 5 in the pre-sleep phase) and despite the statistical similarity between targets and fillers.
Nevertheless, in the post-sleep phase, to avoid this confound and to make sure that the positive effect for REM targets could not be due to these awakenings, BE was computed when excluding all NREM or REM RefRN heard around (micro)-awakenings (Supplementary Fig. 4c ), which led to identical results as in Fig. 3. Offline sleep scoring of polysomnographic recordings. Polysomnographic data was analyzed using a combination of SPM (Functional Imaging Laboratory, Univ. College London, London, UK), FieldTrip  73  , and EEGlab 74 toolboxes running on Matlab (Mathworks Inc. , Natick, MA, USA). Polysomnographic data (EEG, EOG, EMG, and ECG data) was preprocessed according to established guidelines. EEG data was high-pass filtered above 0. 1 Hz and then low-pass filtered below 30 Hz (5th order two-pass Butterworth filters).
During the night, ITPC was computed by dividing all sleep cycles into fixed windows of 20 stimuli presentations (either RefRN or RN). This was done either by cycle when focusing on the within-cycle dynamics (Fig. 7b-c ) or when considering the entire night (Figs. 4b  and 7d ). These fixed windows were slid trial-by-trial. Thus, if a cycle (or a night) contained n RefRN trials, we obtained n-19 ITPC values (that were then binned in 18 bins for each cycle, Fig. 7b ). When examining the entire sleep recordings, we pooled data across participants. As the number of windows differed between RefRN and RN trials, statistical tests are unpaired in Fig. 4b  and we subtracted the average ITPC for RN trials to the ITPC values for RefRN trials in Fig. 7d. To obtain the power spectra displayed in Supplementary Fig.
43  and the 'robust correlation' toolbox for Matlab). We therefore also reported the correlation coefficients when including these two data points in the Results section. However, as these correlation coefficients were not obtained when including all participants, they should be considered with caution. For the null results illustrated in Fig. 3a , a nonparametric method (Bayes factors) was used to test the plausibility of the null hypothesis  34. These Bayes factors are reported in the text. A Bayes factor comprised between 3 and 20 is usually considered as positive evidence for the null hypothesis, while a Bayes comprised between 20 and 150 reflects strong evidence for the null hypothesis  79. We also used a stepwise regression analysis (with forward selection) to examine the respective influence of NREM and REM sleep substages on the learning effects observed upon awakening.
Acknowledgements This research was supported by  ANR  grants ( ANR-10-LABX-0087  and  ANR-10-IDEX-0001-02 ), by the  European Research Council  ( ERC  project  METAWARE  to S. K. and  ERC  project  ADAM  to D. P. ), by the  EU   H2020 program  (COCOHA # 644732  to DP), and by the  Ministère de la Recherche  and the  Société Française de Recherche et Médecine du Sommeil  (T. A. ). We thank  V. Bayon ,  A. Dalbin ,  L. de Sanctis ,  M. Elbaz ,  S. Rio , and  C. Varazzani  for their help.
ACKNOWLEDGMENTS MMi work's was funded by the  French National Research Agency (Agence Nationale pour la Recherche: SeasoNRG   ANR-09-JCJC-0049-01  and HypNeurogen  ANR-16-CE37-0006-01 ) and FL's work was funded by  French National Research Agency  ( PLASTMATBEHAV ANR12-BSV7-0017 ). This work has benefited from the facilities and expertise of the cellular imaging platform (PIC),  UMR PRC, INRA Centre Val de Loire, France. We thank  Prof. J. Lonstein  for his kind revision of the manuscript.
This implementation features automatic estimation of the hyperparameters, enhanced speed and similar-to-better reconstruction performances and superresolution capabilities. It is benchmarked on both synthetic and real data. We additionally provide thresholding guidelines and theoretical bounds on its performance, in terms of precision-recall and temporal accuracy, for large range of realistic experimental conditions. The program is available at https://github. com/jertubiana/BSD. The article is organized as follows. In section 1, we present the principles of non-negative sparse deconvolution methods. In section 2, we focus on the determination of the sparsity prior λ; we compare our method to existing solutions and compare performances on synthetic data. In section 3, we compute the spike detection theoretical limits in terms of precision-recall and temporal resolution.
This reflects in the fact that the computing time is largely dependent on whether or not the noise level is provided. Notice that the exact gain in speed depends on which version of con-oopsi is used (here, Matlab implementation, con-oopsi version of Dec. 2015, with cvx). Although we did not test the PAVA optimizer, we expect a gain of the same order of magnitude between constrained-PAVA and BSD-like PAVA. Such a difference in computation load may prove highly beneficial for real-time inference in high data-throughput recordings, as illustrated in section 6. N f rames BSD/ fast-oopsi (s) con-oopsi (s) con-oopsi σ user-provided 10 TABLE I: Comparison of BSD, fast-oopsi and con-oopsi computational speed. For con-oopsi, under-estimation of the noise level σ, even for synthetic data, can lead to a large increase in computational time.
Lastly, the spike amplitude a and frequency ν can be deduced from the following equations, that hold under the model assumption: < F >= aν∆t i K [i∆t] < F 2 > -< F > 2 = a 2 ν∆t i K [i∆t] 2 (30) Although they yield very good results for synthetic datasets, these estimators can fail in several frequently encountered situations in practice: • When the neural activity is not sparse, we do not expect b to be the most frequent fluorescence value. An error in the estimation of b can result in a misestimation of σ as well. • When the neuron displays bursting activity (i. e. several spikes in short time intervals), the hypothesis that the N i are independent usually fails. This may result in overestimating τ r and/or τ d. • In the same situation, Eqn 30 is incorrect and a can be overestimated. • When the noise exhibits temporal correlation (streaking artefacts in light sheet imaging, small sample drifts, fluctuations in laser intensity, etc.
A. Joint Electrophysiology and Fluorescence recordings on mice data We first test it on joint electrophysiology and fluorescence recordings, as the former can be used as a ground truth for comparison. Recordings were performed on mice visual cortex at the Svoboda laboratory  [17, 18] , using the GCaMP5k, GCaMP6s and GCaMP6f probes, see summary in Table  II. [37]. For all datasets, the fluorescence is recorded at 60Hz, and the electrophysiology at 10kHz. For each neuron, the fluorescence signal is computed by simple averaging of the raw voxel-scale fluorescence over a region of interest. A baseline is computed using a moving percentile (window: 10s, quantile q = 0. 15); it is subtracted to the fluorescence trace to remove the temporal fluctuations of the baseline. The resulting pre-processed signal is then fed to the following deconvolution algorithms: • con-oopsi.
The overall agreement is good, but the experimental width are ∼ 30% larger than the theoretical bounds. This is likely due to the fact that real spikes are not isolated, and neighboring spikes tend to decrease the temporal accuracy. We find that the super-resolution improves the temporal resolution by ∼ 30% for both GCaMP6f and GCaMP5k, a gain which can be considered as very significant given that it is here obtained with no prior knowledge of the convolution kernel. A more modest gain is obtained for GCaMP6s, likely due to the higher noise and larger rise time associated with this dataset. Compared to standard fluorescence microscopy technique, such as confocal or two-photon point-scanning technique, light-sheet imaging allows for a parallelization of the recording, yielding ∼ 100-fold increase in data-throughput  [19? ? ].