dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, increasing the inhibitory drive into the motor cortex could represent a useful strategy to improve motor symptoms. While there is an emerging therapeutic potential of optogenetics  [54] [55] [56] [57] [58] [59] [60]  , its medical application is still in its infancy, with the first human clinical trial underway to treat retinitis pigmentosa (clinicaltrials. gov, NCT03326336 and NCT02556736). More work is necessary to fully explore and optimize safe transfection and light delivery in humans  58, 60, 61. Nevertheless, targeting cortical GABAergic networks with pharmacology or non-invasive brain stimulation  61  such as transcranial stimulation  62  could provide less invasive strategies than DBS, thus benefiting a larger population of parkinsonian patients.
Data were sampled at 25 kHz via a CED 1401 interface using the Spike2 data acquisition program (Cambridge Electronic Design, Cambridge, UK). The STN ipsilateral to the recorded pyramidal cells was stimulated with a bipolar electrode (SNE-100; Rhodes Medical Instruments, Woodlands Hill, CA) at 8. 1 mm depth. Electrical stimulation consisted in either single-shock STN stimulations or 130 Hz DBS (60 μs, 2-4 V), using a stimulus isolator (DS2A, Digitimer, WelWyn Garden City, UK) driven by a pulse stimulator (Pulsemaster A300, WPI, Hitchin, UK). In vivo juxtacellular electrophysiological recordings Rats. Unilaterally 6-OHDA-lesioned or sham-lesioned rats were anesthetized with chloral hydrate (400 mg/kg, i. p. , supplemented by continuous injection delivered at 60 mg kg -1 h -1 ). Craniotomies: see intracellular recordings.
Because Pv::ChR2 and Sst:: ChR2 mouse strains display a small proportion of off-target expression of ChR2 (Supplementary Fig. 2 ), we used cluster analysis to combine opto-activation and waveform characteristics for a better identification (Supplementary Fig. 3 ). Principal component analysis (Matlab) was applied to waveform characteristics (time and amplitude of the trough and of the second peak relative to the first peak, on the filtered waveform) and opto-activation success rate. Neurons were clustered using hierarchical and k-means clustering algorithms (Matlab), testing all combinations of the first 2, to all principal components, with 2-7 target clusters. The resulting cluster silhouettes were ranked and the best score was selected (mean silhouette = 0. 73, with 2 principal components, 2 clusters, k-means algorithm), resulting in two clearly separated clusters.
In the second session, mice were injected (6 μL/g, i. p. ) with levodopa (6 mg/kg) with benserazide (3. 75 mg/kg) and underwent the same tests in the same order. All the behavioral testing occurred within 30-100 min after the saline or levodopa injection. Open field. Mice were placed in a 40 × 25 × 20 cm arena for 30 s habituation period. For opto-and DBS-stimulation sessions consisted of alternating opto-or DBSstimulation periods off and on for 1 min, for 10 min. Saline and levodopa sessions lasted for 5 min. Head position was tracked using DeepLabCut algorithm  66  , then custom Matlab scripts. Cylinder test. Mice were placed in a glass cylinder (12 cm diameter). For opto-and DBS-stimulated mice, sessions consisted in 3 epochs (before, during and after stimulation) of 10 rearing episodes each.
Histology Assessment of 6-OHDA lesion. Coronal slices (50-80 μm thickness) of striatum and SNc were processed for Tyrosine hydroxylase(TH)-immunohistochemistry, after endogenous peroxidases inactivation, using a rabbit anti-TH (1/1000, 36 h at 4 °C, AB152, Merck Millipore), and biotinylated goat antibody against rabbit IgG (1/200, BA 1000, Vector Laboratories, CliniSciences, Nanterre, France), followed by revelation using an ABC kit (PK-6100, Vector Laboratories). Efficacy and specificity of transgenic mouse lines. Images were taken with a stereozoom fluorescence microscope (Axiozoom, Zeiss) or confocal microscope (SP5, Leica), and analyzed using ImageJ software. Statistics. Unless otherwise stated, normal data are displayed as mean ± SEM, and differences between groups were assessed using two-tailed unpaired or paired t-test.
Non-normal data are presented as boxplots (Matlab built-in boxplot function defaults) where the center line is the median, the box represents the 25% and 75% quartiles (Q1 and Q3), and the whiskers extends to the last data point within 1. 5* the interquartile range outside of the box range (bottom whisker: Q1-1. 5* (Q3-Q1), and top whisker: Q3 + 1. 5*(Q3-Q1)) (data outside the whisker range are not shown, but are included in the statistical analysis), and differences between groups were assessed using two-tailed Mann-Whitney U test (unpaired data) and Wilcoxon's signed rank test (paired data). Normality of each dataset was tested using D'Agostino and Pearson's Omnibus K² test. 2-way ANOVAs were performed using Prism 5. 0 (GraphPad, San Diego, CA, USA). Computational model: spiking network model.
Additional tests with different current amplitudes, ranging from 200 to 800 pA were also simulated (Supplementary Fig. 7 ). Simulations of the network activity were done using a custom code developed in Matlab R2016 (The Mathworks). Computational model: analysis Firing rates. Average firing rates were computed for each population from 1 s simulations of network activity. When mimicking PV or SST opto-activation, the average firing rates of PV or SST interneurons were calculated only based on the neurons that were directly activated. Heatmaps of pyramidal cell firing rate modulation were obtained by varying the intensity (from 0 to 120 pA) of I DBS (with default parameters: square pulses of 2 ms, repeated at 130 Hz) received by two populations at the same time, while keeping the default intensity (120 pA) for the third population.
M, where the weight matrix W is a random matrix, identical for all stimuli presentations, with each element generated from the uniform distribution on the interval [0, 1] (Fig. 7a ). These S matrices were then used as inputs for the supervised-learning algorithms (nearest centroid classifier, multinomial logistic regression, linear discriminant analysis and support vector machines  71, 72  ). For each condition, the dataset consisted of 100 repetitions for each of the 20 stimuli (with the same network parameters and convergence matrix W, but independent realizations of the intrinsic noise ξ). The classifiers were trained to discriminate the population response given the stimulus on 80% of the data sample, using stratified k-fold cross validation (k = 5). This procedure was repeated using 5 independent seeds.
The training and testing of the classifiers were run using scikit-learn and keras packages in Python 3. 5 (Python Software Foundation, www. python. org). Statistics. Normally distributed data are displayed as mean ± SD, and differences between conditions were assessed using one-way ANOVA for each feature and Tukey-Kramer post-hoc test. Since some distributions of correlation coefficients were not normal, all data is summarized using the median and presented in details as boxplots (using the same convention as described above; Supplementary Fig. 6 ) and differences between conditions were assessed using the Kruskal-Wallis test (with Tukey-Kramer post-hoc test) on every stimulus. The statistics for the Pearson correlation coefficients and classifier accuracies were performed considering the full model (Supplementary Fig.
Acknowledgements We thank the L. V. lab members,  Christian Giaume  and  Pierre Magistretti  for helpful suggestions and critical comments. We thank  Sylvie Perez ,  Alessandra Romei ,  Farah Hadj-Idris ,  Angèle Roudeau  and  Charlotte Branco  for technical assistance for 6-OHDA lesioned-rodents and immunohistochemistry. We thank  France Maloumian  for her help in the schematic illustrations in Figs. 3 and 5. This work was supported by grants from  Fondation de France (Maladie de Parkinson) CRCPEN ,  Fondation du Collège de France, France Parkinson ,  Fondation Patrick Brou de Laurière, INSERM ,  Collège de France  and  CNRS. C. P. is supported by  Ecole Normale Supérieure , G. G by  Fondation Patrick Brou de Laurière , and S. V. , W. D. and M. V. by the  Collège de France.
, CA, USA) using a paired-end 150 bp reads strategy. One lane of HiSeqX5 flow cell was used per sample specific library in order to reach an average sequencing depth of 30x for each sequenced individual. Sequence quality parameters have been assessed throughout the sequencing run and standard bioinformatics analysis of sequencing data was based on the Illumina pipeline to generate FASTQ file for each sample. FastQ sequences were aligned on human genome hg37 using the BWA-mem program  (25). Variant calling was performed using the GATK HaplotypeCaller (GenomeAnalysisTK-v3. 3-0, https://software. broadinstitute. org/gatk/documentation/article. php?id=4148) tool followed by recalibration. Single nucleotide variants (SNVs) that succeeded the "PASS" filter were then annotated using Annovar  (26).
To comply with the autosomal dominant mode of inheritance of the observed PSD, we selected as candidate culprit PSD causing variant any rare variant that has either never been reported in public database or at a very low allele frequency (<1‰) and that were present at the heterozygous states in the 5 whole genome sequenced patients but not present in 2 healthy relatives. Interrogated public databases were dbSNP, GnomAD (https://gnomad. broadinstitute. org/), Ensembl (https://www. ensembl. org/index. html), 1000 genomes (https://www. internationalgenome. org/), ExAC (http://exac. broadinstitute. org/) and FrEx (http://lysine. univ-brest. fr/FrExAC/).
Sanger sequencing validation Sanger sequencing validation of the identified candidate variant was carried out using primers designed to span all the putative transcription factor binding sites described by de Wolf (  27 ) (Supplementary Table  2 ). PCR amplification of genomic DNA was performed using Phusion™ Green Hot Start II High Fidelity DNA Polymerase with GC Buffer in presence of 10% of DMSO (ThermoFisher). Big dye sequencing chemistry was used to sequence the PCR products in both directions using the ABI 3500xL Genetic analyser (Applied Biosystems, Foster City, CA) and the sequence were analyzed by the SeqScape Software (Applied Biosystems, Foster City, CA).
RNA isolation and RT-qPCR analysis Total RNA was isolated from the transfected HeLa cells using the RNeasy mini kit (Qiagen) following the manufacturer's instructions. Reverse transcription of 2 µg of total RNA to single-stranded cDNA was performed using High Capacity cDNA kit (Applied Biosystems). To quantify PROS1 transcripts, qPCR were carried out on the generated cDNA (diluted at 1/5) using PowerUp SYBR master mix (Thermofisher Scientific) in a final volume of 10 µl and 40 cycles of amplification on QuantStudio3 Real-Time PCR System (ThermoFisher). Results were analyzed with the QuantStudio design and analysis software. Transcript levels were normalized to the reference RPL32 gene. Relative quantification of PROS1 in different samples was conducted according to threshold cycle (Ct) value based on the ΔΔCT method.
The PreTIS software (41) dedicated to 5'UTR variants predicted a "moderate" impact of the variant while the DeepSea suite's predictions  (42, 43)  were neither supportive for a strong functional impact of the variant. While the identified c. -39C>T mutation segregates with PSD, it is also important to emphasize that all but one family members with PSD and complete clinical information experienced one or more VT events, with age of onset of first VT varying between 16 and 38 years old. A part from the PSD patient with missing clinical information (individual IV-4), the sole affected patient carrying the disease mutation that has not (yet) developed a thrombotic event is the youngest one. She is currently 16 yrs old and her free PS levels were 18%. Free PS levels in other relatives with combined PSD & VT ranged from 22% to 38% while such values ranged from 88% to 125% in healthy family members.
investigated (Fig 2B). The human PROS1 cDNA (GenBank NM_000313. 4) was PCR-amplified from HepG2 cDNA and cloned into pcDNA3. 1/myc-His(-) plasmid (Invitrogen). The PCR amplification of PROS1 from c. -44 to c. 2028 was performed using Phusion High Fidelity (HF) DNA Polymerase (ThermoFisher) and primer pairs designed to be either complementary to the wild-type sequence or carrying the c. -39C>T variant on the forward primer only (Supplementary Table.
Acknowledgments O. S and M. R were financially supported by the  GENMED Laboratory of Excellence on Medical Genomics  [ ANR-10-LABX-0013 ]. DA T was supported by the «EPIDEMIOM-VTE» Senior Chair from the  Initiative of Excellence of the University of Bordeaux. This work was partially supported by the  GENMED Laboratory of Excellence on Medical Genomics  ( ANR-10-LABX-0013 ) and the  French Clinical Research Infrastructure Network on Venous Thrombo-Embolism (F-CRIN INNOVTE) , two research programs managed by the  National Research Agency (ANR)  as part of the  French Investment  for the Future.
, t k+1 }, d k , m) + 2 s K. By construction, the procedure is sensitive to the choice of K max since a least squares regression is realized for the "largest" values of K and we preferred to chose the largest reasonable value of K max. A software was written with Octave software (also executable with Matlab software) and is available on http://samm. univ-paris1. fr/IMG/zip/detectchange. zip. 2 × J n (K, t, d, m) + 2 × s × K (in red).
The general aim of future research on SSB shall be to work on a retracked dataset that is free from the retracker-related noise, in order to correct for the physical effects of the interaction between the radar signal and the waves. Meanwhile, this approach offers a first step to improve the correction currently used in the standard products of the missions considered in this study. The advantage of the ALES retracker coupled with the recomputed SSB correction in improving the quality and quantity of the altimetric records has been studied in recent years in several regional studies. The retracked SSH time series show a strong abatement in the number of outliers in the Baltic Sea/North Sea transition zone compared to the global along-track Sea Level CCI dataset  21. Chereskin et al. 22  have shown that the spatial spectrum of the SSH for the Jason missions retracked by ALES is comparable to the one obtained from Sentinel-3 measurements, despite the latter having an intrinsically better signal-to-noise ratio.
legos. obs-mip. fr/) in order to optimize the completeness and the accuracy of the SSH derived from satellite altimetry in coastal ocean areas. It is tailored for extending the use of altimetry data to coastal ocean applications and already provides freely available 1-Hz (i. e. with a resolution of 6-7 km in the along-track direction) sea level time series covering all the coastal oceans, distributed by the AVISO + operational centre. It is based on the standard ocean MLE4 (Maximum Likelihood Estimator 4) altimeter waveform retracker. The X-TRACK system is described in detail in  7, 25, 26  (see also https://www. aviso. altimetry. fr/index. php?id=3047). In the context of the ESA SL_CCI + project, X-TRACK has been extended to the processing of high-rate altimetry measurements (20-Hz in the case of Jason missions) instead of the 1-Hz data and then adapted to the ingestion of data provided by the ALES retracker.
The resulting new processing system, called X-TRACK/ALES is illustrated by Fig. 4. Like X-TRACK, the X-TRACK/ALES processing system works on a regional basis (the regional domain can be easily defined before the processing in a parameter file). It first uploads all the parameters needed to compute the product (orbit solution, altimeter ranges, instrumental, environmental and geophysical corrections), which were previously ingested and indexed in the altimetry database of the French National Observations Service for altimetry called CTOH (http://ctoh. legos. obs-mip. fr/). These parameters come from the Geophysical Data Records (GDRs) data sets distributed by the space agencies for the different altimetry missions, ALES products from TUM (Technical University Munich) and additional geophysical corrections provided by the RADS (Radar Altimetry Database System) altimeter database (http://rads.
tudelft. nl/rads/rads. shtml) and the University of Porto for the GPD (GNSS-derived Path Delay) plus wet tropospheric correction  27. In terms of altimetry corrections, the best altimeter standards defined in the ESA SL_cci project were selected in order to calculate sea level anomalies for climate studies (Table  1 ). As already mentioned above, the characteristics of ocean altimetry measurements and corrections change significantly when approaching the coast, resulting in an important degradation of the corresponding sea level products (see  28  for a complete review of the technical and processing issues related to coastal altimetry). To cope with this, the X-TRACK/ALES system first selects valid ocean data through a precise land mask (based on the GSHHS -Global Self-consistent, Hierarchical, High-resolution Geography-shoreline database distributed by NOAA, http://www.
At this stage of the processing, we obtain a regional dataset of SLA time series with a temporal resolution of 10 days and a spatial resolution of ~300 m along the tracks of each Jason altimetry mission. The computation of a single long-term multi-mission product requires the application of inter-mission biases in order to remove instrument and corrections biases, thus to obtain stable merged sea level time-series. Inter-mission biases are computed during the "calibration phases" between two consecutive missions (about 3 month-long between Jason-1 and Jason-2 in 2008, and between Jason-2 and Jason-3 in 2016), when both satellites measure the same sea level on the same ground track, with about 1 minute time lag. The biases are computed for each of the six studied regions, excluding altimetry points located at less than 10 km from the coast, as well as points where more than 20% of data are missing in the time series.
In order to remove the high-frequency variations in the resulting 20-Hz along-track bias values, known to be associated with measurement noise, the data are low-pass filtered (with a 40-km cutoff frequency) and averaged over 1° × 1° boxes. The corresponding smoothed 1° × 1° bias values are then interpolated at the original 20-Hz along-track altimetry points and applied to the SLAs. The Jason-1/2 inter-mission bias is applied to Jason-2 SLAs first, and then the Jason-2/3 inter-mission bias to Jason-3 SLAs. It is worth mentioning that no orbit error reduction has been applied to the coastal sea level product. This is because such a correction based on differences between ascending and descending satellites tracks due to orbit errors needs to be computed globally. However, the orbit error is supposed to be small at the coast because of its large-scale behavior.
Finally, the long-term multi-mission SLA time series are monthly averaged, and a linear trend v and associated error are derived at each 20-Hz along-track point (see section 4. 2 for details on the trend calculation). A first version of the processing system described above has been successfully evaluated and validated in  30, 31. It significantly increases the number of valid SLAs in the coastal zone. As an example, in the Mediterranean Sea, 80% of valid sea level data are still available at distances of <2 km from coast, compared to distances of several km in the standard X-TRACK product  30.
Data records Using the regional data processing system described above, the measurements of the three Jason-1, Jason-2 and Jason-3 altimetry missions are combined in single homogeneous 10-day SLA time series over the period June 2002 -May 2018. These 10-day time series at each 20-Hz point, from 20 km offshore to the coast, represents the basic SLA data set called 'Coastal Sea Level Product 1'  32, 33. It is available to users and distributed as NetCDF files. This data set contains 628 portions of 20 km-long tracks, crossing land at different locations across all regions. The 'Coastal Sea level Product 1' is not subject to any other further editing than the one done during the X-TRACK/ ALES processing. The Jason track coverage for Northeast Atlantic, Mediterranean Sea and West Africa on the one hand, and for North Indian Ocean, Southeast Asia and Australia on the other hand, is presented in Fig.
5  and Fig. 6  respectively. We further constructed another edited product called 'Coastal Sea Level Product 2' (delivered with this paper), that not only includes SLA time series (expressed as monthly averages), at each 20-Hz point along a track portion of 20 km from the coast, but also sea level trends and associated standard errors. This 'Coastal Sea Level Product 2' contains a much smaller number of track portions because based on a severe data selection, largely based on trend estimates (see below). The product includes monthly SLAs, simply computed by averaging available 10-day data (the monthly SLAs are based on a maximum of 4 values but sometimes only 2 or 1 value are available in the monthly interval). At each along-track 20-Hz point from 20 km offshore to the coast, annual and semi-annual signals were removed by fitting sinusoidal functions to the SLA time series.
An editing was further applied by computing the mean of the deseasonalized and detrended SLA time series, and removing outliers located outside a 2-sigma threshold around the root mean squares of the time series (as in  31  ). After re introducing the initial trend, a new trend and its associated 1-sigma formal error were estimated through a least-squares fit of a linear function to the edited SLA time series. Note that we do not correct the SLA time series for the regional contribution of Glacial Isostatic Adjustment. Such a correction is small (<1 mm/yr) and can be further removed from the estimated trends by the users. The criteria considered for the selection of the monthly SLAs plus trends of the Coastal Sea Level Product 2 are: (1) Number of valid data of the SLA time series at each 20-Hz points (missing data <50%); (2) Distribution of the valid data as uniform as possible across the three Jason missions.
In a number of cases, Jason-1 data were much less numerous than the Jason-2 data. The corresponding SLA time series were then discarded; (3) Trend values in the range -15 mm/yr to +15 mm/yr (this threshold is based on occasional spurious discontinuities observed in sea level trends from one point to another, on the order of 10-15 mm/yr); (4) Standard errors on trends <2 mm/yr; (4) Continuity of trend values between successive 20 Hz points. Too abrupt changes in trends over very short distances were considered as spurious and the corresponding point was removed. This mostly occurred close to the coast but sometimes, at a larger distance from the coast. With this selection approach, we discarded a large number of along-track 20-Hz points considered as not accurate enough to compute reliable trends.
1 mm/yr. The mean value for all tracks amount to 2. 6 mm/yr. Note that this trend value is not GIA-corrected. Several GIA models exist and users can apply the GIA correction of their choice using available gridded GIA data sets. Because of differences between models, we prefer not to apply this correction. The mean and median trend errors are also similar for ascending and descending tracks, and on the order of 1. 1 mm/yr. Note that there is no trend error larger than 2 mm/yr, a consequence of one of our selection criteria (see section 4. 2). In Fig. 8  are also shown the distributions of the closest distance to the coast (first valid point) for the same configurations as for the trends. Again little difference is observed between ascending and descending tracks, with a mean distance of 3.