dataesr/scientific-paragraphs-categorization huggingface dataset:
Estimates of IOP Parameters The ranges of IOP parameters are constrained based on SPM solutions (Figure  5 ) and reported in Table  8. Among all IOP parameters , backscattering mass-specific coefficient (b * bp (700)) is most correlated with SPM relative to the other optical parameters. The Yangtze dataset exhibited the most constrained range in IOP parameters. The table below reports the range of IOP parameters and its median solution by dataset, region, and year of data acquisition (when applicable). For example, Gironde Estuary data reported on Knaeps18 dataset, were acquired at two different dates, therefore it was split here (Gironde Estuary, FRA (2012) and (2013).
Validation of IOP Estimates The only datasets with optical parameters available for comparison are NewRiver12 and MCR13. The two datasets do not represent a large dynamic range of SPM but are adequate for testing of the MW algorithm assessment of IOPs. The NewRiver12 and MCR13 datasets allowed for comparison between derived and measured b bp (λ), and γ. In order to compare information, b * bp (650) was selected from the SPM solution pool, and field γ was calculated based on the slope between measured b * bp (λ) at available wavelengths (see Figure  6  for b bp (650) and γ). There is a good agreement between derived and measured backscattering for both datasets. The number of matchup measurements is small (N = 10 for NewRiver12; N = 13 to MCR13) but metrics show promising results. The shape parameter γ (Figure  6 ), however, had agreement.
Therefore, the assumption of using one model to our algorithm might imply a bias. It is suggested that future work test the sensitivity to the specific R rs -IOP models chosen. Numerous water absorption datasets exist  [31] [32] [33] [34] [35] [36] [51] [52] [53] [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [66]. Some, however, show considerable differences as a function of wavelength. The observed differences may come from differences in measurement methodology (i. e. , temperature control, equipment accuracy) as well as the method employed to obtain a "pure" water sample  [67]. Temperature is also known to affect water absorption up to 0. 7% per degree kelvin (e. g. , 725-750 nm; 1145-1155 nm). Pure water absorption increases by almost a factor of 10 from the visible to the NIR (changes in temperature therefore potentially have significant impact on inversions)  [13].
Absorption by particles is generally assumed negligible in the NIR part of the spectrum (e. g. , Reference  [13] ). The study by Reference  [38]  however, shows that this might not be the case, therefore applying the suggested NIR offset would improve SPM estimates. Sensitivity tests were realized with and without the introduction of a NIR offset for non-algal particles (a * N AP (750)). The offset suggested in the study of Reference  [38]  for the Elbe River was added (Equations (  7 ) and (  8 )) with the expectation of improving SPM estimates specially in highly turbid waters. Our sensitivity analysis demonstrate that the addition of a NIR offset lead to an overall improvement on SPM retrievals of about 2%-3%. With the NIR offset, the dataset with highest SPM concentrations (Yangtze19) showed an improvement of more than 30% while the retrievals of datasets of low SPM concentration (MCR13, NewRiver12) worsen by 1%-2%.
Note, however, that the computation performed here does not include accounting for uncertainties associated with the process of computing reflectance from top-of-the-atmosphere measurements. In routine applications we recommend that such uncertainties should be taken into account (e. g. , References  [68, 69]  on how to compute them for a specific satellite). Finally, the MW algorithm may help to obtain information regarding the composition and size of SPM. Both NewRiver12 and MCR13 datasets, show high uncertainties regarding the estimate of backscattering spectral shape. The low correlation between measured and modeled backscattering spectral shapes could be related, at a first order, to the high number of degrees of freedom provided by the many different combination of mass-specific coefficients and shape parameters.
Performance of the SPM Algorithms We compared the performance of the MW algorithm to two commonly used SPM algorithms using an extensive in-situ dataset covering about four orders of magnitude in SPM concentration. While all algorithms performed relatively well over the SPM range for which they were designed, they performed less well outside of these ranges. The MW algorithm had the lowest percentage error for the dataset with highest SPM concentration, 22. 6%. Some of the uncertainty may be due to variability in the methods used to measure R rs (λ) among the six in-situ datasets, and different processing procedures (see different methods in Table  2 ). Additionally, it is not possible to have uncertainties in retrieved SPM significantly lower than the ranges observed of mass-specific IOPs of SPM, as these will directly propagate into the possible solutions.
Conclusions A semi-analytical multi-wavelength SPM algorithm using reflectance spanning the range from 630 nm to 1300 nm was developed. We demonstrated that using all available bands is useful in environments with large ranges of SPM. Comparison with state-of-the-art algorithms shows similar performance. The MW algorithm has the advantage of being easily adapted to any range of wavelengths available. This enhances applicability for extremely turbid waters such as the world's most turbid estuaries like Yangtze River and the Gironde Estuary. Application of the MW algorithm to satellite sensors shows great promise (despite the lack of a validation dataset) and has the advantage of providing a map of uncertainty (see Figure  7 ). It takes into account the different spectral ranges, bandwidths, signal to noise, and others characteristic parameters of different space-borne sensors.
Acknowledgments: The authors would like to thank the  National Natural Science Foundation of China  ( 41776052 ),  Binbin Jiang , and  Dac Ve Nguyen  from  Tongji University  for collecting R rs (λ) and SPM data in the 2018 Yangtze Estuary Cruise. The  RIVERCOLOR  project for making the dataset available. The data efforts from the SOMLIT and MAGEST projects, specially Sabine Schimit. Special thank you to  Tim Milligan  and  Jing Tao  for data acquisition and processing of NewRiver12 and MCR13 datasets. Finally, we would like to thank two anonymous reviewers whose comments have significantly improved this manuscript.
Dataset SPM [g. m -3 ] Temperature [ • C ] N Site Location Yangtze19 574-3981 21. 2-25. 6 16 Yangtze River, CHI Knaeps18 86. 3-1400. 5 17. 5-21. 5 72 Gironde Estuary, FRA 49. 6-402. 0 20 32 Scheldt Estuary, BEL 48. 3-110. 0 17. 4 33 Rio del Plata, URY Nechad15 6. 0-330. 0 29 119 Indonesia, IDN 0. 4-31. 2 14 48 North Sea Rivercolor14 2. 58-2355. 4 20 51 Gironde Estuary, FRA MCR13 1. 5-4. 9 15 33 Columbia River, OR, USA NewRiver12 2. 9-11. 8 21 16 New River, NC, USA.
Statistical Analysis We defined each trial as the third element of a high-or lowprobability triplet. Trills (e. g. , 1-2-1) and repetitions (e. g. , 1-1-1) were eliminated from the analysis because participants tended to show preexisting response tendencies to these types of triplets  (Howard et al. 2004; Unoka et al. 2017; Janacsek et al. 2018; Takács et al. 2018). The first 5 button presses were random; thus, only the eighth button press could be evaluated as the last element of a valid triplet. Therefore, the first 7 trials were excluded from the analysis. Blocks were collapsed into 4 epochs in the different instruction phase (Epoch 1-4), and one epoch in the same instruction phase (Epoch 5) to facilitate data processing and to reduce intraindividual variability. We calculated the median reaction times (RTs) separately for high-and lowprobability triplets for each participant and each epoch.
Funding  National Brain Research Program  (project  2017-1. 2. 1-NKP-2017-00002 );  Hungarian Scientific Research Fund  ( NKFIH-OTKA K 128016 , PI: D. N. ,  NKFIH-OTKA PD 124148 , PI: K. J. );  Janos Bolyai Research Fellowship of the Hungarian Academy of Sciences  (to K. J. and A. M. );  EFOP-3. 6. 1-16-2016-00008 (to A. M. );  IDEXLYON Fellowship  of the  University of Lyon  as part of the  Programme Investissements d'Avenir  ( ANR-16-IDEX-0005 ) (to D. N).
A platform to analyze USVs and their synchronization with behaviors We needed to automatically detect USVs within background noise (i. e. , animals moving in the bedding, continuous electronic or ventilation noises) and to extract their acoustic features, all these aspects being synchronized with behavioral monitoring. In our system, sounds were recorded using Avisoft SASLab Pro Recorder and synchronized with behavioral monitoring using LMT (Supplementary methods -Wave record and synchronization; Figure  1a-d ). Mechanical noise (e. g. , amplitudes at continuous frequencies from electronic devices or ventilation) was removed (Supplementary methods -filtering mechanical noise) and the USVs extracted (Figure  1e ; Supplementary methods -USV detection / USV detection validation). The USVs were then synchronized with behaviors and the acoustic characteristics (Figure  1f ) extracted and stored as metadata in the database (Supplementary methods -USV audio feature extraction).
Higher speed and longer duration of behaviors during which USVs are emitted may reflect high excitement We next hypothesized that behaviors accompanied by USVs were more intense (i. e. , longer and with higher mean speed) than behaviors not accompanied by USVs. As a proxy for excitement, we considered the speed of the animals. We thus quantified the mean speed of the animals during behavior accompanied or not by USVs, as well as their duration. Our hypothesis was not verified for longChase and Train2 events in any age class at the individual level. Indeed, within each individual, although the speed of Train2 was the highest of all events, the duration of the event and the speed of the mice did not vary much, regardless of whether USVs were emitted or not (Figure  5a ). In contrast, follow (Figure  5b ), approach contact (Figure  5c ), contact, getaway, and oral-genital contact (data not shown) showed a significantly longer duration and higher speed when accompanied by USVs, in agreement with our hypothesis at the individual level.
The frequency and location of USV emission during urination is altered in Shank3 -/-female mice Concerning USVs emitted during specific behavioral contexts, the sounds emitted by mice jumping down the walls and the harsh USVs, short-flat USVs, and long-modulated USVs were preserved in Shank3 -/-mice (Supplementary Figure  S17 ). In contrast, Shank3 -/-mice emitted fewer urine-specific USV bursts than WT mice (Mann-Whitney U-test: W = 88. 5, p = 0. 001) and, when recorded, they took place not only in corners, as for WT mice, but also in the middle of the cage in some cases (Supplementary data -Specific USV types, Urination sequences). Nevertheless, their acoustic characteristics were not significantly different from those of WT mice (Figure  7a ). This suggests that the activation but not performance of urine marking behavior is perturbed in Shank3 -/-mice.
3,  (Arriaga et al. , 2012; Chabout et al. , 2015))  or in an open repertoire determined by the data themselves (MUSE,  (Neunuebel et al. , 2015) ; MUPET,  (Van Segbroeck et al. , 2017) ; DeepSqueak,  (Coffey et al. , 2019) ). Nevertheless, most of these systems do not handle background noise, and only MUSE provides the synchronization with behavioral monitoring, along with a heavy triangulation system that could not be easily replicated and adapted to our Live Mouse Tracker behavioral monitoring system.
USV Segmentation Method Vocalization waveform to spectrum We process wav files recorded at a sampling rate of 300 kHz with a resolution of 16 bits per sample. We first apply an FFT (overlap of 0. 75 and FFTSize = 1024 points) to the original audio signal to get its spectrum. We note here spectrum  [t] [f] each magnitude data of the spectrum. The signal recorded presents three main problems: 1/ The continuity of the USVs can be interrupted if the signal gets too low. 2/ The animals produce a lot of noise by interacting with their environment. 3/ The animal facility environment itself produces interfering ultrasonic noise. We illustrate in the Supplementary Figure  S22  these noise types. To overcome these problems which might prevent correct classification, we need to filter the signal of the spectrum.
Filtering out wave files containing only noise During an experiment, we do not record continuously the audio information. We use Avisoft-RECORDER automatic triggering capability which monitors the audio and starts the recording when a predetermined power threshold is reached. Therefore, the dataset of an experiment is composed of thousands of files that may contain USVs or just noise due to the activity within the cage. Therefore, data of an experiment can be preprocessed to filter out wave files containing only noise, which represent 50% of the files generated in our experiment. An expert sorted files containing USVs from files containing noise to train a random forest classifier. We use the following features as machine learning features: for each file, we extract the mean power of the whole file, the number of presumed USVs detected, the duration of the USVs over the overall length of the file, the average duration of USV and its standard deviation, the mean peak frequency and the standard deviation of the peak frequency.
We then train the random forest classifier. We provided a pre-trained classifier but one can retrain the classifier with its own data. One just needs to start the python script and point two different folders (noise and USVs) to train the system. For our experiments, we trained the system with 451 files containing USVs and 247 with only noise. The accuracy of the training is 97%, using 10 folds. Then, the system can be used in predictive mode to sort dataset containing both USVs and noise. The script copies the file in a noise and USV folder, so that the user can easily control the sorting accuracy.
To synchronize USV recording with the tracking, we use the "Trigger control" of Avisoft-RECORDER. This function allows to launch an external program at each start and end of records. We use the free software PacketSender (https://packetsender. com/) to perform communication between Avisoft-RECORDER and Live Mouse Tracker (LMT). Through PacketSender, we send an UDP string packet containing the file number currently recorded by Avisoft-RECORDER. This information is recorded by LMT within the database as an "USV event". The goal of the synchronization is to match the USV record with the current data frame number recorded by LMT. USV Toolbox, an open-source, free and online USV analysis pipeline The currently available methods to detect and analyze mouse USVs need specific installations and software.
Breton, François Rimlinger, Didier Montéan and Christophe Joubert for animal facility support, Benoît Forget and Nicolas Torquet for critical reading of the manuscript. This work was partially funded by the Institut Pasteur, the Bettencourt-Schueller Foundation, the Cognacq-Jay Foundation, the Conny-Maeva Foundation, the ERANET-NEURON SYNPATHY program, the Agence Nationale de la Recherche through grant number ANR-10-LABX-62-IBEID, France-BioImaging infrastructure through grant number ANR-10-INBS-04 and the INCEPTION program through grant number ANR-16-CONV-0005, the Centre National de la Recherche Scientifique, the University Paris Diderot, the BioPsy Labex, and the Foundation for Medical Research (Equipe DEQ20130326488). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
08  (Byrne, 1994). To find out if the tested model is invariant, we compared the differences in approximate fit statistics between subsequent models (e. g. , between configural and metric or between metric and scalar), whose values should not exceed. 015 in RMSEA and. 01 in CFI  (Chen, 2007). We carried out all the structural analyses using robust maximum likelihood estimation in Mplus v. 7. 2  (Muthén & Muthén, 2012). We made all the used scripts and data available at the OSF project site: https://osf. io/8nsc3/?view_only=18e7f1b0bd7449ffb9116f7eed93a6cd. We also anonymized the link for peer review purposes.
They assigned every novel UMI read sequence to a novel UMI. However, with this strategy, the higher sequencing error rate of longread sequencers causes serious issues. PacBio sequencers have a circular consensus error rate of about 1%. In consequence, about 10% of the 10 nt. UMI reads are expected to have at least one error. Fake UMIs are an even more serious issue with the higher Nanopore sequencing error rate of 5-8%. For 45% of the UMIs identified by Nanopore sequencing, Volden et al. 5  could not find a corresponding UMI in the Illumina dataset of the same sample. The authors suggest that at least one-third of the UMIs were misassigned with their strategy due to Nanopore sequencing errors. We addressed those issues and designed a long-read single-cell sequencing approach that combines the high throughput of Nanopore sequencing with high accuracy cellBC and UMI assignment.
8% and 97. 4%, respectively (Fig. 1d ). We also examined the precision of cellBC and UMI assignment with a second strategy where we compared the number of cellBC and UMI assigned reads obtained with the Illumina dataset of the same and of another unrelated 885 cell mouse brain sample. We found 140 times less cellBC and UMI assigned reads with the unrelated Illumina data, suggesting a combined cellBC/ UMI assignment accuracy of 99. 3%. The accuracy is likely higher since cellBCs and UMIs were found at higher edit distances (ED) with the unrelated Illumina dataset (mean ED: cellBCs, 3. 01; UMIs, 2. 28; n = 38,310) than with the short-read To evaluate whether the additional cycles of full-length PCR amplification (see "Methods" section) or Nanopore library preparation skew the long-read dataset, we compared the cellBC and UMI assigned Nanopore data to the Illumina data of the same sample.
On average, 79% of the RNA molecules (UMIs) and 91% of the genes identified in each single cell after Illumina sequencing were also found in our Nanopore dataset (Fig. 2a, b ). The cellBC and UMI assigned Nanopore reads (median 28,120 /cell) reflect a median of 2427 genes and 6047 UMIs per cell with a good correlation between Nanopore and Illumina gene counts (Fig. 2a ; r = 0. 99), UMI counts (Fig. 2b ; r = 0. 99) and gene expression for individual cells (Fig. 2c , mean r = 0. 90). In consequence, our cellBC and UMI assigned Nanopore dataset represents well the transcriptome captured in the 10x Genomics workflow. Identification of transcript isoforms in full-length E18 mouse brain transcriptome. We next analyzed the transcript isoforms in our long-read dataset. In total we found 33,002 Gencode vM18 annotated transcripts where all annotated exon-exon junctions were supported by at least one UMI.
It was recently suggested that single cells tend to express dominantly one transcript isoform of a gene  6. While we noticed this for certain genes (e. g. , Pkm), we also found many instances of well-expressed genes showing expression of several isoforms in a single cell (Supplementary Fig. 5 ). We also identified 4388 novel isoforms in our dataset (Supplementary Fig. 6 , Supplementary Data 2, Supplementary Data 3). We required that novel transcript isoforms: (i) are backed by at least five UMIs; (ii) have all splice junctions confirmed in a mouse brain Illumina short-read dataset; (iii) have a 5′ end within 50 nt. from CAGE-seq identified transcription start site; (iv) have a polyadenylation site within 50 nt. of the transcript 3′ end (see methods section and Supplementary Note for details).
Globally, the novel isoforms were detected at far lower levels (Gencode: median 3795 UMIs/cell; novel: 60 UMIs/ cell) than Gencode isoforms suggesting that some of those novel isoforms might reflect a certain leakiness of the splicing or transcription machinery. A t-SNE plot of the Illumina short-read gene expression data reveals typical cell types for E18 mouse brain (Fig. 2d ). t-SNE projection of transcript isoform expression defined by Nanopore sequencing (Fig. 2e ) yielded a similar clustering without revealing novel well-defined sub-clusters. Globally, the isoform-based clustering was more diffuse than the gene-based clustering. This is likely due to: (i) a split of the UMIs for a given gene in a cell between several isoforms (16,612 genes vs. 33,002 isoforms). This results in globally lower isoform counts and a higher dropout rate (cells with zero UMIs for a given isoform).
(ii) Only 63. 6% of the UMIs could be assigned to exactly one transcript isoform, resulting in a further reduction of isoform UMI counts (Illumina: median 7605 UMIs/cell; nanopore: median 6047 geneUMIs/cell, 3795 isoformUMIs/cell). In two independent technical replicates, the 951 cell and the 190 cell datasets, the corresponding clusters correlated well in gene expression based on either Illumina or Nanopore data and in isoform expression deduced from Nanopore data (Supplementary Fig. 7 ). We next searched for genes with a transcript isoform expression that differed between clusters. We noticed cell-type selective isoform usage for 76 genes and 174 differentially expressed isoforms (Supplementary Data 1, Supplementary Fig. 8b ). For instance, Clathrin light chain A (Clta) (Fig. 3a-d ) and Myosin Light Chain 6 (Myl6; Supplementary Fig.
(ii) This is the first approach that enables accurate UMI assignment to long reads. (iii) The additional short-read dataset of the same sample provides a quality control that allows detecting bias introduced by the additional amplification of full-length cDNA during Nanopore library preparation. One principal advantage of our approach is the accurate UMI assignment. The use of UMIs is even more important for long read than for short-read sequencing. Chimeric cDNA generated during PCR amplification is not an issue in short-read sequencing when only one extremity of the cDNA is sequenced. Conversely, PCR artifacts can severely affect the quality of long-read sequencing data. UMIs allow elimination of most of those PCR artifacts (Supplementary Fig. 4a ). This will be particularly important for the identification of rare chimeric transcripts in single-cell studies of tumor heterogeneity.
7% and 14. 3%; targeting 1200 and 200 cells). We extended the PCR elongation time during the initial PCR amplification of the cDNA from the manufacturer recommended 1 min to 3 min to minimize preferential amplification of small cDNAs. Half of the amplified cDNA was used for short-read sequencing library preparation following the 10x Genomics protocol and sequenced on an Illumina Nextseq 500 sequencer (26bases + 57bases). We generated 43 M and 70 M Illumina reads for the samples targeting 200 and 1200 cells respectively and mapped them to the mouse genome (build mm10) with the 10x Genomics Cell Ranger software (v2. 0. 0). For each PromethION flow cell (Oxford Nanopore) we re-amplified 2-10 ng of the 10x Genomics PCR product for eight cycles with the primers NNNAAGCA GTGGTATCAACGCAGAGTACAT and NNNCTACACGACGCTCTTCCG ATCT (Integrated DNA Technologies, IDT).
Mapping of Nanopore reads. Nanopore reads were aligned to the Mus musculus Genome (mm10) with minimap2 v2. 17 in spliced alignment mode (command: "minimap2 -ax splice -uf -MD -sam-hit-only -junc-bed"). The splice junction bed file was generated from the Gencode vM18 GTF using paftools. js, a companion script of minimap2. For reads matching known genes, the gene name was added to the corresponding SAM record (SAM Tag: GE) using the Sicelore (ScNaUmi-seq companion java toolkit) AddGeneNameTag method. Before cellBC and UMI assignment, SAM records were annotated with their Nanopore read sequence (SAM Tags: US) and read qualities (SAM tag: UQ) using the Sicelore AddBam-ReadSequenceTag method. CellBC and UMI assignment to Nanopore reads. Our Java software performs the following analysis steps (Supplemantary Fig.