dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-Seq libraries and sequencing RNA-Seq data were generated from three independent polysome profiling experiments, each comprising eight different samples as follows: cytoplasm and polysomal fractions from unfertilized eggs and 1-h (one-cell) postfertilization embryos, in the absence or presence of puromycin. All eight samples from a biological replicate were taken from eggs or embryos arising from a single female and male pair. The independent experiments correspond to three independent sets of parents. RNA purification and NGS libraries construction are described in Supplementary methods. Description of the 24 libraries and corresponding data generated for this study are provided in Supplementary Table  S1.
Translatome analysis The maternal transcriptome was generated from the cytoplasmic RNAs corresponding to unfertilized and fertilized eggs (three independent samples for each condition) on a de novo assembly pipeline (Supplementary methods). The de novo transcriptome was annotated using the Strongylocentrotus purpuratus gene database Echinobase (genome version 3. 1) (http://echinobase. org; (19, 20) ). The transcriptome was filtered, selecting transcripts with the best BLASTn hit values <10 -5 and a transcript count (FPKM) greater than 5, leading to a final set of 14 002 transcripts. The translation status of each transcript was established using the following analysis. Normalization and differential analysis were carried out using the generalized linear model framework according to the edgeR model and R package  (21) (22) (23) , using the raw read counts from the 24 libraries (Supplementary Table  S1 ) to generate three log 2 -fold change values to evaluate the translation status in unfertilized eggs (UnF vs UnFpuro), in fertilized eggs (F vs Fpuro) and the recruitment into or exit from polysomal fractions induced by fertilization (F vs UnF).
The scaling factors s i j and s c i j were calculated using the trimmed mean of M-values (TMM)  (24). We used the edgeR Bioconductor package with a matrix of offsets [κ i jg ] to fit a negative binomial model per gene with a genewise dispersion as calculated in  (22, 23). The normalization as pointed out in  (25)  has an important impact on all the downstream analyses. The matrix of offsets was assumed to account for all normalization issues (here sequencing depth, numbers of polysome gradients and cytoplasmic values). We computed GLM likelihood ratio tests for UnF vs UnFpuro differences, F vs Fpuro differences and F vs UnF differences within females, to select for mRNAs translated in unfertilized eggs and in 1-h embryos and for mR-NAs for which translation was modified by fertilization. The R code for translatome analysis is available upon request.
80; Supplementary Figure  S2 ). Comparison between unfertilized eggs and 1-h post-fertilization embryos showed no significant differences (Supplementary Figure  S3 ), suggesting that there was no variation in the abundance of the transcripts between these two stages. An estimation of the repertoire of transcripts present in the maternal transcriptome was done in comparison with data available for the American sea urchin Strongylocentrotus purpuratus, for which a genome is available  (19, 20, 33). Annotation against the 23 000 gene predictions of the S. purpuratus genome assigned a best BLASTn hit with an e-value<10 -5 for 14 002 transcripts, corresponding to 7685 Trinity unique genes and to 6518 S. purpuratus gene predictions. The maternal transcriptome thus represented roughly 28% of the total gene number, which is within the range of other species (20-45% in mouse, 42% in S.
A negative binomial generalized linear model was fitted to quantify each individual transcript in the heavy fractions of the gradient, on three independent biological replicates, taking into account the cytoplasmic abundance of the transcript and the paired nature of the samples (see Methods). Pairwise comparison in unfertilized and fertilized samples allowed the selection of transcripts for which the entry into the heavy polysome fractions was modified after fertilization. Transcripts with significant differences (BH adjusted P-value < 0. 05) between unfertilized eggs and fertilized embryos were selected. Data were then filtered for translated mRNAs in both stages to distinguish between bona fide translated mR-NAs and co-migrating mRNPs. The translation efficiency for each transcript was assessed by comparing puromycintreated and untreated samples.
Functional enrichment of mRNAs translated upon fertilization When the translated mRNAs were classified from lowest to highest P-values, cyclin B mRNA ranked first, indicating a strong biological constraint on its polysomal recruitment. Consistent with the fact that the greatest biological change triggered by fertilization is the progression into embryonic cell cycles, many other mRNAs encoding proteins involved in cell cycle regulation were found in this dataset. To estimate the enriched functions in the translatome, GOterms were retrieved from the Trinotate annotation of the maternal transcriptome, and functional classes were compared between the translated mRNA and the maternal mRNA datasets. We used a binomial test to compare the observed number of translated genes with the expected number for each functional class, assuming random representation of the maternal mRNAs in the translated set.
Over-represented biological processes in the translatome were 'mRNA processing', 'metabolic processes', 'cell cycle' and 'signal transduction'; over-represented molecular functions were 'RNA-binding', 'transporter' and 'kinase' activities (binomial test, P-value < 0. 05; Figure  2E ). We further classified the maternal mRNAs according to their translational status upon fertilization (recruited, translated from mRNP stocks, released from polysomes and unchanged). Half of the maternal mRNAs belonging to the 'cell cycle' and the 'RNA-binding' functional categories were in the recruited set. A striking feature of the mRNAs from the signaling functional class was their abundance in the translated pools from mRNP stocks (Supplementary Figure  S5 ). Altogether, these data demonstrate that polysomal recruitment at fertilization is selective.
In the presence of PP242, the mRNA distribution across the polysome gradient was similar to that displayed in untreated fertilized embryos. Puromycin treatment in the presence of PP242 triggered the disassembly of heavy polysomes and reloca-Table  1. Identification of mRNAs translated after fertilization from RNA-Seq analysis of polysomal mRNAs. Each mRNA, associated with a transcript ID from the de novo assembly of the maternal transcriptome, is identified by its best BLASTn hit against a Strongylocentrotus purpuratus gene model (Echinobase; http://echinobase. org; (19, 20) ). The mRNA translation index (log 2 FC(F/Fpuro)) and its polysome fractions after fertilization (log 2 FC(F/UnF)) are indicated with their respective adjusted P-values (padj). Abundance of the transcript is expressed as fragments per kilobase million (FPKM).
DISCUSSION Coupling polysome gradient optimization in sea urchin  (5)  with high-throughput sequencing, we re-examined the translational changes occurring upon fertilization by carrying out a large-scale translatome analysis in the sea urchin P. lividus. To our knowledge, this is the first time that such a dataset has been produced in a model system in which eggto-embryo transition occurs independently of meiotic maturation resumption. Here, precise normalization to correct for the technical effects affecting translatome data analysis was included in a generalized linear model framework. This approach to modeling and analyzing the data demonstrated that fertilization triggers the selective translation of maternal mRNAs, and also revealed an alternative model to classical cap-dependent translation in early development.
Translation of a subset of maternal mRNAs upon fertilization The repertoire of mRNAs present in the egg and the 1-h embryo was sequenced from RNA-Seq libraries constructed after poly(A) selection. Various comparisons of the transcript repertoire showed no variation in the abundance of transcripts. These data suggest that no transcripts are produced nor degraded during this short lapse of time, in accordance with the transcription initiation of early zygotic genes and early degradation of maternal transcripts occur-  S]-methionine labeled embryos were cultured in presence of emetine, which was added 5 min after fertilization to inhibit protein synthesis, and the lysate prepared from embryos harvested at 90 min was used for p13 suc1 -sepharose bead purification (F+emet). The experiment was performed twice.
Another example is RKHD/MEX3B, a KHand ring-domain containing RNA-BP, involved as a translation repressor in embryonic cell fate (reviewed in  (53) ). In sea urchin, RKHD has been identified as an ubiquitous maternal and localized zygotic mRNA  (54) , but its function has not yet been investigated. In our study, CUGBP and RKHD were strongly recruited onto polysomes after fertilization. No data is available on the maternal presence of the proteins encoded by these mRNAs, but the finding that they are newly synthesized after fertilization suggests that control of mRNA fate (stability, localization, translation, etc. ) may be set up or modified at the egg-to-embryo transition, and gives insights into possible mechanistic regulations. Interestingly, these two mRNAs were translated even in PP242-treated embryos, and also before fertilization in some individuals (data not shown).
The positions of the 18S and 28S ribosomal RNAs are indicated. (D) Distribution on a 15-40% sucrose gradient of mRNAs coding for cyclin B (CycB, positive control) and initiation factor 4A (eIF4A, negative control) before (UnF) and after (F) fertilization. mRNAs were detected by RT-PCR amplification in each fraction (a representative experiment shown). Distribution of the mRNA along the gradient is shown as a percentage of total mRNA, error bars represent SEM on five biological replicates (UnF vs F: * P-value < 0. 05). Presence of the mRNA in active polysomes was assessed by treating embryos in vivo with puromycin before polysome gradient fractionation (F+puro in vivo, n = 3). (E) Diagram of the translatome analysis, performed on three independent polysome profiling datasets.
are shown in bold. The full translatome dataset is presented in Supplementary data 2 SPU best Transcript ID Gene F vs UnF F vs Fpuro FPKM BLAST Hit logFC padj logFC padj Cell-cycle related genes comp77341 c2 seq1 Cyclin B 2. 964 2. 14E-19 1. 631 2. 33E-07 3202. 66 SPU 015285 comp80046 c1 seq2 Ribonucleotide reductase small 2. 645 1. 67E-12 1. 195 7. 15E-04 4679. 83 SPU 024933 subunit R2 comp76547 c0 seq2 Regulator of chromosome 2. 426 9. 07E-17 1. 687 2. 51E-09 103. 06 SPU 023992 condensation 1 RCC1 comp79240 c1 seq1 Cyclin A 2. 339 5. 65E-13 1. 513 1. 81E-06 649. 47 SPU 003528 comp69502 c0 seq1 14-3-3 epsilon 2. 223 1. 96E-10 0. 864 1. 17E-02 64. 30 SPU 003825 comp79495 c0 seq1 Cyclin dependent kinase 1 CDK1 1. 583 8. 41E-07 1. 933 8. 15E-10 62. 15 SPU 002210 comp78419 c0 seq1 Early mitotic inhibitor EMI1 1.
While several studies have shown human subjects' impressive ability to detect faces in individual images in paced settings  (Crouzet et al. , 2010) , we here report the details of an eye movement dataset in which subjects rapidly and continuously targeted single faces embedded in different scenes at rates approaching six face targets each second (including blinks and eye movement times). In this paper, we describe details of a large publicly available eye movement dataset of this new psychophysical paradigm  (Martin et al. , 2018). The paradigm produced high-resolution eye-tracking data from an experiment on continuous upright and inverted 3 • sized face detection in both background and no-background conditions. The new "Zapping" paradigm allowed large amounts of trials to be completed in a short amount of time.
BACKGROUND AND SUMMARY We present here a large dataset of visual search in humans, which investigated the number of faces which could be targeted with the eyes each second in embedded and changing scenes continuously and without any experimental pauses between trials. Using a gaze-contingent paradigm with a high definition eye-tracker, we presented detection and targeting tasks to the subject as rapidly as they could solve them. As soon as the subject's eye reached the target face, the next detection task was presented based on the previous target face's location. The experiment sought to determine whether the visual system works as fast continuously as has been observed in paced experiments  (Crouzet et al. , 2010) , and also whether the continuous detection rate is faster for particular types of targets (i.
e. , upright faces) versus that found for other types of visual targets  (Wolfe, 2010; Wu and Kowler, 2013). The methods and text of this paper are modified and expanded versions of the descriptions and text of our related work  (Martin et al. , 2018) , wherein we used the dataset to show that subjects could continuously perform ultra-rapid face detection and localization with saccades at surprisingly fast sustained rates (targeting rates of up to 5. 4 face targets/second when faces were hidden in a background, and up to six face targets/second when there was no background). This previous work investigated continuous saccadic search in the context of either pasting and/or blending the faces into different backgrounds, or pasting them on a simple gray background. Second, we used inverted faces as controls to test for advantages in search for upright faces  (Kanwisher et al.
We have included pupil sizes, microsaccades, saccades, stimulus properties (local contrast, luminance), and 1250 hz eye position data according to each condition for each trial. As it would take an enormous amount of time to investigate all of these covariates and their relationships with the continuous dynamics of visual search, we have decided to release the dataset to the research community. We hope that the dataset will be of interest to a wide range of researchers and experimental disciplines who are interested in visual search and saccadic execution, and will provide a solid public dataset for future scientific exploration into the dynamics of continuous visual search.
Stimuli The face stimuli were cropped from a set of 2316 images of segmented faces from the Humanae project (with written permission from the artist and on the web at: http://humanae. tumblr. com/) (Dass, 2016). Unfortunately, we were not able to redistribute these images in the dataset. However, individual face images can be accessed on the photographer's Tumblr website (currently located at http://humanae. tumblr. com/). Image filenames from the face images on the photographer's website correspond to the filenames stored in the data. stimulusImageFile array (see Table  2 ). Faces were converted to grayscale before the experiment. Faces were resized to 3 • of visual angle in height. We have included the pixel locations for seven facial landmarks for each trial (head top center, forehead top center, left eye, right eye, nose apex, mouth middle, and bottom chin middle).
Background scene image stimuli for all experiments were selected from a large database of images containing 861 images, some of which have been used in previous psychophysical studies  (Thorpe et al. , 1996). Luminance and global contrast (with respect to the pasted face) of the background scene images are available in the dataset. Image backgrounds were resized using the imresize function of MATLAB to cover the entire screen resolution of 2560 × 1440 pixels. The original image backgrounds varied in original size but almost always had an initial size greater than 1920 × 1080 pixels before resizing. (Martin et al. , 2018). (E) Examples of how blended faces would appear on different backgrounds in Experiment 3 (N 3 = 24 subjects, Data Citation 3)  (Martin et al. , 2018). Figure  1C  is a modified reproduction from the source's Figure 3A  (Martin et al.
Presentation Hardware Stimuli were presented on an ASUS ROG Swift PG278Q GSYNC monitor with 1 ms response time and 120 Hz refresh rate at a screen resolution of 2560 × 1440 pixels. The size of screen in cm was 61. 5 cm width × 35. 5 cm height. Subjects' eyes were at an approximate distance of 85. 0 cm to the screen (as measured with a laser distance measure device with distance measurement precision of ±2 mm). The display therefore subtended approximately 31 • horizontal and 22 • vertical of visual angle. There was no gamma correction done during image presentation. The default parameters for the monitor were used. We have included data on the luminance of each background image. The display was controlled by a custom-built workstation running Gentoo Linux with a 64 bit kernel that was tuned for real-time processing.
Eye-Tracking Hardware Eye movements were recorded using the SMI iViewX High Speed system with a 1250 hz sampling rate on the subject's master eye. Before the first session, we determined each subject's dominant eye and subsequently recorded and calibrated that eye. The eye-tracker recorded the dominant eye at a rate of 1250 hz and sent gaze position samples with a processing delay of approximately 2 ms to the presentation hardware. We used the "microsacc plugin" for saccade detection with a smoothing level of 2 ["the raw data is smoothed over a five-sample window to suppress noise"  (Engbert and Mergenthaler, 2006) ], a velocity factor of λ = 5 to determine the velocity threshold for saccade detection ["thresholds were relative to the noise level, calculated as λ = 5 multiples of a median-based SD estimator"  (Engbert and Mergenthaler, 2006) ], and a minimum saccade duration of 10 samples (corresponding to 8 ms)  (Engbert and Mergenthaler, 2006).
imagestatistics. backgroundLuminance Global luminance of the background image. imagestatistics. facelocations Structure containing the row and column locations of seven facial landmarks for each particular trial. that each face and background image combination appeared equally in each condition across subjects. Participants were told to find the faces with their eyes as fast as they possibly could. • SMI iViewX Hi-Speed 1250 hz • Saccade detection: • Event Detector (v3. 0. 20) • Psychtoolbox (v3. 0. 12) • Matlab (R2017b) • EYE-EEG: (v0. 41), microsacc_plugin (v2. 1)  (Engbert and Mergenthaler, 2006; Dimigen et al. , 2011)  • Parameters • Velocity Factor: 5 • Minimum Saccade Duration: 10 samples • Smoothing Level: 2 • Global Velocity Threshold: Yes • Microsaccade size limit: 1 • • Saccade statistics • Accuracy defined as whether the first saccade landed within a square of size 150px centered on the center of the face image.
Data Records All data and code corresponding to the data citations are stored on the Figshare data publishing site and are accessible from the Internet (Data Citation 1, Data Citation 2, Data Citation 3, Data Citation 4, see Table  1 ). The datasets are provided as three separate MATLAB (Mathworks TM ) ". mat" data files for each of the three experiments. Each file contains the subject numbers for the subjects in the corresponding experiment (which are unique to a subject across experiments), so that one can investigate both within and between subject factors by simply collating on the subject numbers across experiments. The datasets use an indexed approach for ease of access. The fields of the dataset are described in Table  2. For example, to compute the accuracy of upright faces for subject with id 5, one can simply execute the MATLAB command: The structure of data is indexed by trial.
Technical Validation We first verified that the data were similar to that in previous experiments on human eye movements (saccade peak velocity vs. saccade duration, main sequence, saccade reaction time, microsaccade rates, etc. ). Stimulus timing was recorded and validated by a photodiode connected to the screen and put into the SMI system via a high-speed analog card link. Stimulus timing was also duplicated with Ethernet messages. SMI iViewX calibration was performed after each of the eight 500 trial blocks. SMI accuracies are reported in the system's manual and also in scientific papers  (Mantiuk, 2016). We used the standard automatic SMI 13-point calibration/validation procedure, which, according to the SMI manual, typically results in gaze position accuracies of 0. 25 • -0. 5 • of visual angle.
This procedure accepted each point automatically based on the average of 400 ms of data for each point. In addition, there was audio feedback (a beep) when each new point was displaced. As described in the SMI iViewX manual (p. 148 -Calibration Setup), we randomized the order of the display of the points, set the system to "Wait for Valid Data" (which enforced the detection of a valid fixation before proceeding), and set the SMI "Check Level" for each point to "Medium" (which determined "how strict the system was in accepting calibration points and overall geometry")  (Mantiuk, 2016). For the analysis of experimental error and variation, the timing of processing and communication between eye-tracking system and experimental computer configuration was done via an oscilloscope and network latency tests.
Exclusion Criteria One out of 74 initial subjects quit the experiment pre-maturely due to discomfort, and this subject's data were removed from the dataset. Another subject that had problems with eye-tracking calibration (in which case the experiment was prematurely terminated) was also excluded. To complete the experimental cohort, two additional subjects performed the full experiment to complete the planned 24 subjects per experiment. These exclusion criteria were established before the study was conducted.
FUNDING This study was funded by  NEI   R01EY024161 ,  ANR-13-NEUC-0004 , and  ERC Advanced  Grant No. 323711  ( M4 ). This work was also supported by grant  ANR-13-NEUC-0004-01  ( NeCiOL Project ) as part of the  National Science Foundation  ( NSF, United States ), the  National Institute of Health  ( NIH, United States ), and the  Agence Nationale de la Recherche  ( ANR, France) NSF/NIH/ANR Collaborative Research in Computational Neuroscience.
Gonadal histology Samples of ovaries were stored in a Bouin-Holland solution for 1 week, washed once with water, twice with 70% ethanol and stored in absolute ethanol  (Abdulfatah et al. , 2011). Then, fragments of gonads were cut into 6-mm-thick slices, dehydrated with OTTIX solution (DIAPATH SpA, Italy/MM France) and embedded in a paraffin substitute dubbed Diawax (DIAPATH SpA, Italy/MM France) (adapted from  Langeron, 1942). Sections of 6 µm were cut from the Diawax block and stained with a Masson's trichrome-hematoxylin Gill III (Merck, Darmstadt, Germany), 0. 5% phloxine B (VWR, California, USA) and 0. 5% light green (Sigma) according to  Rinchard and Kestemont (1996). The stage of oocyte development was determined according to  Wallace and Selman (1981). Observations have been completed with a light upright optical microscope (Nikon Eclipse Ni-U), Nikon BR software (Nikon France, Champigny sur Marne, France).
Gene expression analysis Primers were designed based on a multiple alignment of sequences (Supplementary Material Table  S1 ) available in the NCBI database, targeting conserved regions of the FSHb subunit, the gonadotropin-releasing hormone 3 (GnRH3) and the β-actin as the first housekeeping gene. Primers sequences of the LHb subunit gene and the RPL8 as the second housekeeping gene were found in the publication of Hermelink et al. (2011). In order to isolate and to sequence pikeperch FSHb subunit and the GnRH3 cDNAs from the brain, a PCR reaction was performed. Polymerase chain reaction conditions were as follows: denaturing cycle of 95°C for 2 min, followed by 35 cycles of 95°C for 1 min, 58°C for 1 min, 72°C for 1 min, followed by a final elongation step of 72°C for 5 min. Real-time PCR was carried out with a StepOne Plus q-PCR Cycler (4376600; Applied Biosystems, Foster City, CA, USA) using QUANTA SYBR green reagent (VWR, 733-1382) in a 20 µl reaction volume (5 µl diluted cDNA, 2.