dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to evaluate more precisely the similarities and differences between classical eye-tracking and digit-tracking regarding how subjects allocate their attentional resources on the images, we develop a second method to compare attention-maps based on a convolutional Neural Network Architecture. This architecture was used to automatically analyze the image content and evaluate among the identified elements those that attract more or less the human eye. To this end, the Matlab (Mathwork Inc. ) implementation of the AlexNet 23 Network was selected. The images and recorded attention maps were cropped and scaled to match the input resolution of the network (227 × 227 pixels). The AlexNet network was then modified to learn how to solve the following regression problem: predicting measured attention maps from input images-in other words, to make a saliency prediction.
Weights learned to solve this regression problem are quantitative indicators of how each image element contributes to how subjects distribute their attentional resources on the images. To predict saliency maps measured with eye or digit-tracking, the pre-trained AlexNet  23  Network was used to generate maps sensitive to various high-level visual features. The selected model was trained on a subset of more than one million images, extracted from the ImageNet database (http://www. image-net. org) for the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC  24  ). Dedicated originally for largescale image classification, the CNN architecture was transformed using the Matlab Neural-Network toolbox (Mathwork Inc. ) to solve a regression problem and generate saliency predictions. First, the outputs of the fifth convolutional layer, rectified by a linear unit (ReLU layer), were interpolated to obtain 256 feature-maps with the same resolution of the input image (227 × 227 pixels).
Then the maps were linearly combined using a weighting function to generate the predicted saliency. This approach can be considered as a simplified implementation of the Kümmerer et al. method for saliency map prediction  51. The optimal weighting function was learned using a small database of 122 couples of image/saliency map and validated with LOO (Leave One Out) cross-validation. Two networks with the same architecture were trained, one using eye-tracking data, and the second using digit tracking data. The quality of the prediction was evaluated by Pearson correlation (CC score) using the data that was not included in the training phase. Then, CC scores obtained with eyetracking (CCeye) and digit-tracking (CCdigit) were tested using non-parametric sign test (Family Wise Error Rate (FWER) corrected) and compared using paired t-test and uncorrected paired sign-test.
Two learning methods were tested for computing the optimal weights: wCorr and SVMregress. wCorr is a fast estimate of the weights by averaging the Pearson Correlation Coefficients calculated between a feature-map and the measured saliency map (the 256 weights are learned independently). ). A learning strategy optimized for high-dimensional data was selected considering the important number of parameters estimated and the correlation in the data after the interpolation step (256 regression parameters, learned using fitrlinear-Matlab r2017a (Mathwork Inc. )-with a 10-fold cross-validated lasso regularization). Both are equivalent in terms of prediction quality (Pearson Correlation = 0. 63 for both methods for digit-tracking prediction-Fig. S4B ). Nevertheless, the first method (wCorr) was selected due to a better consistency in the learned weights (Fig.
S4C ), suggesting a faster convergence and therefore more stable results when the learning image database is small. In order to test whether the two recording methods produce equivalent measurements of saliency, we used the previously described CNN architecture to quantify how much each of the 256 high-level visual features hierarchically contributes to the explorations measured by eye-tracking or by digit-tracking. First, the 61 pictures of the first dataset (set A) were selected to train the network using the group-level attention maps measured on the first population of 11 neurotypical subjects with eye-tracking. Then, the same pictures were used to train the network again, but using the group-level attention maps measured on the second population of 11 neurotypical subjects evaluated with digit-tracking.
The similarity between the learned weights was assessed using Pearson correlation (Fig. 3c ). Lastly, this procedure was replicated using the 61 pictures of the set B. In order to visualize the hierarchical ordering of the contribution of the 256 visual features, the weights were averaged across image datasets first, then across recording methods and ordered in decreasing order (Fig. 3b ). The features with the highest positive weights are those that are the most attractive and conversely, the features with the most negatives weights are the most avoided ones. Thus, these 256 weights can be viewed as a signature of how subjects hierarchically consider high-level visual features to optimize their visual explorations. Finally, in order to visualize these features, pixels in our image database that are activating the most the corresponding features are selected and displayed to assess more intuitively which image elements are the main contributors of the measured visual exploration strategies.
CNN was also applied to visual exploration data from the ASD population. Two types of analysis were conducted on the exploration data recorded with eyetracking and digit-tracking in the group of ASD patients. The CNN architecture was used to identify the high-level visual features that are the best predictor of the measured explorations in ASD patients and compare these to the same data obtained in the control (CTRL) population of neurotypical subjects. Also, for each subject and each recording method, an 'exploration-neurotypicality' score was computed in order to test whether digit-tracking is as efficient as eye-tracking at detecting atypical exploration behavior in the ASD population. Single subject performance prediction using CNN. Since the adopted CNN architecture generates stable estimates of how a population prioritizes high-level visual features guiding their visual exploration, the same architecture was trained to predict the visual exploration behavior of each subject and for each recording method.
Acknowledgements This work was supported by  Centre National de la Recherche Scientifique ,  Labex Cortex University of Lyon I ("Investissement d'Avenir")  Grant  ANR-11-LABEX-0042  ( AS, JRD ),  ANR-13-BSV4-0010-01  (JRD) and  SATT Pulsalys CNRS  (A. S. , G. L. , J. R. D. ). We thank  Stephane Donnet  for continuous support and  Victoire Mironneau  and  Pénélope Lacombe  for help in testing subjects.
The enrichment probes targeted coding and non-coding exons and their flanking intronic regions; large indels were searched by depth-ratio analysis. Raw data was computed through two independent pipelines as previously described. 26 Confirmation and analysis in the relatives from the four families was performed by Sanger sequencing (Figure  3A ). We identified bi-allelic variants in DNAH17, a gene not previously associated with PCD, in three probands and one brother: 18GM00285 (II-1 from family 1), 18GM01913 and 18GM01974 (II-3 and II-4, respectively, from family 2), and 18GM01932 (II-4 from family 3). In addition, a heterozygous in-frame duplication in DNAH17 was identified in the proband 18GM01926 (II-1 from family 4). Importantly, all individuals born to consanguineous unions were shown to carry the identified DNAH17 variations in the homozygous state (Figure  3A ).
DNAH17 (GenBank: NM_173628; Ensembl: ENSG00000187775) is located on chromosome 17 and contains 81 exons (transcript GenBank: NM_ 173628. 3), predicting a 4,462-amino-acid protein (NCBI: NP_775899. 3; UniProtKB: DYH17_Human Q9UFH2). The DNAH17 protein comprises nine principal domains: the Figure  1. TEM Analysis of Spermatozoa from Individuals 18GM01913, 18GM01932, and 18GM01926 Who Have an MMAF Phenotype (A) Left: a longitudinal section of control spermatozoa with the head, comprising the nucleus (N) and acrosome (Ac). The flagellum extends from the head and comprises the axoneme (Ax), which is surrounded by mitochondria (M) in the midpiece, and the fibrous sheath (FS) in the principal piece. Right inset: a transversal section of control spermatozoa in the principal piece region. The axoneme comprises nine doublets of microtubules (MTD) circularly arranged around a central pair (CP) of microtubules.
741 3 10 À6 (2 out of 228,814 alleles in gnomAD). The c. 7994_8012del deletion was absent from the gnomAD database. Both mutations intro-   E  and F ). Human sperm cells and a nasal biopsy were processed for TEM analysis as previously described. 51, 52 ce a premature stop codon (positions 431 and 2,665, respectively, in the 4,462 amino acid DNAH17 protein sequence) and are therefore expected to induce nonsense-mediated mRNA decay that is likely to prevent protein production. The two siblings 18GM01913 and 18GM01974 from family 2 (II-3 and II-4, respectively, in Figure  3 ) were found to be homozygous for the c. 5486G>A (p. Cys1829Tyr) missense variation. A DNA sample from their father was not available, but their mother was heterozygous for the variant, and their brother, who fathered two children naturally, did not carry the variant.
To date, very few data on ODA composition of mammalian spermatozoa have been reported in the literature. Therefore, we first intended to better define the expression profile of the established human genes, namely DNAH5, DNAH8, DNAH9, DNAH11 and DNAH17, encoding the ODA heavy chains. First, by comparing the distribution of ODA heavy chains in tissue expression databases (EMBL-EBI Expression Atlas) and performing quantitative RT-PCR analyses on human adult tissues (Figure  S2 ), we observed that DNAH8 and DNAH17 were clearly expressed in the testis and less abundant in the lung, whereas DNAH5, DNAH9, and DNAH11 were the most abundant heavy chains detected in the lung. 9] [30]  In keeping with the expression pattern of the genes involved in spermiogenesis, DNAH8 and DNAH17 expression was plainly detected in the germ cells from the early spermatocyte to late spermatid stages but not in the somatic cells (Leydig, Sertoli cells) (Figure  S3A ).
Taken together, the above data indicate that DNAH8, DNAH9, and DNAH17 transcripts are expressed in the germ cells during spermiogenesis. However, only DNAH8 and DNAH17 are detected by proteomic analysis in mature spermatozoa. In order to further confirm these data, we analyzed the presence and location of all ODA heavy chain proteins in  AECs and spermatozoa from control individuals by performing immunoblot and immunofluorescence analyses. By means of an immunoblot that used antibodies directed against DNAH5, DNAH8, DNAH9, DNAH11, and DNAH17, we first show that DNAH8 and DNAH17 are detected at the expected molecular weights in sperm cells but not in AECs, whereas DNAH5, DNAH9, and DNAH11 are only detected in AECs (Figure  4A ). Next, by means of immunofluorescence with co-staining of the axoneme with anti a-tubulin antibody, we confirm the above results.
To date, no crystal structure from an axonemal dynein heavy chain is available. In order to retrieve more information about the impact of some of the DNAH17 mutations that we identified on conserved residues among dynein heavy chain proteins, we used the available crystal structure of the closest human cytoplasmic heavy chain, DYNC1H1 (MIM: 600112) (PDB: 5NUG). Figure  S7  presents the crystal structure of DYNC1H1 with the location of the three equivalent amino-acids targeted by the p. Cys1829Tyr, p. Pro3499Leu, and p. Leu3595Pro DNAH17 mutations. From this representation, the Cys1829 residue (1,888 in DYNC1H1) appears close to the ADP binding site, whereas the proline 3,499 (3,647 in DYNC1H1) residue is located at the end of an alpha-helix; the consequences of its change to a leucine residue are difficult to predict.
e. , the wildtype DNAH17 encoded by the second allele and DNAH8). This would imply dimerization and/or physical interaction of DNAH8 and DNAH17, but importantly, also a transmission of the mutation by the mother or a neo-mutation in ) and airway epithelial cells (AECs) (AEC1 and AEC2) from two control individuals; DNAI1 was used for loading control. AECs and spermatozoa, obtained by nasal brushing and masturbation, respectively, were suspended in lysis buffer (50 mM Tris, 150 mM NaCl, 1% Triton X-100, and complete protease inhibitor cocktail, Roche Diagnostics). Lysates were sonicated and cleared by centrifugation. Equal amounts of cells were loaded on 4%-12% gradient SDS-PAGE and transferred overnight on a PVDF membrane. The primary antibodies (SIGMA DNAH8 HPA028447, DNAH9 HPA052641, DNAH17 HPA024354, and DNAI1 HPA021649) were incubated overnight at 4 C.
A secondary antibody coupled to HRP (SIGMA rabbit HRP A0545) and Amersham ECL Select Western Blotting Detection Reagent were used for detection. (B) Immunofluorescence staining of AECs and spermatozoa from a control with ODA heavy chain antibody (in red) and a-tubulin (in green). Cells were counterstained with DAPI (blue) as a nuclei marker. The scale bars represent 5 mm. AECs obtained by airway brushing were cytocentrifugated onto glass slides and fixed for 15 min with 4% paraformaldehyde. Spermatozoa were smeared onto glass slides and fixed for 10 min with 4% paraformaldehyde and then epitope retrieval in citrate buffer (pH 6) was performed. Cells were permeabilized in Triton X-100 for 10 min (0. 1% for AECs and 0. 2% for spermatozoa) and labeled with rabbit polyclonal antibody directed against ODA heavy chains DNAH5 (SIGMA HPA037470), DNAH8 (SIGMA HPA028447), DNAH9 (SIGMA HPA052641), DNAH11 (SIGMA HPA045880), and DNAH17 (SIGMA HPA024354) and were co-stained with acetylated a-tubulin (mouse monoclonal Abcam ab24610) for AECs and a-tubulin (mouse monoclonal SIGMA DM1A T9026) for spermatozoa.
Lysates were sonicated and cleared by centrifugation. The same amount of cells was loaded on a 4%-12% gradient SDS-PAGE gel that was transferred overnight on a PVDF membrane. The membrane was incubated overnight at 4 C with primary antibodies (SIGMA DNAH17 HPA024354 and DNAI1 HPA021649). After incubation with the secondary antibody (rabbit HRP SIGMA A0545), proteins were detected with Amersham ECL Select Western Blotting Detection Reagent. (B) Immunofluorescence staining of spermatozoa from a control individual and from individuals 18GM01926 (II-1 from family 4, in Figure  3 ) and18GM01932 (II-4 from family 3, in Figure  3 ) with DNAH17 (in red) and a-tubulin (in green). Spermatozoa were counterstained with DAPI (blue) as a nuclei marker. The scale bars represent 5 mm. Spermatozoa were smeared onto glass slides and fixed for 10 min with 4% paraformaldehyde before epitope retrieval in citrate buffer (pH 6).
Spermatozoa were then permeabilized in Triton X-100, 0. 2% for 10 min and blocked for 1 h with 1% bovine serum albumin (BSA). Finally, cells were labeled with rabbit polyclonal antibody directed against DNAH17 (SIGMA HPA024354) and a-tubulin (mouse monoclonal DM1A SIGMA T9026), then a secondary antibody incubation (anti-rabbit Alexa 568 and anti-mouse Alexa 488, Invitrogen) and mounting in DAPI Vectashield (Vector) were performed. spermiogenesis, DNAH9 (b-HC) might fulfill a transient function in the spermatid cells, and the protein amount, if present in mature spermatozoa, might be very low. Although the loss of the ODAs in sperm cells from all individuals carrying mutations in DNAH17 was highlighted by TEM analyses, we observed by immunoblot and immunofluorescence analyses that DNAH17 and DNAH8 were both absent, but DNAI1 was present, although its amount was found to be reduced.
Spermatozoa were counterstained with DAPI (blue) as a nuclei marker. The scale bars represent 5 mm. Spermatozoa were smeared onto glass slides and fixed for 10 min with 4% paraformaldehyde before epitope retrieval in citrate buffer (pH 6). Spermatozoa were then permeabilized in Triton X-100, 0. 2% for 10 min and blocked for 1 h with 1% bovine serum albumin (BSA). Finally, cells were labeled with rabbit polyclonal antibody directed against DNAH8 (SIGMA HPA028447) or DNAH2 (SIGMA HPA067103) and a-tubulin (mouse monoclonal DM1A SIGMA T9026), and then secondary antibody incubation (antirabbit Alexa 568 and anti-mouse Alexa 488, Invitrogen) and mounting in DAPI Vectashield (Vector) were performed. within the globular domain of the ODA, faces the same fate. The presence of DNAI1 in the sperm flagellum of individuals carrying mutations in DNAH17 suggests that the entirety of the ODAs might not be lost and that the intermediate domain of the ODAs might succeed in being assembled or stabilized in the absence of the heavy chains, as was recently demonstrated in the case of DNAH9 mutations.
(B) Immunofluorescence staining of spermatozoa from a control individual and from individuals 18GM01926 (II-1 from family 4, in Figure3) and18GM01932 (II-4 from family 3, in Figure3) with DNAH17 (in red) and a-tubulin (in green). Spermatozoa were counterstained with DAPI (blue) as a nuclei marker. The scale bars represent 5 mm. Spermatozoa were smeared onto glass slides and fixed for 10 min with 4% paraformaldehyde before epitope retrieval in citrate buffer (pH 6). Spermatozoa were then permeabilized in Triton X-100, 0. 2% for 10 min and blocked for 1 h with 1% bovine serum albumin (BSA). Finally, cells were labeled with rabbit polyclonal antibody directed against DNAH17 (SIGMA HPA024354) and a-tubulin (mouse monoclonal DM1A SIGMA T9026), then a secondary antibody incubation (anti-rabbit Alexa 568 and anti-mouse Alexa 488, Invitrogen) and mounting in DAPI Vectashield (Vector) were performed.
Spermatozoa were then permeabilized in Triton X-100, 0. 2% for 10 min and blocked for 1 h with 1% bovine serum albumin (BSA). Finally, cells were labeled with rabbit polyclonal antibody directed against DNAH8 (SIGMA HPA028447) or DNAH2 (SIGMA HPA067103) and a-tubulin (mouse monoclonal DM1A SIGMA T9026), and then secondary antibody incubation (antirabbit Alexa 568 and anti-mouse Alexa 488, Invitrogen) and mounting in DAPI Vectashield (Vector) were performed.
Acknowledgments We thank all the individuals and their families for their cooperation, as well as all the referring physicians and  Denise Escalier  for initial TEM analyses. This work was supported by the  Institut National de la Santeet de la Recherche Medicale (INSERM) , the  Centre National de la Recherche Scientifique (CNRS) , the  UniversiteParis Descartes , the  UniversitePierre et Marie Curie , the  French National Research Agency  (grant  ANR MASFLAGELLA 14-CE15-0002-03 ), the  Fondation pour la Recherche Medicale  (grant  DEQ20120323689 ), RaDiCo (funded by the  French National Research Agency  under the  specific program  '' Investments for the Future '' [cohort grant agreement  ANR-10-COHO-0003 ]), the  Legs Poix grant  from the  Chancellerie des Universites of Sorbonne Universite , and the  BEAT-PCD (Better Experimental Approaches to Treat Primary Ciliary Dyskinesia) COST (European Collaboration in Science and Technology) action.
Acknowledgements W. X. is supported by the  Chinese Scholarship Council. A. P. receives funding from  European Union   Horizon 2020 Marie Skłodowska-Curie Innovative Training Network  (ITN-ETN). G. K. is supported by the  Ligue contre le Cancer  (équipe labellisée);  Agence National de la Recherche (ANR) -Projets blancs; Association pour la recherche sur le cancer (ARC);  Cancéropôle Ile-de-France ;  Chancelerie des universités de Paris  ( Legs Poix ),  Fondation pour la Recherche Médicale (FRM) ; the  European Research Council (ERC) ;  Institut National du Cancer (INCa) ;  Inserm (HTE) ;  Institut Universitaire de France ; the  LabEx Immuno-Oncology ; the  RHU Torino Lumière ; the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; the  SIRIC Cancer Research and Personalized Medicine (CARPEM).
The North Atlantic spatial pattern of correlation shows significant and positive correlations of 0-100 m temperatures from 1950 with A. islandica growth in SPM encompassing the subpolar gyre area. These global-scale relationships are refined and the mechanisms leading to them explained by comparing A. islandica growth chronology to regional environmental datasets. These relationships existing between the A. islandica shell growth record at SPM and environmental datasets covering different geographical scales could yield details about past North Atlantic basin and regional environmental conditions through their influence on SPM coastal environment.
Crosssections were ultrasonically cleaned with deionized water between each grinding or polishing step to remove residual abrasive material. The polished shell sections were then etched in a Mutvei's solution  (Schöne et al. , 2005a)  for 45 min at room temperature, soaked in a deionized water bath, and left to air dry before imaging. Treatment with Mutvei's solution results in a threedimensional display of growth patterns and reveals clear annual growth lines. Shell sections were photographed under reflected light (Carl Zeiss, KL 2500 LCD) using an AxioCam MRC 5 installed on a Carl Zeiss SteREO Lumar. V12 stereomicroscope equipped with a motorized stage, under 25 × magnification. Photomosaics were constructed using AxioVision 4. 9. 1 software (Carl Zeiss). Annual growth increment widths corresponding to the curved distance between two successive annual growth lines were measured digitally in the outer shell layer  (Schöne et al.
Chronology Construction To isolate environmental signals from time series of increment width, ontogenetic age trends were individually removed. This ontogenetic trend can be estimated mathematically by a growth equation. In the present study, a dynamic optimized model deriving the generalized von Bertalanffy growth function  (von Bertalanffy, 1938)  was fitted to each measured increment width time series. This code was written with Scilab 6. 0. 0, free and open source software (distributed under CeCILL license -GPL compatible) developed by Scilab Enterprises (2012) and is available upon request. Growth indices (GIs) were then calculated for each year and each individual by dividing the measured increment width by the predicted increment width  (Schöne, 2013) , as follows: GI t = L t+1 -L t L p t+1 -L p t Where GI t is the growth index at t (in years), L t+1 -L t is the measured shell increment at t, and L(p) t+1 -L(p) t is the predicted shell increment length at the same time t.
Environmental Datasets In order to reveal potential relationships between shell growth and local phytoplankton dynamics, SGI master chronology was compared with monthly satellite chlorophyll a measurements  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015). The data were downloaded from the GlobColour website 1  and are weighted monthly averages of single-sensor products (SeaWiFS/MERIS/MODIS/VIIRSN merged chlorophyll concentrations) over the area 46. 6-47. 3 • N/56. 0-56. 6 • W (i. e. , waters surrounding the SPM archipelago within ca. 30 km). Temperature measurements were obtained from oceanographic Station 27  (at 10, 20, 30, 50, 75, 100, 125, 150, and 175 m; 47. 55  • N, 52. 583 • W), the oldest regularly monitored site of the LC inshore branch (Figure  1 ).