dataesr/scientific-paragraphs-categorization huggingface dataset:
Direct, single molecule, measurements of initiation frequency Detection of DNA synthesis on individual DNA fibers is the most direct way to localize sites of DNA replication. Since the preponderance of the literature describes low-frequency, heterogeneous selection of replication initiation sites in mammalian cells, single-molecule measurements may be the only means to accurately map the relative frequencies of initiation events in the human genome. However, to date, single-molecule replication-mapping technologies, such as DNA combing and SMARD, which have throughput of a few hundred fibers, have provided insufficient depth of coverage for metazoan genomewide analyses. Recently, nanopore sequencing has been used to map replication incorporation tracks genome-wide on single DNA fibers, but the current technology only allows for relatively small datasets of relatively short fibers  (Georgieva et al.
, 2019; Müller et al. , 2019; Hennion et al. , 2020). The largest dataset from a recent study describing the DNAscent approach to nanopore-based replication mapping in budding yeast was 3. 8 Gb (the equivalent of just over 1x coverage of the human genome) with an average fiber length of 32 kb  (Müller et al. , 2019). Therefore, ORM, for which current typical datasets are 150-times larger, provides a unique opportunity for single-molecule, genome-wide mapping of metazoan replication at high coverage on long fibers. Using ORM, we have mapped the replication initiation events occurring in the first 1. 5% of S phase in aphidicolin-synchronized HeLa cells. These initiation events, as others have reported  (Petryk et al. , 2016; Long et al. , 2020; Pourkarimi et al. , 2016; Ganier et al. , 2019; Cayrou et al.
Cell Labeling and Sample Preparation HeLa S3 cells were grown in DMEM plus 10% Cosmic Calf Serum (GE Life Science SH30087) and Pen/Strep. For asynchronous HeLa datasets, cells were grown to ~70% confluency, washed with warm PBS, trypsinized, and electroporated (Lonza, Nucleofection kit SE, HeLa S3 HV program) in the presence of 40 µM Aminoallyl-dUTP-ATTO-647N (Jena Bioscience NU-803-647N). For G1/S synchronized data sets, cells were synchronized at metaphase by incubation with 0. 05 µg/ml nocodazole for 4 hours followed by shake off. The percentage metaphase cells was quantified by metaphase spread. Briefly, 3X volumes of ddH20 was added to an aliquot of media from shake off and was incubated at 37˚C for 15 minutes. Cells were fixed by adding several drops of ice-cold methanol:glacial acetic acid (3:1).
Cells were spun down and pellet were resuspended in ice-cold methanol:glacial acetic acid (3:1) and spotted on to a clean slide. Slides were allowed to dry, spotted with Vectasheild (Vector Laboratories H-1000) containing DAPI and covered with coverslips. Metaphase and interphase cells were counted on a Nikon Eclipse Ti microscope. Metaphase cells were released into fresh media containing 10 µg/ml aphidicolin (Millipore Sigma 178273) for 16 hours. For 0-minute release datasets, cells were washed three times with warm PBS (or PBS plus aphidicolin, in the case of B. 0) to remove aphidicolin, trypsinized, and electroporated (Lonza, Nucleofection kit SE, HeLa S3 HV program) in the presence of 40 µM Aminoallyl-dUTP-ATTO-647N (Jena Bioscience NU-803-647N). Cells were recovered in warm media plus aphidicolin for 30 minutes in a 37˚C water bath with agitation every 5 minutes to prevent cells from settling in the tube.
We further assume that there is an exponentially decreasing amount of label over time as it is consumed by replication. Combining these two assumptions results in the depicted fit (Eq. 14, Supplemental Mathematical Methods), with an inferred initial incorporation rate of about 1/900 thymidines and a depletion half length of about 75 kb for the merged 0-minute dataset. See Supplemental Mathematical Methods for details. Combining the models in Figures  S2A  and B , we calculate that 20% of the signals in Figure  S2B  (corresponding to the 20% of the fluors that we infer from Figure  S2A  are not individually resolved by the Saphyr) are located within 1. 3±0. 3 kb of a neighboring fluor, consistent with the reported resolution of the Saphyr and the observation that there are almost no inter-signal distances below 1.
The value of 40% was chosen because, when aggregated around IZ peaks, the ORM signal density decreases with distance from the peak at a similar rate until about 40% then decreases much more slowly. This process was performed on all 4 biological replicates of HeLa 0' data as well as the combined dataset. Only initiation zones identified in all 4 replicates and with a relative peak height in the combined dataset (as measured by the difference in signal density between the peak and the edges of the zone) of greater than 0. 015%, or those identified in at least 2 replicates and with a relative peak height of greater than 0. 05%, were retained. To estimate the minimum number of initiation sites in each initiation zone, we calculated number of replication track centers separated by more than the resolution of our ability to localize initiation sites, which is about 15 kb.
Comparative Analysis of ORM Data with Other Datasets The raw Repli-Seq data of HeLa S3 cells were downloaded from the Encode project <http://genome. ucsc. edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeUwRepliSeq>  (Hansen et al. , 2010)  and S50 (the fraction of S phase at which 50% of the DNA is replicated in a defined genome region) was computed and used to identify replication timing peaks as previous described  (Chen et al. , 2011). Published H9 ESCs replication timing and peak calling data were used  (Zhao et al. , 2020). ORM signal enrichment around timing peaks was calculated in R (version 3. 5. 1 <https://www. r-project. org>). Correlations between ORM initiation efficiency and published genome-wide HeLa cell replicationinitiation-mapping datasets-OK-seq  (Petryk et al. , 2016) , SNS-seq  (Picard et al.
Published G4 sequence locations were retrieved (Puig  Lombardi et al. , 2019). We analyzed 3 G4 datasets (G4L3, G4L7 and G4L12) with similar results; we therefore used the G4L12 (G 3 N 1-12 G 3 N 1-12 G 3 N 1-12 G 3 ), which contains the largest dataset and give the best correlation with ORM data, in our final analysis. The genome-wide pairwise correlation analysis and enrichment around ORM IZs were performed by deepTools  (Ramírez et al. , 2016)  or with R (version 3. 5. 1 <https://www. r-project. org>). Initiation zones were classified by timing-early (S50<0. 25), mid-early (0. 25 ~ 0. 5), mid-late (0. 5~0. 75) and late (S50>0. 75)-using the S50 of the IZ center. The partial correlation analysis between ORM initiation efficiency and G4 sequences with controlling the effect of GC percentage (obtained from UCSC <http://hgdownload.
The average fiber length is 338 kb and the fiber N50 is 369 kb. D) The correlation of labeling between the three biological replicates that make up the C. 0 0-minute dataset. The number of signals in each 10 kb bin across the genome is plotted and the correlation coefficient is reported. E) The correlation of labeling between the four biological replicates (A. 0, B. 0, C. 0, D. 0) of the 0-minute dataset and one asynchronous dataset, as in panel D. The correlations between the biological replicates are higher than those between the technical replicates because the biological replicates are larger, reducing the counting noise in infrequently labeled regions of the genome. B) The distribution of inter-signal distances in the combined 0-minute dataset. The fit to the data (Eq. 14, Supplemental Mathematical Methods) between 10 and 500 kb predicts an initial labeling frequency of 1 in every 877±17 thymidines and a depletion half length of 74.
5±0. 7 kb. Similar fits for the asynchronous HeLa and H9 datasets predict labeling frequencies of 1/1025 and 1/850 and depletion half lengths of 57 and 48 kb, respectively. The similar labeling densities suggest the nucleotide uptake is similar in all three experiments, whereas the shorter depletion half length is consistent with previous reports that the number of forks increases during S phase, which would consume nucleotides more quickly  (Yang and Bechhoefer, 2008; Goldar et al. , 2009). C) The enrichment of ORM signals in solo-signal replication tracks from the combined 0-minute dataset around early replication-timing peaks, those that replicate in the first quarter of S phase. D) The distribution of inter-replication-track lengths. E) The distribution of initiation zone sizes. F) The fraction of 50 kb genomic bin with initiation efficiency greater than indicated on the x axis.
Acknowledgements KP, JB and NR were funded by  NIH  grant  GM125872. CLC was supported by the  I. Curie YPI program , the  ATIP-Avenir program  from  CNRS ,  Plan Cancer  from  INSERM , the  CNRS 80|Prime interdisciplinary program ,  ANR  and  INCa. WW was supported by a  COFUND IC-3i International PhD fellowship.
Numerical results We now present numerical results 3 obtained using the previously introduced algorithm. Throughout, we use the following conventions for model parameters: • space S is the discretized one-dimensional torus (the circle), • time horizon is T and the number of time steps is N + 1 (discrete time is indexed by k = 0,. , N ), • θ 1 , θ 2 are the mobility parameters (higher θ means higher movement cost), • σ is the regularization parameter for labour market equilibrium OT problem, • ν 1 , ν 2 are the respective diffusivity parameters for residents and firms, • the congestion/rent function is given by : F (x) = ax p p , hence the Legendre transform (F + χ R + ) is simply : G(β) = 1 a 1 p-1 p -1 p β p p-1 + 3 The Matlab code for the simulations presented in this section is available at https: //github.
com/CesarBarilla/MFG-Cities_Code. Animated GIFs of the simulations and additional cases are also available online at https://cesarbarilla. github. io/ research/mfg-cities. higher a and p mean stronger congestion, • the ground cost is taken to be either the geodesic distance (labeled as linear ), either its square root (labeled sqrt) or its square distance (labeled quadratic). The simulations below only vary in the parameters (T, N, θ 1 , θ 2 , σ, ν 1 , ν 2 , p, a) and the ground cost -all of which are specified for each corresponding plot at the bottom of the figure. For interpreting graphs, recall that we take population 1 (blue/solid curves) as inhabitants and 2 (red/dashed curves) as firms. Generally, we study cases with θ 1 < θ 2 i. e it is more costly for firms to relocate and ν 1 > ν 2 i.
initi ative-pim. org) project (Fig. 2 ). The site of Molarotto (A Zone) takes its name from the nearby granitic rocky islet surrounded by countless rocky outcrops protruding from a large sandy area. The site is characterized by granitic boulders and rocky ridges, where the steepest and highest crest-shaped rocky outcrop rises to 20 m from around 30 m depth. The investigated site has a threedimensional terrain surface area of approximately 3860 m 2 (2D area = 3500. 46 m 2 ; data retrieved from TerraExplorer Pro, Skyline Software Systems Inc. ). The site of Secche Papa (B Zone) consists of two series of limestone pinnacles, mostly covered with coralligenous formations, rising from the sandy bottom at a depth of about 45 m up to a maximum of 15 m depth. At this site, the approximate three-dimensional terrain surface area that was investigated-within the 35 m isobath-is 3305 m 2 (2D area = 2283.
41 m 2 , TerraExplorer Pro, Skyline Software Systems Inc. ). The outstanding underwater seascapes and the rich biodiversity characterizing these rocky banks have made this location a renowned Mediterranean diving site, where fishing is prohibited within a radius of 100 m. Taking this into account as well as the popularity of diving tourism in this MPA  (Micheli and Niccolini 2013) , the features of this location favour the involvement of recreational scuba divers in data collection and educational projects. Field-work surveys were carried out during two consecutive summers (2017 and 2018) at the two above-mentioned sites. Data collection was spatio-temporally extended owing to the involvement of recreational scuba divers within the frame of a collaborative project (see below in the "photographs collection" section).
These photos were shared via e-mail, with clear indication of the site where they had been taken and the date. Poorquality images shared by recreational divers were discarded (see details below) to maintain data quality and consistency within the dataset. From the resulting digital archive, a total of 968 high-quality photographs/video frames (taken from 2006 to 2018) were selected for the identification of grouper individuals by cephalic blotches and software matching.
Photo-ID: matching software The software used to store grouper photographs in a digital library and to recognize as well as compare lateral cephalic patterns was the Interactive Individual Identification System I 3 S Pattern (Version 4. 0. 2; Den Hartog & Reijns 2014), freely available from the I 3 S website (http:// www. reijns. com/ i3s). I 3 S Pattern has been used in fingerprinting studies of marine and terrestrial reptile species such as sea turtles (Chelonia mydas and Eretmochelys imbricata,  Calmanovici et al. 2018 ) and lizards (Liopholis slateri,  Treilibs et al. 2016). I 3 S Pattern is a semi-automated matching software that requires user interaction. The user must point out three fixed reference points and identify the relevant region bearing the individually identifiable pattern of the unknown animal.
In this study, the edge of the upper lip, the anterior base of the dorsal fin and the opercular caudal edge were manually entered and used as reference points (Fig. 1 ). Once the reference points are entered, the software automatically extracts all key points located within the reference area, generating a unique fingerprint for each image. To match fingerprints, the software compares between the key point patterns of any new unknown individual (test image) and known individuals (catalogued images), ranking every image from the database based on how closely the sizes and locations of key point pairs match. In particular, the numerical ranking, termed the distance metric, is calculated from the sum of the distances between each key point pair, divided by the square of the number of key point pairs (Den Hartog and Reijns 2014).
The lower is the score, the better is the match. Since automatic key point extraction is sensitive to visual circumstances (i. e. , water clarity, uneven natural lighting on the fish, reflections, backscatter-particles illuminated by the flash), poor-quality photographs were a priori excluded to reduce incorrect matches. High-quality images were defined as those that (1) portrayed individuals with the standard mottled or silver streaked colour patterns, (2) contained the reference area, (3) were clear and sharp, and (4) were taken with an approximately perpendicular viewing angle with respect to the animal. The software interface allows detailed visual comparison of an unknown image against a ranked list of photos from the database, thus allowing the user to check the match, include the image of a confirmed individual in the database, and to prevent the co-identification of different individuals (i.
e. , false positive error,  Stevick et al. 2011). To test the validation process performed with the software and to reduce the likelihood of failing to match two photographs of the same individual (i. e. , false negative error,  Stevick et al. 2011) , pattern matching performance was investigated using multiple identifying features for the identification (scars and/or damaged fins) and by manual comparison of all the pictures in the database. After having completed the catalogue, a reference image per individually identified grouper was selected and compared by eye against the entire identification database twice. This exhaustive comparison in the image library also allowed the detection of false negative errors, and thus the matching of a few photographs by eye. Data about the accuracy of I 3 S Pattern in the photo-recognition of the dusky grouper are not provided as this goes beyond the scope of this work.
5 linear km away (site 1, Fig. 2 ) since 8 July 2012. From 2012 to 2018, her presence was recorded during summer on 17 calendar days (7 at Secche Papa, 10 at site 1). This female moved between site 1 and Secche Papa at least once in July 2017 and at least thrice between July and August 2018. The male M-SP-002 (Fig. 1 ) has been the most recorded grouper, with a total of 32 sightings on different days during the summers of 2017 and 2018 (22 sightings only in 2018, Fig. 7 ). In 2018, it was first photographed on 16 June 2018, at a diving spot more than 4 linear km away from Secche Papa (site 2, Fig. 2 ) and then, from 30 June until 23 September 2018, it was only recorded at Secche Papa. Therefore, even if we cannot rule out that this individual moved back and forth from the study site during the summer of 2018, the collected data support strong site-fidelity.
Acknowledgements The authors thank all the divers who contributed photographs and attended public information events. In particular, we would like to thank, in alphabetical order,  Eric De Keyser ,  Luigi Onnis ,  Gianluca Pecetti ,  Irina Podda ,  Luigi Prosperi  and  Claretta Spada  for their significant contributions, without which re-sightings and key information would not otherwise have been possible. We thank  Andrea Deiana  for his help with GIS. We also thank  Jurgen den Hartog  and  Renate Reijns , the developers of the I 3 S application, for their helpful advice to use I 3  S Pattern. Finally , we wish to thank  Michael Paul  for the revision of the English language.
Nenni 14/A,  Padova  within the  CRUI-CARE Agreement. This work was carried out within the framework of the  PhD  project of  E. Desiderà  and the  Citizen Science Project  " TavolaraLab ", both funded by the  Marine Protected Area of Tavolara-Punta Coda Cavallo, Ministero dell'Ambiente e della Tutela del Territorio e del Mare (MATTM). This work was also partially supported by funding from the  Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR)  and  Consorzio Nazionale Interuniversitario per le Scienze del Mare (CoNISMa).
Structural differences between RLS and age-matched control group. The structural MRI data of RLS patients and age-matched healthy controls were analyzed using voxel-based morphometry (VBM) analysis. No significant between-group differences were identified in an independent t-test using the cluster-correction threshold combined with a liberal voxel-wise threshold (p < 0. 01). Resting-state differences between RLS and age-matched control group. The resting-state fMRI (rsfMRI) data of RLS patients were analyzed in relation to those of the age-matched healthy controls. No significant group differences were identified in an independent t-test for any of the voxel-wise or region-based rsfMRI measures using the cluster-correction threshold combined with a liberal voxel-wise threshold (p < 0. 01).
We, however, did not find any group differences in functional connectivity, which may be due to the fact that the RLS symptoms in pregnancy are largely transitory. The neuroimaging data were obtained shortly after childbirth with most of the women already experiencing relief or partial mitigation of the symptoms. While we did not seek to quantify CNS iron deposits by means of MRI, some studies suggest that the assessment of brain iron through quantitative magnetic susceptibility measurement reveals differences between healthy con-   3 ). trols and RLS patients in the basal ganglia and the dentate nucleus  29, 30. However, further investigations are needed to determine if regional brain iron concentrations are consistently lower in patients with RLS  31.
To control for the exclusion criteria, a brief non-standardized clinical interview was conducted according to the DSM V criteria by an experienced psychiatrist (NC). A detailed description of the study population is given in Tables  1  and 2. Based on the multimodal neuroimaging data (n = 108) collected at T0 (within 1 to 6 days postpartum), 25 participants had RLS symptoms during the recent pregnancy (aged 23-42 years). The remainder of the RLS group (n = 32) was not eligible for the MRI. Neuroimaging data of the age-matched control group without RLS during pregnancy were collected from the same data pool. A detailed description of the sample that participated in the MRI experiment is given in Table  3  (please refer also to Fig. 4  with the flowchart of inclusion into the study). In addition to the IRLSS, the following questionnaires were used at T0: the Stressful Life Events Screening Questionnaire (SLESQ)  43  was used as a self-report instrument to help assess the participants' encounter with 13 particularly traumatic experiences, and the Edinburgh Postnatal Depression Scale (EPDS)  23  was used as a self-report instrument for the screening of postpartum depression.
The structural image was acquired with an anatomical 3D T1-weighted MPRAGE sequence (176 slices, TR = 2300 ms, TE = 1. 99 ms, FoV = 256 × 256 mm 2 , flip angle = 9°, voxel resolution = 1 × 1 × 1 mm 3 ). The brain tissue was segmented into gray matter, white matter and cerebrospinal fluid, the resultant adjusted volume measurements representing the amount of gray matter corrected for individual brain size. Structural MRI data were preprocessed using the Computational Anatomy Toolbox (CAT12) and SPM12 (https:// www. fil. ion. ucl. ac. uk/ spm/ softw are/ spm12/) toolbox implemented in Matlab 2015b (MathWorks, Inc. , Natick, MA) to derive voxel-wise gray matter volumes for each subject. The default settings of CAT12 were applied for spatial registration, normalization and segmentation of the T1-weighted structural brain images.
For a precise spatial normalization into standard (MNI), the Diffeomorphic Anatomic Registration Through Exponentiated Linear algebra algorithm (DARTEL)  46  was performed. The images were segmented into gray matter, white matter, and cerebrospinal fluid, and modulated with Jacobian determinants. Finally, the modulated gray matter images were smoothed with an 8 mm isotropic FWHM Gaussian kernel. Subsequently, the association between brain structure and clinical indicators of patients experiencing RLS in pregnancy was systematically charted. Data of 25 women with RLS during pregnancy and n = 26 age-matched women without RLS during pregnancy, were analyzed using SPM12 toolbox. Smoothed gray matter segment of all participant were implemented in an independent t-test analysis, using the theory of Gaussian random fields.
Resting-state functional connectivity (RSFC). For preprocessing of resting-state data of women with RLS during pregnancy and n = 26 age-matched women without RLS during pregnancy, the SPM12 software was used, implemented in Matlab 2015b (MathWorks, Inc. , Natick, MA). Images were realigned, unwarped, and coregistered to the structural image, spatially normalized using structural information, and smoothed by a Gaussian convolution with 6 mm FWHM. A gray matter (GM) mask was applied to reduce all analyses to gray matter tissue. Images were further processed in the CONN toolbox version 18. b  49. White matter (WM), cerebrospinal fluid (CSF) and 24 motion parameters (Friston-24) were added as first-level covariates when computing voxeland region-based measures of interest. Global Correlation was calculated as the average of bivariate correlations between the BOLD signal of a given voxel and every other voxel  49.