dataesr/scientific-paragraphs-categorization huggingface dataset:
Article number, page 49 of 62 A&A proofs: manuscript no. main Appendix H. 7: IRAS4B IRAS4B (or NGC1333-IRAS4B) is a Class 0 protobinary system in the Perseus molecular cloud at a distance of (293 ± 20) pc  (Ortiz-León et al. 2018). The binary companions 4B1 and 4B2 are separated by ∼11  (Looney et al. 2000; Jørgensen et al. 2007 ). The source harbors a young bipolar outflow almost aligned along a north-south direction (Choi 2001). Podio & CALYPSO (in prep. ) distinguish two outflows: a first associated with the main protostar 4B1 at 167 • and a second with a PA of -99 • (see Table  1 ). IRAS4B is in the vicinity of IRAS4A which undergo a collapse triggered by a fast external compression with a velocity of v > 7. 30 km s -1  (Belloche et al. 2006). Thus, collapse of IRAS4B could have triggered in the same way.
23). To minimize this contamination and probe rotational motions in the equatorial axis, we used the method in the image plane in the PdBI dataset instead of working in the visibilities to constrain in the inner envelope (200-500 au). At outer envelope scales (r >1000 au), the N 2 H + emission shows a reversed gradient dominated by external cloud compression. Moreover, the integrated intensity of N 2 H + emission from the 30m datasets (see right top panel on Figure H. 22) trace the a filamentary structure which are consistent with the filament in which the source is embedded. The panel (g) of Fig. 16  shows the PV rot diagram of IRAS4B built from the velocity gradients observed at scales of r <1100 au. The index of the fitting by a power-law (α ∼-0. 6, see Table  3 ) is consistent with a Keplerian rotation.
However, we also obtain a good reduced χ 2 when we fit the PV rot diagram by an infalling and rotating envelope by fixing the index of the power-law at -1 (∼0. 6, see Table  3 ). Thus, for this source, the CALYPSO dataset only allow us to estimate a range of the power-law index between -1 and -0. 6 (see Table  3 ) at the scales of 170-1100 au probed in our analysis. Moreover, Keplerian rotation is not detected at the smaller envelope radii investigated by  Maret et al. (2020). Thus, the Keplerian rotation due to a large disk is not a robust interpretation for this source.
Acknowledgements. We thank the  IRAM  staff for their support carrying out the CALYPSO observations. This work has benefited from the support of the  European Research Council  under the European Union's  Seventh Framework Programme  ( Advanced Grant ORISTARS  with grant agreement no. 291294  and  Starting Grant MagneticYSOs  with grant agreement no. 679937 ). M. G. thanks the  Max-Planck Institute for Radio Astronomy  for its support toward the end of this work. We would like to thank  Cecilia Cecarelli  for comments and suggestions on the estimation of column density, and  Nagayoshi Ohashi  and  Jaime E. Pineda  for valuable discussions on the interpretation. We also thank the referee for the useful comments which helps to improve this paper.
Let note + sequences aligned to the positive strand, and -those aligned against the negative strand, keeping orientation so as to match BAM alignments (i. e. , sequence mapping against the negative strand are displayed in the same orientation as the positive strand). A total count of those ancient C residues that were methylated and present in a CG context is thus provided by the cumulated sum of CG+ → TG+ and CG-→ CA-misincorporations, where CG+ and CG-refer to the reference genome sequence at a given position, and TG+ and CAto that sequenced in the ancient specimen. Methylation rates are estimated by normalizing this numerator denominator of all counts where the sequence data show and lack evidence of DNA deamination, i. e. CG+ → CG+, CG+ → TG+, GC-→ GT-, and GC-→ GC-, thus providing so-called Ms ratios  (Pedersen et al.
, 2017). For maize and barley, the softmasked version of the respective reference genomes (including nuclear and organelle DNA) were used for read alignments. Those references are available on the Ensembl Plants Database 2 (maize: B73 reference genome v4; barley: IBSC reference genome v2)  (Schnable et al. , 2009; International Barley Genome Sequencing Consortium et al. ,  1 https://w3. pierroton. inra. fr/QuercusPortal/index. php?p=GENOMIC_SEQWe 2 https://plants. ensembl. org 2012;  Kersey et al. , 2018)  and represent a nuclear genome size of 2. 4 and 5. 3 Gb, respectively. Mapping statistics are provided in Supplementary Tables  S2, S3. For sequence data generated in the absence of USER treatment, ancient DNA damage patterns and quantification were carried out through PALEOMIX, using the methodology implemented in MapDamage  (Jónsson et al.
, 2019) , with important projects reconstructing the genomic history of maize, grape, and barley domestication  (Fonseca et al. , 2015; Mascher et al. , 2016; Ramos-Madrigal et al. , 2016 , 2019; Vallebueno-Estrada et al. , 2016; Swarts et al. , 2017; Kistler et al. , 2018). The findings reported here suggest that the ever-increasing amounts of sequencing data underlying such projects will soon open for an evaluation of the epigenetic impact of domestication. By extension, when other taxa and other archeological and environmental contexts will be included, the production of larger ancient sequence datasets will help researchers characterize the epigenetic response of plant species to many other selective pressures, including climate change, pollution, and more  (Allaby et al. , 2015; Gutaker and Burbano, 2017).
Previous work has shown that signatures that could inform about gene expression, such as nucleosome positioning along genes and their phasing across cells, could be obtained from the sequencing data underlying ancient mammal genomes  (Hanghøj et al. , 2016). Whether such signatures could also be collected in plants remains unknown and will require further research. Future work should also focus on improving the methodology proposed here, following what recently done for mammals where statistical models, such as the one implemented in the DamMet package  (Hanghøj et al. , 2019) , have been developed to account for the contribution of other factors than DNA methylation to C → T mis-incorporations. This should reduce the impact of sequencing errors and non-reference variants present in the individual sequenced, and thus improve the accuracy of DNA methylation inference.
Extending DamMet to trinucleotide contexts will, however, be challenging as the number of underlying genotypes could become rapidly intractable and machine learning approaches may prove more powerful to disentangle nucleotide mis-incorporations pertaining to post-mortem deamination reactions affecting methylated C residues from those resulting from all other processes contributing to sequencing errors. Once the methodology will have been refined, and following the amount of sequence data that were found necessary for retrieving accurate DNA methylation estimates in mammals, we anticipate that ancient genomes characterized at a minimal sequencing depth of 20-25× may be used for in depth studies on ancient plant methylomes. Interestingly, such datasets have already started to be produced in plants (e.
FUNDING This work was supported by the  Initiative d'Excellence Chaires d'Attractivité, Université de Toulouse (OURASI) , and the  Villum Fonden  miGENEPI research project. CP has received funding from the  ANR  ( epitree  project,  ANR-17-CE32-0009-01 ,  2018-21 ). LO has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Program  (Grant Agreement No. 681605-PEGASUS ).
, 2017 JK1699 Zea mays Turkey Pen Shelter Utah 2026 ± 20 Cob Desiccated Swarts et al. , 2017 JK1700 Zea mays Turkey Pen Shelter Utah 1903 ± 20 Cob Desiccated Swarts et al. , 2017 For all samples, we used paired sequence data generated based upon USER-treated and raw extracts. See Supplementary TableS1for accession numbers and details. Frontiers in Ecology and Evolution | www. frontiersin. org FIGURE 2 | Ms ratios for CG, CHG, and CHH contexts. Results are shown for paired sequence data of USER-treated and raw extracts of ancient oak, maize, and barley samples, for chloroplast and nuclear DNA, respectively. Scores are reported for full read length. For Ms ratios computed for outer and inner read regions, see Supplementary Figure S1.
Quantification of cell numbers, performed using Imaris software on confocal images from the macula, revealed that 7m8 infected 10,310 RGCs, whereas NHP#9 infected 4,296. In contrast, NHP#9 infected 2202 cones, compared with 1019 cones infected by 7m8 (Figure  5 , G and H). qRT-PCR, performed using the ΔΔCT method, revealed an 11. 71-fold (10. 37 to 13. 22) increase of GFP expression in foveal cones relative to 7m8. The top-ranking variant from the GFP-barcoded screen, LoopSwap variant ~583-LQRGVRIPSV-LEVNGQ (designated NHP#26), was also tested for validation despite the limitation that reduced production of this vector enabled only a low dose. Approximately 5 × 10 10 particles of NHP#26-scCAG-eGFP were injected intravitreally into 1 eye of a cynomolgus monkey. Although the number of particles injected was 30-fold lower than for the other tested vectors, efficient expression of GFP was observed in the fovea and in regions across the retina (Figure  6 ).
DNAse-resistant viral genomic titers were measured by qRT-PCR using a Bio-Rad iCycler. Deep sequencing of AAV libraries from rounds of selection. An approximately 75-to 85-bp region containing the 7-mer insertion or LoopSwap mutation sites (semirandom mutations at surface-exposed regions; see ref. 15  for a description of LoopSwap library construction) was PCR amplified from harvested DNA. Primers included Illumina adapter sequences containing unique barcodes to allow for multiplexing of amplicons from multiple rounds of selection (Supplemental Table  2 ). PCR amplicons were purified and sequenced with a 100-cycle single-read run on an Illumina HiSeq 2500. Custom Python code was written for analysis. First, the DNA sequences encoding amino acid insertions, found between constant linker DNA sequences, were identified.
PCR amplicons were purified and sequenced with a 100-cycle single-read run on a MiSeq. Read counts were normalized by total number of reads in the run. Analysis of barcode abundance was performed using custom code written in Python, followed by creation of plots in Pandas. Barcodes were identified as variable DNA sequences found between constant sequences in the expression cassette, which surrounded the barcode. Best performing variants were selected based on the fold increase in the percent of total library, relative to the injected library (percentage of total in recovered sample/percentage of total in injected library). Analysis was performed on n = 1 primate. Gene expression analysis. GFP expression was calculated relative to GAPDH, using the ΔΔCT method on RNA collected from retinal sections and extracted using AllPrep DNA/RNA FFPE Kit (QIAGEN).
insight. jci. orghttps://doi. org/10. 1172/jci. insight. 135112insight. jci. orghttps://doi. org/10. 1172/jci. insight. 135112Acknowledgments Deep sequencing was performed at the  QB3 Vincent J. Coates Genomics Sequencing Laboratory. Confocal imaging was performed at the  Berkeley Biological Imaging Facility. Injections for AAV selection in primates were performed at  Valley Biosystems. We thank  Yvonne Lin , and  Jaskiran Mann  for technical assistance. Funding was provided by the  Ford Foundation ,  National Eye Institute/NIH  ( F32EY023891 ,  PN2EY01824 ),  4D Molecular Therapeutics , and  Foundation Fighting Blindness. Address correspondence to:  David V. Schaffer , 278  Stanley Hall, Berkeley , California 94720-3220, USA. Phone: 510. 642. 4923;Email: schaffer@berkeley. edu. Or to:  John G.
The use of high-quality multispectral aerial imagery has enabled the estimation of the AGBD by using Unmanned Aerial Vehicles (UAVs)  [6] [7] [8] [9]. In  [10] , a lightweight UAV was used for the above-ground estimation of biomass and panicles of rice. Spatio-temporal variations in several VIs were analyzed by fusing both visual (RGB) and multispectral (NIR) images into a single crop surface model. Linear regressions models were used for correlating the VI variations with the Above-Ground Biomass (AGB). Experimental results determined that both MVARI and VDVI indices enabled higher AGB estimations (R 2 = 0. 9), mostly due to the computation of the crop surface model with fused VIS/NIR imagery. However, the approach in  [10]  required expensive offline image processing calculations. Several authors have also tackled the estimation of plant growth-related traits by data fusion from different sensors  [11] [12] [13] [14]  for the computation of crop surface models based on image mosaicing methods  [15] [16] [17] [18].
Rice crops and UAV System Here, we report on 72 sampled areas, evaluated from the vegetative through the ripening stage, capturing around 2. 000 NIR images per crop stage, yielding a dataset of 6. 000 images per trial. We conducted two trials (3 months each) of in-field testing, in which our UAV platform performed 10 flights per crop stage, capturing an overall of 12. 000 images. In this regard, the machine learning algorithms applied for the estimation of biomass (as detailed in Fig  1 ) used 60% of the dataset for training, whereas the testing accounted for the remaining 40% of the images. Along with the UAV-based aerial sampling, we manually collected several plants corresponding to sampled areas previously mentioned. Next, we present how the proposed GFKuts segmentation technique enables the precise selection of the crop areas under study, during each crop stage.
Since the estimation of the accumulated biomass depends on the accuracy and reliability of the extracted VI-based features, it is important to compare the correlations between the aerial and the ground-level features, i. e, canopy-plant scales. To this purpose, the VI-based features were calculated by applying the formulas in Table  2  to both ground-level and aerial samples. Given that, the former relies on the assembled ground-truth database described in Fig  2(d) , while the latter relies on the canopy imagery. Fig  9  presents the feature correlation results obtained for both rice production systems: upland (dry soils) and lowland (flooded paddies). Correlations were calculated using Eq 4, as follows: r ¼ P n i¼1 ðx i À � xÞðy i À � yÞ ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi Table  2.
At canopy-level, several factors affect the spectral reflectances of the crop: solar radiation, plant morphology and color, leaf angles, undergrowth, soil characteristics and water. In our system, the Sequoia multispectral camera comes with an integrated sunshine sensor to compensate light variations in the resultant image. Also, the image segmentation method deals with the filtering of undergrowth and other soil noises. In this regard, the change in the leaves color is the most notably variation of the crop through the phenological cycle. Next, we present the experimental results regarding the estimation of the AGBD using artificial neural networks trained with the selected VI-features. As previously mentioned in Section 2, datasets were divided 60% for training while the remaining 40% for testing.
Biomass estimations As shown in Fig  12( e), a significant improvement was obtained for the AGBD estimation based on the GFKuts-driven input data. The performance was measured in terms of the linear correlation (r) and the coefficient of determination (R 2 ). On average, we obtained an AGBD correlation of 0. 9568 with R 2 = 0. 9154, increasing the estimation in about 13%. The samplesaxis in both figs (b) and (e) correspond to the aerial imagery used for the estimation of biomass thought the phenological cycle. As previously mentioned in Section 2, biomass destructive measurements were conducted for selected crop plots, in order to assemble the Ground-Truth dataset. Given that, our system selects those aerial samples matching with the GPS coordinates of the ground measurements. Lastly, Fig 12c-12f present the comparative results of both approaches for the estimation of the AGBD according to the physiological response of two rice genotypes under lowland and upland production systems.
This cycle was divided into three growing stages: vegetative, reproductive, and ripening. Here, we report on 72 sampled areas, evaluated from the vegetative through the ripening stage, capturing around 2. 000 NIR images per crop stage, yielding a dataset of 6. 000 images per trial. We conducted two trials (3 months each) of in-field testing, in which our UAV platform performed 10 flights per crop stage, capturing an overall of 12. 000 images. In this regard, the machine learning algorithms applied for the estimation of biomass (as detailed in Fig1) used 60% of the dataset for training, whereas the testing accounted for the remaining 40% of the images. Along with the UAV-based aerial sampling, we manually collected several plants corresponding to sampled areas previously mentioned. Fig 2(c) depicts this process for destructive biomass sampling.
The Ground-Truth was defined by weighting the collected samples from each plot. The estimated dry weight of total aerial biomass was estimated after 3 days in the oven at 65˚C. Fig 2(d) shows an example of the assembled Ground-Truth database. Our aerial samples were acquired with the commercial quadcopter UAV Asctec Hummingbird, manufactured by INTEL's Ascending Technologies GmbH. By using the UAV's onboard ARM High Level Processor (HLP), we integrated the Parrot Sequoia multispectral sensor. Fig 3 gives a detailed presentation of the UAV setup.
Acknowledgements The " Faidherbia-Flux " platform is supported by  CIRAD ,  UMR Eco&Sols , LMI IESOL, ISRA, CERAAS and several on-going projects:  EU-LEAP-Agri RAMSES II ; USAID SIIL-SIMCO; Agropolis and  Total Foundation  DSCATT; CGIAR GLDC;  EU-DESIRA CASSECS ;  EC2CO   ENCAS. The UAV equipment is supported by the  Bill and Melinda Gates Foundation  through the " Terra-SGT" Project. We thank the people running the  Health 35 and Demographic Surveillance System (HDSS) , and especially  Laurence Fleury ,  Valerie Delaunay , and  Richard Lalou. Among the native people from Sob and Niakhar, we are extremely indebted to  Ablaye Diouf ,  Ibou Diouf , and  Robert Diatte. We also thank all the students who are participating in other experiments nearby:  Espoir Gablo ,  Adama Tounkara ,  Sidy Sow ,  Seydou Diatta ,  Sophie Djiba.
The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts.
Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and opensource software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images. The study of cell motility typically involves recording cell behavior, using live-cell imaging, and tracking their movement over time  1, 2. To enable the analysis of such data, various software solutions have been developed  [3] [4] [5] [6] [7] [8] [9].
Deep-Learning approaches have demonstrated their robustness against these two issues 11. In this work, we present a new analysis workflow that builds upon a Deep-Learning segmentation tool and a cell tracking tool to achieve robust cell tracking in cell migration assays. We combine StarDist, a powerful deep learning-based segmentation tool, and Track-Mate, a user-friendly tracking tool, into a tracking pipeline that can be used without requiring expertise in or specialized hardware for computing (Figure  1 )  [12] [13] [14] [15]. The use of deep learning networks, such as StarDist, often requires the user to train or retrain a model using their images. While high-quality StarDist pre-trained models are readily available, they are likely to underperform when used on different data with, e. g.
Training a StarDist model requires a set of images and their corresponding masks (Figure  1  and Figure  2 ). Generating a training dataset is by far the most time-consuming part of the analysis pipeline presented here as it requires the manual annotations of the images to analyze (Supplementary protocol). For instance, to generate the training datasets presented in Figure  2 , each cell/nuclei contour was drawn manually using the freehands selection tool in Fiji. The creation of a highquality training dataset is a critical part of the process as it will impact the specificity and performance of the StarDist model. However, the generation of a training dataset is only required once per dataset type. If a StarDist model already exists for similar images it can be used to significantly accelerate the creation of the training dataset via semi-automated annotation (see Supplementary protocol).
One of our analysis pipeline's key features is that, once a StarDist model has been satisfactorily trained, movies of migrating cells can efficiently be processed in batch. Indeed, while individual tracking files can be analyzed one by one using the TrackMate graphical interface, we also provide a Fiji macro to analyze a folder containing multiple tracking files. Our batch processing macro will provide basic quantitative information for each track, including median and maximal speeds. If more information is needed, the tracking results generated by our script are directly compatible with the Motility lab website, where they can be further processed  16. To illustrate our analysis pipeline's functionality and flexibility, we first trained a StarDist model to analyze the behavior of breast cancer cells migrating collectively (Figure  2A , Video 1).
The cancer cell's nuclei were fluorescently labeled, and the cells imaged using fluorescence-based microscopy. The creation of the training dataset used in this example was greatly facilitated by the availability of a StarDist model, released by the StarDist creators, capable of segmenting fluorescent nuclei. In this case, the StarDist Fiji plugin was used to segment the location of nuclei in the training images, and all miss-annotations were manually corrected (Supplementary protocol). To highlight that our pipeline can also be used to analyze brightfield images, we generated a StarDist model to track T cells migrating on ICAM-1 or VCAM-1 (Figure  2C -E, Video 2). Importantly, automated analysis of these data could reproduce the results obtained via manual tracking  17. Finally, we used our pipeline to automatically track non-adherent cancer cells flowing in a microfluidic chamber (Figure  2F   Here we show that StarDist and TrackMate can be integrated seamlessly and robustly to automate cell tracking in fluorescence and brightfield images.