dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on these analogous investigations, and due to the brevity and amplitude of GW170814, we expect systematic biases to be significantly smaller than the statistical error reported in this work. In addition to a waveform model, the coherent Bayesian analysis also incorporates the detectors' noise power spectral densities at the time of the event  [13, 118]  and marginalizes over the calibration uncertainties described in Sec. II, as in  [4, 5, 119]. We note that the likelihood used in our analyses assumes that the noise in the detectors is Gaussian in the 4 s window around the event. While some non-Gaussian and nonstationary features exist in the data, initial investigations suggest that the non-Gaussian features in the data do not significantly impact the reported parameters, but we defer a detailed study of these effects to future work.
For GW170814 this is dominated by the two LIGO detectors. The inclusion of Virgo data into the coherent analysis significantly improves the inference of parameters describing the binary's position relative to the Earth, as shown in Fig. 3 , since those parameters are predominantly determined by the relative amplitudes and arrival times observed in the detector network  [67, 146, 147]. Because of the inferred orientation of the binary, we do not see a significant improvement in parameters such as inclination and polarization angle for GW170814.
VIII. CONCLUSION On August 1, 2017, Advanced Virgo joined the two Advanced LIGO detectors in the second Advanced Detector Observation Runs. On August 14, 2017, a GW signal coming from the merger of two stellar mass black holes was observed with the Virgo and LIGO detectors. The three-detector detection of GW170814 allowed for a significant reduction in the search volume for the source. The black hole characteristics of GW170814 are similar to GW150914 and GW170104, and are found to be consistent with the astrophysical population and merger rate determined with previous detections. The addition of Virgo has allowed us to probe the polarization content of the signal for the first time; we find that the data strongly favor pure tensor polarization of gravitational waves, over pure scalar or pure vector polarizations.
Dyballa et al. used these data to develop a map of the progenitor cells in the whole otic vesicle. The findings help to fill the void in our understanding of the link between gene activity and tissue architecture in the inner ear. The next challenge is to use these findings as a basis to further explore models for how sensory neurons and hair cells develop, and to understand how to overcome the inner ear degenerati as we age. analyses of additional embryos, which we provide in supplementary form. Data from selected experiments can be downloaded from URL: http://bioemergences. eu/eLife2016.
These findings establish a link between the place and order of Table  2. Datasets used for the study of the different biological questions. Datasets used for each addressed biological question, and Figures in which the corresponding analyzed data are displayed. Each dataset corresponds to one imaged embryonic inner ear, except for 140402aX in which both ears were imaged. All datasets correspond to control samples, except for 140519aX and 140513aX that correspond to MO-neurog1 embryos (see Table  1 ). Note that we have performed deep and detailed analyses in few datasets, and supported the observations and conclusions with partial analyses of other datasets mainly included in supplementary figures.
Remodeling of the neuronal progenitor domain upon delamination We were interested in understanding whether cell proliferation had any role in conferring neuroblasts categories. To investigate this question we used the previous dataset (ID Dataset: 140426aX, Tables  1 2 ) to undertake the neuroblast clonal analysis (Figure  3a ), which showed that SAG-neuroblasts originate from a neuronal progenitor pool with different division behaviors: one third of the analyzed neuroblasts divided before exiting the epithelium (n = 42 red cells in Figure  3a , ). Thus, cell position within the otic epithelium is not relevant for these cell division behaviors because no specific spatial distribution can be observed (Figure  3 figure  supplement 1c ). In addition, cell division and delamination are independent events, in contrast to what was proposed for epithelial invagination of the Drosophila tracheal placode  (Kondo and Hayashi, 2013).
Neuroblasts dividing before delamination give rise to two daughter cells that will delaminate (Figure  3a-c ). In most cases, sister cell delaminations happened within a two hours interval, however in a few cases the time of delamination between two sister cells can be as long as seven hours (Figure  3b ). Neuroblasts giving rise to two daughter cells that delaminated within a delay interval of more than two hours were evenly distributed in the otic epithelium (n = 11/116 bicolored cell centers, Figure  3 -figure supplement 1d). Overall, these analyses suggest that (i) epithelial neuroblasts' divisions give rise to cells that will delaminate (Figure  3c ), and (ii) cell division is not necessary as a trigger for delamination. Finally, we used the same dataset (ID Dataset: 140426aX; Tables  1 2 ) to understand how the neuronal progenitor domain changes upon cell delamination and morphogenesis of the inner ear over time (orange circles in Figure  3d ; n = 131 at 18 hpf).
Video 5. Position of epithelial neuroblasts along the anteroposterior axis prefigures their location within the SAG. Reconstructed cell centers from neuronal progenitors were color-coded for position along the anteroposterior (AP) (left) or mediolateral (ML) (right) axes in the otic epithelium and followed from 18 hpf to 30 hpf. Note that the relative position of neuroblasts along the AP but not the ML axis is maintained from the otic epithelium to the SAG. These neuroblasts are the same set of cells as used in Video 3, but analyzed for epithelial position along the AP/ML axes instead of delamination time. ID Dataset: 140426aX. DOI: 10. 7554/eLife. 22268. 014.
In order to understand how Previous findings indicated that a specific pool of neuronal progenitors switches its fate to hair cells of the PM upon abrogation of neurog1 function (Sapede et al. , 2012). However, they were unable to clearly assess the position of this progenitor pool and how it behaved. To do so, we first undertook the dynamical analysis of maculae generation upon neurog1-downregulation by injection of translation-blocking morpholino (ID Dataset: 140519aX, Tables  1 2 ), which fully recapitulates the phenotype of neurog1 hi1059 mutants (Sapede et al. , 2012). In the absence of neurog1, sensory neurons do not form and supernumerary hair cells are produced in the PM (Figure  4f  To unveil the organization of the PM hair cell progenitor pool we generated the map of hair cell progenitors in the whole otic vesicle over time (ID Datasets: 140507aX, 140326aX).
For this, differentiated hair cells at 42 hpf were backtracked to their progenitor state, and information about differentiation time (color-code) and progenitor distribution was combined (Figure  4d -e; Figure  4 -figure  supplement 1c ). 3D-reconstructions revealed that progenitors for hair cells of the PM were distributed over the ventromedial domain of the otic vesicle at early stages of embryonic development (see color-coded cell centers in Figure  4d  and Figure  4 -figure supplement 1c), and that upon neurog1 inhibition the hair cell progenitor pool expanded along the anteroposterior dimension allocating more medially (ID Dataset: 140519aX; Figure  4i, Video 7 ). Furthermore, the position of hair cell progenitors foreshadows the organization of differentiated cells within the sensory patch, demonstrating that otic epithelial cells did not rearrange during early stages of hair cell differentiation.
However, the relative positions of the maculae changed upon morphogenesis, resulting in the growth of the PM towards the anterior region while growth of the AM was mainly anterior and towards dorsal (Figure  4c, h ). Finally, our analysis allowed us to compare the progenitor maps for hair cells and neurons at the onset of hair cell differentiation (24 hpf): they were largely segregated with a small region of putative overlap (Video 8), consistent with the existence of a pool of dual progenitors (Sape  de et al. , 2012). 1 2 ). Anterior is always to the left. ID Datasets: 140507aX for control, 140519aX for MO-neurog1; see Figure  5 -figure supplement 1 for additional analyses. (o) Graphic depicting the total number of cells in the otic vesicles for wild type (control, n = 3), neurog1 hi1059/ hi1059 mutant in the Tg[Isl3:GFP] background (n = 3), and MO-neurog1 embryos (n = 2) at 24 hpf.
Sensory and non-sensory domains of the otic vesicle display different proliferative activity As overall shape and size of the otic vesicle remain relatively constant during neuroblast delamination, we assumed that there was some homeostatic process involving cell proliferation elsewhere and we further validated this hypothesis. To understand the proliferative behavior of the distinct otic territories, we reconstructed during 16 hr the lineages of 51-64 neighboring cells located either in the non-sensory (dorsolateral region of the otic vesicle; Figure  5a-c, Video 9 ) or in the sensory domain (ventromedial epithelial region; Figure  5d-f, Video 9 ). For this we used the Tg[Brn3c:GFP] H2B-mCherry injected embryo (ID Dataset: 140507aX). Cell behavior was assessed according to cell division (dividing/nondividing) or cell differentiation (progenitor/differentiated) status.
Embryo mounting and SPIM imaging Embryos were anesthetized in 0. 17 mg/ml tricaine in system water and mounted in 0. 75% LMP-agarose in glass capillaries size 2 (volume 20 ml, BRAND GMBH). Time-lapse imaging was performed at 26. 5 ˚C (to avoid melting of LMP-agarose) on a Zeiss Lightsheet Z. 1 microscope using a 20x or a 40x objective and the developmental stage was corrected accordingly (at 26. 5 ˚C development is delayed about 0. 7 fold). Nuclei, plasma membranes and cell fate were recorded simultaneously for the entire system (Figure  1a , Figure  1 -figure supplement 1). The cohort of embryos and datasets used in this study are depicted in Tables  1  and 2. Each dataset corresponds to the imaging of a distinct embryo inner ear, except for 140402aX in which both ears were imaged. Note that we have performed deep and detailed analyses in few datasets, and supported the observations and conclusions with partial analyses of additional datasets mainly included in supplementary figures.
3D+time image analysis pipeline Image pre-processing (Figure  1 -figure supplement 1) Image pre-processing was done using the Zeiss ZEN software and involved dual (illumination) side fusion and deconvolution (Regularized Inverse Method). To compensate for morphogenetic movements during image acquisition a semi-automated rigid registration was carried out using developed FIJI-scripts: the user is guided through the time steps of the 16-bit. czi data set and chooses by clicking a fixpoint of the structure (eg. a tether cell or a nucleus on the dorsolateral wall of the otic vesicle) for which the x, y, z coordinates are recorded. After navigating through all time steps of the video and having specified the fixpoints for all time steps the dataset is processed according to the recorded coordinates.
This registration allows as well decreasing the size of region of interest, which substantially reduces data size. A separate 8-bit. vtk file with the corresponding transformation is generated for each time step and channel. Changes on the levels of the transgene fluorescent protein expression were compensated for by modifying dynamic ranges upon 16-bit to 8-bit mapping. Different datasets were generated for this work as displayed in Tables  1 2. Center detection and automatic tracking (Figure  1 -figure supplement 1) The. vtk files were then uploaded to the Bioemergences platform  (Faure et al. , 2016)  and center detection was launched. Detected centers were validated using the CenterSelect application available in the platform. Subsequently, automatic tracking of the validated centers was launched and once completed the tracking data was validated and curated using MovIT, a custom-made graphical interface that offers the tools for segmentation and tracking of cells to accurately reconstruct their positions, movements and divisions  (Faure et al.
, 2016). Videos of the developmental processes displayed in the manuscript were generated using FIJI (RRID:SCR_002285), MovIT, or a combination of both. Only validated/curated cell tracks were used for further analyses. MovIT has several display modes of time-lapse data such as: (i) orthoslices through the volume showing one xy-plane, one xzplane and one yz-plane, used to validate cell tracks, (ii) oblique volume slices, which show a slice of a density map that is not parallel to the faces of the volume box and allows orienting the image along the axes, and (iii) volume rendering allowing to display a 2D projection of a 3D discretely sampled dataset; this permits to display cell centers in the context of the raw images in the whole volume (Video 1). 1 -figure supplement 1, Tables  1 2 All used datasets are clearly stated in the Figure legends, and mostly throughout the text.
Datasets analyses (Figure For manual segmentation of the delamination domain, ITK-Snap software was used on three time steps of an imaging sequence of Tg[neuroD:GFP] embryos injected with lyn-TdTomato mRNA (Figure  1b-d ). The delamination domain (identified by otic epithelial neuroD:GFP expression) and the otic vesicle volume (determined from the lyn-TdTomato signal) were segmented, and the resulting. vtk files were used to display neuroD-epithelial neuroblasts and membrane signal of the otic epithelium only (FIJI-3D viewer). For cell lineage analysis, validated centers were grouped in selections and displayed with different appearances either alone or in context of the original data (Figure  1h -i; Figure  2b-g  For analysis of cell proliferative activities over time, cell centers were grouped in selections according to cell states (eg.
dividing or non dividing) and these data were further explored within SciPy, a Python-based ecosystem for scientific computing (Figure  5 ). Namely the data was imported as Pandas DataFrames; different time points and samples were registered using the iterative closest points transformation algorithm as implemented in. vtk and the results were plotted using matplotlib and seaborn. To estimate the local cell density the other cell centers within 20 mm of each center were counted, and this number was then divided by the volume of a sphere of that radius (Figure  5m -n, Video 10). The nearest neighbor distance (NN-distance) was used instead when comparing subsets of cell centers to limit boundary effects (Figure  5 -figure supplement 1a,b). Both estimates were calculated using k-d-trees as implemented in the SciPy library.
(e-g) Time-lapse stills showing a segmented delaminating neuroblast (red; Video 2); (e'-g') magnifications of framed regions in (e-g). ID Dataset: 140426aX. (h-i) Still images from Video 1 displaying: otic tissue architecture (h), and cellular distribution (i) upon SAG formation. Reconstructed cell centers are color-coded according to cell position/identity (see legend). ID Dataset: 140423aX. SAG/ALLg, statoacoustic/anterior lateral line ganglia. AM/PM, anterior/posterior maculae. DOI: 10. 7554/eLife. 22268. 003 The following figure supplements are available for figure 1: Figure supplement 1. 3D+time image analysis pipeline. DOI: 10. 7554/eLife. 22268. 004 Figure supplement 2. Posterior expansion of the otic neuroblast delamination domain. DOI: 10. 7554/eLife. 22268. 005.
ID Dataset: 140423aX. DOI: 10. 7554/eLife. 22268. 006 Video 2. Segmentation of delaminating neuroblasts. Tg[cldnb:lynGFP]Tg[Isl3:GFP] embryos were injected with H2B-mCherry mRNA at 1cell-stage, and single delaminating neuroblasts (n = 5) were automatically segmented. A representative segmentation (red colored cell) is shown. Transverse and lateral views (top and bottom rows) with their respective high magnifications on the right hand side. Note that the neuroblast changes shape and exits the otic epithelium basally within one hour. ID Dataset: 140426aX. DOI: 10. 7554/eLife. 22268. 007 this, we used the Tg[cldnb:lynGFP]Tg[Isl3:GFP] embryo injected with H2B-mCherry (ID Dataset: 140426aX, Tables.
[cldnb: lynGFP] Tg[Isl3:GFP] embryo was injected with H2B-mCherry mRNA at 1cell-stage. ID Dataset: 140426aX. DOI: 10. 7554/eLife. 22268. 012 Video 4. The anterior and posterior SAG neuronal populations derive from the anterior and posterior otic epithelium, respectively. Neuroblasts belonging to distinct SAG neuronal populations, anterior or posterior (cyan/red), were backtracked to their otic epithelial origin prior to delamination. Note that cells of the anterior and posterior SAG delaminate from the anterior and posterior otic epithelium, respectively, and they maintain this relative position. The video is played backwards. These neuroblasts are the same set of cells as used in Video 3, but analyzed for AP position in the SAG instead of delamination time. ID Dataset: 140426aX. DOI: 10. 7554/eLife.
The color intensity of cell centers depicts the position of cells along the dorsoventral axis of the otic vesicle. The map was built after following the lineages from 18 hpf to 26 hpf of all encircled cells. Note how neuroblast delamination impacts on the size and position of the progenitor domain (orange cell centers) over time. Tether cells are depicted as black circles. For this analysis, Tg[cldnb:lynGFP] Tg[Isl3:GFP] line was injected with H2B-mCherry mRNA at 1cell-stage (Tables 1-2). ID Dataset: 140426aX. DOI: 10. 7554/eLife. 22268. 015 The following figure supplement is available for figure 3: Figure supplement 1. Spatial distribution of epithelial neuroblasts according to division behavior or delamination time. DOI: 10. 7554/eLife. 22268. 016.
ID Datasets: 140507aX for control, 140519aX for MO-neurog1; see Figure 4-figure supplement 1 for additional analyses DOI: 10. 7554/eLife. 22268. 017 The following figure supplement is available for figure 4: Figure supplement 1. Temporal pattern of hair cell differentiation in AM and PM. DOI: 10. 7554/eLife. 22268. 018.
Figure5continued color-coded cell centers across the whole otic epithelium (Video 10). Tg[Brn3c:GFP] embryos injected with H2B-mCherry and with/without MO-neurog1 at 1cell-stage were used for full lineage reconstruction (Tables1-2). Anterior is always to the left. ID Datasets: 140507aX for control, 140519aX for MO-neurog1; see Figure5-figure supplement 1 for additional analyses. (o) Graphic depicting the total number of cells in the otic vesicles for wild type (control, n = 3), neurog1 hi1059/ hi1059 mutant in the Tg[Isl3:GFP] background (n = 3), and MO-neurog1 embryos (n = 2) at 24 hpf. DOI: 10. 7554/eLife. 22268. 022The following figure supplement is available for figure5:.
Local cell densities across the whole epithelium in control and MO-neurog1. All cells of the otic epithelium in a control and a MO-neurog1 embryo are plotted and color-coded according to their estimated local cell density at 24 hpf. Tether cells are depicted in black for better orientation. The animation displays a rotation of otic vesicles around the anteroposterior axis. Note that the anteroventral territory displays higher cell density in both cases. ID Datasets: 140507aX for control, 140519aX for MO-neurog1. DOI: 10. 7554/eLife. 22268. 025.
Acknowledgements We are grateful to  T Pujol  for his valuable help in SPIM imaging and Zeiss for use of the Lightsheet Z. 1 microscope. We thank members of  Peyrieras and Pujades laboratories  for insightful discussions and technical help, especially  D Fabreges  and  G Recher. We acknowledge  P Daniel  for help in experiments related to otic vesicle point cloud segmentation. We thank  V Lecaudey ,  A Nechiporuk  and  B Riley  for kindly providing transgenic fish lines, and  S Fraser  for his critical reading of the manuscript and valuable comments. This work was supported from  Spanish Ministry of Economy and Competitiveness (MINECO)  by Grant  BFU2012-31994  to CP,  Unidad de Excelencia Marı  ´a de Maetzu  2015-19 MDM-2014-0370  to  DCEXS-UPF ,  Centro de Excelencia Severo Ochoa   2013-17, SEV-2012-0208  and  Swiss National Science Foundation  ( SINERGIA CRSII3 141918 ) to CRG.
Video 8. 8 Figure5continued color-coded cell centers across the whole otic epithelium (Video 10). Tg[Brn3c:GFP] embryos injected with H2B-mCherry and with/without MO-neurog1 at 1cell-stage were used for full lineage reconstruction (Tables1-2). Anterior is always to the left. ID Datasets: 140507aX for control, 140519aX for MO-neurog1; see Figure5-figure supplement 1 for additional analyses. (o) Graphic depicting the total number of cells in the otic vesicles for wild type (control, n = 3), neurog1 hi1059/ hi1059 mutant in the Tg[Isl3:GFP] background (n = 3), and MO-neurog1 embryos (n = 2) at 24 hpf. DOI: 10. 7554/eLife. 22268. 022The following figure supplement is available for figure5:.
Video 10. 10 Local cell densities across the whole epithelium in control and MO-neurog1. All cells of the otic epithelium in a control and a MO-neurog1 embryo are plotted and color-coded according to their estimated local cell density at 24 hpf. Tether cells are depicted in black for better orientation. The animation displays a rotation of otic vesicles around the anteroposterior axis. Note that the anteroventral territory displays higher cell density in both cases. ID Datasets: 140507aX for control, 140519aX for MO-neurog1. DOI: 10. 7554/eLife. 22268. 025.
This applies to infection-related cancers such as Kaposi sarcoma, Burkitt lymphoma, Hodgkin lymphoma and hepatocellular carcinoma, and also to two common embryonal cancers -retinoblastoma and nephroblastoma. These (and other) observations are unlikely to be artefact, and are of considerable interest when considering possible aetiological factors, including ethnic differences in risk (and hence genetic/ familial antecedents). The data reported are the most extensive so far available on the incidence of cancer in sub Saharan Africa, and clearly indicate the need for more resources to be devoted to cancer registration, especially in the childhood age range, as part of an overall programme to improve the availability of diagnosis and treatment of this group of cancers, many of which have-potentially-an excellent prognosis.
This necessary awareness is provided by the cancer registries. In Africa, only South Africa operates a national childhood cancer registry. In other countries, however, regional population-based or hospital-based registries exist for many years. Presently, they are members of the African Cancer Registry Network (AFCRN), which has become, with international support, a regional hub for cancer registration in sub-Saharan Africa. This monograph on childhood cancer statistics in sub-Saharan Africa contains information from 16 registries. It contributes to the knowledge about children's cancer in sub-Saharan Africa, presenting data directly from source. This approach distinguishes it from the national estimates of cancer incidence in Globocan, prepared by the International Agency for Research on Cancer (IARC) which, due to the more limited data sets available requires extrapolation the statistic findings from one region to the surrounding ones.
The result will be, obviously, that figures will differ from those produced by IARC. It is, nevertheless, highly necessary to make known -to the scientific community and to the political factors of decision on health carethese data, which at the moment represent the best image of the malignant disease in children in the respective regions. Furthermore, a first essential step is to use the data as they exist now in order to drive continued improvement in cancer intelligence in the continent in the future. I am convinced that this monograph is a significant step forward towards improving the care for childhood cancer.