dataesr/scientific-paragraphs-categorization huggingface dataset:
Our georeferencing process and accuracy validation, as we have elaborated in substantive detail, have important methodological values for future expansions of spatial dam inventories using similar approaches, such as Geo-Wiki and OpenStreetMap. To further ensure the optimal inclusion of the world's largest dams, we harmonized the georeferenced WRD (or GeoDAR v1. 0) carefully with GRanD v1. 3. Using the harmonized dam points as spatial identifiers, most of their reservoir boundaries were then retrieved from high-resolution water body datasets. This ICOLD-GRanD harmonization and the subsequent reservoir retrieval resulted in GeoDAR v1. 1, our end product, which holds 23,680 dam points (including 22,569 linked to WRD) and 20,214 reservoir polygons. This product spatially resolved 40% of the entire ICOLD WRD by dam count and more than 90% by reservoir storage capacity.
Since most of the world's largest reservoirs (e. g. , >0. 1 km 3 ) are already included in GRanD, GeoDAR adds limited improvements (by 4-27%) to the total reservoir area, storage capacity, and catchment area. However, by including many smaller dams particularly in lower and middle latitudes, GeoDAR is triple the size of GRanD in terms of dam and reservoir quantity. For this reason, one of the major improvements of GeoDAR is its unparalleled ability to capture relatively small dams, or in other words, to enhance the spatial detail of global dam and reservoir distributions. Besides an enhanced spatial detail, another unique value of GeoDAR is its capability of bridging the locations of dams to a broad suite of attributes that are essential to scientific applications. A standing dilemma of existing global dam datasets is the divergence between the focus on dam quantity or spatial detail and the provision of detailed attributes for a limited dam quantity.
This dilemma was partially ameliorated by GeoDAR because its georeferenced dams and reservoirs were explicitly indexed to WRD and/or GRanD records where many attributes are available. Since the original ICOLD WRD is not georeferenced, our perception was that the task of georeferencing WRD to enable a spatially explicit application of the attribute information, even at regional scales, often fell on individual users. To avoid the duplication of efforts and to facilitate scientific applications, we performed this comprehensive georeferencing on the entirety of ICOLD WRD as thoroughly as possible, and hereby released the resultant dam coordinates and reservoir polygons to the public as part of GeoDAR. We would like to reiterate the disclaimer that GeoDAR does not directly contain, and neither do we intend to release, the original WRD attribute data which are proprietary to ICOLD.
In this sense, even just with the 24,000 or so geometric dam points, GeoDAR contributes yet another fundamental extension to global water infrastructure databases. If these dam points are rectified to high-resolution hydrographic networks (such as MERIT Hydro  (Lin et al. , 2021; Yamazaki et al. , 2019) ), GeoDAR, together with other existing dam and barrier datasets, can help refine our understanding of how human water infrastructure fragmented global rivers and their ecosystems  (Belletti et al. , 2020; Grill et al. , 2019; Kornei, 2020) , especially with a more exhaustive inclusion of smaller and/or headwater catchments. Alongside the detailed dam points, GeoDAR's reservoir boundaries provide thus far the most comprehensive global base maps for assessing reservoir dynamics and the impacts of human water regulation.
Python 3. 7. 0 programming language was used with the Jupyter notebook web-based interactive computational environment to perform these tasks. An Intel Core i5-5250U CPU @ 1. 60 GHz, 8. 0 GB RAM, macOS 10, 64-bit computer was used for the computations. The Python codes and the raw text data are currently hosted in the following GitHub repository: https://github. com/cnicolas662/NLP_SmartCities.
Top-down text data extraction This study was built upon the urban management-related text datasets of Boston, Helsinki, Seoul, and Taipei. Digital notices issued from governmental portals were scrapped and selected to properly reflect smart city strategy, since they carry key top-down messages aiming to inform and align their citizens with the current urban strategy  (Capdevila & Zarlenga, 2015; Kong & Woods, 2018; Saad-Sulonen, 2012). The shortest (i. e. , less than 40 words) public notices and duplicated ones were dropped. Also, all notice tags, such as Analytics Team, Arts and Culture, Budget, Environment, Food Access, and Public Safety, were retained to transparently reflect wider smart city strategy trends. Ultimately, even if some announcements sporadically convey unrelated information, massive web-scraping was conducted to produce voluminous datasets (Table  3 ) on top of which topic modeling was used for dimensionality reduction, allowing to clearly discern the major trends of smart city strategies  (Blei, Ng, & Jordan, 2003; Mimno, Wallach, Talley, Leenders, & McCallum, 2011).
For instance, non-smart city specific content (e. g. , music festival notice) would be made irrelevant at the topic modeling stage through unsupervised LDA. Conversely, this content would happen to be revealed at the topic modeling stage if recurrent and fully integrated in the wider smart city strategy (e. g. , active  emphasis on cultural experience). A typical pitfall could have been to only retain announcements containing keywords, such as "smart city" or "smart initiatives". Indeed, the concept of smart city is highly ambiguous, subjective, and often linked to a huge misconception that would focus on digital-centered content and miss human-centered communication efforts  (Bibri & Krogstie, 2017; Martin et al. , 2019). According to the proposed methodology, the text datasets were extracted from publicly available online governmental portals, as indicated in Table  3.
To automatically extract text data, a web scraping approach was implemented using the Python library BeautifulSoup. Web scraping collects and converts unstructured web data, typically coded in hypertext markup language (HTML) format, into structured data that can be stored in a central, local database for further analysis. BeautifulSoup provides simple methods and pythonic idioms for dissecting a web page and extracting specific content from HTML code  (Richardson, 2007). For a given city, government announcements are typically encoded into individual web pages by a common HTML template. The structure of these HTML documents (e. g. , script tags) was examined to implement a Python BeautifulSoup based program that navigates such templates and extract textual information. The program also stored the retrieved data into external text files.
4 ). The quality of statistical inference induced by K topics can be quantified through topic coherence measures, as presented in the next paragraph. Gensim Python library was used to implement the LDA algorithm on sets of preprocessed text documents. After implementation, core topics were automatically extracted. The performance of metrics used to examine words within topics and quantify their coherence has been validated by experts  (Mimno et al. , 2011). Therefore, these metrics effectively assesshow LDA models align with human judgment  (Röder, Both, & Hinneburg, 2015). In this study, two coherence indicators are considered: C UMass and C v. To measure the internal coherence of a given topic, t, C UMass (t; W (t) ) averages the sum of the pairwise score logarithmic function, as described in Eq.
e. , α = 0. 21 and β = 0. 91) yielded a 33% C v improvement over the baseline score, resulting in more interpretable topics. After the LDA models had been tuned to optimize coherence metrics for each case study, pyLDAvis, an interactive web-based LDA visualization Python package  (Sievert & Shirley, 2015) , was used to visualize the outputs and validate the human interpretability of the extracted topics; t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction was employed to plot the LDA topics on a two-dimensional plane (Fig. 6(a) ). In pyLDAvis, the LDA topics are represented as circles, and their area is proportional to the overall topic frequency in the corpus. Inter-topic distance in the 2D plane is an indicator of inter-topic similarity. As suggested in  Sievert et al.
(  4 )). HD(p, q) = 1 ̅̅ ̅ 2 √ ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅ ∑ T i=1 ( ̅̅̅̅ p i √ - ̅̅̅̅ q i √ ) 2 √ √ √ √ (4) The Hellinger distance returns a dimensionless output in the range [0,1], with lower values referring to higher similarities. Since topics are modeled as probability distributions over words in LDA, the Hellinger distance could be applied to the topics. In this study, the inter-topic Hellinger distance was transformed into a similarity measure by simply subtracting it from 1. To identify significant similarities and emerging complementarities among strategic domains, inter-topic Hellinger similarity matrices were computed for the four smart cities. The results were presented in diagonal heatmaps using the Seaborn Python library.
This framework was employed for analyzing the top-down discourse of four cities engaged in smart city projects (Boston, Helsinki, Seoul, and Taipei) and for understanding their relationships with wider public policies aiming to leverage urban performances. Specifically, a web scraping process was first used to collect the content of metropolitan top-down announcements, then the retrieved text datasets were processed and LDA based topic modeling was implemented to quantify the prevalence of core topics in smart city management policies, inter-topic Hellinger similarities were also computed to measure the city governments' efforts to bridge disciplinary perspectives, and ultimately, the top-down trends were compared with levels of urban performance. Based on realworld levels of performance, the proposed framework could analyze the top-discourse of smart cities as a reflection of wider political strategies.
2017). Here we use geometric morphometrics to analyse three-dimensional endocast shape in a sample of 57 marsupial species across all major clades, including 12 fossil species. We aim to determine if a common pattern of shape variation emerges in marsupials, and whether suggestions for allometric patterning of endocast shape, posited for primates, can be generalised to marsupials and could generally explain mammalian brain shape evolution. We also assess suggestions that endocast shape corresponds with locomotor function  (Ahrens 2014; Bertrand et al. 2019b ) and phylogeny (e. g. Silcox et al. 2009; Thiery and Ducrocq 2015; Bertrand et al. 2016; Bertrand et al. 2019b ). To further understand if brain shape is indicative of internal brain structure, we use volume dissections of our endocasts and a limited dataset of neocortical and isocortical grey matter volumes (noting that isoand neocortex largely refer to the same structure; Palomero-Gallagher and Zilles 2015) to assess the degree to which brain partition volumes and brain shape correspond in a large, phylogenetically diverse mammalian radiation.
Specimens This study is based on cranial endocasts, which have been shown to be a good approximation of brain shape for mammals and marsupials in particular  (Jerison 1973; Macrini et al. 2007a). Virtual reconstructions of endocasts from conventional and micro-Computed Tomography (µCT) scans from adult skulls of 57 marsupial species were prepared in Materialise Mimics (v. 17-20) through virtually "flood-filling" the endocranial cavity of specimens, mostly by two operators, with some help from technical staff. The list of specimens with their museum numbers is available in the main data file used in this analysis (Supplementary Table  1 ). The 3D endocasts files that were landmarked are available on Figshare ( 10. 6084/m9. figshare. 12284456. ) One specimen per species was sourced for 38 species, and multiple specimens, where possible including males and females, were sourced for 19 species to assess how representative a single endocast is of a species.
Scans of extant species were conducted at either the University of Texas High-Resolution X-ray Computed Tomography Facility (Austin, Texas, USA), a SkyScan 1074 at the University of Queensland (School of Biological Sciences), or a Siemens Inveon Pet-CT scanner at the UQ Centre of Advanced Imaging at the University of Queensland (Australia). To allow easy manipulation of large scans, these were cropped to just the brain case and/or subsampled by either using every second slice only and/or reducing the resolution of each slice in ImageJ  (Schneider et al. 2012). All but three endocasts were derived from museum specimens (for accession numbers, see Supplementary Table  1 ). The study also used post-mortem CT scans of one captive dunnart (Sminthopsis macroura), and two captive koalas (Phascolarctos cinereus), whose deaths were unrelated to this study.
Endocast landmarking and protocol comparisons Endocast shape was characterised in three dimensions with 29 fixed landmarks, ten curve semilandmarks, and 32 surface sliding semilandmarks (Figure  2 ; for anatomical description and numbering of the landmarking protocols, see Supplementary Information 1). The landmarking of endocasts represents a challenge because the cerebral hemispheres have large areas with few landmarkable homologous features. This was exacerbated due to varying scan resolutions, and the high diversity of brain shapes within our sample, so that landmark repeatability was expected to be an issue (Pereira-Pedro and Bruner 2018). To capture endocast shape effectively, and test the repeatability of our resulting landmark dataset, one of us (VW) landmarked each endocast twice and trialled two different protocols for surface landmark placement.
This included a fully manual protocol using only the Checkpoint programme  (Wiley 2015). The other protocol used the Checkpoint software  (Wiley 2015)  for fixed and curve semilandmarks, and automatic placement of surface semilandmarks using the Morpho R package (v. 2. 8). The performance and similarity of the two surface landmarking protocols was tested in several ways. To assess repeatability of landmarking, the shape variance from each replicate and each protocol was evaluated against among-species variance using a Procrustes analysis of variance (ANOVA)  (Zelditch et al. 2012). To test the correspondence between the two protocols, principal components analyses (PCA) was used to construct two multi-dimensional spaces, and the distribution of species in each space was compared using a Mantel test in the Vegan R package v.
2. 5-6  (Oksanen et al. 2016 ). In addition, scatter plots of the first two principal components for each protocol, as well as graphs of the shape changes along the axes, were produced for visual comparison. All automated landmarking procedures, analyses and results were conducted in the R statistical environment (R core team 2018) and can be replicated with our code on github; the ply files that can be used for automatic landmarking are in 10. 6084/m9. figshare. 12253409.
Endocast partitions and additional neo/isocortex data The endocasts were virtually "dissected" into four partitions representing the cerebrum, olfactory bulbs, cerebellum, and brain base (the protocol for how the boundaries were defined is in Supplementary information 1). Volumes of the four dissected partitions were measured using Mimics (versions 17-20) and averaged per species if multiple specimens per species were available. Such partitions are only approximate representations of brain regions because they do not allow for internal brain boundaries. However, this method has been successfully used as a general indicator of overall brain proportions  (Sakai et al. 2016; Sakai et al. 2018; Bertrand et al. 2019a ) and is used with this caveat here. A log-shape ratio approach was used to standardise the partition volumes with respect to scale  (Mosimann 1970).
We computed the log-shape ratios of neocortex volumes from  Pirlot (1981)  for 17 species of marsupials, including members of Didelphimorphia, Dasyuromorphia, Peramelemorphia and Diprotodontia; and isocortex grey matter volumes for 19 Diprotodontian species from  Jyothilakshmi et al. (2020). These log-shape ratios were based on the larger datasets of histology-based brain region volumes of which the neo/isocortical volume data were part (for computation of these from the full datasets, refer to the relevant file on github). In one case for  Pirlot (1981)  and five cases for  Jyothilakshmi et al. (2020) , a species in our dataset was matched with a value from a congeneric species of a similar body weight (noted in Supplementary Table  1 ).
Phylogeny, body mass, and locomotor mode The details on tree estimation are in Supplementary information 1; the tree files are part of the raw data folder on this study's github repository. For all phylogenetic analyses, we used three topologies differing in their placement for the fossil Yalkaparidon, whose phylogenetic affinities are uncertain  (Beck et al. 2014). Figure  2  displays one of these phylogenetic topologies. All polytomies were considered as soft polytomies, and randomly resolved to zero branch lengths using the multi2di function from the ape package v. 5. 4-1  (Paradis et al. 2004 ) in R. In all phylogenetically-informed analyses in this paper, the use of different topologies had minimal impact on test statistics and no impact on significance levels, and thus an average of the test statistics from the three trees is presented here.
Body mass estimates for extant species were taken from  Weisbecker et al. (2013) , and for fossil species were derived from multiple publications as outlined in Supplementary Table 1  (Wroe et al. 1999; Argot 2003; Turney et al. 2008; Travouillon et al. 2009; Black et al. 2012; Sharp 2016 ). All species were allocated broad locomotor categories: arboreal, terrestrial, gliding, scansorial, hopping and fossorial (Supplementary Table  1 ). Locomotor data for Australian species were taken from  Weisbecker et al. (2020)  , and for American species was taken from  Nowak (2018). Note that the locomotion of the tree kangaroo Dendrolagus lumholtzi was scored as "hopping", because this is the main locomotor mode of the species despite the fact that it lives in trees. Note also that only two dasyurids (the yellow-footed antechinus and the northern quoll) in the dataset were classified as "scansorial"; due to this small sample size, these two were classified as "arboreal" because most other dasyurids are terrestrial, so that a capacity to climb as part of the scansorial locomotor repertoire is likely to have arisen from terrestrial ancestry.
Comparison of endocast shape with partition volume evolution, and relationship with histological neo/isocortical volumes For an estimate of how well endocast partition volumes correspond with their shapes, the distribution of species in multi-dimensional PC space between the shape vs. volume PCA was computed using a Mantel test in the Vegan package  (Oksanen et al. 2016 ) in R. We also computed phylogenetic signal (K mult ) and evolutionary allometry of brain partition volumes for comparison with K mult of shape variation. Lastly, we assessed if there was any association between shape, cerebral volume, and the literature-derived neocortical/isocortical data (explained above), which might be the case if changes in the relative size of the neocortex are reflected in changes in the relative size of cerebral hemisphere volume.
2019). Shape diversification partially associated with elongation has also been postulated for the therian cranium (e. g. Cardini et al. 2015) , although this so-called "Cranial Rule of Evolutionary Allometry" is also associated with size variation, which has no strong influence in our dataset. The high variability of endocast shapes across short evolutionary time scales (or even within species) suggests that the diverse brain tissues within the endocast are integrated to produce a limited set of shapes reflected in PC1  (Felice et al. 2018). But what determines this strong main axis of variation? The pattern resembles an evolutionary "line of least resistance"  (Schluter 1996) , a genetically favoured direction of morphological evolution. In mammals, strong allometric variation is often portrayed as a hallmark of such evolutionary lines of least resistance (e.
Acknowledgements: This research was funded by an  Australian Research Council   Discovery Early Career Award  ( DE120102034 ),  Discovery  Project  DP170103227 , and  Future Fellowship   FT180100634  to V. W;  ARC DP170101420  to MA and SJH;  ATM MNHN 'Biodiversité actuelle et fossile ' to S. L. ; and a  University of Adelaide  Fellowship to E. S. Scans provided by the Digimorph platform were funded by  National Science Foundation  ( NSF) IIS 9874781  and  IIS 0208675 (TR) , and several scans were done by T. M. under  NSF   DEB 9873663  ( Nancy Simmons, AMNH ) and  NSF   DEB 0309369  ( Rowe and Macrini ). For involvement in the collection of the fossils, we thank  P. Creaser  and the  CREATE Fund , K. and  M. Pettit , the  University of New South Wales, Environment Australia, Queensland Parks and Wildlife Service, Outback at Isa, Mount Isa City Council , The  Riversleigh Society  and the Waanyi people.
Data Availability: All endocasts are available on Figshare: For the original stl files and endocast dissections, the doi is http://10. 0. 23. 196/m9. figshare. 12284456). For ply files used for automatic landmark placement, the doi is http://10. 0. 23. 196/m9. figshare. 12284456. The full R code for data read-in, analysis, and Figures, including, auxiliary files, volume data, and landmark coordinate data are available in github repository https://github. com/VWeisbecker/Brain_shape_study (see README instructions there).
, 2021)  found that DNA damage can be triggered by confinement independently of NE rupture events some cell lines, including MDA-MB-231 cells. We thus assayed these cells and found that DNA damage upon confinement was both NE rupture-and TREX1-independent (Fig. S7G, H ). These results suggest that tumors might be differentially sensitive to TREX1 inhibition. Our experiments do not prove that TREX1 is the main driver of DNA damage in the human desease nor that DNA damage associated with nuclear deformation plays a role in the human disease progression. TREX1 has been described to promote pro-or anti-tumoral effects depending on different cellular and tumoral contexts  (Benci et al. , 2019; Erdal et al. , 2017; Vanpouille-Box et al. , 2017). To get further insight into this question, we analyzed the association of TREX1 expression levels in breast cancer with overall survival using public datasets.
and drawing the graphical abstract, Aastha Mathur for data analysis of the γH2AX-positive nuclei relative to the xenograft edge, Zahraa Alraies for the preparation of cDNA samples for the quantitative RT-PCR analysis, Dipanjan Bhattacharya who developed the method to automatically segment nuclei at IFOM, Dr. Ines Ferrara for providing technical support for the human tumor samples. Finally, we also thank the patients and their families who contributed tissue samples to these studies. This study was supported by the following grants: Institut Pierre-Gilles  and icGAS were plated on glass bottom dishes and imaged for several hours. Arrows point to NE rupture events. (K) Single cell curves showing the relationship between NE rupture and the appearance of 53BP1 foci. (L) Box and whisker plot showing the median value and 10-90 percentiles of the ratio between the number of 53BP1 foci after and before NE rupture.