dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Tamas Balla  ( National Institute of Child Health and Human Development, Bethesda, MD ),  Sergio Grinstein  ( The Hospital for Sick Children and University of Toronto, Toronto, ON, Canada ), and  Christopher Burd  ( Yale University School of Medicine, New Haven, CT ) for PI4KIIα and PI4KIIβ cDNAs, the P4C probe, and helpful discussions,  Pietro De Camilli  ( Yale University School of Medicine, New Haven, CT ),  Juan Bonifacino  ( National Institute of Child Health and Human Development, Bethesda, MD ), and  Rytis Prekeris  ( University of Colorado, Aurora, CO ) for critical antibodies,  Shanna Bowman  and  Linh Le  ( Children's Hospital of Philadelphia and University of Pennsylvania, Philadelphia, PA ) for advice on video microscopy and image quantification,  Dawn Harper  ( Children's Hospital of Philadelphia, Philadelphia, PA ) for laboratory assistance, and  Leslie King  ( University of Pennsylvania, Philadelphia, PA ) for helpful comments.
Templating is also a key element of trauma surgery, although not as standardised as in arthroplasty surgery. Several forms of templating in trauma surgery have been examined in the literature, though most studies have been retrospective and observational, and randomised controlled trials are sparse and report varying results  [4]. Despite this lack of evidence, the proverb 'If you fail to plan, you are planning to fail!' applies well to fracture surgery. The aim of this narrative review is to recount the past, present and future of preoperative templating in orthopaedic fracture surgery. Relevant literature was identified from the PubMed and Embase databases using the keywords "Orthopaedic surgery", "Trauma", "Fracture", "Preoperative planning" and "Templating". In PubMed, MeSH search terms were used to narrow the search, and the reference lists of the included papers were checked for additional studies eligible for this review.
They were not able to prove a reduction in hospital stay as Chen et al. , and likewise could not show a difference in patientreported outcomes and neither complication rates  [ 28 , 29 ]. Jia et al. also looked at intertrochanteric hip fractures  [33]. They retrospectively collected data from 1221 patients receiving surgery for intertrochanteric hip fractures. 465 patients received computerassisted virtual preoperative planning and 756 patients had conventional preoperative planning. They saw significant lower mortality in the virtual planning group as well as a lower reoperation rate. As seen in Wang et al. a steeper learning curve was seen among the surgeons  [ 29 , 33 ]. In addition to preoperative templating, perioperative templating has progressed. With the invention of 3D printing, templating both prior to and during surgery has expanded  [34].
Newer programs incorporating these elements are in development and could help address the challenge of manoeuvring around soft tissue  [30]. Boudissa et al. conducted a review of articles concerning computer-assisted surgery, especially regarding acetabular fractures  [30]. Several software programs were mentioned, both when it came to virtual preoperative planning (SQ PELVIS, MIMICS etc. ), but also custom-made plates and implants (3D Studio Max, MIMICS, Imageware 12. 1 etc. ). The value of adding a haptic component to preoperative planning software programs for a more realistic experience is already well known in teaching settings. With the addition of tactile sensation, surgeons can improve their awareness of the tension of nearby ligaments and their placement in relation to the fracture.
The rest of this paper is organized as follows. In section 2, we build a nonparametric spatial scan statistic for functional data based on the Wilcoxon-Mann-Whitney statistic proposed by  Chakraborty and Chaudhuri (2015)  and we evaluate its statistical significance using random permutations. In section 3, first, the spatial scan statistic is compared to other methods on simulated datasets. Then, we apply it to a real dataset illustrating the demographic evolution over time in Spanish provinces and we exhibit a specific behaviour in the North-West of Spain in the last twenty-two years. We conclude with a discussion and a brief scope for future work.
Computing the statistical significance After computing the scan statistic WMWFSS and the most likely cluster Ĉ , it is necessary to evaluate its significance. However, the distribution, under H 0 , of a variable window scan statistic has no analytical form. To overcome this problem,  Dwass (1957)  proposed a test procedure based on Monte-Carlo simulations allowing to give an approximation of the null distribution. This method was subsequently extended by  Barnard (1963) and Hope (1968). It relies on comparing the observed scan statistic to scan statistics issued from datasets simulated under H 0. Here, since no assumption is made on the distribution of the functional marks, the only way to obtain such datasets is by running a method called random labelling  (Cucala, 2014) : a simulated dataset is obtained by randomly associating the functional marks X i to the spatial locations s i.
Based on T random permutations, let (1) WMWFSS ,. , (T )   WMWFSS be the observations of the scan statistics associated with the simulated datasets. Then, as stated by  Dwass (1957) , the p-value of the scan statistic WMWFSS , observed in the initial sample, is given by p value = 1 + T i=1 1 { (i) WMWFSS > WMWFSS } T + 1. Of course, the larger the number of permutations T , the better the estimation of the p-value of the scan statistic. However, since the computational cost cannot be neglected, one needs to find a trade-off between the two aspects. The most likely cluster Ĉ is said to be significant if p value is less than the type I error α.
Simulation study We decided to run a simulation study to evaluate the performance of the functional scan statistic WMWFSS proposed in the previous section. We generated artificial datasets using the geographic locations of the administrative centres of the The functional marks associated with each location take values in χ = L 2 ([0, 1], R) and are defined as follows: ∀i = 1,. , 94, X i (t) = ∞ k=1 Z i,k e k (t) + (t)1 {s i ∈C} , where for all k 1, e k (t) = √ 2 sin(t/σ k ) is an orthonormal basis of χ , σ k = ((k -0. 5)π ) -1 and Z i,k 's are independent random variables which correspond to the projection of X i on the Karhunen-Loève basis  (Karhunen, 1947; Lévy and Loève, 1948). The decomposition of the functional marks above is based in the Karhunen-Loève expansion which is widely used in several issues related to image processing and functional data analysis  (Ahmed et al.
,n. Each mean value is associated with its location and the univariate Wilcoxon-Mann-Whitney scan statistic introduced by Cucala (  2016 ) is computed, using the same set of potential clusters and same random permutations than the functional one. This mean-based univariate scan statistic is denoted by MBUSS. • the second one, inspired from the LISA function defined by  Mateu et al. (2007) , relies on the deviations from the mean function of the marks D i = 1 0 (X i (t) -X(t)) 2 dt, i = 1,. ,n, where X(t) = 1 n n i=1 X i (t) is the mean function of the observed functional marks. Each deviation is associated with its location and the univariate Wilcoxon-Mann-Whitney scan statistic is computed. This deviation-based univariate scan statistic is denoted by DBUSS. To compare the three scan methods, we generated 100 simulated datasets for each distribution of the marks and each value of the cluster intensity c and we computed three distinct criteria for each method: the alarm rate (AR), the True Positive (TP) rate (also called the sensitivity) and the False Positive (FP) rate.
These three criteria were calculated as follows: -The alarm rate (AR) was defined as the proportion of datasets exhibiting a significant cluster with a type I error equal to 0. 05 and based on T = 99 random permutations. -The TP rate, denoted by %TP, was defined as the mean proportion of the True Positive (TP) départements over all simulated datasets. It was calculated as the number of départements included both in the significant cluster Ĉ and in the true cluster C divided by the number of départements included in C. -The calculation of the FP rate, denoted by %FP, is similar to the TP one. It was defined as the average proportion of the False Positive (FP) départements i. e. , the number of départements included in the most significant cluster Ĉ but not in the true cluster C divided by the number of départements not included in C.
Application to real data Here, we give an example of the use of our scan statistic to extract features in Spanish province population growth, as presented by  Cronie et al. (2019). In order to study the structure of the Spanish population, we considered one of the most important population characteristics which is the demographic evolution. This latter can change over time because of factors like birth and death rates, immigration rate or economical situations. The Spanish province population is provided by the Spanish Institute of Statistics (www. ine. es) and the boundary and centre coordinates of the 47 provinces of Spain (see Fig. 2 ) by the R package raster  (Hijmans, 2019). For geographical reasons, we decided to exclude from the study Baleares and Canarias islands as well as the Spanish autonomous cities (Melilla and Ceuta) which are located on the Northwest coast of Africa and sharing a border with Morocco.
Analysis of the real dataset Our objective here is to detect a spatial area where the demographic evolution would be significantly different. In order to identify such a cluster, we computed the functional scan statistic on this dataset: WMWFSS = 2. 72025. Remark that here the computation of the scan statistic is slightly different from what is done in the simulation study since it is estimated from 22 observation points. Based on T = 999 permutations, the value of the statistic is highly significant (p value = 0. 001) and the most likely cluster Ĉ is plotted in Fig. 4. This cluster includes 13 provinces in the west of Spain (Asturias, Galicia, Extremadura and the west of Castilla y León) in which the marks are significantly lower than in the rest of the observation domain. In the west part of Castilla y León, the most likely cluster includes the región leonesa and the west of the Castilla la Vieja (Ávila, Palencia and Valladolid).
46 in 2006 and 15. 75 in 2018). This explains why Zamora has the lowest evolution demographic (see Fig. 5 ) and is close to becoming a demographic desert. Such a demographic decrease can be explained by the emigration in the last years of the youngest population abroad and to other regions of Spain like Cataluña and Madrid where the average hourly wage is higher and the unemployment rate is lower than the autonomous communities detected by the functional scan statistic (for more details, see the website of the Spanish Institute of Statistics). Then, using the same dataset, we also computed the univariate scan statistics MBUSS and DBUSS and their p-values and we recorded the computation time. The results are given in Table  3. Contrary to the deviation-based univariate scan statistic, the mean-based univariate scan statistic detects a very significant cluster which is very similar to the one detected by WMWFSS (see Fig.
This performance was achieved thanks to the use of the function NPFSS from the R package HDSpatialScan introduced very recently by  Frévent et al. (2021). Finally, after identifying the most likely cluster, we have tested the presence of a secondary cluster, following the method by Z. Zhang et al. (2010) : once a significant cluster is found, remove the data included in that cluster and restart the analysis. However, on this dataset and using the functional scan statistic, the secondary cluster is not significant since its p-value equals 0. 282, using T = 999 permutations.
Analysis of the sensitivity of the method Secondly, we decided to add noise to the preceding real dataset in order to test the sensitivity of the proposed method. We also investigated the choice of the number of permutations. We considered the noisy data. , 47, ∀t ∈ [1998, 2019] , X i (t) = X i (t) + α i (t), ∀i = 1,. where the X i 's are the initial functional marks (the demographic evolution of the Spanish population measured in each province), the i 's are independent centred sBm processes and α is the parameter controlling the variance of the added noise. We simulated 100 noisy datasets with different levels of variance α and computed the functional scan statistic and its p-value based on different numbers of random permutations T. As in section 3. 1, Table  4  presents the alarm rates, TP and FP rates we obtained.
Discussion Nowadays and with the development of modern technology, scientists often observe functional data instead of univariate or multivariate ones. As a consequence, there is a need for testing procedures adapted to these infinite dimensional data. To this end, this paper proposes a nonparametric spatial scan statistic based on the Wilcoxon-Mann-Whitney two-sample test for functional data introduced by  Chakraborty and Chaudhuri (2015). As shown in the application to simulated and real data, this scan procedure is much more suitable for functional data than existing ones, and its implementation in the R package HDSpatialScan makes it easy and quick to compute. For sake of simplicity, we decided to focus on functional data belonging to an Hilbert space. We must mention that extending this work to data belonging to a more general Banach space is straightforward since the Wilcoxon-Mann-Whitney statistic of  Chakraborty and Chaudhuri (2015)  can be generalized to such a space.
Activity guided identification of active compound C. crenata bur was extracted with boiling water in a reflux system. The aqueous extract (10. 15%) was suspended in EtOH (70%, v/v), then the soluble part was evaporated and partitioned by chloroform, ethyl acetate, and n-butanol, depending upon the solvent polarity. Thus, the ethyl acetate fraction was further separated by methanol precipitation. After precipitation, the soluble part was separated using silica gel and Sephadex LH-20 column chromatography, and the precipitated fraction was dissolved in dimethyl sulfoxide (DMSO). Figure  6A  shows the schematic of the fractionation of CBE. The HPLC system used in this study was a Waters 2695 (Waters, Milford, MA, USA) system equipped with a Waters 996 Photodiode Array (PDA) Detector. Empower 2 software was used to control the analytical system and perform the data collection and processing.
B Sebocytes were cultured and incubated for 72 h with or without CBE. TG levels were measured using the TG ELISA Kit. C Prepared sebocytes lysates were treated. Protein levels of SREBP-1, PPARγ and GAPDH were detected. The levels of SREBP-1 and PPARγ were normalized to that of GAPDH (n = 3). These data are presented as the mean ± SD, *P < 0. 05 compared to PA-treated control Fig. 3  CBE inhibits 5α-reductase activity. The liver microsomal fraction containing 5α-reductase activity was put in contact to finasteride or CBE. After lyophilizing the reaction solution, the remaining testosterone was analyzed by HPLC (n = 3). The data are presented as the mean ± SD, *P < 0. 05 compared to 5α-reductase-treated control translocation of NF-κB p65 was increased in keratinocytes, while treatment with CBE mitigated the C.
Identification of ellagic acid as an active compound in CBE To elucidate the active compound of CBE responsible for sebum production, we performed a bioassay-guided fractionation (Fig. 6A ). Among the fraction obtained, we found that the ethyl acetate fraction most effectively inhibits sebum production (Additional file 1: Figure  S1 ). The inhibitory activity on sebum production was found in the precipitate (Additional file 1: Figure  S2 ), and the The HEK293-TLR2 cells were cultured in serum-free conditions and PA was treated with or without CBE for 18 h. TLR2 activity was measured through the secreted alkaline phosphatase. The values are expressed compared to the control, which was considered to be 100% (n = 3). The data are presented as the mean ± SD, *P < 0. 05 compared to the control group main active compound was determined to be ellagic acid using HPLC analysis (Fig.
Highlights • Analysis of a dataset of 127,322 occurrence records corresponding to 1,092 tree species delimitated eight bioregions in warm drylands of the world. • These eight distinct bioregions cover about 19% of Earth's land area across all continents. • Tree species diversity is not evenly distributed among bioregions (from 28 to 360 species) which host high proportions of endemic tree species (from 26 to 97%). • Aridity conditions are highly variable within and among the eight bioregions, providing the opportunity to anticipate various challenging ecological conditions under future climate change.
However, because this zonation is based on administrative boundaries and not on any biogeographic pattern, it might not be relevant to tackle the diversity of woody ecosystems in global drylands and group them in a higher hierarchical level. Bioregionalization, which aims to "reflect a range of biological, physical and ecological phenomena"  (Mackey et al. 2007 ) is a prominent approach to biodiversity sciences such as biogeography, macroecology, and conservation biology, for summarizing the diversity of ecological and evolutionary patterns at large spatial scales  (Mackey et al. 2007 , Montalvo-Mancheno et al. 2020). Increased access to largescale geospatial datasets (e. g. , georeferenced species occurrence records available on the online repository GBIF, Global Biodiversity Information Facility: https:// www.
gbif. org/), and the advent of new statistical methods and tools that are user-friendly and readily accessible online (e. g. , Infomap Bioregions:  Edler et al. 2017 , Yusefi et al. 2019 ) have allowed researchers to use bioregionalization to characterize species assemblages across spatial scales. In this study, we aimed to delineate bioregions in warm drylands at the global scale, based on tree species assemblages and using large species occurrence databases. To achieve our objective, we spatially defined warm drylands based on climate data, then, we collated occurrences of tree species obtained from online databases and used the Infomap clustering algorithm  (Edler et al. 2017) , based on a co-occurrence network approach, to delineate bioregions.
Materials and Methods Three steps were implemented to delineate bioregions in warm drylands based on trees occurrences: (i) defining and mapping warm drylands based on climate data, (ii) retrieving and curating tree species georeferenced occurrence records from three reliable online databases, within warm drylands and (iii) running a bioregionalization analysis based on tree species assemblages. Data manipulations and spatial analysis were performed with R v4. 0. 3 and QGIS v. 3. 4 software.
Defining warm drylands Contrary to previous attempts to delineate warm drylands climatic envelope using temperature and precipitation data  (Köppen 1900 , Whittaker 1970) , we based our climatic envelope on the Aridity Frontiers of Biogeography 2022, 14. 4, e56435 © the authors, CC-BY 4. 0 license 3 Index because it considers both atmospheric water input (via precipitations) and output (via potential evapotranspiration). Thus, it gives a better estimation of the actual water availability for the vegetation to grow than variables based only on temperatures and precipitations. We defined drylands using a Global Aridity Index (AI) map (CGIARCSI, https://cgiarcsi. community; Trabucco and Zomer 2018) for deriving hyper-arid (AI < 0. 03) and arid (0. 03 < AI < 0. 2) maps following the UNEP classification (UNEP 1997).
Tree occurrence data collection We used the Desert Trees of the World Database (Aronson et al. 2019) as a species checklist. This database includes 1,574 species of trees, i. e. , long-lived plants that develop one sturdy long-lived trunk equal or above one meter in height  (Shreve and Wiggins 1951 , Felger et al. 2001 , Schatz 2001) , growing in all drylands around the world. We obtained occurrence data for these species from GBIF 4  (2020) and from the African Plant Database (2020 5  ) to fill important data gaps in GBIF, especially for the Sahara Desert. Then, we cleaned the dataset using GBIF metadata, and CoordinateCleaner R package  (Zizka et al. 2019 ) (see Appendix 1). Occurrence records falling outside of warm drylands area (see 2. 1. ) were removed.
In other terms, grid cells sharing similar species assemblages are assigned to the same bioregion and an indicative species score is calculated  (Edler et al. 2017)  to inform the most indicative taxa in each bioregion. Infomap does not implement any constraint for spatial contiguity. This method has proven to outperform methods based on species composition similarities between grid-cells because being less sensitive to species sampling biases  (Vilhena and Antonelli 2015). The tree occurrence dataset was analysed a first time with the Infomap Bioregions algorithm to assign species to bioregion(s). Then, we manually checked whether each species was native in each bioregion to which it was assigned by the algorithm, by consulting the Kew Plant of the World Online portal 6 (2021), and additional literature  (Ghazanfar 2003 -2017 , Miller and Morris 2004 , Arbonnier 2009 , African Plant Database 2020, Euro+Med Plant Base 2020 7 ).
We filtered out records occurring outside of the native area of the species to perform the bioregionalization design only on occurrences of native species. Twenty-four species had no native occurrence record in our dataset and thus were removed from our study list. The Infomap Bioregions algorithm was then run on the native occurrence dataset. After preliminary analysis, the algorithm was parametrized to allow a cell size from 2 to 4° and prioritize a number of occurrence records from 10 to 100 and a number of species from 2 to 100 per grid cell. Also, we calculated the percentage of endemic species for each bioregion, i. e. , the percentage of species occurring only in the given bioregion Depending on data amount and spatial density, as well as species diversity distribution, several solutions, differing by the number and the size of bioregions, can be obtained.
Bioregions The final dataset (i. e. , native occurrences of tree species) included 127,322 occurrence records corresponding to 1,092 species ((min) mean ± sd (max): (1) 117±220 (3,182) records per species). Of the 1,574 tree species initially present on the checklist (Aronson et al. 2019), 482 were excluded either because they do not grow in warm and hyper-arid or arid areas (n=377), because they were not informed by any valid occurrence record, considering our cleaning procedure (n=59), or because they were synonyms (n=46). Of the 1,092 species remaining, 8 species were not attributed to any bioregion by the algorithm because their occurrence records belong to grid cells with less than 10 occurrence records and/or less than 2 species. The number of records (from 10 to 6,001; Fig. 2A ) and the number of species (from 2 to 99; Fig.
However, all the main warm drylands commonly recognized are represented in our study. We recognize that gathering occurrence data from large online databases may introduce various sources of uncertainty together with taxonomic and geographic biases inherited from the opportunistic nature of the data collection  (Meyer et al. 2015). For instance, more data are available for North America and Australia compared to South America, the Sahara, or Asian deserts (fig. 2A ). Whether a low number of occurrence records for a given species reflects a rarity of the species may be uncertain, therefore a low number of tree species within a grid cell may not reflect a low species richness. Nevertheless, it seems that occurrence records sampling size is not necessarily associated with species richness in our study.