dataesr/scientific-paragraphs-categorization huggingface dataset:
france-genomique. org/bases-de-donnees/frex-the-french-exome-project-database/ (accessed on 12 November 2021)). Their frequency in the general population was taken from the Genome Aggregation database (gnomAD; http://gnomad. broadinstitute. org/ (accessed on 6 November 2020)). Their pathogenicity was evaluated using the Varsome tool (The Human Genomic Variant Search Engine; https://varsome. com (accessed on 12 November 2021))  [50] , according to guidelines  [33]  and CADD score (Combined Annotation Dependent Depletion; https://cadd. gs. washington. edu/snv (accessed on 6 November 2020)). In addition, the in silico prediction tools PolyPhen-2 (Polymorphism Phenotyping version 2; https://genetics. bwh. harvard. edu/pph (accessed on 6 November 2020)), Provean (Protein Variation Effect Analyzer; https://provean.
1% for one hour at room temperature then incubated overnight at 4 • C with a recombinant anti-LRP6 antibody (ab134146) from Abcam ®. The membrane was washed three times with 0. 1% TBST, followed by the addition of peroxidase-conjugated affiniPure Goat anti-Rabbit IgG secondary antibody from Jackson ImmunoResearch for one hour at room temperature. The membrane was then washed three times with 0. 1% TBST, treated with the ECL detection kit (Clarity TM Western ECL Substrate, from BioRAD ® , Hercules, CA, USA) and detected using the iBright TM FL1500 imaging system from ThermoFisher ® Scientific. Protein was quantified by ImageJ software (National Institutes of Health, Bethesda, MD, USA). An internal reference, ß-actin, was detected using a monoclonal anti-ß-actinperoxidase antibody (AB3854) from Sigma-Aldrich.
Data represent four independently performed assays. (D,E) LRP6 expression in HEK293T cells after transfection with LRP6-WT or mutated plasmid (p. (Thr1479Ile) and p. (Tyr972Cys) variants). Proteins were extracted from transfected cells, separated by electrophoresis and then transferred onto PVDF membrane. The membrane was incubated with primary antibody (anti-LRP6), followed by incubation with secondary antibody before detection using the iBright TM FL1500 imaging system. Protein was quantified by ImageJ software. Equal loading was confirmed using the ß-actin antibody. Data represent three independent assays. (F) LDL uptake in HEK293T after transfection with an empty vector, LRP6-WT or mutated plasmid. The median fluorescence intensity of 100,000 events was acquired for each sample, but only the median fluorescence intensity of living cells is presented.
Acknowledgments: We thank all family and the proband cohort for participating in the study. We thank  Sekar Kathiresan ,  Gina Peloso  and  Nathan Stitziel  ( Broad Institute of MIT, Boston, MA, USA ) for their help in the exome sequencing experiments. Sekar Kathiresan was supported by  NIH   R01 HL107816. We acknowledge the use of the bioresources of the  Necker DNA Biobank  ( BB-033-00065 ). We thank  Silvia Friedrichs  for the technical assistance in sterol and bile acid analyses by GC-MS.
603dupC p. (Ser202LeufsTer19) c. 604_605delTCinsCA p. (Ser202His) rs781585299 rs386629678 Clathrin-mediated endocytosis 112. 23. P LB. PD. N (-2. 072) P LB. LRP6 (NM_002336) c. 2915A > G p. (Tyr972Cys) c. 4144G > T p. (Val1382Phe) rs772441071 rs139480047 Vesicle- 0. 001193% (3/251,364) 0. 08379% (237/282,856) 0. 002640% (3/113,656) 0. 1061% (137/129,164). 0. 261% VUS B PD B D (-7. 227) N (-1. 246). LB 26. 9 22. 3 Acceptor gain (0. 04) Donor gain (0. 02) c. 4436C > T p. (Thr1479Ile) rs144175121 mediated transport 9. 662 0. 02263% (64/282,836) 0. 04335% (56/129,168). B B N (-1. 906). 23. 3 No-consq (0) c. 4835C > T p. (Ser1612Phe). 0. 0008097% (2/247,016) 0% (0/111,624). VUS PD D (-2. 879). 29. 4 Donor gain (0. 02) # Gene expression in the liver, from the Genotype Tissue Expression database (GTEx).
Understanding the role that range size plays in the K E Y W O R D S Bergmann's rule, body length, climate, conservation, extinction, morphology, NDVI, range size, rodent extinction risk of rodents can help to predict which species might become threatened, especially if the relationship between range size and body size, in addition to the environmental drivers of the latter, are also clarified. Here, we use a recently compiled, unprecedentedly large dataset of morphological measurements of rodents  (Alhajeri, Fourcade, et al. , 2020) , in addition to publicly available data on environmental factors, phylogeny, range size and extinction risk, to disentangle the complex relationships between environmental correlates of body size, range size and extinction risk in rodents. Previous evidence showed that although ecogeographical patterns, such as Bergmann's rule, generally hold for vertebrates, they might not necessarily do so in rodents, whose size seems to covary more with precipitation-related climatic factors  (Alhajeri & Fourcade, 2019; Alhajeri & Steppan, 2016) , in accordance with the "resource availability hypothesis" (see  Alhajeri, Porto, & Maestri, 2020).
| Species data This study relies largely on a considerable, recently compiled morphological dataset, originally used to investigate the relationship between appendage length and climate (i. e. , Allen's rule;  Alhajeri, Fourcade, et al. , 2020). In that dataset, head-and-body length was collected from 285 different sources, including museum databases and the literature, forming a total of 81,880 measurements of 2212 rodent species. From this dataset, we extracted the mean and standard deviation of the head-and-body length of each species. According to  Burgin et al. (2018) , there are a total of 2552 described rodent species, which means that our dataset covered >86% of all described species. Although the dataset compiled by  Alhajeri, Fourcade, et al. (2020)  also contains measurements of body mass, we used head-and-body length as a proxy for body size here because the former was available for only 1191 species and because the two variables were highly correlated.
The date of collection was not always available, but the specimens examined span at least the 19th and 20th centuries. We obtained species range maps from the International Union for Conservation of Nature (IUCN, 2017) for all 2212 species. These data are distributed in the form of shapefiles (available at: www. iucnr edlist. org/resou rces/spati al-data-download) depicting the known global distribution of each species as assessed by the IUCN. Such distributional data are mostly produced by expert knowledge, supplemented by empirical records and habitat filtering. As such, they might fail to account for fine-scale variation in species presence and absence  (Herkt et al. , 2017)  but provide a nearly comprehensive assessment that would otherwise be difficult to gather at such a large spatial and taxonomic scale  (Schipper et al.
, 2008). In particular, we expect them to be accurate enough to rank species properly by their range area (see e. g. ,  Alhajeri, Fourcade, et al. , 2020; Newsome et al. , 2020; Rodrigues et al. , 2006) , and they have been shown to be as effective as true occurrence data in estimating basic climatic conditions experienced by each species  (Alhajeri & Fourcade, 2019). Here, we extracted species range size as the total area covered by all polygons where each species is present currently, using the "letsR" package  (Vilela & Villalobos, 2015)  for the software R v. 4. 0. 2 (R Core Team, 2020). The full description of the database and collection methods, including morphology and species range, is available in the paper by  Alhajeri, Fourcade, et al. (2020). From the IUCN maps, we also extracted the mean and standard deviation of the latitudinal coordinates of each species range.
In addition, we used the IUCN database to obtain the conservation status of each species and the criteria that led to this status (IUCN, 2017), which was used to characterize extinction risk and filter species (see section 2. 5). The IUCN reports conservation status using a scale of seven categories from "least concern" to "extinct"; this ranks species on whether they are threatened with extinction according to a series of criteria related to population trends and the size of the remaining natural range  (Rodrigues et al. , 2006). Among the 2212 rodent species that we analysed, 351 were classified as "data deficient", which means that their conservation status was evaluated, but insufficient information was available for a reliable conclusion regarding their extinction risk. These species were thus not used in the analyses involving extinction risk but were retained in all other steps.
In total, 83% of the remaining species (1548) are not immediately threatened (categories "least concern" and "near threatened"), while 17% (311) are at risk of extinction (categories "vulnerable", "endangered" and "critically endangered"), and another two species are already extinct. The conservation status of species was accessed with the R package "rredlist"  (Chamberlain, 2020). When investigating interspecific variation in species traits, it is essential to consider that species are not independent of each other, because they are all phylogenetically related. Therefore, in all analyses described below, we adopted statistical approaches that accounted for the phylogenetic relatedness of the species. For this purpose, we obtained a sample of 100 trees (because there are uncertainties in phylogenetic placements and node ages) from the phylogenetic trees estimated by  Upham et al.
(2019) , which are currently the most up-to-date source for these data. We used both "DNA-only" trees, which are those that contain only the species for which DNA data were available, and "completed" trees, where species missing DNA data were imputed. Some species in our morphological database were missing from the phylogenetic dataset; therefore, ultimately, analyses were based on 2170 species when using the completed trees and 1466 species when using the DNAonly trees. Additional information regarding phylogenetic trees and the way in which we handled taxonomic matching between morphological and phylogenetic databases can be found in the papers by  Upham et al. (2019)  and  Alhajeri, Fourcade, et al. (2020).
| Environmental variables We obtained climatic variables from the WorldClim project (v. 2), which compiles mean temperature and precipitation from a set of weather stations, from the years 1970 to 2000, to interpolate global raster maps of climate  (Fick & Hijmans, 2017). Specifically, we downloaded all 19 bioclimatic rasters at a resolution of 2. 5 arc-min (from https://www. world clim. org/), which are biologically meaningful climate variables derived from monthly temperature and rainfall. Instead of considering all bioclimatic variables and testing which is correlated best with body size, we reduced our set of predictors using a varimax-rotated principal components analysis (PCA), as implemented in the "psych" R package  (Revelle, 2020). The resulting principal components 1 and 2 represent >70% of the total climatic variation and are strongly related to temperature and precipitation variables, respectively (Supporting Information Table  S1 ; Figure  S1 ).
This allowed us to summarize global climatic variation into two predictors that are based on the climatic aspects that are most relevant to the species examined. Given that there might be discrepancies between the dates of morphological measurements and climate data, we tested whether climate variables from different time periods would lead to the same results. For this, we created the same climate variables using monthly precipitation, minimum and maximum temperature for the periods  1901-1905 and 2001-2005 (i. e. , two climate datasets separated by one century), downloaded from the CHELSAcruts database  (Karger et al. , 2017; Karger & Zimmermann, 2018). In addition, we used the normalized difference vegetation index (NDVI), which is a remote sensing-derived index of vegetation productivity, as a proxy of global variation in resource availability.
As a source of NDVI data, we compiled all bimonthly rasters from the Global Inventory Modelling and Mapping Studies (GIMMS) NDVI3g database, for the period 1990-2009. The GIMMS project is a longterm NDVI time series derived from several Advanced Very-High-Resolution Radiometer (AVHRR) sensors  (Zeng et al. , 2013). Here, we accessed the GIMMS NDVI data using the "gimms" R package  (Detsch, 2020)  and calculated an average global NDVI at 5 arc-min resolution using the mean values over the aforementioned period. To account for intraspecific variability in the environmental conditions experienced by species within their distribution, we extracted, for each species, the mean and standard deviation of both climate variables (PCA axes) and NDVI across their entire range.
There are additional hypotheses regarding the drivers of body size in rodents, namely that precipitation and resource availability might play a role in explaining geographical variation in body size  (Alhajeri, Porto, & Maestri, 2020; Alhajeri & Steppan, 2016). Therefore, we also estimated the association between headand-body length and the second axis of the varimax-rotated PCA of bioclimatic variables (associated with precipitation variables; see Supporting Information Table  S1 ; Figure  S1 ) and NDVI, respectively. We followed here the approach developed by  Alhajeri, Fourcade, et al. (2020). Analyses were carried out as phylogenetic linear regressions to correct for relatedness among species, which were computed using the "phylolm" R package  (Ho & Ané, 2014)  with a Pagel's λ phylogenetic model of trait evolution  (Pagel, 1999).
To account for phylogenetic uncertainty, all analyses were repeated for each of the 100 phylogenetic trees, for both the DNA-only and the completed trees. Given that intraspecific phenotypic variability can also be a source of bias in cross-species analyses  (Silvestro et al. , 2015) , we also repeated each analysis 100 times, each time sampling: (1) an arbitrary value of head-and-body length from a truncated normal distribution (ensuring that sampled values were positive) with the mean and standard deviation obtained from the observed values from our database of morphological measurements, and (2) an arbitrary value of latitude, climate (temperature or precipitation) or NDVI, following a normal distribution with the mean and standard deviation of the observed values within the species range.
| Modelling body size constraints on range size Following  Agosta and Bernardo (2013)  and  Newsome et al. (2020) , we described the association between range size and body size (head-and-body length) as a triangular log-log relationship, where a breakpoint in the lower bound is expected to occur at mid-values of body size. For this purpose, we fitted quantile regressions for the 95 and 5% quantiles to estimate the upper and lower bounds of the relationship. Specifically, the lower quantile was fitted with a segmented regression to allow for a change in the slope, and the location of the breakpoint was estimated using the maximum-likelihood approach implemented in the "segmented" R package  (Muggeo, 2008). Quantile regressions were computed with the "quantreg" R package  (Koenker, 2021). Phylogenetic interdependence between the species was accounted for, following the approach of  Newsome et al.
| Extinction risk and range size To describe how the constraints that body size imposes on range size influence, in turn, the extinction risk of the species, we first defined relative range size as the ln-transformed ratio of the observed range area for the species divided by its theoretical minimum range area given its body size, as predicted by the lower bound of the quantile regression described in the previous section (see  Newsome et al. , 2020). For illustrative purposes, we plotted the variation in mean estimated relative range size of species across the phylogenetic tree of rodents, computing ancestral values using the rerooting maximum-likelihood method of the "phytools" R package  (Revell, 2012). In addition, we mapped the global variation in relative range size across assemblages of rodents, defined as 2° equal-area grid cells where species were considered present if their distribution overlapped any surface of it.
05 level. Given that IUCN conservation categories are essentially an ordinal variable, we acknowledge that our continuous interpretation of extinction risk is imperfect. This approach was taken here in order to keep a consistent framework consisting of 20,000 repetitions of phylogenetic regressions fitted with the "phylolm" R package (which does not include ordinal regressions). For comparison, we also fitted a Bayesian phylogenetic ordinal model using the "brms" R package  (Bürkner, 2017). Owing to computation time, we would have been unable to fit 20,000 such models. Therefore, we present in the Supporting Information (Figure  S4 ) only one ordinal model fitted to one sample of phylogenetic tree and morphology.
| DISCUSS ION We did not find support for Bergmann's rule in rodents; however, it appeared that larger species tended to be associated with regions characterized by high precipitation. Although this result had been demonstrated before using a relatively smaller rodent dataset  (Alhajeri & Steppan, 2016) , in the present study we confirmed this outcome using much larger datasets and more rigorous statistical approaches. It is possible that this relationship reflects an effect of resource availability on the body size of species, whereby productive regions (i. e. , those associated with high precipitation) are able to support larger species. We tested this hypothesis by exploring the relationship between species body sizes and NDVI, as a proxy for productivity, and found that there was indeed a clear positive relationship, with larger species generally being found in regions with higher NDVI.
Yet, this relationship remained weak, as was found before in a smaller rodent dataset  (Alhajeri, Porto, & Maestri, 2020). This demonstrates that resource availability might play a role in shaping the evolution of body size in rodents, but also that more work is needed to find the factors, or combination of factors, that fully explain the observed variation of body size across rodent species. The body size of species, which is constrained, in part, by environmental factors, especially resources as we demonstrated, in turn constrains the evolution of species range size. We found support for a log-log triangular relationship between body length and range area, as was hypothesized earlier  (Brown & Maurer, 1987). The lower bound of this relationship is because a larger range size is necessary for acquiring the resources needed to sustain larger species.
Therefore, species closer to this boundary are potentially more susceptible to disturbance, and therefore might exhibit elevated extinction risk. The IUCN database of rodent species we used lists a couple of extinct species; one of them, the Bramble Cay melomys (Melomys rubicola), became extinct in 2015, most probably because of sea-level rise  (Woinarski et al. , 2017). It was a medium-sized species (c. 15 cm) that was found in a very restricted range (2 ha) on a tiny Australian island, making it the species with the smallest relative range in our database (see Supporting Information Table  S4 ). This anecdotal evidence is congruent with a real link between extinction and our estimate of relative range size. c ) Estimated relationship between the extinction risk of species (coded according to IUCN conservation status: 0 = least concern and 1 = extinct) and relative range size.
, 2008). It appears, based on our result, that it should be among the priority targets for conservation of rodents. Given that the relative range size we report originates in the Despite the nearly comprehensive database on the morphological traits, distribution and conservation status of rodents that we used in the present study, one must keep in mind that we explored only a few factors as correlates of body size, range size and extinction risk and that most of the relationships we revealed were relatively weak. A large number of additional factors are certainly associated with extinction risk. In this regard, some traits frequently linked to higher extinction risk include habitat specialization  (Clavel et al. , 2011)  or long generation time  (Staerk et al. , 2019). In addition, many aspects of human activity are clearly responsible for extinction of species in the wild, such as land conversion, invasive species or overexploitation  (Harfoot et al.
DATA AVA I L A B I L I T Y S TAT E M E N T All analyses are based on publicly available data from the IUCN (range area and conservation status: https://www. iucnr edlist. org/), WorldClim (climate data: https://world clim. org/), museum databases and the literature (morphological data were compiled and made available by  Alhajeri, Fourcade, et al. , 2020).
2%, a median duration of response of 10. 3 months, and a median OS of 7. 1 months  (16). In the phase II CONDOR trial, patients with PD-L1-low/negative expression (TC < 25%), who were treated with durvalumab alone, had an ORR of 9. 2% and median OS of 6. 0 months  (17). In the phase III EAGLE trial, data from the durvalumab monotherapy arm demonstrated a median OS of 9. 8 months in patients with tumor PD-L1 expression ≥25%, compared with 7. 6 months in patients with tumor PD-L1 expression <25%  (18). Collectively, data from the second-line trials of durvalumab in R/M HNSCC suggest that there may be a more effective cut-off point for optimal identification of patients with survival benefit from durvalumab monotherapy. Here, we present an analysis of a mature dataset with a substantial cohort of patients from the HAWK and CONDOR trials to evaluate different PD-L1 cut-off points in both ICs and TCs to develop an algorithm for optimal patient selection.
Cut-off Point Determination Durvalumab efficacy data were pooled from the HAWK and CONDOR trials at the cut-off point date of  March 31, 2017. TC PD-L1 expression was scored as the percentage of TCs with membrane staining at any intensity above the control. The scoring bins were <1%, ≥1%, ≥5%, ≥10%, and ≥20% for CONDOR samples and 25%, 30%, 40%, 50%, 60%, 70%, 75%, 80%, 90%, and 100% for HAWK samples (Supplementary Table  S1 ). ICs with PD-L1 staining were expressed as a proportion of the ICs present (ICP) in the tumor area. When ICP was ≥1%, IC scores were estimated and scored as deciles and quartiles (i. e. , 0%, 10%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 75%, 80%, 90%, 100%). In the rare cases that the ICP was <1%, PD-L1 IC expression was categorized as either 0%, <100%, or 100% (Supplementary Table  S2 ).
The prevalence of PD-L1 (at TC ≥ 25%) in the HAWK and CONDOR screened patients (i. e. , natural prevalence) was 27% (4). Because of the enrichment of TC ≥ 25% as a result of selection criteria in the HAWK study, the PD-L1 TC ≥ 25% prevalence in the pooled population was substantially higher (62%). It was not representative of an all-comers population. Thus, bootstrapping modeling of the OS HR was performed across the various combined TC/IC subgroups. All statistical analyses were conducted using SAS software, version 9. 4 (SAS Institute).
Efficacy of Durvalumab Monotherapy with Various PD-L1 Cut-off Points An initial regression analysis suggested that PD-L1 expression on both TCs and ICs was associated with tumor shrinkage. We did not observe a correlation between TC and IC staining in pooled samples from durvalumab-treated patients (Supplementary Fig. S1 ). Using pooled data from the HAWK (n = 112) and CONDOR (n = 67) trials, we assessed the association of PD-L1 expression with durvalumab efficacy. There was a general trend toward increasing median OS with increasing TC PD-L1 expression (Supplementary Fig. S2 ). The trend was less marked for PFS (Supplementary Fig. S3 ). The longest median OS (9. 8 months [95% confidence interval (CI), 5. 0-13. 9]) and median PFS [3. 4 months (95% CI, 1. 9-5. 4)] for TC PD-L1 expression were observed at the TC ≥ 50% cut-off point.