dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods To estimate fragmentation sensitivity of species populations, we used BIOFRAG software  [28]  to analyse a refined and updated version of the BIOFRAG dataset  [77]. BIOFRAG provides a direct estimate of the effects of landscape-level fragmentation on each population of each species  [9, 28]  (see Supplementary material). Populations of the same species may be identified as fragmentation sensitive in some landscapes and insensitive in others. We define fragmentation-sensitive populations as those avoiding forest edges in fragmented landscapes and occurring mainly in the forest core. We follow methods explained in greater depth elsewhere  [9, 28, 77] , summarising the key points in the following sections, with details of updates and modifications.
Study landscapes and surveys We compiled bird assemblage data from published surveys of fragmented forest landscapes, coupled with fragmentation data extracted from GIS vegetation layers. The core sample was downloaded from the BIOFRAG database, containing species abundance from 32 abundance surveys and tree-cover maps of associated study landscapes from the year 2000  [9, 77]. We excluded 11 studies from our analysis because of potential pseudoreplication (see Supplementary materials). To expand our sample, we gathered further post-1998 bird survey data from (or cited within) forest fragmentation studies via a literature search of Web of Science core collection, using the default "Topic" search for literature published after 2010, with terms: Birds OR Bird OR Avian AND Forest OR Wood* OR Rainforest AND Sample OR Survey OR Census AND Fragment* AND Plot* OR Site* AND Abundance.
After adding 10 new studies to the original BIOFRAG dataset, the final sample contained 31 survey datasets sampled between 1998 and 2013 across six continents (Africa, 3; South America, 5; North America, 10; Europe, 7; Asia, 3; Oceania, 3) (Fig. 1 , Supplementary dataset 1). We calculated the absolute latitude of each study landscape as the centroid latitude of all the sampling points within each survey, using the geosphere package in R  [78]. In all cases, surveys targeted both forest and non-forest matrix with varying levels of tree-cover heterogeneity and multiple (average = 153) sampling locations. We omitted surveys reporting only presence-absence, and limited our sampling to surveys reporting abundance or relative abundance of bird species at each sampling location. We converted raw abundance estimates to relative abundance to allow comparison across the full sample of landscapes.
Although we limited sampling to landscapes in which the original forest cover had become fragmented by a non-forest matrix, this included a range of forest types embedded in various matrix types (Supplementary dataset 1). We included mist-netting, point-count and line-transect surveys in our sample and account for differences in survey method among studies using a mixed-effects modelling approach. We excluded studies using multiple survey methods inconsistenly across the study landscape. Accurate geolocation of avian populations is key to identifying the affinity of species to forest edges. However, geolocation is challenging in field surveys, particularly in dense forest where detectability of birds is often low and 95% of birds are identified through auditory signals  [79] [80] [81]. To maximise accuracy of geolocation, we excluded point-count radii greater than 100 m and transects larger than 100 x 100m.
To minimize distortion of the distance and direction between sample points, we projected maps and sample points into azimuthal equidistant projection (AEQD), giving coordinates in meters with origin equal to the sample points centroid  [9]. Following methods proposed by Pfeifer et al. [28] , we quantified the level of edge influence (EI) within a specified radius from sample points. We used BIOFRAG software to calculate 30m resolution EI maps using the tree-cover maps downloaded from Google Earth Engine. We then specified the "Depth of Edge influence" (DEI) -i. e. the size of radius -for each pixel, and calculated the level of tree cover heterogeneity as a function of the mean and standard deviation of point-cover values within the DEI radius. DEI was set to 1 km as default, with adjustments where necessary according to the scale of particular datasets (see Supplementary materials).
Thraupis sayaca). We examined latitudinal patterns of fragmentation sensitivity by extracting the centroid latitude of species geographical ranges from published data  [34]. To assess whether fragmentation sensitivity was non-randomly distributed across the global bird phylogeny  [86] , we created a majority rule consensus tree from 100 random phylogenies downloaded from BirdTree (www. birdtree. org), using the Hackett backbone. We then quantified phylogenetic signal in fragmentation sensitivity as the sum of changes in estimated nodal values for binary traits (d)  [87]. Values of d close to 0 indicate that fragmentation sensitivity is phylogenetically conserved; values close to 1 suggest a random distribution across the phylogenetic tree. In our dataset, families with a high proportion of fragmentation-sensitive species tend to be sedentary and largely restricted to the tropics, such as Trogonidae (Restricted: 70% sensitive; Expanded: 93% sensitive), Furnariidae (Restricted: 53% sensitive; Expanded: 61% sensitive,) and Pycnonotidae (Restricted: 47% sensitive; Expanded: 47% sensitive).
Dispersal limitation To estimate variation in dispersal ability across species, we compiled Hand-wing Index (HWI) for all 1034 study species using global datasets  [15, 34]. HWI is a measure of wing-shape -and specifically wing-elongation -correlated with wing aspect ratio  [91, 92]. HWI is therefore linked to flight efficiency, with high values of HWI strongly indicative of dispersive, migratory or aerial lifestyles  [15, 21]. HWI predicts dispersal distance in birds  [21, 93] , thus providing a morphological metric widely used as a proxy for dispersal ability in macroecological studies  [15, [94] [95] [96] [97]. Variation of HWI across our study sample (n = 1034 species) is large (range = 1. 9-71. 8; mean = 22. 7; SD = 12. 2) and broadly representative of all birds (n = 9993; mean = 25. 7; SD = 15.
Defining scales We performed analyses using data calculated across three different scales. Landscapelevel analyses (e. g. Fig. 3b ) used geographical or climatic data extracted from the landscape or species-specific data averaged across all species within the landscape assemblage (e. g. community mean dispersal limitation). Population-level analyses (e. g. Fig. 4 ) used data specific for each population and therefore capture intra-specific variation (e. g. fragmentation sensitivity varying across different localities). Species-level analyses (e. g. Fig. S1 ) use data averaged across all populations of the same species (e. g. fragmentation sensitivity), generated at species level (e. g. mean body mass)  [34] , or extracted from GIS layers and averaged across all cells of the species distributional range (e.
To account for possible conflation between dispersive traits and migratory behaviour, we removed long-distance migrants from our sample and re-ran our Restricted analysis (Fig. S4 ). Finally, to ensure results were not driven by exteme values of nHWI, we repeated our analyses with Apodiformes removed from the dataset (see Supplementary materials). Models were constructed using the brms package in R [106] with markov chain iterations and priors kept consistent across all models. We selected 10000 total iterations with a 2000-iteration warmup-phase. We used the no u-turn sampler (NUTS) to reduce autocorrelation between successive iterations and as such no thinning was required. We assigned weakly informative priors, normal (0,10), to the slope parameters and the intercept as recommended by  Gelman [107].
Inferring causal mechanisms To identify drivers of fragmentation sensitivity, we constructed structural equation models (SEMs) using the phylopath package in R  [109]. The results of SEMs provide insight into the relative importance of different drivers, but should be treated with caution given the hierarchical complexity of our data structure (see Supplementary materials; Fig. S1 ). Therefore, in addition, we modelled the effects of historical disturbance, latitude and seasonality on nHWI (Table  S3  & S4) to test the role of these factors in explaining variation in dispersal limitation across study species (n = 1034). Given that extinction filters may act at a local scale through removing and then preventing recolonisation by maladapted species in the landscape  [12] , we calculated historical disturbance, latitude and seasonality using landscape-level data.
For each species, we used GIS layers to extract the historical disturbance score (binary variable: High = 1, Low =0), local temperature variation [104] and absolute latitude of the landscape centroid, for all landscapes where the species was present. We then averaged these scores to obtain species-level values based on variables extracted from the relevant study landscapes. We then ran three phylogenetic generalised least-squared models using 100 random phylogenies (see above) for (1) all study species, (2) residents, short distance migrants and partial migrants (i. e. excluding long-distance migrants), and (3) residents only. Data and definitions for these migratory classes are provided by  Tobias & Pigot [110]. In each case (1-3), we first assessed the relationship between historical disturbance and nHWI as a univariate model.
Second, we added latitude as an additional covariate, and third, we added seasonality as a third covariate. Multicolinearity between the three covariates was addressed by assessment of VIFs and found to be acceptable (VIF < 6). For each model, we established the relative proportion of independent variance explained by each driver using hierarchical partitioning implemented with the R package hier. part  [111]. Evolutionary drivers such as habitat disturbance and climate may act on species traits at regional rather than local scales, so we re-ran these analyses with disturbance, seasonality and latitude estimated across the distribution of each study species (see Supplementary materials).
Acknowledgements We thank the  BIOFRAG  project for supplying data and numerous data contributors, including  Victor Arroyo-Rodriguez ,  Daniel Cleary ,  Herve Jactel ,  Jordan Karubian ,  Jesse Lasky ,  Stephanie Melles ,  Jose Carlos Morante Filho ,  Vania Proenca ,  Shankar Raman ,  Philip Round  and  Julien Terraube. Trait data collection was supported by  Natural Environment Research Council  grant  NE/I028068/1  and  UKRI Global Challenges Research Fund  grant  ES/P011306/1  (JAT). Analysis was funded by the  Natural Environment Research Council  studentship through the  Science and Solutions for a Changing Planet Doctoral Training Programme   NE/S007415/1  (TLW). Illustrations are reproduced with permission of  Cornell Lab of Ornithology.
The ORR were 76%, 89% and 82%, with CR rates of 57%, 61% and 73%, in the ipilimumab + BV, nivolumab + BV and tritherapy groups, respectively. PFS at 1 year was 61% for the ipilimumab + BV group, 70% for the nivolumab + BV group, and 80% for the tritherapy group. Median PFS was 1. 2 years for the ipilimumab + BV group but was not reached for the two other arms after a median follow-up of 2. 6 years for ipilimumab + BV, 2. 4 years for nivolumab + BV and 1. 7 years for the tritherapy. The median OS has not yet been reached for any of the groups. 39 A phase II trial is currently underway to determine the efficacy and tolerance of these combinations more precisely (NCT01896999).
DATA AVA I L A BI L I T Y S TAT E M E N T References for this review were identified through searches of PubMed with the search terms "CD30", "CAR", and "brentuximab vedotin" for articles published from 1980 until June 2022. Articles were also identified through searches of the authors' own files. Only papers published in English were reviewed. The final reference list was generated on the basis of originality and relevance to the broad scope of this review. ORC I D M. Veyri https://orcid. org/0000-0002-7020-8160.
This indicates that the mechanical behavior of an ultimately deformed rubber-like network can be experimentally obtained by embedding it as the primary network in a DN material and deforming it. Such real mechanical data of an ultimately deformed rubber-like material are crucial for investigating and modeling the elasticity of a rubber-like material up to its ultimate deformation and fracture. As particular experimental data of the ultimately deformed rubber-like materials were obtained through measurements of DN materials, here, we report inverse mechanical-swelling coupling of the DN gels. Generally, the swelling and mechanical behaviors of a gel are coupled  (33). An example of such coupling, which is termed "mechanical-swelling coupling" hereinafter, is the extension-induced swelling of a conventional gel.
The data are from single experiments. The w(t)/w(0) of the specimen was determined by capturing images of the specimen using a camera EOS 90D (Canon Inc. ) and analyzing the distances between the dots on the specimen. We selected four pairs of the stamped dots near the center of the specimen and measured the distance between them manually using ImageJ. The error bars in the graphs represent the standard deviation in measurements of four different pairs within the same sample. λ of the extended specimen was similarly determined by analysis of the distance of the dots.
Acknowledgments: We thank  Y. Katsuyama ,  M. Matsumoto , and  M. Matsushita  ( Hokkaido University ) for help with experiments. The Institute for Chemical Reaction Design and Discovery ( WPI-ICReDD)  was established by the  World Premier International Research Initiative (WPI) ,  MEXT, Japan. Funding: This work was supported by  JSPS KAKENHI  (grant numbers  17H04891  and  22H04968 ) and  JST ,  PRESTO  (grant number  JPMJPR2098 ). Author contributions:.
AMS 14 C age dating The age model for core 1085 was previously constructed using various AMS radiocarbon data (n = 12;  Jang et al. , 2021). In this study, new AMS 14 C data were obtained for marine mollusc shells from cores 1094 (n = 8), 1100 (n = 1), and 1106 (n = 4), analysed at the BETA Analytic Laboratory (Miami, USA) and the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (Bremerhaven, Germany) (Supplementary Table  S2 ). In addition to previous radiocarbon data (n = 4) for core 1100  (Braun, 2019) , all measured 14 C ages were converted to calendar ages based on the Marine13 dataset of the Calib Rev 7. 0. 4 program  (Stuiver and Reimer, 1993)  with a marine reservoir offset R = 105 ± 24 yr  (Mangerud et al. , 2006). For consistency with the previously published age model for core 1085  (Jang et al.
, 1996). The quantification of mineral phases was achieved through the QUAX full-pattern method using a reference material database  (Vogt et al. , 2002). The mineralogical composition of studied samples is dominated by carbonate, quartz, feldspar, clay minerals and various accessory minerals. Crystalline FeOx including haematite, goethite, lepidocrocite and magnetite were identified and quantified based on an extensive pure mineral database. Note that FeOx (mostly Fe-oxyhydroxide) also occurs as amorphous phases in Svalbard sediment cores that cannot be recognized with X-ray diffractograms. In most samples, a single crystalline FeOx mineral phase is dominant with the other near the determination limits. As a consequence, FeOx abundances in this study are reported as the sum of recognized FeOx except for magnetite.
According to the internal analytical quality test using the prepared mixtures including the Reynolds Cup samples at the XRD laboratory (e. g. ,  Raven and Self, 2017) , the relative error (1 SD) on measured FeOx contents was better than 10% (for example, 10±1 wt%). Well-crystallized minerals such as quartz, calcite, aragonite, pyrite and some FeOx including haematite and magnetite were determined with uncertainties less than 1-3% when testing on simple artificial mixtures  (Vogt et al. , 2002). Values for FeOx are reported in Supplementary Tables  S3-S8 , and full report of the mineralogical abundances can be accessed from the PANGAEA databank (http:// www. pangaea. de).
4 ), hence suggesting minor diagenetic controls on sedimentary FeOx downcore. Note also that the abundance of sedimentary FeOx and TOC contents does not display any particular relationship in all studied cores (0. 00 < r 2 < 0. 18) (Supplementary Fig. S2 ), providing further evidence for the minor diagenetic control on the downcore variability of FeOx in this study. (Hughes et al. , 2016). (B) The ice reconstruction at 10. 8 kyr BP shows slightly more expanded coverage than the recent reconstructions at 11. 0 kyr BP based on the SVALHOLA database  (Farnsworth et al. , 2020)  but much larger extent than the most-incredible DATED-1 timeslices at 11 kyr BP, which showed complete deglaciation in inner Wijdefjorden  (Hughes et al. , 2016). Note that the ice at 10. 4 kyr BP is suggested to have retreated landward, which is consistent with the previous reconstructions.
To estimate the uncertainty in T, we first collected all iterative Markov chain Monte Carlo-derived ages for a given depth during BACON simulations (n = 6587, 7969 and 6484 for cores 1085, 1094 and 1100, respectively). We randomly resampled age data from these databases by applying a bootstrap method  (Efron and Tibshirani, 1985). The resampled ages were used for the Monte-Carlo calculation of T (n = 100,000), and 2-sigma variability was reported for the uncertainty of T in the 95% confidence interval   (Box et al. , 2009; Buizert et al. , 2018). Both past reconstructions (A) and recent observations (B) show that glacier melting rapidly increases beyond a threshold summer temperature value of approximately +1. 8 • C above pre-industrial value, suggesting accelerated glacier retreat in the future (C)  (Pattyn et al.
Acknowledgements This study was supported mainly by the  Basic Core Technology Development Program   for the Oceans and the Polar Regions  ( NRF-2021M1A5A1075512 ) ( KOPRI-PN23013 ) and the  Basic Science Research Program  ( 2017R1A6A3A01076729 ) from a  National Research Foundation of Korea  Grant from the  Korean Government (MSIT ; the  Ministry of Science and ICT). We would like to thank all the participants of the cruises on RV  Helmer Hanssen  for their support of the core sampling, as well as  D. Kim ,  K. Park ,  Y. Joe ,  Y. J. Joo , and  Y. Son  for laboratory assistance. We also thank the  XRD lab  crew of the  Crystallography & Geomaterials group at Geosciences Bremen  for keeping the machines always online, in particular  J. Birkenstock ,  Th. Messner ,  R. X. Fischer  and  E. M.
Data Analysis All data analyses were performed with R stat (R 3. 6. 2 version). The effect of treatments on N-mineralization and soil respiration rates were analyzed with a one-way ANOVA and plant biomass and N accumulation were analyzed with a two-way ANOVA. The homogeneity of variances and the normality of the ANOVA residuals were verified using the Levene and Shapiro-White tests, respectively. If these tests were significant (p < 0. 05), the data were log-transformed before analysis. The differences between means were analyzed using Tukey's HSD post hoc test and the "predictmeans" package (version 1. 0. 4).
Funding: This research was funded by the  French Agency of National Research  through the  In-tens&Fix project  ( ANR-10-STRA-004-04 ) awarded to J. -P. B. K. W. was supported by the  Ministry of Science and Technology and Silpakorn University  for her PhD project. The two sites belong to the  SOERE F-ORE-T network , which is supported annually by  ECOFOR, AllEnvi  and the  French National Research Infrastructure ANAEE  "https://www. anaee-france. fr(accessed on 7 February 2023)". The work was also supported by the program Investments for the  Future  ( ANR-10-LABX-04-01 ) through the use of the  Ecotrop platform from CeMEB labEx. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Acknowledgments: We thank  Lourival Fermiano  and  Rildo Moreira  of the  Itatinga Experimental Station (ESALQ-USP ), and  Tiburce Matsoumbou  of the  Pointe-Noire Experimental Station (CRDPI ) for their technical support for pot experiments.
Purpose: Within the Solve-RD project (https://solve-rd. eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the "ClinVar low-hanging fruit" reanalysis, reasons for the failure of previous analyses, and lessons learned. Methods: Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of singlenucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar.
The working group focused on the detection of singlenucleotide variants (SNVs) and small insertions and deletions (indels) of less than 40 base pairs (Solve-RD SNVindel Working Group) aims to initially programmatically identify SNVs or indels already annotated in ClinVar as pathogenic or likely pathogenic (P/LP) variant: the so-called "low-hanging fruit" variants. 19, 24 ClinVar is a freely accessible public database aggregating information about genomic variations and their relationship to diseases. It features more than 1. 9 million recorded submissions  25  and contains, in particular, variants reported as P/LP by the genetics community. In this article, we present the results of this first focused reanalysis, referred to as "ClinVar low-hanging fruit," on the first batch of the data set composed of ERN-ITHACA negative ES and report the causes underlying missed diagnoses at first analysis.
ES reanalysis and reinterpretation ES and bioinformatics analyses were performed on platforms as previously described  [26] [27] [28]  and are detailed in the Supplemental Methods. Raw data were reanalyzed using a centralized, automated analysis and filtering approach developed within the RD-Connect GPAP and in the context of the Solve-RD project. 19 We retained SNVs and indels (1) located in genes associated with ID or neurodevelopmental diseases, according to a list of clinical and research candidate genes provided by ERN-ITHACA (in Supplemental Gene List Table ), (2) having a Genome Aggregation Database (gnomAD) allele frequency of < 1%, (3) having an internal RD-Connect GPAP allele frequency of < 2%, and (4) reported in ClinVar (v. 13-01-2020)  29  as being likely pathogenic or pathogenic.
Methods of this step have already been described in Matalonga et al. 19  Variants were prioritized if zygosity of the variant matched with the inheritance patterns of the gene (based on OMIM and the Developmental Disorder Gene-to-Phenotype [DDG2P] database) in which the variants were observed and, in addition, filtered on gnomAD frequency and internal frequency depending on the mode of inheritance (Figure  2 ). Frequency filters allow discarding frequent variants that are not involved in rare diseases and then avoid having to interpret irrelevant variants, including variants wrongly reported as being P/LP in ClinVar. The annotated variants were subsequently returned in February 2020 to the original submitting center that had initially produced the sequencing data for clinical interpretation.