dataesr/scientific-paragraphs-categorization huggingface dataset:
We acknowledge that these maps bear some uncertainties and controversial aspects. For example, the very early Andean rise to their modern elevations consequently precociously lowers the predicted bulk sediment flux during the Neogene period while diversification continues to increase (Figs. 3  and 4 ). It is worth noting that even though the PALEOMAP dataset forms the basis of this study, from a methodology standpoint, other datasets  [64] [65] [66]  could be used. To simulate riverine processes, the palaeo-precipitation dataset used was generated using a variant, HadCM3BL-M2. 1aD (ref. 15), of the coupled atmosphere-ocean-vegetation Hadley Centre model. This climate model also uses the PALEOMAP Atlas 67 but at a lower resolution (3. 75° × 2. 5°). The reconstructed palaeo-precipitation regimes are obtained for each individual palaeo-elevation map after running the climate model for at least 5,000 model years to reach a dynamic equilibrium of the deep ocean  15.
This simulation runs until dynamic equilibrium is reached (that is, erosion rates compensate tectonic uplift rates) within the first million years of landscape evolution (Extended Data Fig. 2c ). The outputs of this second simulation are then used to evaluate water and sediment flux for the considered time slice, as well as the major catchment characteristics (river networks, drainage areas, and erosion and deposition rates). The parametrization of equation (  1 ) is obtained by calibrating its variables with modern estimates of average global erosion rates  18  (mean value of 63 m Myr -1 with a standard deviation of 15 m Myr -1 ; Extended Data Fig. 6c ) and of suspended sediment flux from the BQART model  19, 20  (corresponding to 12. 8 Gt yr -1 ). Following calibration, we predict an average present-day global erosion rate of 71 m Myr -1 , and a sediment flux of 12.
   Sr Sr CR We find that the contributions of mantle and terrigenous sources relative to those of the present day covary at long wavelengths, with two periods of reinforced influx from both sources separated by a quieter period during Pangaea. This trend mirrors the Wilson cycle. It indicates that the periods of high erosion, coinciding with periods of continental aggregation and contraction, increased elevation and wetter climates, also match periods of reinforced tectonic activity. Seafloor expansion is faster during periods of continental dispersal, and the total length of ridges increases during breakup  77. As the mantle input to the  87  Sr/  86  Sr ratio of seawater is partially driven by seafloor kinematics, we find several similarities between the predicted mantle Sr flux and subduction rates  72, 74, 76  that can be taken as a proxy for oceanic spreading rates; Extended Data Fig.
4 ). Notably, over the past 250 Ma, we deduce that mantle fluxes were low during Pangaea, and subsequently increased during Pangaea breakup; flux decreased during the late Palaeogene, mirroring the decrease in crustal production rates in the Atlantic and Pacific oceans  78  and the consumption of the East Pacific ridge. Our model predictions of sediment flux compare at first order to the observed increase in the  87  Sr/  86  Sr ratio of seawater over the past 150 Myr (Extended Data Fig. 4 ). Assuming that weathering scales with erosion rates, it corroborates the first-order impact of Cenozoic orogenesis  79  on the Sr ratio. Likewise, over the entire Phanerozoic, short-lived (20-40 Myr) increases in predicted erosion flux can explain the increase in the  87  Sr/  86  Sr record during major orogenic phases  80  , whereas the long-term variations of the record can be at first order explained by the coevolution of the terrigenous and mantle sources during the Wilson cycle.
As plate tectonics-and the breakup of Pangaea in particular-form the cornerstone of such studies  11, 40, 41  , we thus compared continental fragmentation  41  with Phanerozoic marine diversity, which yields only moderate correlations (Pearson coefficients of 0. 46 with number of marine families 2 ; 0. 54 to 0. 58 with the number of genera  2, 5  ; Extended Data Fig. 8 ).
Acknowledgements This work was principally supported by the  Australian Research Council DARE Centre  grant  IC190100031. This research was undertaken with the assistance of resources from the  National Computational Infrastructure , which is supported by the  Australian Government , and from  Artemis HPC Grand Challenge  supported by the  Sydney Informatics Hub  at the  University of Sydney. In addition, we acknowledge  C. Nielsen ,  M. Laugié  and  A. Lettéron  for providing the Aptian palaeo-elevation and palaeo-precipitation grids used in Extended Data Fig. 5c. The manuscript was improved by the reviews and suggestions of  R. Martin  and  A. Pohl.
Data availability The PALEOMAP Paleodigital Elevation Models for the Phanerozoic from ref. 14  can be downloaded from https://doi. org/10. 5281/ zenodo. 5460860 (last accessed 3 October 2023). Palaeoclimatic maps from the HadCM3BL-M2. 1aD model 15 are available from the Bristol Research Initiative for the Dynamic Global Environment website at https://www. paleo. bristol. ac. uk/resources/simulations/. Palaeoclimate simulations from ref. 93 are made available in the original article, as referenced. All palaeo-elevation reconstruction maps  16  for the Phanerozoic generated in this study are available from HydroShare: http:// www. hydroshare. org/resource/0106c156507c4861b4cfd404022f9580.
Code availability The scientific software used in this study, goSPL  12  , is available from https://github. com/Geodels/gospl and the software documentation can be found at https://gospl. readthedocs. io. We also provide a series of Jupyter notebooks used for processing the datasets and model outputs that can be followed to reproduce some of the figures presented in the paper, which can be accessed from https://github. com/ Geodels/paleoPhysiography. The open-source python interface for the Generic Mapping Tools (https://www. pygmt. org) was used for global two-dimensional map visualization and the three-dimensional global maps in Fig. 1  were created with the open-source Paraview software (https://www. paraview. org).
Extended Data Fig. 8 | 8 Plate tectonics and the Phanerozoic marine biodiversity. a. The index of continental block fragmentation derived from Zaffos et al. 41 (orange curve) with value close to 1 indicating no plates are touching and a value of zero corresponding to contiguous continental blocks arranged in a single mass. Pearson correlation (0. 46) indicates a positive but moderate relationship with the number of marine families (black line from Sepkoski's dataset 2. b. Correlations between the fragmentation index and the total number of marine genera from Sepkoski 2 (SO2) and Rohde & Muller 5 (RM05) show a slightly stronger but still moderate positive trends (Pearson values of 0. 58 and 0. 54 respectively). Extended Data Fig. 9 | Hierarchical classification of physiographic relevant morphometrics derived from model outputs.
a. From standardized topographic position index (TPI S ), slopes calculated at 0. 05 o resolution, and reconstructed water flux, series of categories characterizing the physiography are produced at global scale (chosen example at 26 Ma). b. Zoomed-in maps of the physiographic categories for Northeast Africa. Extended Data Fig. 10 | Biodiversity and sediment flux through time. a. Predicted trend in net sediment flux to the ocean (purple line) and estimates of total number of marine genera from S02 2 and RM05 5 ; Pearson correlation of 0. 61. Sepkoski's dataset (S02) downloaded from http://strata. geology. wisc. edu/jack/. Paleo-marine life by Rebecca Horwitt, available at full size and open access from https://sites. psu. edu/rhorwitt/. The grey line shows the least square regression of the sediment flux to the ocean using SciPy Savitzky-Golay filter.
Trial Protocol After ethical approval is obtained, the trial protocol, following SPIRIT guidelines  [48] , will be publicly registered and published. It will include all research processes relating to the main trial and analysis (WP3, WP4), the equity, access and inclusion evaluation (WP5) and the process and implementation evaluation (WP6). Ethical approval will first be obtained from the Sponsor site. Once obtained, each partner involved in data collection will obtain ethics approval from their local ethical committee before the start of the trial. The results of the ethical committees' judgements will be collected by the WP3 lead for reporting to the Research Executive Agency. Informed consent to participate in the trial will be obtained from all participants before enrolment on the trial.
To ensure protocol fidelity is maintained, an Intervention Manual and training sessions will be provided for trial teams and rehabilitation practitioners delivering the intervention at each participating site. The manual and training will be provided and delivered by the WP2 and WP3 teams before and during the trial. We aim to recruit from oncology and palliative care services and tumour groups where existing rehabilitation services are limited. This will ensure high fidelity as intervention participants have access to a finite rehabilitation resources. In addition, partner leads and project managers from each site will sit on the Trial Management Group and will receive reports from independent Trial Steering and Data Management Committees. The Inspire Intervention In brief, the Inspire intervention comprises up to three manualised sessions (face to face and/or by telephone) delivered by an expert rehabilitation practitioner (physiotherapist, occupational therapist, dietitian, speech and language therapist or rehabilitation nurse), delivered in addition to standard oncology and/or specialist palliative care.
For quantitative components, sex and gender variables will be included as relevant to explore differences in outcomes and access. For qualitative components, gender will be considered within data collection and analysis: purposive sampling will be used to ensure representation of participants with different genders, and reflexive practice considering the influence of the researchers' personal characteristics on interpretations (including gender) will be undertaken. This mixed-methods approach will support identification of potential mechanisms, mediators and moderators of access and treatment effect relating to person, intervention, and service characteristics, and cross-country and within-country barriers and facilitators to access and equitable delivery of the intervention. Not all patients report good experience from rehabilitation and palliative care and access and equity challenges are apparent, e.
g. , according to diagnostic groups and tumour types. Inequality in health care applies especially to vulnerable groups of patients with multimorbidity, socio-economic disparities or cultural and personal disadvantages  [64, 65]. Our implementation process evaluation will utilise implementation science methodologies  [66] [67] [68]  to evaluate if the findings can be confidently attributed to the intervention as delivered  [69]. The evaluation will assess uptake, reach and fidelity of intervention delivery and explore how the trial processes and intervention components were received and experienced by patients, family caregivers and healthcare professionals. Analysis of quantitative and qualitative data will identify potential mechanisms of action, and mediators and moderators of effect relating to person, intervention, service, and contextual characteristics.
Acknowledgements: We acknowledge the work and support of all stakeholders who provided their valuable insights and assistance to  INSPIRE's research. Azienda USL-IRCCS di Reggio Emilia, Italy :  Luca Braglia ,  Dr Barbara Bressi ,  Silvio Cavuto ,  Dr Stefania Costi ,  Virginia Dolcini ,  Debora Formisano ,  Dr Stefania Fugazzaro ,  Dr Monica Guberti ,  Dr Elena Turola   European Association for Palliative Care :  Dr Julie Ling ,  Fódhla Ní Chéileachair ,  Dr Cathy Payne ,  Patricia White European Cancer Patient Coalition :  Nicola Di Flora ,  Eduardo Agustín Veber ,  Dr Juan Jose Ventura Fonden  for Almen Praksis, Denmark:  Professor Mai-Britt Guldin ,  Dorthe Nielsen ,  Dr Lise  Nottelmann  Hospices Civils de Lyon , France:  Anne-Sophie Belmont ,  Laure Christophe ,  Dr Guillaume Economos ,  Julie Haesebaert ,  Sophie Jacquin-Courtois ,  Elise Perceau-Chambard ,  Julia Romeyer Fondazione IRCCS Instituto Nazionale dei Tumori , Italy:  Dr Maria Grazia Blandini ,  Constanza Bono ,  Simona Braghieri ,  Professor Augusto Tommaso Caraceni ,  Giovanni Scoazec ,  Dr Elisa Vanzulli King 's  College London, UK :  Dr Joanne Bayly ,  Emily Boocock ,  Megan Bowers ,  Professor Irene Higginson ,  Professor Matthew Maddocks ,  Professor Charles Normand ,  Carmine Petrasso ,  Professor Toby Prevost ,  Joana Carvalho Vasconcelos Lyon Ingénierie Projets, France: Marta Esteban University of Edinburgh , UK:  Dr Barry Laird ,  Amy McLuskie Universitet I Bergen , Norway:  Dr Hilde Hjelmeland Ahmedzai ,  Professor May Aasebø Hauken ,  Professor Line Oldervoll ,  Dr Guro Birgitte Stene  This study is supported by the  National Institute for Health and Care Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King's College Hospital NHS Foundation Trust.
Modern sequencing technology enables the detection of complex structural variation (SV) across genomes. However, extensive DNA rearrangements arising through series of mutations, a phenomenon we term serial SV (sSV), remain understudied since their complexity poses a challenge for SV discovery. Here, we present NAHRwhals (https://github. com/WHops/NAHRwhals), a method to infer repeat-mediated series of SVs in long-read genomic assemblies. Applying NAHRwhals to 58 haplotype-resolved human genomes reveals 37 sSV loci of various length and complexity. These sSVs explain otherwise cryptic variation in medically relevant regions such as the TPSAB1 gene, 8p23. 1 and the DiGeorge and Sotos syndrome regions. Comparisons with great ape assemblies indicate that most human sSVs formed recently and involved non-repeat-mediated processes.
sSV occurrence and SV complexity across Hominidae Having identified abundant sSV loci in humans, we next set out to examine to which degree sSVs in these regions are human-specific, as a prerequisite to gaining an understanding as to how these regions may have emerged evolutionarily. Repeat-associated variation is known to have contributed substantially to the evolution of modern humans  38  , and hundreds of genomic regions display SD-mediated inversions between non-human primates and humans  [39] [40] [41]  , frequently accompanied by secondary copy-number variations (CNVs) near their breakpoints  41. We considered that a fraction of our human sSV loci, too, may have undergone substantial restructuring during great ape evolution, which may be explicable through sSVs. To test this, we turned our attention to sSVs in the four great ape genome assemblies  22  included in our dataset (Methods).
The further distant species Gorilla and Orangutan display a somewhat analogous configuration, but also harbor additional insertions that cannot be explained by NAHR alone. We also find instances of more isolated non-NAHR events, such as a duplicated section on Xq28 containing cancer/testis antigen 1 (CTAG1A/CTAG1B) genes, which are implicated with a variety of cancers  43. In our dataset, the CHM13-T2T assembly, Bonobo and Chimpanzee share the same locus configuration, which is distinct from the non-duplicated region in Orangutan. NAHR was again not sufficient to explain the implicated duplication event. Across these examples and the remaining dataset, our results suggest that in the majority of human sSV loci, NAHR alone is insufficient to explain inter-species variation, where consequently also other mutational mechanisms are likely at play.
Mutated matrices are assigned an alignment score using a customized Needleman-Wunsch algorithm  56  in which we treat the condensed matrix like a regular pairwise sequence alignment. SV-chains producing a pairwise alignment of sequence identity larger than a threshold (default: 98%) are considered successful. Simulation experiments. We implemented a framework to simulate and mutate SD-containing sequences (available at https://github. com/WHops/NAHRwhals/blob/main/scripts/simulation-dev. R ). For each pair of SDs parameters of three different similarity values (90%, 95%, 99%) and four different lengths (100 bp, 500 bp, 1. 000 bp, 10. 000 bp), we created 50 genomic sequences with 2 pairs of non-overlapping SDs of randomized position and orientation each. Subsequently, we simulated a pool of sequence derivatives, realizing all NAHR-concordant combinations of Inv, Del and Dup up to depth 2.
Six of the samples (GM19129, HG02282, HG02769, HG02953, HG03452, HG03520) have not been reported on by the HGSVC previously. The average CPU time of hifiasm was 323h at a peak memory usage of 105Gb. Additionally, four hifiasm-based great-ape assemblies (Bonobo, Chimpanzee, Gorilla and Orangutan) were acquired from a recent publication  22. (available at https://doi. org/10. 5281/zenodo. 4721957 ). In these four datasets, contigs labeled as 'primary' were used. Core-duplicons. sSV loci were tested for overlap with genes and gene families mapping to 'core-duplicons'  47. To create a table of relevant genes, we assembled a list of 22 prominent core duplicon -associated gene families (NBPF, RANBP2, RGPD, PMS2, PPY, C9orf36, ZNF790, SPRYD5, NPIP (also known as Morpheus), GOLGA, LRRC37, TBC1D3, USP6, SMN, CCDC127, TRIM51, GUSBP, FAM75A, SPATA31, OR7E, DPY19, SPYDE).
62, 63. We subsequently queried the gencode gene annotation (version 35)  64  for all members of those families, leading to a list of 131 gene instances (Table  S3 ). Automated sequence validation with Nanopore reads. We used ONT reads generated by the HGSV Consortium for 11/26 samples -GM19129, HG00512, HG00731, HG00733, HG02282, HG02769, HG02818, HG02953, HG03452, HG03520, NA19239 (data availability). After removing adapters with Porechop (https://github. com/rrwick/Porechop), ONT reads were mapped to both haplotypes of their respective samples (i. e. , reads from each sample were mapped both to the h1 and h2 assembly). We invoked the sniffles2  36  SV caller to identify homozygous variants which correspond to assembly regions not supported by any reads, i. e. likely assembly errors. Using the liftover coordinates provided by NAHRwhals, we queried the sequences corresponding to their CHM13-T2T-counterpart individually for each assembly.
Acknowledgements We thank  Celia Tsapalou ,  Thomas Weber  and  Michael Jendrusch  for valuable feedback on the code and the manuscript, and the members behind the deutsch_studio account on fiverr. com for designing our logo. Marschall *,  Ryan E. Mills ,  Zepeng Mu ,  Katherine M. Munson ,  David Porubsky ,  Benjamin Raeder ,  Tobias Rausch ,  Allison A. Regier ,  Jingwen Ren ,  Bernardo Rodriguez-Martin ,  Ashley D. Sanders ,  Martin Santamarina ,  Xinghua Shi ,  Chen Song ,  Oliver Stegle ,  Michael E. Talkowski ,  Luke J. Tallon ,  Jose M. C. Tubio ,  Aaron M. Wenger ,  Xiaofei Yang ,  Kai Ye ,  Feyza Yilmaz ,  Xuefang Zhao ,  Weichen Zhou ,  Qihui Zhu , and  Michael C. Zody.
Funding Funding was provided by:  National Institutes of Health (NIH)  grant  U24HG007497  (to C. L. , E. E. E. , J. O. K. , T. M. ),  Federal Ministry of Education and Research (BMBF)  grant  031L0181A  (LAMarCK to J. O. K). The  EMBL International PhD Programme  provided additional support.
of 48 morbid CNV-regions of interest. ) Merge any intervals if they have at least 50% overlap. (4) Elongate each region by 25% of its length on either end. (5) Subtract centromeres and ALR repeat regions. (6) Merge regions with <100 kbp distance to each other (7) filter to regions >40 kbp. The resulting regions contain between one and several thousand SV/Inv calls, with region lengths ranging from 40 kbp to 26 Mbp (median: 170. 8 kbp). The scripts and all input data are available at https://github. com/WHops/NAHRwhals_rois. Loci containing disease-associated CNVs were based on four separate lists from 44 (Table 1; 44 regions. Table 2: 14 regions) and 49 (Table S2; 19 regions. Table Collection.
There are available iOS (https://apps. apple. com/us/app/terrhum/id1366575 503, accessed on 24 June 2023) and Android (https://play. google. com/store/apps/details? id=fr. inra. terrhum&hl=en_US&gl=US, accessed on 24 June 2023) applications in Italian, French and English languages, for tablets and mobile phones. During field observations, these applications can provide quick and useful indications for classification and photographic examples of horizons, forms and systems of humus. From what has been said, we must conclude that it is necessary to plan and practice specific management for forest soil. When you walk on the forest floor, your feet are resting on a very useful living sponge, which exists not only as a support for the static anchoring of trees, which play a dominant role as the driving force of the ecosystem, but also as a source of energy for all the whole forest life.
More generally, also considering wetlands, living organisms and the mechanisms for recharging and maintaining energy in the soil have evolved for each ecosystem environment (Figure  11 ). There are freely available iOS (https://apps. apple. com/us/app/terrhum/id1366575503, accessed on 24 June 2023) and Android (https://play. google. com/store/apps/details?id=fr. inra. terrhum&hl=en_US&gl=US, accessed on 24 June 2023) applications in Italian, French and English languages, for tablets and mobile phones. During field observations, these applications can provide quick and useful indications for classification and photographic examples of horizons, forms and systems of humus. Figure  11. Synthetic keys for the classification of Humipedons in reference humus systems for terrestrial environments (non-asphyxiated soils) or for semiterrestrial environments (more or less submerged soils, peats).
This research was funded by the  PROVINCE OF TRENTO (Italy)  and the  EUROPEAN COMMUNITY  (d. d. 437 and  1159 ,  08/03/2002  and  25/05/2002 ). Work in  Grand-Follié  forest (Savoy, France) was supported by the endowment fund: Museum for the  Planet-Sainte-Foy-Tarentaise  fund. Coordination and writing were carried out in the context of ordinary funding for the research of the  University of Padua for Augusto Zanella. Institutional Review Board Statement: Ethical review and approval were waived for this study. Data were collected and studied in 2002-2005 in accordance with the  Declaration of Helsinki. The  TESAF (University of Padua) department's current Ethics Committee  does not pass judgment on past research.
ICs are anatomically diverse and complex To characterize the diversity of ICs, we developed a program for morphometric analysis of segmented cells called CellWalker that allows for calculation of morphological features of segmented cellular structures, including length, diameter, volume, surface area, and curvature (see Supplementary Methods). Using CellWalker, we saw that IC lengths ranged from 1. 25 to 3. 3 μm with an average of 2. 25 μm (Fig. 5A ), comparable to in vitro and in vivo CBs, which exhibit lengths between 3 and 5 μm  (37). To assess trends in the thickness (or diameter) of ICs, we sliced each IC at 50-nm intervals along their lengths to obtain a series of cross sections (Fig. 5, B  and D ). To assist the determination of the axis for cross sectioning, we skeletonized ICs using the Kimimaro algorithm  (38)  (see Supplementary Methods).