dataesr/scientific-paragraphs-categorization huggingface dataset:
We attribute the overall consistency between parameter values derived using the two different methods to the fact that all reflectors in our seismic data show a similar pattern: long-distance transport beginning from the earliest stages of post-rift margin evolution followed by the largely depositional draping of successive units atop previous deposits. In this respect the modern surface is not geometrically distinct from the subsurface reflectors, which may explain why Preprint of article published in Basin Research, https://doi. org/10. 1111/bre. 12698 incorporating the subsurface reflectors leads to little improvement in model-data fit. A model can either achieve parameter values that allow it to develop these types of deposits (i. e. , in the nonlocal, nonlinear model) in which case the specific number and age of reflectors used does not have a significant effect on inferred best-fit parameter values, or it cannot achieve parameterizations that allow long-distance, deposition-driven stratal stacking patterns (i.
The improvement in model-data fit gained from adding nonlocal, nonlinear sediment transport dynamics exists regardless of whether we use only the modern surface or all reflectors as a basis for comparison. The misfit values between the two models are much closer when all reflectors are used for the inversion (Figure  10 ). This arises from the introduction of seven additional constraints on the model, many of which it must inevitably fail to match (Figure  5 ) even in its best-fit parameterization. However, the consistent reduction in misfit that accompanies the nonlocal, nonlinear model signals that those dynamics are required to produce stratigraphy that matches observations. The only scenario where this would not hold true is one in which a misfit function was used that did not take into account the distal portions of the basin at all.
ACKNOWLEDGMENTS C. M. Shobe  was supported by  H2020 Marie Sklodowska-Curie Actions  grant no. 833132  (STRATASCAPE). We acknowledge time on the West Virginia University Thorny Flat highperformance computing cluster which is supported by the  National Science Foundation   under Major Research Instrumentation program  award # 1726534. We thank  Benoît Bovy ,  Tim Carr ,  Rachel Glade ,  Kim Huppert ,  Luca Malatesta ,  Delphine Rouby ,  Jaime Toro , and  Amy Weislogel  for helpful discussions. Thanks to  Guillermo Franco ,  Nate Garver-Daniels , and  Daniel Turpen  for HPC support and to  Xuesong Ding ,  Oriol Falivene , and  Associate Editor Peter Burgess  for constructive reviews.
Furthermore, this study predicted that patients treated with full-length FVIII (infusions every 48 h) spent more time with FVIII above the 10 IU dL-1 than patients treated with BDD-FVIII product infusions every 72 h. Therefore, full-length FVIII could be characterized by higher plasma stability than B domain-deleted FVIII substitutes compared to BDD-FVIII  [1]. This data raised a controversy  [4] , but the high plasma stability of FL-FVIII was confirmed at pre-clinical level  [5]. FVIII half-life is strongly influenced by von Willebrand Factor (VWF), which is reported to bind with FVIII  [6] [7] [8]. Several studies reported that the binding sites of VWF at FVIII are in the A3 and C1 FVIII domains  [9] [10] [11] [12]  Therefore, with the aim to improve BDD-FVIII plasma stability, a novel FVIII substitute has been developed, efanesoctocog alfa (BIVV001).
Efanesoctocog alfa is an investigational biologic drug where VWF D'-D3 domain is covalently bound (through a cleavable XTEN polypeptide linker) to an engineered BDD-FVIII  [13]. The atomic model of cryo-electron microscopy (Cryo-EM) of efanesoctocog alfa was built and deposited on Protein Data Bank (PDB:7KWO); in this atomic model VWF binding to A3 and C1 domains of FVIII is confirmed. Mature FVIII is a 2351 a. a pre-protein that, during processing in the endoplasmic reticulum, is cleaved and reassembled in a heavy and a light chain, interacting by means of Van der Waals interactions, and through a metal complex interaction with a divalent cation (Figure  1 ). FVIII) biologics have been developed to improve the biotechnological production of these proteins. Furthermore, BDD-FVIII conjugated with Fc immunoglobulin fragment was reported to have the highest plasma half-life, providing the opportunity of scheduling a low number of infusions  [2].
Efanesoctocog alfa is an investigational biologic drug where VWF D'-D3 domain is covalently bound (through a cleavable XTEN polypeptide linker) to an engineered BDD-FVIII  [13]. The atomic model of cryo-electron microscopy (Cryo-EM) of efanesoctocog alfa was built and deposited on Protein Data Bank (PDB:7KWO); in this atomic model VWF binding to A3 and C1 domains of FVIII is confirmed. Mature FVIII is a 2351 a. a preprotein that, during processing in the endoplasmic reticulum, is cleaved and reassembled in a heavy and a light chain, interacting by means of Van der Waals interactions, and through a metal complex interaction with a divalent cation (Figure  1 ). After secretion, the processed full-length FVIII (FL-FVIII) is cleaved mainly at a. a 1313, corresponding to the secreted inactivated form of FVIII, containing about 572 amino acids of B domain (secreted full-length FVIII) (Figure  1 )  [14, 15].
After energy minimization steps, the optimized models of full-length FVIII (secreted FL-FVIII) and B domain-deleted FVIII (BDD-FVIII) were then subjected to protein-protein docking with a the D'D3 domain of von Willebrand Factor (PDB: 6N29) through PyDock, which provided the prediction of binding free energy of protein complexes (https://life. bsc. es/pid/pydock/ from 2016 to 2022)  [38]. Pydock output also includes the scoring of predicted complexes. Rescoring of predicted complexes was also carried out with Prodigy web server (http: //milou. science. uu. nl/services/PRODIGY/ from 2016 to 2022)  [39].
On the basis of network clustering, the participation coefficient P is defined as: P i = 1 - k si k i 2 ( 10 ) where k si is the number of links the i-th node shares with nodes belonging to its own cluster. The participation coefficient is able to identify residues' role in transmitting signals between functional protein regions (protein network clusters)  [30, 34, 40, 44]. The PCN methodology is now implemented in open-source software  [45]. We projected values of participation coefficient as b-factor and colored the ribbon structures of the complexes by means of an in-house Python script, according to the method previously described [27].
Protein Docking Protein-protein docking studies were carried out with PyDock on the model of fulllength secreted form of FVIII, to predict VWF/FL-FVIII complex (Figure  2 ). To validate the protein-protein docking and the computational structure interface analysis approaches, we also docked the BDD-FVIII (PDB: 2R7E) with the fragment of von Willebrand factor (PDB: 6N29) (Figure  3 ). Pydock predicted that von Willebrand factor interacts with the B domain of secreted full-length FVIII with slightly more negative predicted binding free energy, compared to the BDD-FVIII/VWF complex (Table  1 ). A is the network adjacency matrix, as defined in Equation  (1). Cluster partition is based on the value of the Fiedler vector v2 (the eigenvector corresponding to the second minor eigenvalue of L): the cluster number nc is user-defined.
The v2 components interval  = {min( ) , max( )} is divided into nc subintervals, so that nodes (residues) are parted in clusters according to which subinterval their v2 components fall into. On the basis of network clustering, the participation coefficient P is defined as: 𝑃 = 1 - 𝑘 𝑘 ( 10 ) where  is the number of links the i-th node shares with nodes belonging to its own cluster. The participation coefficient is able to identify residues' role in transmitting signals between functional protein regions (protein network clusters)  [30, 34, 40, 44]. The PCN methodology is now implemented in open-source software  [45]. We projected values of participation coefficient as b-factor and colored the ribbon structures of the complexes by means of an in-house Python script, according to the method previously described [27].
Protein Docking Protein-protein docking studies were carried out with PyDock on the model of fulllength secreted form of FVIII, to predict VWF/FL-FVIII complex (Figure  2 ). To validate the protein-protein docking and the computational structure interface analysis approaches, we also docked the BDD-FVIII (PDB: 2R7E) with the fragment of von Willebrand factor (PDB: 6N29) (Figure  3 ). Pydock predicted that von Willebrand factor interacts with the B domain of secreted full-length FVIII with slightly more negative predicted binding free energy, compared to the BDD-FVIII/VWF complex (Table  1 ). 3  and 4 )  [13, 47]. 3  and 4 )  [13, 47]. Fuller et al. , 2021, have solved the structure of the bioengineered clinical-stage FVIII substitute, BIVV001 or efanesoctocog alfa (PDB: 7KWO). In efanesoctocog alfa, the VWF-D′D3 is covalently linked to an Fc domain of a BDD-FVIII, resulting in a stabilized VWF-D′D3/BDD-FVIII complex (Figure  4 ).
Therefore, our computational approach is in accordance with the experimental findings reporting highly stable VWF and FVIII interactions in efanesoctocog alfa or BIVV001. Fuller et al. , 2021, have solved the structure of the bioengineered clinical-stage FVIII substitute, BIVV001 or efanesoctocog alfa (PDB: 7KWO). In efanesoctocog alfa, the VWF-D D3 is covalently linked to an Fc domain of a BDD-FVIII, resulting in a stabilized VWF-D D3/BDD-FVIII complex (Figure  4 ). The two structures of predicted VWF/BDD-FVIII and of efanesoctocog alfa have been superimposed, and RMSD of alignment was 1. 4 Å. PRODIGY rescoring (Table  2 ) provided binding energy and dissociation constants for analyzed complexes. The binding energy (-14. 1 kcal/mol, pKd 10. 4) of the predicted complex VWF/FL-FVIII, characterized by the binding of VWF with B domain, was higher (less favorable), although comparable to the binding energy of VWF bound to the A3-C1 domains of BDD-FVIII (-15.
e. , FVIII A3-C1 domain is the most stable binding site of VWF. Our computational approach suggested that one of the driving forces of VWF binding at this preferential binding site could be related to conformational modifications of FVIII, through modulation of allosteric residues. The PISA and PCN analysis of topological parameters suggested an unfavorable binding of VWF at B domain of FVIII, due to an increased stability of the interface between the heavy and the light chain, compared to VWF/BDD-FVIII and efanesoctocog alfa. In fact, the most stable VWF/FVIII complex (efanesoctocog alfa) was characterized by highest (less favorable) interaction energy between heavy and light chains of FVIII. Differences between the modeled VWF/BDD-FVIII complex and the atomic model of efanesoctocog alfa are not likely to be attributed to the lower resolution of BDD-FVIII X-ray structure (PDB:2R7E), compared to the atomic model of efanesoctocog alfa (PDB: 7KWO).
Pydock output. Predicted interactions between FVIII structure models and von Willebrand factor fragment (PyDock). Shiltagh et al. in 2014 identified the D'D3 (PDB: 6N29) as the domain of von Willebrand factor (VWF) that interacts with FVIII [46]. Chiu et al. in 2015 found that the VWF interacts with the A3 and C1 domain in light chain of the B domain-deleted FVIII [47]. The Pydock output for VWF/BDD-FVIII complex is in accordance with the study results from Chiu et al. 2015 and Fuller et al. 2021 (Figures.
Acknowledgments: The manuscript is completed in memory of  Salvatore Asero (BIOVIIIx) , who contributed to conceptualization of the project. Authors wish to thank the company  BIOVIIIx (Via Alessandro Manzoni 1, 80128 Napoli, Italy)  for unrestricted support and fruitful scientific discussion. The manuscript is dedicated to  Salvatore (Totò) Salomone , who spent his career in the field of cardiovascular pharmacology, and unfortunately departed too early from the  Pharmacology section of the BIOMETEC department-University of Catania.
Acknowledgements We acknowledge financial support from a  Marie Curie Initial Training Network iTECC  funded by the  EU REA  under the  FP7 implementation of the Marie Curie Action , under grant agreement  316966. We are grateful to  Laurie Reisberg  for inspiring discussions and an informal review of the manuscript, as well as  Catherine Zimmerman ,  Damien Cividini ,  Aymeric Schumacher  and  Christiane Parmentier  for assistance in sample preparation. Edward Tipper and two anonymous reviewers are thanked for their constructive comments.
This suggests a slightly more rugged conformational free energy surface for hairpin-like conformations, which are likely stabilized by sidechain interactions between residues more distant in sequence. We observe however that the kinetic stability of hairpin-like conformations of apo αS C-term is not observed at longer timescales, suggesting   S4. Moreover, finding a single optimal global geometry of the IDP dataset is often not possible owing to their extreme heterogeneity with almost equal transition probability between different clusters. However, if one necessitates the global preservation, tuning the perplexity 95 , and other parameters like Early exaggeration and Learning rate, initializing with PCA and Multi-scale similarities could be helpful  75, 96. In addition, some of the variations of t-SNE methods such as h-SNE can also be helpful 97.
Another factor that should be considered while using t-SNE on ultra-large datasets is the associated computational cost. Analyzing large data sets with t-SNE (beyond n ≫ 10 6 ) is methods such as Barnes-Hut approximation 98 and the FIT-SNE method to accelerate the computation. In short, Barnes-Hut approximation considers a subset of nearest neighbors for modeling the attractive forces, and the FIT-SNE method relies on a fast Fourier transformation, which reduces the computational complexity to O(N logN ) and O(N ), respectively. To mitigate the slow convergence and fragmentation of clusters, it is often desirable to run t-SNE on a sub-sample of the trajectory that includes all unique populations and then projects 22 the rest of the points onto the existing map. In line with discussing the possible pitfalls of the t-SNE method, it is also understood that adding a new data point onto the existing t-SNE map can lead to erroneous interpretation as the method is essentially non-parametric and does not directly construct any mapping function between the high dimensional and low-dimensional space.
D. Cluster-wise conformational analysis and visualization The conformations corresponding to each cluster are extracted using Gromacs based on the cluster indices. All the conformations were used for estimating the contact probability, binding energy, and homogeneity within individual clusters. Whereas, for visualization purposes, we extracted ten representative conformations from each cluster that is closest to the corresponding cluster centroid (as identified using KD-tree based nearest neighbor search algorithm). The conformations are rendered using VMD. Our current implementation of the model is available on the GitHub repository: https://github. com/codesrivastavalab/tSNE-IDPClustering.
VI. Acknowledgments A. R. thanks the  Wellcome Trust DBT India   Alliance for Early Career Fellowship  (Grant number:  IA/E/18/1/504308 ). A. S. thanks the  Department of Science and Technology (DST) of India  for the early career grant ( SERB-ECR/2016/001702 ). A. S. also thanks the  DST  for the  National Supercomputing Mission  grant ( DST/NSM/R&D HPC Applications/2021/03. 10 ). Computational support from the high-performance computing facility "Beagle" setup from grants by a partnership between the  Department of Biotechnology of India  and the  Indian Institute of Science  ( IISc-DBT partnership program ) is greatly acknowledged. AR and AS are grateful to the  SciNet HPC Consortium, ComputeCanada  for their generous computational support. P. R. and J. K. are supported by the  National Institutes of Health  under award  R35GM142750.
Study design Two pivotal Phase 3, single-arm, open-label trials with a double-blind placebo-controlled, withdrawal period investigated the effect of setmelanotide on body weight in individuals with obesity due to POMC or LEPR deficiency. Patients for each study were identified by the investigators from clinical site databases or genetic obesity registries  [20]. The POMC trial (NCT02896192) had active sites in Germany, France, Canada, the United States, Spain, and Belgium. The LEPR trial (NCT03287960) had active sites in Germany, France, the United Kingdom, and The Netherlands. The POMC trial enrolled patients ≥ 6 years old with homozygous or compound heterozygous POMC or PCSK1 variants. The LEPR trial enrolled patients ≥ 6 years old with homozygous or compound heterozygous LEPR variants. For inclusion in either trial, obesity was defined as body mass index (BMI) ≥ 30 kg/ m 2 for adults ≥ 18 years old and BMI ≥ 95th percentile for age on growth chart assessment for children or adolescents 6-17 years old.
QOL questionnaires were administered in each trial according to the following schedule: at screening and before treatment administration at visits 4 (week 5), 6 (week 13), 9 (week 27), 11 (week 39), and 13 (week 53). In instances when the incorrect patient-reported outcome instrument, by age, was administered to a patient, the data were not included in the analysis. Hunger questionnaires, IWQOL-Lite or PedsQL, and the PHQ-9 were administered at appropriate visits before treatment administration in each trial. Raw scores from both the IWQOL-Lite and PedsQL are transformed onto a scale of 0 to 100, such that 0 represents the worst possible QOL and 100 represents the best possible QOL  [26, 27]. The mean (SD) IWQOL-Lite total score of a comparative population of normal and overweight individuals is 94.
Furthermore, the data are based in part on the IWQOL-Lite, which is not tailored to patients in obesity clinical trials  [36]. The IWQOL-Lite-CT may be a more sensitive measure for this patient population  [36]. Clinically meaningful changes in patient-reported PedsQL scores from a control population of children were used as the reference for both the child and adolescent populations in this study, given that adolescent scores were not available. The comparator population used to define clinically meaningful changes could affect the interpretation of QOL results. However, in this study, patients who experienced a clinically meaningful change often had scores well beyond the minimally defined thresholds referenced. Finally, this trial assessed disease-specific QOL measures for 1 year, which may be an insufficient amount of time for patients to overcome burdens that they may have lived with for multiple years.
, 2013; Trotter et al. , 2019). Specifically, 16S rRNA PCR has high sensitivity and specificity and is proven to be a powerful strategy for bacterial infection diagnosis in case of the absence of etiological orientation by broad-range DNA amplification  (Fenollar et al. , 2006; Renvoisé et al. , 2013). The amplification of the 16S rRNA gene can cover all or some of the nine hypervariable regions scattered among highly conserved sequences. Ensuing sequencing of the amplicons and its comparison to nucleotide database allows assignment to a taxum. However, molecular diagnosis by conventional Sanger sequencing of 16S rRNA amplicons is hampered in case of a polybacterial infection. Metagenomic next-generation sequencing from biological samples has paved the way for a paradigm shift in microbiological diagnosis without a priori etiological assumption  (Goodwin et al.
Clinical Samples Upon clinical suspicion of bacterial infection, amplification and Sanger-based sequencing of a portion of the 16S rRNA gene is routinely performed in our laboratory on culture-negative difficult-to-obtain samples such as bone and joint specimens, cardiac valve, brain abscesses, etc. , as previously described  (Gauduchon et al. , 2003). Around 400 culture-negative and 16S rRNA, PCR-positive samples are obtained annually, of which 25% lead to unsuccessful Sanger sequencing. In the present study, we retrospectively searched the laboratory database for culture-negative and 16S rRNA PCR-positive samples with either a single DNA sequence or an uninterpretable polybacterial pattern chromatogram obtained from patients admitted between January and July 2019. The sample localisation and basic clinical context that came with the bacteriology analysis prescription were collected when available.
Sequencing Run and Data Analysis Sequencing runs on the MinION device were driven by the MinKNOW software (version 3. 6. 5, Oxford Nanopore Technologies) to allow raw data acquisition and live base calling. Sequencing FASTQ reads were analyzed in real-time using the cloud-based platform EPI2ME Agent (v2020. 2. 10; Metrichor, Oxford, UK) using the 16S workflow. This turnkey tool includes quality-check, demultiplexing, primary and secondary analyses to obtain an identification of bacteria and archaea and an NCBIbased taxonomy tree for each sample.
Acknowledgments We would like to thank  Bert Vercnocke  for collaborations on earlier versions of this work. We thank  Nikolay Bobev ,  Geoffrey Compère ,  Pierre Heidmann ,  Tom Lemmens ,  Christian Maes , and  Ruben Monten  for useful discussions. VD would especially like to thank  Natalia Pinzani Fokeeva  for taking a lot of time to explain subtleties regarding [49,50], and  Balt van Rees  for discussing the same papers at the pre-Strings Workshop 2019 in Leuven. The work of VD is supported by a doctoral fellowship from the  Research Foundation -Flanders (FWO). The work of DRM is supported by  ERC  Advanced Grant 787320 -QBH Structure, ERC Starting Grant 679278 -Emergent-BH, and  FWO Research Project G. 0926. 17N. The work of VSM was supported by a doctoral fellowship from the  FWO. This work is also partially supported by the  KU Leuven   C1  grant  ZKD1118 C16/16/005.
2 ) interpreted shipboard to be deposited in a shallow marine shelf environment  (Barker et al. , 1988). Age control based primarily on the presence of calcareous nannofossils  (Wei and Wise, 1990; Villa et al. , 2008) ; and a revised dinoflagellate cysts age model  (Houben et al. , 2013 (Houben et al. , , 2019) )  places the studied section at 33. 2 to 37. 6 Ma (Table  1 ), with sediments encompassing the EOT and EOIS event recovered between 571. 5 to 569. 1 m b. s. f. (Houben et al. , 2013). A total of 35 samples from the late-middle Eocene to earliest Oligocene (643. 73-520. 88 m b. s. f. ) were analysed for their pollen and spore content. Raw data collected are available from the PANGAEA database  (Thompson et al. , 2021). All palynological slides were prepared using standard chemical palynological processing techniques following the protocols at the University of Northumbria, Department of Geography and Environmental Sciences, and the Laboratory of Palaeobotany and the Laboratory of Palaeobotany and Palynology of Utrecht University, published previously (e.
Bioclimatic analysis Estimates for terrestrial mean annual temperature (MAT), mean annual precipitation (MAP), warmest month mean temperature (WMMT), and coldest-month mean temperature (CMMT) were obtained using the NLR approach in conjunction with the probability density function (PDF) method. Fossil taxa used and their NLRs are shown in Table  2. Climate estimates based on the NLR approach use presence or absence data and are independent of the relative abundance of individual taxa. This makes this method ideal https://doi. org/10. 5194/cp-18-209-2022 Clim. Past, 18, 209-232, 2022 for sporomorph-based climate estimates from marine sediments, where hydrodynamic sorting of grains may cause variations in the percentages of individual taxa  (Arias, 2015)  and also helps reduce taphonomic biases  (Klages et al.