dataesr/scientific-paragraphs-categorization huggingface dataset:
The model spatial resolution was 2° × 2°, and the resolution for time and age dimensions was one month. The skipjack tuna reference model was obtained by integrating all available geo-referenced data-catch, length-frequency of catch and tagging release-recapture data-into a likelihood function and obtaining the solution using the maximum likelihood estimation (MLE) approach (Supplementary Note 7). The initial habitat and movement parameters for bigeye and yellowfin tuna were also estimated by integrating tagging data into the model; however, the final parameterizations of the reference models for these two species were based mainly on fisheries data. The methodology and optimal reference solutions obtained for skipjack, yellowfin and bigeye tuna, and model validations with statistical metrics, are described in other publications documenting the use of SEAPODYM  13, [63] [64] [65].
Acknowledgements This article is dedicated to our co-author  Sue Taei , who lost her battle with cancer recently. Sue was a regional thought leader and was instrumental in helping to design this study and obtain the resources needed to extend the tuna modelling from exclusive economic zones to high-seas areas to examine the implications for Pacific Island economies. We thank  T. Ruaia  for providing data on access fees;  C. Appel ,  B. Colas ,  A. Desurmont  and  E. Johansen  for assistance with figures; and  P. Gardiner ,  S. Kay ,  J. Kittinger  and  P. Obregon  for helpful discussions/comments on the draft manuscript. J. D. B. , I. S. and P. L. thank the  Moccasin Lake Foundation  and the  GEF-funded ,  World Bank-implemented Ocean Partnerships for Sustainable Fisheries and Biodiversity Conservation , a sub-project of the  Common Oceans ABNJ Program  led by  UN-FAO , for financial support for this research and preparation of this article.
SEAPODYM simulations were also partially funded from the  European Union 's  Horizon 2020 research and innovation programme COMFORT , under grant agreement no. 820989  (noting that the  European Commission  and their executive agency are not responsible for any use that may be made of the information from the simulations). Q. H. , H. H. -D. , Y. O. and K. L. S. acknowledge support from the  Nippon Foundation Ocean Nexus Center  at the  University of Washington Earthlab.
Data analysis This study involves model simulations and analysis of model outputs. The simulations done with custom SEAPODYM software written in C++, and all data processing and algorithms for the model output analyses developed using R programming language and R open source software, are available at https://osf. io/qa8w4/ For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e. g. GitHub). See the Nature Research guidelines for submitting code & software for further information.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Raw data were generated by the Earth System Models and the SEAPODYM ecosystem model described in the Methods. The 3-D ocean data from the Earth System Models in netcdf format are available at http://data. umr-lops. fr/pub/AFCM85/ The 2-D forcing input data files for SEAPODYM, and the output data files to reproduce the results are available at https://osf. io/qa8w4/ The spreadsheets used to produce Supplementary Tables 5-20 are available at https://osf.
Purpose: To evaluate the adherence rate and the contribution of self-rehabilitation (SR) guided by a mobile application after anterior cruciate ligament reconstruction (ACLR) in combination with physical therapy sessions on early knee function. Methods: This study was a retrospective analysis of prospectively collected data from a single health care facility. All patients who underwent ACLR by a single surgeon from December 2019 to September 2020 were included. Two groups were formed and compared based on use of the mobile app: users (>10 days of use) and nonusers ( 10 days of use). Outcomes included physical examination at 3 and 6 weeks postoperatively. Results: A total of 65 patients were analyzed: 19 in the nonuser group and 46 in the user group. Adherence rate was 91% at 10 days, 71% at 15 days, 62% at 21 days, and 44% at 45 days.
Methods The study was approved by our hospital's research and ethics committee and was performed in accordance with the Declaration of Helsinki's ethical standards. Informed consent was obtained from all participants before enrollment in the study. A retrospective analysis of prospectively collected data from a single-center database was performed. All patients operated on from December 2019 to September 2020 by a single senior surgeon (J. M. F. ) and reviewed in person by a single independent practitioner (L. B. , nonsurgical sports medicine physician) at the 3-and 6week follow-up examinations were included. In our center, patients are randomly reviewed by 4 different sports medicine physicians. Inclusion criteria were all patients undergoing primary ACLR with or without meniscus repair, meniscectomy, or lateral extraarticular reconstruction.
Despite enormous research efforts, the genetic component of longevity has remained largely elusive. The investigation of common variants, mainly located in intronic or regulatory regions, has yielded only little new information on the heritability of the phenotype. Here, we performed a chip-based exome-wide association study investigating 62 488 common and rare coding variants in 1248 German long-lived individuals, including 599 centenarians and 6941 younger controls (age < 60 years). In a single-variant analysis, we observed an exome-wide significant association between rs1046896 in the gene fructosamine-3-kinase-related-protein (FN3KRP) and longevity. Noteworthy, we found the longevity allele C of rs1046896 to be associated with an increased FN3KRP expression in whole blood; a database look-up confirmed this effect for various other human tissues.
Exome Chip Genotype Calling and Quality Control in the German Study Population The samples were genotyped on the Infinium HumanExome-12v BeadChip (244,770 SNVs) (Illumina Inc. , San Diego, CA; Supplementary Figure  1 ). Quality control was done with the software Plink 1. 9  (21). In total, 224 samples were removed because they had failed one or more of the following inclusion criteria: concordant sex information, missing genotype < 3%, heterozygosity rate greater or lower than ± 4SD from the mean, and no relatedness of individuals. Relatedness was estimated using the identity by descent (IBD) metric  (22). In case of relatives (IBD > 0. 1875; halfway between the expected IBD for third-and second-degree relative (  22 )), only one individual was included in the analysis. SNVs were excluded if the missing rate was too high (> 3%) or, for common SNVs in the control sample, if they deviated from the Hardy-Weinberg equilibrium (HWE) (p <.
The Sanger imputation service (http:// www. sanger. ac. uk/science/tools/sanger-imputation-service) and the 1000 Genomes phase I v. 3 reference panel were used to enrich the pool of common SNVs. In addition to the single-variant association testing, a genebased analysis was performed. All SNVs that passed quality control (112,977 SNVs) were used to generate the gene set (Supplementary Figure  1 ). Potential cumulative effects of rare and common variants with longevity were tested using both burden and non-burden (ie, SKAT) approaches (application of the RC-SKAT algorithm from the R-package SKAT  (26) ). Both approaches were considered because burden tests were shown to perform better when multiple variants in a region are causal and influence the phenotype in the same direction  (27) , while non-burden tests, like SKAT  (28) , are more advantageous when SNVs in the region interact or show opposing directions of effect  (29).
The MDR interaction model describes the percentage of entropy (information gain) by each SNV or SNV interaction. Positive values of entropy indicate synergistic or nonadditive interactions, while negative entropy values indicate redundancy between the markers or lack of synergy between the interacting markers. The model (ie, combination of SNVs) that appeared most consistently among replicates in the training balance accuracy level was considered the best model. Significance was calculated via permutation tests using 1000 permutations and a significance level of α = 0. 05. MDR was implemented employing the open-source MDR software package version 3. 0. 2 (https://www. mybiosoftware. com/mdr-2-0-multifactor-dimensionality-reduction. html)  (31). The functional implications of the longevity variants were assessed using our previously published gene expression data from whole blood samples of 55 LLI and 73 control individuals (independent cohort from Germany)  (32)  as well as publicly available data from the Blood eQTL browser (https://genenetwork.
Genotyping in the Replication Cohorts The Danish LLI were genotyped using the Illumina HumanOmniExpress Array (Illumina Inc. ). Preimputation quality control included filtering of SNVs on genotype call rate <95%, HWE p < 10E-04, MAF < 1% and filtering of individuals on sample call rate <95%, relatedness, and sex mismatch. After imputation to the 1000 Genomes phase I v. 3 reference panel, genotype probabilities were converted to hard-called genotypes in PLINK 1. 9 (using a cutoff of 90%)  (21). For the Danish controls, data for the SNVs rs1063192, rs1046896, and rs13119846 were extracted from quality-controlled genotype data (as above) created using the Illumina Infinium PsychArray (Illumina Inc. ). French individuals were genotyped by TaqMan (Thermo Fisher Inc. , Waltham, MA) on a 7900HT Fast Real-time PCR System (Thermo Fisher Scientific Inc.
8 based on the CEU subpopulation from the 1000 Genomes Project Phase 3; Supplementary Table  3 ) influence local or distant gene expression. In our previously published whole blood transcriptome data from 55 LLI and 73 control individuals (independent cohort from Germany)  (32) , we observed significant cis-eQTL associations of rs1046896 (and high LD SNVs) with the expression of FN3KRP. FN3KRP gene expression was higher in the presence of the rs1046896 longevity allele C (major allele) (Figure  2A ). Using publicly available data from the Blood eQTL browser (https://genenetwork. nl/   Full color version is available within the online issue. bloodeqtlbrowser/)  (33)  and the GTEx eQTL database (https:// www. gtexportal. org/home/; accessed April 5, 2019), we confirmed the allele-dependent expression of FN3KRP in several tissues (eg, brain regions, testis, pancreas; Supplementary Table  4 ; Figure  2B ).
Funding G. G. T. was funded by the  Cluster of Excellence "Inflammation at Interfaces " ( EXC 306 ). This work was supported by the  German Federal Ministry of Education and Research (BMBF)  within the framework of the e:Med research and funding concept (SysInflame grant  01ZX1606A ). The biobank PopGen and the PopGen 2. 0 network are financed by the  BMBF  (grant  01EY1103 ). The CEPH centenarian cohort, maintained at the  CEPH Biobank, Paris, France  [BIORESOURCES], was supported by the  "Ministère de l'Enseignement supérieur et de la Recherche. "The SU. VI. MAX cohort was funded by the  French Institut National de la Santé et de la Recherche Médicale , the  Institut National de la Recherche Agronomique , the  Université Paris 13. The genotyping of French samples was supported by the  Commissariat à l'Energie Atomique-Centre National de Recherche en Génomique Humaine.
The Danish replication study was supported by The  National Program for Research Infrastructure 2007  (grant no. 09-063256 ), the  Danish Agency for Science Technology and Innovation , the  Velux Foundation , and the  U. S. National Institute of Health  ( P01 AG08761 ). Genotyping of the Danish controls was conducted by the  SNP&SEQ Technology Platform, Science for Life Laboratory, Uppsala, Sweden  (http://snpseq. medsci. uu. se/genotyping/snpservices/) and supported by  NIH   R01 AG037985  (Pedersen). The  KORA research platform (KORA, Cooperative Research in the Region of Augsburg)  was initiated and financed by the  Helmholtz Zentrum München -German Research Center for Environmental Health  which is funded by the  BMBF  and the  State of Bavaria. Furthermore , KORA research was supported within the  Munich Center of Health Sciences  ( MC Health ),  Ludwig-Maximilians-Universität , as part of  LMU innovativ.
In this paper, we present GMG-BCU -a local search algorithm based on block coordinate update for estimating a generalized median graph for a given collection of labeled or unlabeled input graphs. Unlike all competitors, GMG-BCU is designed for both discrete and continuous label spaces and can be configured to run in linear time w. r. t. the size of the graph collection whenever median node and edge labels are computable in linear time. These properties make GMG-BCU usable for applications such as differential microbiome data analysis, graph classification, clustering, and indexing. We also prove theoretical properties of generalized median graphs, namely, that they exist under reasonable assumptions which are met in almost all application scenarios, that they are in general non-unique, that they are NP-hard to compute and APX -hard to approximate, and that no polynomial α-approximation exists for any α unless the graph isomorphism problem is in P.
Introduction Labeled graphs provide a powerful structure to represent, process, and analyze data in a wide variety of scientific domains such as cancer detection  [1] , keyword spotting in handwritten documents  [2] , and many more  [3]. However, determining fundamental tools such as a distance or an average graph is non-trivial. Given a space G of labeled simple graphs, the graph edit distance (GED) is a natural choice for comparing two graphs  [4, 5]. It measures the minimal amount of distortion needed to transform one graph into another by means of edit operations. Given cost functions for the edit operations, GED can be defined as the minimum cost of a node map (i. e. , a relation between the nodes of the source and the target graph) that specifies for each node whether it should be substituted, inserted, or deleted.
As an outlook, we discuss potential applications. Here, we show to use GMG-BCU for computing representative graphs for microbiome data, which can then be used for downstream differential data analysis. We also point out to the fact that, once scalable estimators are available, generalized median graphs can be used beneficially for tackling higher-level problems such as graph clustering, classification, and indexing. Finally, we report the results of an extensive empirical evaluation. The evaluation shows that our algorithm clearly outperforms existing competitors, and that it yields extremely promising results when used within the aforementioned applications scenarios. In sum, the paper contains the following contributions: • We show that generalized median graphs exist under mild assumptions, that they are in general non-unique, and that they are hard to compute and approximate.
• We present GMG-BCU, a scalable and efficient local search algorithm based on BCU that produces locally optimal estimated generalized median graphs and can be configured to run in linear time w. r. t. the size of the graph collection whenever median node and edge labels are computable in linear time. • We point out to the fact that, thanks to our new efficient algorithm, generalized median graphs can be beneficially applied in an application from bioinformatics as well as for higher-level problems such as graph clustering, classification, and indexing. • We report the results of extensive experiments, showing that GMG-BCU clearly outperforms the state of the art. • A well-documented, ready-to-use C++ implementation of GMG-BCU is freely available at https://github. com/dbblume nthal/gedlib/.
Increasing the order Checking whether augmenting the order of the current median might yield a lower SOD proves a more complex task. We allow the algorithm to add a single isolated node to the median, and the objective is to infer both the label and the assignments of the new node that minimize the variation of the SOD. If the minimal variation is negative, a new node will be added to the current median. For each graph G p ∈ G and its current node map π p , we define I p as the set of indices of nodes that are inserted in π p , plus the dummy index ϵ. Let Z = I 1 × I 2 × • • • × I |G|. Each element z := {z 1 ,. , z |G| } ∈ Z encodes a configuration that describes how to map the new potential node in each graph of the dataset, where z p is the index of the node in G p to which the new potential node in G ⋆ is mapped.
Experimental setup 7. 1. 1. Datasets We tested on three widely used benchmark datasets, two datasets containing synthetically generated graphs, and one microbiome dataset from a biomedical application. Table  3  summarizes the most important properties of the datasets. In the following paragraphs, we describe them in more detail. All datasets and edit cost functions respect constraints (C1) to (C3). Benchmark We tested on the widely used benchmark datasets letter, aids, and muta from the IAM Graph Database Repository  [75]. The graphs in aids and muta represent molecules that do or do not exhibit activity against HIV and mutagenicity, respectively. They have symbolic labels both on the nodes and on the edges. The graphs in letter represent distorted drawings of all capital roman letters that can be drawn with straight lines only.
Synthetically generated datasets. We also tested on the two synthetically generated datasets s-mol and aids-edit to evaluate scalability (s-mol) and quality (aids-edit). The dataset s-mol contains 5 • 10 4 synthetic molecule graphs, which were generated as follows: In a first step, the order n of the graph G was randomly selected from the range {10,. , 15}. Subsequently, we randomly generated a Prüfer sequence of length n -2, constructed G as the corresponding tree on n nodes, and randomly selected node labels (modeling atoms) from {1,. , 5} and edge labels (modeling valence) from {1, 2}. For the tests on s-mol, we used the same edit cost functions  [28, 76]  as for aids and muta. The aids-edit dataset was generated such that we (almost) know the optimal SOD for each sub-collection. This was achieved as follows: In a first step, we randomly selected a seed graph G 0 from the aids dataset.
Subsequently, we generated all graphs H that can be reached from G 0 via exactly one edit operation, computed lower bounds for ⌊GED⌋(H 1 , H 2 ) for each pair (H 1 , H 2 ) of generated graphs, and created a conflict graph X with edges (H 1 , H 2 ) for all (H 1 , H 2 ) with ⌊GED⌋(H 1 , H 2 ) = 0. In the last step, we heuristically computed a maximum independent set S for X and put all 542 surviving graphs H ∈ S in the dataset aids-edit. If used with uniform edit costs, this construction ensures that all graphs contained in aidsedit are at distance 1 from G 0 and at distance at least 1 from each other. Therefore, the optimal SOD for each sub-collection G of aids-edit is lower-bounded by |G| -1 and upper-bounded by |G|. Microbiome dataset. Finally, we tested on the real-world dataset ibd which contains graphs that model the gut microbiome of 133 patients with inflammatory bowel disease (IBD) and 6 controls  [77].
Test protocol and implementation For each dataset D except ibd and each p ∈ {10, 20,. , 90}, we randomly sampled five collections (D p i ) i∈  [5]  containing p % of the graphs. For each p, we ran the tests on all five collections and aggregated the results. First, we evaluated various configurations of GMG-BCU on the datasets letter-eucl, aids, and muta. We then selected the three best performing configurations and compared them against the state of the art on these three datasets and additionally on letter-squared, aids-edit, and s-mol. Subsequently, we compared the ibd prototypes computed by the configurations of GMG-BCU to those computed by the state of the art, and preliminarily evaluated the performance of one GMG-BCU configuration within heuristics for graph clustering, classification and indexing.
Number of iterations. The first row of Fig. 2  shows that the maximal number of iterations of the configurations. To improve the readability of the plots, we only show the results of those configurations that were not Pareto dominated on at least one dataset. We see that the maximal number of iterations mainly depends on the label types. Since the graphs contained in aids and muta have symbolic node and edge labels, the label domains L V and L E are finite, which leads to a very small number of iterations. Graphs contained in letter are labeled with Euclidean coordinates, i. e. , L V is continuous, which increases the number of iterations. In contrast to that, the average size of the graphs turns out not to increase the maximal number of iterations. Although letter graphs are smaller than aids and muta graphs, GMG-BCU required more iterations on letter-eucl than on muta and aids.
Another important observation is that, on all datasets, the number of iterations is independent of the size of the graph collection G, of the initialization strategy, and also of the GED heuristic used during the BCU. (The low number of iterations of configurations that use medoid initialization on muta and aids is due to the fact that these methods exceeded the time limit during initialization. ) Unless medoid initialization is employed, the runtime behavior of GMG-BCU is hence linear in the size of G, even if run without a maximal number of iteration. Effect of algorithm used during descent. The second row of Fig. 2  shows how the non-dominated configurations performed w. r. t. mean runtime and SOD. Interestingly, the dominated configurations include all configurations that use IPFP during the BCU, although IPFP has been shown to produce tighter upper bounds for GED than BRANCH-FAST and 2-REFINE  [9, 10].
The reason for this is that IPFP is by far the slowest of these heuristics and therefore very often reached the time limit already after very few iterations of the BCU. Most GED heuristics led to pretty similar SODs on all datasets; in terms of SOD, GMG-BCU is hence robust w. r. t. the choice of this heuristic. On letter-eucl, the best configuration used BRANCH-FAST during the BCU; on aids and muta, the best configuration used 2-REFINE. Another interesting observation is that, on letter-eucl, different GED heuristics did not have a great effect on the runtime. The reason for this is that the graphs contained in letter are very small and that their node labels are Euclidean coordinates (cf. Table  3 ). In this setting, computing node maps via any GED heuristic is fast and evaluating median-label(•) hence becomes the dominant operation.
This operation is the same for all GED heuristics. On the other datasets, computing the node maps is the dominant operation. Therefore, configurations that use the fast GED heuristic BRANCH-FAST during the BCU and do not carry out the IPFP-based tightening clearly outperformed all other configurations in terms of runtime. Effect of initialization type. The second row of Fig. 2  also shows that, on all datasets, randomly initialized configurations with tightening computed the tightest SODs, and that also among the fast configurations without tightening the best results were achieved with random initialization. In view of these findings, we included the following three configurations in the time limit tests and in the comparison against the state of the art: GMG-BCU (I) Random initialization, BRANCH-FAST during BCU, with tightening (yielded best mean SOD on letter).