dataesr/scientific-paragraphs-categorization huggingface dataset:
Mapping and SNP identification in the Avalon, Cadenza and bulk segregant datasets (Figure  2a ). The sequence data was mapped to both of the pseudo-chromosome reference sequences individually using the short read mapping software BWA's (v 0. 7. 10; Li and Durbin, 2009)  bwa-MEM algorithm. Indexing of the reference sequence involved use of the 'IS' algorithm. All unmapped, non-uniquely mapped/low quality (MAPQ < 10) and duplicate reads were removed from the analysis. SAMtools mpileup (v 0. 1. 18) (Li et al. , 2009a)  was implemented on the four datasets and SNP calls were filtered out using VarScan (v2. 3. 7) (Koboldt et al. , 2012)  with the following parameters: discard SNPs covered by 20 or fewer reads, discard sequencing reads with a quality less than 15 and if the alternate allele has less than two supporting reads passing the quality filter discard it.
For this SNP analysis indels were removed from the VarScan output. Implementing the mapping, SNP calling and homozygote haplotyping algorithm in iPlant. Our mapping, SNP calling and haplotyping pipelines are available on iPlant as a workflow within the Discovery Environment; 'Hexaploid Wheat Mapping By Sequencing Haplotyper'. This workflow requires a mutant parental line, a wild-type parental line and a bulk segregant mutant pool as input, as was used within this study. Users will also require one of two pseudo-chromosome wheat reference genomes, along with their corresponding files containing chromosome length, these are also available publicly on the iPlant datastore:/iplant/home/ shared/iplant_uk_training/mapping_by_seq_hexaploid-wheat. To use the workflow with the default settings used in this study, users can simply enter the sequence data files, reference sequence and chromosome length file as prompted in the app followed by clicking launch analysis.
Figure3. Homozygosity scores calculated for the bulk segregant dataset along each pseudo-chromosome. Magenta: Scores plotted for 'Cadenza specific homoeologous homozygote SNP alleles' found in the bulk segregant dataset. Blue: Scores plotted for 'Avalonspecific homoeologous homozygote SNP alleles' found in the bulk-segregated dataset. Scores calculated per 500 000-bp window along each chromosome at 10 000-bp intervals.
Detailing candidate homozygous SNP alleles for stripe rust resistance. Homoeologous homozygous SNPs found in 23-43% of reads in the bulk segregant dataset that are within the peak interval (13 650 001 and 14 150 001 bp) and that could be associated with known disease resistance genes. These SNPs are conserved with unique homozygous SNP alleles, in 23-43% of reads, which were found in the Cadenza parent. Gene annotations if SNP region can be associated with a BLASTN gene hit (E-value 1e-5).
ACKNOWLEDGEMENTS DNA sequence was generated by  The University of Liverpool Centre for Genomic Research (United Kingdom ). This project was supported by the  Biotechnology and Biological Sciences Research Council (BBSRC)  via a  Doctoral Training Grant  (LG), a  BBSRC Career Development Fellowship   BB/H022333/1  (AH), a  BBSRC grant   BB/M018431/1  (RJ), a  BBSRC grant   BB/L011786/1  (LG), a  John Oldacre Foundation  PhD studentship (PBB) and a  Natural Environment Research Council  grant (LO). The phenotyping and generation of DNA bulks was carried out by  PBB  and  DOS. The population of doubled haploid (DH) individuals, derived from F1 progeny of a cross between cvs Avalon and Cadenza, was developed by  Clare Ellerbrook ,  Liz Sayers  and the  late Tony Worland (John Innes Centre) , as part of a DEFRA funded project led by ADAS.
e. , previous contests are not "forgotten"). We began by using simple network visualization techniques for small, easily visualizable, social groups of seven individuals. Network graphs were implemented with the "plot. igraph" function in the igraph package  (Csardi and Nepusz, 2006)  for R version 3. 2. 1 (R Development Core  Team, 2015) , to demonstrate how the network and individual fitness (a function of NS), co-vary over time in different nutritional environments. In studies of animal dominance, deconstructive approaches where networks are separated into their constitutive triads, or "motifs, " are increasingly popular tools for understanding social network structure  (Shizuka and McDonald, 2012; Pinter-Wollman et al. , 2013). We used this approach to compare the structures of dominance networks generated via nutritional mechanisms in our model with the typical structure of published animal dominance networks (based on the comparative results of  McDonald and Shizuka, 2013).
We also allowed for draws, or "mutual" relationships (i. e. , instances where individuals won equal number of contests against one another, which were also coded as 1). Triads can take one of 16 different configurations, ranging from null triads (no interactions) to completely reciprocal relations between all three nodes, and we refer to these triads according to the Mutual Asymmetric Null (MAN) system  (Holland and Leinhardt, 1970, 1976). We used the "triad. census" function in the R package statnet  (Handcock et al. , 2008 (Handcock et al. , , 2015) )  to get counts of each type of triad in each network. We also simulated 1000 equivalent random matrices (using the "rguman" function in statnet), to estimate the difference in occurrence of each triad type between our ABM-derived networks and random networks with the same number of nodes, edges and null dyads (similar to the method of McDonald and Shizuka, 2013, although they did not include draws).
To do this, we generated a weighted directional network at a number of time points as above (based on contest matrices and again where competition was intense). We calculated the (normalized) closeness of each individual (node) within the network at a given time with the "closeness_w" function in the R package tnet  (Opsahl, 2009). In weighted directed networks such as which we generated, the closeness of a node is a function of: (1) the direction between two nodes (implying that the shortest path between nodes i and j is not necessarily equal to the shortest path between nodes j and i), (2) the number of edges a node has, and (3) the weight of those edges  (Opsahl et al. , 2010). The tradeoff between the influence that these last two characteristics have on closeness is governed by the tuning parameter α  (Opsahl et al.
In addition, there were on average 7. 47 less The degree of food availability equates to a "competition intensity" (c) of 0. 4, 0. 6, and 0. 8, as described in  Senior et al. (2015a). The script is available in Supplementary File S2. FIGURE 3 | Differences in the representation (%) of triad types between networks generated from the agent-based model and random networks with similar properties (same number of nodes, edges and null dyads) in a three-food environment (identical to Figure  1A ). We created 10 different networks for groups of 20 individuals using our model, with a high level of "competition intensity" (c = 0. 8) and after 20 meals. For each network we calculated the difference between itself and 1000 randomly generated networks, and took the mean of these 1000 differences. These results give the mean of the 10 sets of mean differences (black dots) and the 0.
ACKNOWLEDGMENTS AS is supported by a  Judith and David Coffey fellowship  from the  Charles Perkins Centre, University of Sydney. ML receives support from the  Centre National de la Recherche Scientifique (CNRS) , the  IDEX of the Federal University of Toulouse  and the  Fyssen Foundation. JB was funded by the  Australian Research Council   Future Fellowship  and Discovery Projects programs. DR is an affiliate of the  New Zealand Institute for Advanced Study. SS was supported by an  Australian Research Council Laureate Fellowship.
Currently, much translational research focuses on mindfulness interventions, such as Mindfulness-Based Stress Reduction 6 , which place primary emphasis on regular daily practice and include only brief periods (e. g. , one day) of intensive retreat practice. The current study provides suggestive evidence that intensive retreat practice may deserve increased consideration by researchers as an "active ingredient" in mindfulness training. This may be especially relevant if we hope to go beyond studying mindfulness practice in a short-term intervention model, and investigate its public health impact as an ongoing, long-term health-related strategy for behavior change. One limitation of the current findings is their ecological validity, since data were collected in a laboratory setting. Although respiration recordings were made outside of formal practice settings, it is also possible that LTMs spontaneously engaged in meditation practice during these periods.
Immediately prior to the second and third sessions, LTM participants completed an 8-hour day of formal practice, consisting of either Open Monitoring or Loving Kindness meditation counterbalanced across the two sessions. A subset of Control participants (n = 18) participated in a day of leisure activities immediately prior to the third session. The day of leisure for the Control group was designed to match the days of practice for the LTMs. The sitting sessions were comprised of activities such as reading, playing computer games and watching documentaries. No social interaction, which included the use of internet (email) or cell phones, took place between subjects for either group. The administration of food and drink was held constant for both groups. All data collection occurred after the completion of the day of practice.
Respiration measures. At each study session, inductance plethysmography was used to capture respiration signal during a seated, eyes-open, uninstructed resting period lasting 370 seconds. Respiration signal was recorded on a polygraphic input box developed by Electrical Geodesics, Inc. , using an abdominally placed ProTech DuraBelt #1073622 belt. From each recording session, we calculated mean respiration rate (RR) using time-domain analysis, following established guidelines  32, 33. Processing of the recorded signal was performed using Matlab r2014b (The MathWorks Inc. , Natick, MA). Segments with distorted or missing signal were excluded from further processing; recordings without any detectable signal were excluded from further analysis. Usable data was available for 30 LTM and 35 Controls at the Baseline session; 24 LTM for the Open Monitoring practice session, alongside 30 Control participants; and 21 LTM for the Loving-Kindness practice session, alongside 29 Control participants.
Statistical analysis. Analyses were conducted in R (3. 1. 1, R Foundation for Statistical Computing, http:// www. r-project. org/). Mixed effects models were fit using the lme4 package (1. 1). Results of mixed-effects models were consistent with ordinary least-squares (OLS) regressions on weighted average and session-by-session RR values. In all mixed-effects models, a random effect of Participant was included, with intercept estimated at the first level. All other predictors were modeled as fixed factors. Effect sizes are reported in observed units (e. g. breaths/min). Maximum likelihood t-and F-tests were conducted using Satterthwaite approximations for pooled degrees of freedom, using the lmerTest package (2. 0), with approximated degrees of freedom reported to nearest integer values. Significance tests for post-hoc contrasts used Tukey's HSD correction for multiple comparisons.
Acknowledgements The authors wish to acknowledge  Donal MacCoon ,  David Bachhuber ,  Tammi Kral , and  Amy Cayo , among many who contributed to study planning and execution;  David Perlman ,  Robin Goldman ,  Melissa Rosenkranz , and  John Dunne  for critical comments and suggestions;  Jeanette Mumford  for statistical advice,  Kaley Edwards ,  Nagesh Adluru ,  Amy Cayo , and  Chad Sebranek  for technical assistance; and  Isa Dolski  for administrative assistance. This work was supported by grants from the  National Center for Complementary and Integrative Health (NCCIH)   P01AT004952  to  Richard J. Davidson , a gift from the  Adam J. Weissman Foundation , and a core grant to the  Waisman Center  from the  National Institute of Child Health and Human Development (NICHD)   P30 HD003352  to  Albee Messing.
Methods The synthesis was carried out in Pyrex glass bottles with screw cap and a volume of 100 mL. Powder X-ray diffraction (PXRD) data was collected on a STOE Stadi P diffractometer equipped with a linear position sensitive detector (PSD) using monochromated CuK α1 radiation in transmission geometry. All processing of the crystallographic data was done with TOPAS. 28 XRD data depending on humidity was collected on a Bruker PANalytical Empyrean diffractometer (CuK α -radiation). Elemental analysis was measured with an Eurovektor EuroEA Elemental Analyzer. IR-spectra were measured on a Bruker ALPHA-FT-IR A220/D-01 spectrometer equipped with an ATRunit. The thermogravimetric analyses were recorded using a TA instruments Q500 with a heating rate of 10 K min -1 under oxygen flow. Sorption experiments were performed using a BEL JAPAN INC.
Both the 1 H and  13  C spectra are referenced with respect to TMS (tetramethylsilane) using the secondary standard adamantane. Finally, the 27 Al (156. 4 MHz) 1D MAS experiments were acquired using a rotor-synchronized spinecho with central transition selective 90°and 180°pulses of 12. 5 and 25 µs (to remove the resonances from the satellite transitions) at a spinning speed of 20 kHz (3. 2 mm MAS triple resonance probe, Bruker). The reference was set using a solution of AlCl 3 adjusted to a pH of one with hydrochloric acid. The  27  Al spectra were fitted using the simulation package SOLA included in Bruker's Topsin3. 2 software. The force field based-geometry optimizations were conducted using the Forcite software available in the Materials Studio package and the generic Universal Force Field Lennard-Jones parameters for all atoms of the MOF framework.
Point correspondence? Fig. 1  Point-to-point correspondence problem in complex cardiac morphologies. Widely used parametric methods to build statistical shape models are based on the so called Point Distribution Model (PDM)  [5] , in which shapes are parameterised by landmarks. Bookstein et al. [40]  define landmarks as points on the structure's surface for which "objectively meaningful and reproducible […] counterparts  […] " exist in all other structures present in the dataset. In complex cardiac structures however, those point correspondences are difficult to establish, as illustrated here for two aortic arch models from the CoA cohort compute a template (i. e. an "anatomical mean shape") and describe shape variability around this template, the framework is based on a forward approach  [14] , which essentially describes each subject as a deformation of the template plus some residual information (Fig.
To describe the deformation data with the minimum number of required parameters, a statistical method called partial least square regression (PLS)  [12, 22] , is employed (Fig. 6a ). PLS allows the extraction of shape modes  [5] , which represent the dominant, most common shape features observed in the population that are most correlated with a specific parameter of interest (such as a clinical parameter measured on the individual patient). Here, shape modes most related to body surface area (BSA) and the functional parameter ejection fraction (EF) were extracted. Extracted shape patterns described by PLS shape modes are visualised by deforming the computed template shape with the transformations along the direction of the mode (Fig. 6a ). To determine whether the obtained shape patterns are correlated with a response parameter, shape modes need to be broken down to numbers that allow statistical analysis.
6c ). A perfect correlation of shape vector and response would imply that the derived shape mode showed exactly those shape patterns associated with low or high response values (such as high or low EF) when moving along the shape mode from low to high shape vector values. For mathematical details about the shape modelling process as outlined above, we refer to Appendix 1 and the referenced literature. The entire mathematical framework has been published under the name "exoshape" and is publicly available as a Matlab (The MathWorks, Natick, MA) code  [12, 22] , (https://team. inria. fr/asclepios/software/exoshape/). A similar, open-source code has been recently published in C as "Deformetrica" by Durrleman et al. [23]  (http://www. deformetrica. org/). The described SSM framework was applied to the CoA patients following the steps as explained in detail in the next sections (Fig.
Patient population, image data and 2D morphometry Images were segmented using thresholding and regiongrowing techniques combined with manual editing in commercial software (Mimics, Leuven, Belgium)  [24]. A previous study comparing physical objects and their respective 3D segmented and reconstructed computer models found an average operator induced error in the order of 0. 75 mm, which equals about half the voxel size in our study  [25]. In order to reduce irrelevant shape variability, aortas were cut such that only the root, the arch and the descending aorta up to the diaphragm were kept. As the focus of this analysis lies on the arch shape, coronary arteries and head and neck vessels were cut as close as possible to the arch. This is a common pre-processing step in shape analysis of aortic arches  [26] [27] [28].
The final segmented surface models of the aortas were stored as computational surface meshes. Conventional 2D morphometry was carried out manually on CMR imaging data to measure the ratio of aortic arch height (A) and width (T) as well as the ascending and descending aortic arch diameters (D asc and D desc, respectively) at the level of the pulmonary artery as proposed by Ou et al. [17]  (Fig. 8a ). Diameters at the transverse arch level (D trans ) and at the isthmus level (D isth ) were measured manually as described previously  [24]. 3D shape parameters were computed semi-automatically from the segmented arch surface models using The Vascular Modeling Toolkit  [29]  (VMTK, Orobix, Bergamo, Italy; www. vmtk. org) in combination with Matlab. Extracted geometric parameters included volume V and surface area A surf , as well as parameters associated with the vessel centreline such as length, curvature and tortuosity  [30, 31] , and inner vessel diameters along the centreline (minimum D min , maximum D max and median diameters D med ) (Fig.
Detecting outliers or influential subjects In preliminary studies, PLS regression proved to be prone to be influenced by outliers. Outliers in terms of shape are common in clinical data of pathological shapes; particularly in the field of CHD, where intersubject shape variability is typically large. In order to detect influential observations in the PLS regression, the Cook's distance was measured. The Cook's distance measures how much a specific subject influences the final regression result by leaving out that subject and comparing all remaining fitted values to the original, full data fitted values. It is defined as (Eq. 3)  [35] D i ¼ P n j¼1 y j -y j i ð Þ 2 p⋅MSE ;MSE ¼ 1 n X N i¼1 y i -y ð Þ 2 ð3Þ with y j being the j th fitted response variable and y j (i) being the j th fitted response variable if the fit does not include observation i; p is the number of coefficients in the regression model and MSE is the mean square error.
K-fold cross-validation In order to assure that the final template shape is not overly influenced by adding or leaving out a specific subject shape, k-fold cross-validation was performed  [11]. The entire dataset was divided into k = 10 randomly assigned subsets. The template calculation was run k times, each time leaving out one of the subsets until each patient had been left out once. As the entire set consists of N = 20 datasets in total, in each of the k runs N/k = 2 patients were left out. The 10 resulting templates should all be close to the template calculated on the full dataset of N = 20 patients. This was assessed visually by overlaying the final template meshes and numerically by measuring the surface distances between each of the 10 templates and the original template.
Conclusions In this paper, we presented a non-parametric shape analysis method based on CMR data from the userperspective and applied it to a population of aortic arch shapes of patients post-aortic coarctation repair. The process was described in detail in order to make it more accessible to researchers from both clinical and engineering background. The method has the potential of discovering previously unknown shape biomarkers from medical image databases and could thus provide novel insight into the relation between shape and function. Application to larger cohorts could contribute to a better understanding of complex structural disease, improving diagnosis and risk stratification, and could ultimately assist in the development of new surgical approaches. Appendix 1: Detailed description of the statistical shape modelling framework.
e. how shape features are distributed. The crucial parameter is the standard deviation of the kernel λ W , which allows to control how coarsely or finely a surface is resolved by currents  [14]. If λ W is chosen too small, too many shape features are captured and noise dominates, whereas if λ W is chosen too large, important characteristics may be lost  [12]. Thus, the parameter λ W is referred to as the resolution of the currents representation. It is defined in millimetres and is one of the parameters to be set by the user. To encode all 3D shape information present in the dataset, the template is transformed, i. e. deformed into each of the shapes used to compute it via transformation functions φ i (Eq. 5). Similar to the definition of the space of currents, subject-specific transformation functions φ i are defined within another vector space V as a Gaussian kernel with standard deviation λ V.
Here, we retained the first PLS shape mode only in order to capture only shape features most related to either BSA or EF and thus to avoid overfitting. The shape modes describe the main components of shape information present in the population, so that each subject shape in the dataset can be approximated by linearly combining shape modes. Thus, the shape of subject i is characterised by a unique linear combination of m weights for m shape modes as exemplified in (Eq. 6). Shape i ¼ X m k¼1 weight i k ⋅Mode k ð6Þ Those subject-specific weights constitute the shape vector X S , which is obtained by projecting each patient transformation onto the shape modes  [12]. Correlating the shape vector and response parameter Y shows how well each subject's shape features (supposedly related to Y) are represented by the derived shape mode.
Mathematical details of how our framework allows deriving PLS shape modes and shape vectors based on deformations can be found in  [21]  and  [12]. PLS shape modes were computed using the plsregress function in Matlab. Appendix 2: Preliminary sensitivity analysis of meshing and λ parameters A sensitivity analysis was performed to investigate how the mesh resolution of the input surface models and the setting of the λ parameters affect the template shape and the computational time needed to compute a template. Segmented 3D surface models of 5 randomly chosen aortic arches were meshed from high (5 cells/mm 2 ), to medium (2. 5 cells/mm 2 ) to low (0. 5 cells/mm 2 ) mesh resolution using VMTK. A template was calculated for each of the differently meshed test sets, first with λ W and λ V being constant at 15mm.