dataesr/scientific-paragraphs-categorization huggingface dataset:
Strengths and limitations of study This study has several strengths. Firstly, repeat assessment of physical activity over 28 years allowed us to examine trajectories of physical activity before diagnosis of dementia by using an innovative analytical strategy anchored to the cause of disease, reflected in use of the backwards timescale. Conventional analysis tends to use wave of data collection to study change, which is not optimal when preclinical disease affects risk factor levels. We were also able to assess associations with both mild and moderate to vigorous intensity physical activity. Sensitivity analysis using a case-control approach (six matched controls for each dementia case) ensured better control for confounding (by age, sex, and education) and period effects that may affect diagnosis of dementia.
Comparison with other studies 1] [12] [13] [14] [15]  This inconsistency has raised concerns about the causal nature of the association between physical activity and cognitive outcomes. Interventions use objective measures of physical activity, whereas a lot of the observational data come from self reported measures of physical activity. Our results are based on reported physical activity; however, as reporting biases at the start of the study are unlikely to be affected by dementia status (mean follow-up 26. 6 years), this is an unlikely source of major bias for the observed associations between physical activity and dementia in our study. A further source of inconsistency in results in this domain is that much of the evidence comes from studies in which physical activity was measured at older age (65 years or more) and the follow-up was short (less than 10 years),  making them prone to reverse causation biases.
4  Results from studies with a long follow-up are also inconsistent. [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19]  The Caerphilly Prospective Study and the Adult Health Study found no association between midlife physical activity and incidence of dementia at older ages. 8 48  However, data from the AGES-Reykjavik Study and CAIDE (Cardiovascular Risk Factors, Ageing, and Dementia) show that people who were less active in midlife had a higher risk of dementia at older ages. 18 19  Intriguingly, the CAIDE study found no association between physical activity and dementia when analyses were based on the entire target population (n=544 (15. 3%) dementia cases out of 3559 people) and electronic health records were used to identify dementia cases; in contrast, a protective effect was seen in the subsample (n=250 (16.
Whole-exome massively parallel sequencing DNA from MBC and matched germline of the 35 cases was subjected to whole-exome capture using the SureSelect Human All Exon v4 (Agilent) platform and to massively parallel sequencing on an Illumina GAIIx or HiSeq 2000 at the Institute of Cancer Research, UK (GAIIx) or Memorial Sloan Kettering Cancer Center (MSKCC) Integrated Genomics Operation (IGO, HiSeq 2000) following validated protocols  (22). Paired-end 75/76/101-bp reads were generated (Supplementary Table  S1 ). Sequencing data have been deposited in the NCBI Sequence Read Archive under the accession SRP073692. Whole-exome sequencing analysis was performed as described  (22)  with modifications. CNAs were identified using FACETS  (23)  and the cancer cell fraction (CCF) of each mutation using ABSOLUTE (v1.
Comparative analysis with triple-negative IDC-NSTs and statistical analysis Clinical and molecular data for triple-negative IDC-NSTs from The Cancer Genome Atlas (TCGA, n=69) (9) were obtained from the TCGA Data Portal (https://tcga-data. nci. nih. gov/ docs/publications/brca_2012/). Comparisons of continuous and categorical features were performed using Mann-Whitney U and Fisher's exact tests as appropriate. p<0. 05 were considered statistically significant. All tests were two-sided. Statistical analyses were performed with R v3. 1. 2 and SPSS v24 (IBM, Supplementary Methods).
Acknowledgments The authors would like to acknowledge  Dr Deepu Alex  and  Dr Snjezana Dogan  for their assistance with the HER2 FISH analysis. Financial support: JSR-F is funded in part by the  Breast Cancer Research Foundation. SP is currently funded by  Swiss National Foundation  (Ambizione grant number  PZ00P3_168165 ). Research reported in this publication was supported in part by a  Cancer Center Support  Grant of the  National Institutes of Health/National Cancer Institute  (Grant No. P30CA008748 ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Utility for Teaching and Research A digital 3D model holds spatial information, marking a significant deviation from its static 2D predecessors. It is essentially a code that records scale, texture, and colour in minute detail, building point clouds from hundreds of photographs, connecting them up with a mesh before imposing surface texture and colour. 4 On Sketchfab, this is conveyed on the screen using linear perspective, creating an illusionistic sense of pictorial recession as the image is manipulated by the viewer. It is an accessible, detailed and interactive means of representation that serves art-historical research by providing accurate data and stimulating new perspectives. There are many other objects in museum storage that could be similarly interpreted, bringing long-hidden resources to students and allowing young scholars to engage in primary research early in their careers.
The Micropasts project has also shown that volunteers across the world are willing to contribute to other parts of the photogrammetry workflow. Virtual technologies can aid the contextualization of originals. The data from the 3D reproduction can be used to create virtual reality (VR) and augmented reality (AR) experiences that help communicate the meaning of historical objects. With minimal setup, all models uploaded to Sketchfab can be viewed in VR on desktops and smartphones. As content production for these technologies is still young, the field is wide open for experimentation. In fact, 3D modelling can be used to generate a wealth of experiences, whether real or virtual. 3D files can be downloaded and printed, thereby taking on a third existence. Not only does this bring the public closer to artefacts in a very personal way, but it is also allowing people whose primary means of experiencing the world is through touch rather than sight to learn by feeling the form of an artefact.
Some people have even brought their own 3D prints to meet the original at the museum. 3D data can complement a variety of resources and help to dramatize collections. By combining it with images, audio, and text it is possible to create rich sensory experiences. Museum in a Box is just one of the initiatives seeking to leverage the value of 3D in combination with other media for the benefit of education within the cultural heritage field. Finally, the experiences of operators of virtual material are as real as those of any visitor to the museum gallery. As people spend more time browsing online, 3D digitization offers a chance for historic content to reach new audiences. 3D models from the British Museum have been featured on specifically "tech" oriented sites like Gizmodo and Techcrunch. Likewise, the community on the Sketchfab platform itself is diverse, including video game artists, scientific researchers, VR enthusiasts, and engineers.
By publishing to Sketchfab, which has averaged twenty million visitors per year in five years, the British Museum is able to reach audiences that may not be among the seven million visitors per annum to its galleries. It is probably worth noting at this point that 3D modelling cannot do everything we would hope at the moment. Photogrammetry is an accessible method of digitization but it has its limits-it's extremely difficult to scan very shiny objects and near impossible to get results from images of transparent or translucent subjects. Additionally, there is still a huge amount of work to be done in regards to formalizing 3D modelling within the wider context of museum work: acceptable levels of precision; universal scale and colour standards; how best to archive 3D files and in what formats for future use; the ethics involved in the scanning and display of some subjects; linking 3D data on platforms like Sketchfab to the masses of metadata generated; how to display often huge 3D datasets on the widest variety of devices.
There are also notable examples of negative 3D public engagement: the Palmyra arch debacle is a prominent example of the mistaken belief, perpetrated by the popular press, that recording and documentation processes are equal to the preservation of original artefacts. The future is positive for the ethical use of applied 3D technology, and the public is the key. The production of 3D data is now democratized, the ubiquitous mobile phone in your pocket allied with the power of cloud computing, allows everyone to create or even co-create high quality output. Whether the public wishes to consume them in their everyday activities is to be seen, but opportunities to push this consumption to mainstream audiences will increase annually.
Footnotes B. Spencer, Pilgrim Souvenirs and Secular Badges (London: Her Majesty's Stationery Office, 1998), 13. Beryn, "The Canterbury Interlude", in The Canterbury Tales: Fifteenth-Century Continuations and Additions, ed. J. Bowers (Kalamazoo, MI: Medieval Institute Publications, 1992), chap. 5, ll. 171-73. The second phase of the Digital Pilgrim Project is creating GIS maps to help visualize their proliferation. The Digital Pilgrim 3D models were made using a digital SLR camera and Structure from Motion (SfM) software. See the statement entitled "Methodology" in https://www. gta. arch. ethz. ch/events/digital-art-history-challenges-andprospects (accessed 16 May 2017). Including such initiatives as the CHASE "Material Witness" course, or the University of Cambridge's CRASSH "Embodied Things" research group.
Abstract. We present conventional and swath altimetry data from CryoSat-2, revealing a system of subglacial lakes that drained between June 2013 and January 2014 under the central part of Thwaites Glacier, West Antarctica (TWG). Much of the drainage happened in less than 6 months, with an apparent connection between three lakes spanning more than 130 km. Hydro-potential analysis of the glacier bed shows a large number of small closed basins that should trap water produced by subglacial melt, although the observed largescale motion of water suggests that water can sometimes locally move against the apparent potential gradient, at least during lake-drainage events. This shows that there are important limitations in the ability of hydro-potential maps to predict subglacial water flow. An interpretation based on a map of the melt rate suggests that lake drainages of this type should take place every 20-80 years, depending on the connectivity of the water flow at the bed.
Surface-elevation and elevation-change estimates The primary source of elevation data for this study are the CryoSat-2 baseline-B radar altimetry data collected between November 2010 and February 2015, which we refine through a number of steps to derive both POCA (point of closest approach) and swath-mode elevations. With POCA processing, estimates are made of the height and location of the point on the surface that was closest to the satellite when it transmitted a burst of energy, which produces a short-duration, highenergy reflection. Rather than use one of the level-2 retracked products, we apply a maximum-slope retracker to measure the height of these points  (Gray et al. , 2015) , smoothing each waveform with a Gaussian window with a σ of 4 samples, and using a threshold of 3. 6 × 10 -16 W sample -1 to identify waveform-power slopes significantly above the noise floor, and a coherence threshold of 75 %.
Like other POCA products, our retracked POCA CryoSat-2 data tend to preferentially measure the heights of local rises on the ice sheet, missing local depressions, which can lead to gaps in the coverage of measurements of up to a few kilometres in the TWG region. To help fill these gaps, we derived additional elevation measurements using a swath-processing strategy to calculate surface heights from the "tail" of the return  (Christie et al. , 2016; Foresta et al. , 2016; Gray et al. , 2013; Hawley et al. , 2009) , which measures energy returned after the POCA. To estimate heights from this part of the waveform, we smoothed the complex phase for each waveform to about 80 % of the instrument bandwidth, using a Gaussian kernel with an σ of 5 bins, weighted by the coherence values, and calculated a weighted mean of the coherence using the same weights.
We used only those radar-sounding estimates collected when the aircraft was less than 3000 m above the surface, which removes many of the most error-prone measurements. To interpolate a bed DEM from these data, we used an algorithm similar to that used for the CryoSat-2 surface interpolation (Appendix A), solving for a time-invariant bed-elevation model. The bed-fitting algorithm included adjustable parameters that controlled the smoothness and the flatness of the interpolated bed, corresponding to parameters L x in Eq. (A7) and w x0 in Eq. (A8). A set of constrained, independent parameters allowed for a distinct height bias for each day on which data were collected, to account for inconsistencies in radar-system parameters and bed-return picking by different operators. The solution gave bias magnitudes less than 6.
The basal water system is able to sequester large volumes of water over years, which it then releases rapidly with little or no apparent change in glacier speed. This insensitivity suggests that the details of the basal hydrological system may not be the most important feature of the ice sheet for models to capture, especially now that data assimilation techniques allow us to infer the dynamic properties of the bed (e. g. the coefficients in a sliding law) directly  (Joughin et al. , 2010; Morlighem et al. , 2010). At least at the decadal scale, fixed bed parameters can reasonably reproduce observed behaviour  (Joughin et al. , 2010 (Joughin et al. , , 2014)) , despite large increases in water-layer thickness that accompany a speedup and lake drainages. The lack of sensitivity is probably related to the patchy structure of basal drag beneath TWG and the limited time over which lake drainages supply water.
Existing satellites such as CryoSat, ICESat, and 25 years of SAR missions such as ESA's Sentinel-1 have already provided a wealth of data to explore such issues. The launch of ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) in late 2017 or early 2018, and the launch of the NASA ISRO SAR (NiSAR) in 2020, will improve this situation considerably.
Data availability Most data used in this paper are available through the National Snow and Ice Data Center. The exceptions to this are the Cryosat-2 elevation data and the TSX velocity maps. Because the original level-1 Baseline-B CryoSat-2 data are no longer available for download from ESA, we have included our processed elevation-change grids and digital elevation models in the Supplement; because TSX data are not available to the general public, we have included our velocity maps in the Supplement. B. E. Smith et al. :  Connected subglacial lake drainage beneath Thwaites Glacier, West Antarctica Appendix A: Methods for estimating elevation and elevation change Based on the CryoSat data, we estimated elevation changes and a DEM on overlapping 65 km rectilinear grids. Each grid has one set of nodes defining reference DEM heights (for 1 June 2011), spaced 500 m in each direction, and one set of nodes defining elevation-change surfaces for 3-month increments between 1 June 2010 and 1 March 2015, spaced at 1 km in each direction.
The matrix form of Eq. (  15 ) is (F δz0 m) T F δz0 m. To solve for the elevation model and bias parameters, we find a model that minimizes R by solving for elevation-and bias-model variables that make the derivative of Eq. (  3 ) with respect to the model parameters equal to zero. This leads to a set of linear equations:         G d F 0 F δ F tt F b F δz0           m 0 m δ m b   + =   z 0 0  . ( A14 ) Here z is a vector of surface-height estimates. We solve this by minimizing the quantity T C -1 , where C is a matrix whose diagonal values give the weights for each component of Eq. (  3 ). In principal, this could be solved by standard linear-least-squares techniques  (Menke, 1989)  but, because of the large number of equations and unknowns, we use the Matlab routine lscov, which uses an algorithm designed to efficiently solve large, sparse systems of least-squares equations.
2010). In all cases, decomposition was of genotypes pruned of strong local LD (marker set 2), mean centered, and scaled to unit variance. Of the first 50 PCs, 10 are individually significantly associated with subpanel identity by ANOVA (linear regression of each PC on subpanel identity, tested against an intercept only model by LRT, P , 0. 05 after Bonferroni correction). Seven of the top 10 PCs are significant, though others up to 38 are also associated, showing that multiple sources of structure contribute to the major axes of variation. For DAPC [R package adegenet,  Jombart (2008) ], we used 100 rounds of 10-fold cross-validation to determine the number of PCs required for optimal subpanel assignment accuracy (the mean of per-group correct assignments). This value (40 PCs) was then used to infer groups by unsupervised k-means clustering (default settings of 10 starts, 10 5 iterations) with the number of groups selected on the Bayesian Information Criterion (BIC).
Data for 227 RILs passed filtering (raw data are in File S9, model coefficients are in File S10), coming from RILs of three subpanels (170 A140, 45 GA50, and 12 GT50). Subpanel explains 4% of the variance in this trait, with GA50 RILs having higher mean fertility than the A140 (linear regression of trait values on subpanel identity, regression coefficient = 0. 43, P ¼ 0:01; see Figure  S2  in File S22). Adult hermaphrodite body size: The area of adult hermaphrodite worms was measured using a Multi-Worm Tracker  (Swierczek et al. 2011). Data were generated in two laboratory locations over several years, recording the relative humidity and temperature at the time of assay (see supplemental material for full details). Final trait values were the Box-Cox transformed line coefficients from a linear model incorporating fixed effects of year, nested within location, and humidity and temperature, nested within location (model S3 in Supplemental Methods).
Heritability and phenotype prediction Repeatability: Repeatability was estimated from ANOVA of the line replicate means for each trait as R ¼ s 2 a /(s 2 a þ s 2 e ) , where s 2 a ¼(mean square among lines 2 mean square error)/n 0 , and n 0 is a coefficient correcting for a varying number of observations (124 plate means) per line  (Lessells and Boag 1987; Sokal and Rohlf 1995). Assuming equal variance and equal proportions of environmental and genetic variance among replicates, R represents an upper bound on broad-sense heritability  (Falconer 1981; Hayes and Jenkins 1997). Fertility data were square root-transformed to decouple the mean and variance. Assumptions: In inbred, isogenic lines, broad-sense heritability can also be estimated by linear mixed-effects model (LMM) from the covariance between genetic and phenotypic variances.
GWAS 1D tests: For single trait, single marker association, we fitted LMMs: Y ¼ bX þ g þ e; with g N 0; s 2 g A ; e N 0; s 2 e ( 4 ) where X is the matrix of fixed effects (SNP genotype) and b is the effect on phenotypic variation that is estimated. g are the random effects describing genetic covariances (Equation  3 ) accounting for nonindependence among tests due to an assumed polygenic contribution to phenotype, with A the n 3 n GSM from the most predictive additive fit found for each trait, and e is residual error. The above model was compared to a null model excluding genotype effects by LRT (fit using the LIMIX Python package, https://github. com/limix/limix). GWAS P-values for size and fertility are in File S14, using genetic similarities in File S15 and File S16. To assess the mapping resolution and power of the CeMEE panel, we carried out GWAS according to the model above for simulated phenotypes.
True positives were defined as cases where the exact simulated site was detected, and false positives were 2-LOD drop QTL among all other markers detected at a 5% significance permutation threshold (see below). All 507 lines were used for simulation, and all GWAS tested 248,668 markers with MAF. 0. 05 (marker set 1; code is in File S18, GWAS_traits. py). Significance thresholds were established by permutation  (Anderson and Ter Braak 2003) , with phenotypes generated by permuting phenotype residuals given the estimated relatedness among lines to ensure exchangeability in the presence of polygenic causal effects or structure (A), using the R package mvnpermute  (Abney 2015). Significance level a is the corresponding percentile of the minimum P-values from 1000 permutations. Given correlation between traits, we also tested phenotype residuals for each trait after linear regression on the other, and a multi-trait LMM fitting general and specific effects.
2011 ): M1 was fitted to responses sampled with replacement from M2 (n = 10,000), taking the interaction P-value as test statistic, and then comparing the observed statistic to the null distribution (significance declared at P , 0. 01). Code is in File S19 2D_hierarchical. py. M0 : Y ¼ m þ e M1 : Y ¼ m þ b i X i þ b j X j þ b ij X ij þ e H0 ð1Þ : b i ¼ b j ¼ b ij ¼ 0 M2 : Y ¼ m þ b i X i þ b j X j þ e H0 ð2Þ : b ij ¼ 0 (5) We initially ignored relatedness for 2D testing, then fitted LMMs as above (Equation  2 ) with genetic covariance A for candidate interactions (R package hglm; Shen et al. 2014). From eight candidate interactions for size, we excluded two for which interaction P-values by LMM were almost an order of magnitude higher. The six remaining candidates changed little (three were lower by LMM).
Significance was tested at a single threshold (LR. 16, around the maximum value seen among pairwise interaction null P-values), using the equivalent of the above hierarchical procedure: LRs for M1 vs. M0 were first summed for each marker and compared to a null distribution generated by full phenotype permutation. Candidate markers significant at level 1 (a = 0. 01) were then tested for significance of the interaction terms against a distribution of LR sums from null additive models for tests with LR. 16 in the observed data. This was repeated 1000 times, with permutation order fixed across bootstraps to maintain correlation structure, and significance was declared at P , 0. 01. Code is in File S20, 2D_sumLRbootstrap. R.
Acknowledgments We thank  J. Costa ,  R. Costa ,  C. Goy ,  F. Melo ,  H. Mestre ,  V. Pereira , and  A. Silva  for support with worm handling, sample preparation, and data acquisition;  M. -A. Félix ,  P. C. Phillips ,  S. Proulx ,  D. Speed , and  C. Zheng  for helpful discussion; and  E. Andersen  for discussion and sharing of sequencing data. This research was supported in part by the  National Science Foundation  (grant  PHY-1125915 ), the  National Institutes of Health (NIH)  ( R25-GM-067110 ), and the  Gordon and Betty Moore Foundation  (grant  2919. 01 ); the  NIH  ( R01-GM-089972  and  R01-GM-121828 ) to M. V. R. ; the  Human Frontiers Science Program  ( RGP0045/2010 ) to B. S. , M. V. R. , and H. T. ; and the  European Research Council  ( FP7/ 2007-2013/243285 ) and  Agence Nationale de la Recherche  ( ANR-14-ACHN-0032-01 ) to H.