dataesr/scientific-paragraphs-categorization huggingface dataset:
LogCombiner Java program to combine log and tree files from multiple independent runs of Bayesian MCMC programs  [16]  TreeAnnotator Java program to assist summarizing the information from a sample of treesonto a consensus tree B A C Time T A T B T MRCA (AB) X Y (a) (b) A B C D E A B C D E A B C D E [16] FigTree obtained through the analysis of billions of sequence reads allows quantifying contamination issues that have haunted aDNA research for decades. Whole genomes have now been sequenced from ancient anatomically modern humans  (Fu et al. 2013a,b) , archaic humans  (Briggs et al. 2009; Krause et al. 2010) , ancient pathogens  (Bos et al. 2011 (Bos et al. , 2014;; Rasmussen et al. 2015)  and many animals including megafaunal species  (Shapiro et al. 2004; Gilbert et al. 2008). Following the detection and removal of potential recombining sites (see Section "To date or not to datewhen is tip dating appropriate?" and Appendix S1-A, Supporting information for more details), such timestamped sequence data can be integrated into phylogenetic reconstructions and used as calibration points to estimate the timing of divergence events, the rate at which substitutions accumulate and reconstruct the past demography of many species for which fossil data are not available.
Finally, it may also be important to independently assess the authenticity of posited sampling dates due to their extreme age or because they are contentious. Shapiro et al. (2011)  developed and implemented a leaf-dating method that estimates the age or date of isolation of individual sequences within the Bayesian MCMC framework provided by the software package BEAST  (Drummond & Rambaut 2007). In this method, the sequence's leaf age is treated as a random variable and an additional parameter for the age of the terminal node is introduced and treated identically to the internal node age parameters in terms of proposals made by the MCMC kernel. Methodologically, the leaf-dating method is similar to relaxing the constraints of the molecular clock on specific lineages within a phylogenetic tree.
Step 1: Assembling a dataset of nonrecombining sequences Step 2: Assessing the quality of ancient DNA sequences (optional) Step 3: Estimating the best partitioning schemes and the substitution models. Step 4: Setting up the tip-dating analysis, testing for measurable evolutionary change within a dataset, determining the best-suited molecular clock and tree prior. Step 5: Performing the tip-dating analysis, interpreting and extracting the results. Fig. 5  Major steps to conduct accurate tip dating. This figure summarizes the five main steps that ought to be conducted when performing tip-dating analyses. For each of those steps, additional advices such as the important choices that must be made or the software to be used are given in the form of a practical guide available in Appendix S1 (Supporting information).
Didelot et al. (2014) introduced a Bayesian inference scheme which allows superimposing the transmission network upon a phylogenetic tree applicable to well-sampled outbreaks while accounting for within-host evolution. Also of note, is the recently developed Bayesian MCMC algorithm by  Gavryushkina et al. (2014)  to infer sampled ancestor trees, that is trees in which sampled individuals can be direct ancestors of other sampled individuals. When analysing an HIV data set from the United Kingdom, they were able to detect sampled ancestors and estimate the probability that an individual will be removed from the process upon sampling (i. e. diagnosis). They could also demonstrate that even if sampled ancestors are not of specific interest in the analysis, failing to account for them leads to significant bias in the branching model and clock rate estimates.
Acknowledgements We thank  Mark Achtman ,  Stephane Hue ,  Dan Jeffares ,  Gael Kergoat ,  Angelis Konstantinos ,  Pierre Lefeuvre  and  Marie Pages  for helpful discussions and constructive comments on previous versions of this manuscript. The work was funded by an  ERC  ( ERC 260801 ) and a  NERC  ( NE/M000338/1 ) grant to FB and supported by the  National Institute for Health Research University College London Hospitals Biomedical Research Centre.
In order to achieve these results, global open data, such as globally available forcing data, and exposure datasets, if possible complemented by open local data sets, will be used so that the obtained results can be shared publicly after the project has been completed. For GDP data we will rely on GFDRR to provide the most appropriate and updated dataset, while for the other assets, including critical infrastructure will be taken from available exposure datasets. One of the innovation presented in this work is related to the use of a high resolution DEM, such as TanDEM-X. One of the challenges to applying DTMs to real flooding scenarios has been the absence of the availability of a standard DTM at a sufficient level of resolution. Low resolution DEMs such as the STRM 90m version are globally available free of charge, but offer limited use for flood modellers in most geographic areas, either because of the errors in water flow or the inability to distinguish critical urban infrastructure.
With   commercially available DEMs at higher resolution such as the TANDEM-X DEM at 10-12m resolution, significant progress will be possible through the application of these technologies to risk assessment. The outstanding characteristics of TanDEM-X are the provision of a truly global and homogenous data set and greatly improved spatial and vertical resolution. Recent analysis comparing DTMs ranging from 1m to 90m resolution have shown that DTMs offering around 10m resolution are the most useful for flood modelling, with 50m and 90m models being too coarse and the 1m model requiring too much computation time for only marginally better results. The global coverage TanDEM-X offers high precision elevation information in areas above 60° latitude that were not covered by SRTM before. It also facilitates the transfer of risk mitigation and vulnerability assessment techniques, enabling truly global applications.
Meteorological forcing Rainfall and (potential) evapotranspiration (PET) are the two main driving forces for the hydrologic model to simulate surface runoff and river discharge. The rainfall input was obtained from the freely available EU-WATCH dataset  [3, 4]. This set was produced as part of WKH (8 )3 µ:$7&+ ¶ SURMHFW IRU UHJLRQDO DQG JOREDO studies of climate and water. The EU-WATCH dataset describes rainfall at 3 hours temporal and ~50km spatial resolution for the full 20th century. However, a test run of the hydrological model revealed that the pre-1970 rainfall over Malawi has a different distribution with higher average flows. Probably, the post-1970 rainfall was bias-corrected by using a data source that was not available before 1970. The pre-1970 data was therefore excluded from the analyses.
Rainfall statistical analysis To force the hydrologic model, a design rainfall event for every return period is needed. This was derived from Intensity-Duration-Frequency (IDF) rainfall statistics. The IDF-table or IDF curve is a common representation of the probability of exceedance of rainfall thresholds over a given duration. The 32-year EU-WATCH data at 3-hour temporal resolution were used to derive IDF curves for northern Malawi. The IDF curves were obtained for several locations spread over northern Malawi and mutually compared. The average over all stations was taken to obtain an average location IDF (Figure  3 ). $Q HPSLULFDO IRUPXOD NQRZQ DV :LVQHU ¶V IRUPXOD was fitted to the IDF table data:  (1)  Where: td = duration (minutes) i = average intensity in period td (mm/hr) a, b, c = parameters, depending on the return period T Next, a design rainfall event, or hyetograph, was derived from the IDF curves using the µDOWHUQDWLQJ EORFN PHWKRG ¶ DV GHVFULEHG LQ &KRZ HW DO  [6].
Hydrological modelling The hydrologic modelling was done using the opensource WFLOW-HBV software that is developed by Deltares (http://www. openstreams. nl). The WFLOW-HBV model is a fully distributed version of the HBV-96 model, where the original routing function (MAXBAS) was traded for a kinematic wave function  [7]. The workflow is shown in Figure  4. The required WFLOW model parameters were taken from freely available global data sources, such as the GlobCover land cover database of the European Spatial Agency (ESA)  2  for land use and the FAO Harmonized World Soil Database (HWSD) and Soil Map 3  for soil type. Results from earlier studies show that WFLOW produces very reasonable results by using model parameters based on these global data, without further calibration to local gauge data.
2D flood modelling The flood maps for three hotspot areas were calculated using a model called D-Flow Flexible Mesh (D-Flow FM) 4. This open source software engine for hydrodynamic simulations is being developed by Deltares. D-Flow FM is a shallow-water solver based on the finite-volume method applied on unstructured grids. It solves the full Saint-Venant equations and thus guarantees conservation of mass and momentum, while also accurately representing the drying and flooding of grid cells  [8, 9]. D-Flow FM uses a flexible mesh and a variable grid resolution. This enables the modeler to use a higher resolution and more accurately simulation of the flow of water in topographically complex areas, while keeping the rest of the model at a lower resolution for optimal computational performance.
Exposure layers for Malawi Damage computation is subjected to the availability of exposure and vulnerability data at relative high resolution. Access to high resolution base layer datasets such as 12m resolution TanDEM-X allows a fairly detailed implementation of hazard models, aimed to predict the effects of floods. However, in data-poor context, such as Malawi, exposure data have to rely on global datasets, which contain information only at national/provincial scale and cannot be directly used on damage computation. To sort out this inconsistency, downscaling procedure has been applied to exposure data to make them consistent to high-resolution hazard layers. This chapter provides background information on the preparation of exposure data of built-up area, population and annual crops for Malawi.
Population The basic reference population layer for Malawi, expressed in terms of number of people per pixel, is the population layer developed by WorldPop 5. This raster layer which represents the predicted number of people per ~100 m pixel, obtained using as input census/population count datasets and mapping of settlements and applying the random forest (RF) model as described in Stevens, et al. (In Press). The result is then obtained adjusting the national total in order to match UN population division estimates (2012 revision).
When possible, FAO data [17] have been preferred; in some specific cases other sources have been applied, specifically Negri and Porto  [18]  for tobacco, Nsanjama  [19]  for potatoes, MAFAP  [20]  for groundnuts, ICLARM ± GTZ  [21]  for Cotton, Sweet Potato and Pigeon Peas. Then, crop loss functions are applied whenever a water depth higher than zero is present on the cropland. A damage factor corresponding to the most flood prone period of the year (December-January) is considered. Referring to buildings, Population and Housing Census of Malawi divides house types in the country into 3 categories  [12] : permanent, semi-permanent and traditional. In order to obtain vulnerability functions for these three typologies, the starting point has been the CAPRA flood vulnerability library, and more precisely to the functions adapted to Malawi.
We propose and detail a deformation-based morphometry computational framework, called Longitudinal Log-Demons Framework (LLDF), to estimate the longitudinal brain deformations from image data series, transport them in a common space and perform statistical group-wise analyses. It is based on freely available software and tools, and consists of three main steps: (i) Pre-processing, (ii) Position correction, and (iii) Non-linear deformation analysis. It is based on the LCC log-Demons non-linear symmetric diffeomorphic registration algorithm with an additional modulation of the similarity term using a confidence mask to increase the robustness with respect to brain boundary intensity artifacts. The pipeline is exemplified on the longitudinal Open Access Series of Imaging Studies (OASIS) database and all the parameters values are given so that the study can be reproduced.
INTRODUCTION An important topic in neuroimaging is to analyse the progression of morphological changes in the brain observed in time series of images, in order to model and quantify normal or pathological biological evolutions  (Scahill et al. , 2002). Deformation-Based Morphometry (DBM)  (Ashburner et al. , 1998)  characterizes the morphological changes of the brain in terms of spatial transformations (here called deformations), estimated by means of non-linear registration. A sub-field of DBM, called Tensor-Based Morphometry (TBM) focuses on the first derivatives of the deformation. Depending on the cross-sectional or longitudinal nature of the dataset used, we can define on one hand cross-sectional DBM and on the other hand longitudinal DBM  (Chung et al. , 2001 ) that we will focus on in this article.
Longitudinal DBM main steps can be summarized as (i) quantifying the evolution of the morphology of each subject by estimating the individual's longitudinal deformation from the time series of images, and (ii) characterizing how this evolution varies among a sample using a suitable normalization for the individual biological variability. A variety of DBM approaches can be found in the literature (e. g. ,  Davatzikos et al. , 2001; Cardenas et al. , 2007; Lorenzi et al. , 2011; Südmeyer et al. , 2012) , each of them associated to specific non-linear registration methods, and processing pipelines. The comparison between the different DBM methods is not straightforward: the efficiency of each DBM pipeline is generally demonstrated on different data sets (or different subsets of the same data set) and the tools the processing pipeline is composed of are generally not all available.
We bring a complement to the already existing literature by explicitly detailing all the processing steps required for the longitudinal analysis of neuroimages by relying on freely available tools. In addition to this contribution, we integrate a modification to the non-linear registration algorithm by adding a masking to the similarity term as proposed by  Brett et al. (2001)  while keeping the symmetry of the formulation. This change increases the robustness of the results with respect to intensity artifacts located in the brain boundaries. The proposed processing pipeline is based on freely available software and tools [the complete list can be found in Appendix (Supplementary Materials)]. The paper is structured as follows: in Section 2, we develop a comprehensive processing pipeline called Longitudinal Log-Demons Framework (LLDF); we present each elementary modules it is based on, and after introducing the mathematical formalism related to DBM, we modify the LCC log-Demons to incorporate a confidence mask.
Skull-Stripping It is often necessary (e. g. , in Section 2. 2. 1) to process the brain without its surrounding skull. For this reason, the pipeline includes a skull-stripping step (also called non-brain removal tool). We selected Robex  (Iglesias et al. , 2011)  for the robustness of its results with no parameter fine tuning:  Iglesias et al. (2011)  showed it generally performs better than six other popular algorithms (BET,  Smith, 2002; BSE, Shattuck et al. , 2001 ; FreeSurfer 3  ; AFNI 4  ; BridgeBurner,  Mikheev et al. , 2008; and GCUT, Mahapatra, 2012). Our experiments were in agreement with this affirmation: when using Robex on our datasets, we no longer had large parts of the skull remaining which was sometimes the case when using FSL-BET with the default parameters. Inputing I, the image with the brain and its surrounding skull, Robex outputs I robex and I mask , the skull stripped brain and the corresponding region mask respectively.
Longitudinal Rigid Registration For a single subject, the acquisition at different time points is usually not performed with the same position of the head in the scanner. This creates a global rigid (six degrees of freedom) misalignment of each subject data series. Since the aim of this work is to model the subtle local longitudinal brain changes, we need to account for this source of variability that generally exceeds the longitudinal variability. Taking the baseline I 0 as the reference position, we rigidly align the follow-up images I 1 ,. , I N-1 to the baseline I 0 , using the rigid transformations φ 1 R ,. ,φ N-1 R , to obtain the rigidly aligned image I al 1 ,. , I al N-1 (cf. Figure  3 ). We choose to use FSL-FLIRT  (Jenkinson and Smith, 2001; Jenkinson et al. , 2002)  for the linear registration as it is the benchmark linear registration framework used in the influential work of  Klein et al.
OASIS Database The clinical cohort considered in this study is composed of 64 patients diagnosed with very mild to moderate Alzheimer's disease, and 72 healthy individuals. For these subjects, 2 to 5 longitudinal brain acquisitions (T1 Magnetic Resonance Imaging) were available, corresponding to a follow-up time  t 0-j = t jt 0 of 0. 5 to 6. 9 years. Further information can be found in Appendix (Supplementary Materials).
Methods and Results After applying the processing pipeline to the database (the parameters used for the different steps are summarized Table  1 ), we obtain the transported subject-specific longitudinal deformation trajectories φi T (t) = exp(t • vi T ) for each subject i in the study-specific template: we thus get 72 subject-specific longitudinal SVFs vi T for the healthy controls and 64 for the patients with Alzheimer's disease. Concerning the non-linear registration parameters for the LCC log-Demons, the optimal parameters we propose here would of course be different for another study, and we recommend to fine-tune in priority the amount of regularization (-b) and the number of iterations (-a). As for the SVF exponentiation (and log-Jacobian), all the computations were performed using an Euler forward integration scheme (option -z 1 in SVFLogJacobian tool).
CONCLUSION AND DISCUSSION We proposed and detailed a new processing pipeline 8  for the longitudinal analysis of image data series. It is based on freely available software and tools so that anyone can reproduce our study, use this pipeline to replicate and verify findings conducted with other pipelines or use it to perform new studies. Moreover, we also implemented a masking of the similarity term in the nonlinear registration (with a formulation that ensures symmetry). It enhances the robustness of the registration results with respect to intensity artifacts in the boundary of the brain, thereby increasing the sensitivity of the statistical studies done on the longitudinal deformations. We finally showed on an open-access database that the results obtained with this pipeline are consistent with the findings from the literature.
(2013) -where again, some repeated interpolations are avoided, while other interpolations are symmetric by virtue of being in MNI space rather than in the native baseline space. It could be possible to go one step further and to avoid any explicit interpolation by initializing the non-linear registration (in the LCC log-Demons software) with the combined affine/rigid transformation using the software parameter:-initial-linear-transform. However, this would still imply an implicit internal resampling and in this case we would no longer follow the assumption made in LDDMM and the SVF framework that all the field tends toward zero when we get away from the center of the image (i. e. , beyond the borders of the image). In practice, we observe edge-effects and a proper way to deal with the problem should be to revise the LCC log-Demons algorithm in order to explicitly handle the two transformations separately and make sure that the criterion (and the discretization) would be affine invariant.
3. 3. This model uses all the possible combinations of SVFs in order to avoid favoring any specific time point. Notice that this approach is sub-optimal with unbalanced data where large variations exist in the number of time points N i between the subjects. This can be corrected using methods like the one described in  Guillaume et al. (2014). However, in the study presented here, only 13 subjects out of 136 had more than three time points. The majority had two or three time points which did not unbalance the data too much. Apart from the bias, one can wonder what would be the best method between LDDMM and the SVF framework. At first sight, LDDMM might appear as a better theoretical model for an elastic mechanical deformation since it is based on the conservation of the Hamiltonian. However, it is not completely clear that the longitudinal evolution of a brain (intra-subject) is an elastic deformation that conserves the energy.
, 2013; Guye et al. , 2008). Several recent studies have endeavored to combine structural and functional approaches in epilepsy  (Chiang et al. , 2015; Douw et al. , 2015; Vaessen et al. , 2014; Voets et al. , 2012; Zhang et al. , 2011). Function-structure relationship has been studied in the epileptic brain in generalized epilepsies  (Zhang et al. , 2011) , frontal lobe epilepsy  (Vaessen et al. , 2014)  and in TLE  (Voets et al. , 2012). While focusing only on limbic and pre-limbic structures  Chiang et al. (2015)  found decreased structural-functional relationship in TLE. Douw et al. (2015)  observed higher anatomical between-module connectivity which correlated with default mode disintegration in TLE compared to a control group. Structural parameters of brain networks such as Euclidian distance or the combination of more complex graph analytical metrics of network communication such as path length, search information and path transitivity have also provided good predictions of functional connectivity in the normal brain  (Goñi et al.
Fiber tracking The fiber orientation distribution (FOD) function was estimated within MRtrix software  (Tournier et al. , 2012) , by performing constrained spherical deconvolution (CSD, lmax = 8) of the diffusion signal  (Tournier et al. , 2008)  within single-fiber (FA N 0. 7) populations. The probabilistic streamline algorithm (iFOD2)  (Tournier et al. , 2010)  was employed to reconstruct 2. 5 million whole-brain fiber tracks of high-angular resolution. As streamlines were generated from random seeds throughout the brain volume, two tractography runs (i. e. 2 × 2. 5 million) were performed to ensure comprehensive brain coverage. A set of plausible fiber trajectories are generated by iFOD2 by random sampling from the orientation likelihood inherent in each FOD along each candidate path. The default tracking parameters were employed for the acquisition parameters of the present study (step size = 1.
fMRI preprocessing The T2*-weighted MRI volumes were corrected for movements and slice timing using the SPM8 toolbox (http://www. fil. ion. ucl. ac. uk/spm/ software/spm8/). The subdivided AAL atlas (512 regions, without cerebellum, see above) was linearly transformed into the subjects T1-image space (FSL 5. 0 FLIRT). Then the T1 volume and regional parcellation were coregistered with the T2* image (SPM8). For each time point all voxels in one region were averaged, followed by regressing out head movement parameters (six parameters, calculated during the movement correction step), cerebrospinal fluid (CSF) signals, white matter signals (both extracted from a manually selected 5 mm spherical ROI; Marsbar Toolbox 0. 43, http://marsbar. sourceforge. net/) and global mean signal according to  (Goñi et al.