dataesr/scientific-paragraphs-categorization huggingface dataset:
30 mm diameter. The samples were analysed on a Thermo QExactive Orbitrap mass spectrometer, equipped with the AP-SMALDI10 ion source (TransMIT, Giessen, Germany). The mass spectrometer was operated in the positive ion mode (scan range m/z 225-900) at mass resolving power 140,000 at m/z 200, and a mass accuracy better than 1 ppm was ensured by using a matrix background peak as lock-mass. The samples were imaged using a pixel size of 10 µm. Subsequent microscope analysis showed laser ablation craters of approx. 7 µm diameter, confirming that oversampling was not performed. The raw data were converted to imzML using an imzML converter  49  (available from www. maldi-msi. org), and MSiReader 0. 09 was used to generate images  50. The images were created using a bin width of 5 ppm.
Acknowledgements The authors thank  Ana Ricci Nielsen  and  Brian Vendelbo Hansen  for expert technical help,  Dr Anna-Maria Andersson  for help in setting up the study,  Cecilie Melau  for help with figures, and pathologists at  Rigshospitalet  for assistance with collection of human samples. The studies carried out at the  Department of Growth & Reproduction , Rigshospitalet were supported by a grant of the  Danish government  to the  Center for Research and Training in the Endocrine Disruption of Male and Child Health (EDMaRC). The studies performed at the IRSET-INSERM UMR1085 were supported by  INSERM ,  EHESP , and University of Rennes 1. MALDI-MSI instrumentation was supported by the  Carlsberg Foundation  and The  Danish Council for Independent Research | Medical Sciences  (grant no.
It also replicates the relation between spike pause duration and pre-complex spike inter-spike interval (ISI) duration observed through electrophysiological recordings  [44]  (Fig 2C; R 2 = 0. 9879; p<0. 0001). Only ISIs immediately following complex spikes were considered for this analysis. This relation was measured by maintaining the CF stimulation constant whilst incrementally increasing the amplitude of the PF input current. The probability distribution of post-complex spike ISIs is also consistent with experimental data  [44]  (Fig  2D ). The kurtosis ('peakedness') of the ISI distribution is 4. 24, which is in the range of kurtosis values measured after tetanisation of mouse Purkinje cells  [44]. Model post-complex spike ISI values are skewed rightward (positive skewness value of 0.
This work was supported by the  European Union  (www. europa. eu),Project consolidation; phase-reversal adaptation; (4) explaining the reversal VOR gain discontinuities during sleeping. SpikeControl 658479 (recipient NL), the  Spanish Agencia Estatal de Investigacion and European Regional Development Fund  (www. ciencia. gob. es/portal/site/MICINN/aei), Project  CEREBROT TIN2016-81041-R (recipient ER ), and the  French National Research Agency  (www. agence-nationalerecherche. fr)-Essilor International (www. essilor. com), Chair SilverSight  ANR-14-CHIN-0001  (recipient AA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Subjects and MRI acquisition MRI images of subjects without neurological or psychiatric disease were obtained (age mean±SD 29±6 years, range 22-42 years; 11 female, 9 male) with a Siemens 3 Tesla Prisma system equipped with a 64-channel head coil. An axial 3D T1-weighted imaging dataset covering the whole head was acquired for each participant (286 slices, voxel resolution = 0. 7 mm3, echo time (TE) = 2. 17 ms, repetition time (TR) = 2400 ms, flip angle = 9°). Resting state functional MRI (rs-fMRI) images were obtained using T2*-weighted echoplanar imaging (EPI) with blood oxygenation level-dependent (BOLD) contrast. EPIs (TR/TE = 2050/25 ms) comprised 42 axial slices acquired with a multiband pulse  (Feinberg et al. , 2010; Moeller et al. , 2010; Setsompop et al. , 2012; Xu et al. , 2013)  covering the entire cerebrum (voxel size = 3 mm3) including 290 brain scan volumes in one run of 10 minutes.
A diffusion-weighted imaging (DWI) acquisition sequence, fully optimised for tractography, provided isotropic (1. 7 × 1. 7 × 1. 7 mm) resolution and coverage of the whole head with a posterior-anterior phase of acquisition, with an echo time (TE) = 75 msec. A repetition time (TR) equivalent to 3500ms was used. At each slice location, six images were acquired with no diffusion gradient applied (b-value of 0 sec mm-2). Additionally, 60 diffusion-weighted images were acquired, in which gradient directions were uniformly distributed on the hemisphere with electrostatic repulsion. The diffusion weighting was equal to a b-value of 2000 s/mm-2. This sequence was fully repeated with reversed phase-encode blips. This provides us with two datasets with distortions going in opposite directions. From these pairs, the susceptibility-induced off-resonance field was estimated using a method similar to that described in  (Andersson et al.
Resting-state functional MRI analysis -overview Resting state functional MRI images were corrected for artefacts in Funcon-Preprocessing tool of the Brain Connectivity and Behaviour toolkit (http://toolkit. bcblab. com; Foulon et al. , 2018; Jenkinson et al. , 2002; Woolrich et al. , 2009). In a next step, they were registered to T1 high resolution individual structural images and normalized to MNI152 standard space using Advanced Normalization Tools (ANTS; http://stnava. github. io/ANTs; Avants and Gee, 2004; Avants et al. , 2010).
Individual DMN maps in the "functional space" To achieve the proposed optimized map of the DMN, the same twenty individual maps were functionally aligned with each other in a new "functional space" using the following steps: Individual DMN maps (obtained in 2. 2. 1. ) were aligned with each other using ANTs' script buildtemplateparallel. sh, defining cross-correlation as the similarity measure and greedy SyN as the transformation model  (Avants et al. , 2011 (Avants et al. , , 2008;; Klein et al. , 2009). This approach consists in an iterative (n=4) diffeomorphic transformation to a common space. The group map was obtained by calculating the median of all DMN maps after functional alignment. The resulting maps correspond to alignment of the 20 individual DMN maps in a "functional space" (figure  2 ).
Functional connectivity comparison of the two DMN For the functional and structural-based DMN maps, time-series of rs-fMRI of each individual in the different regions of interest identified in the DMN maps were extracted  (Jenkinson et al. , 2012). Correlation and partial correlation coefficients were calculated as measures of functional connectivity. Two matrices, representing the median correlation values in the MNI152 space and in the functional space, were created using BrainNet Viewer  (Xia et al. , 2013). A paired t-test was calculated for each cell of the two connectivity matrices using Python's Scipy package, version 0. 19. 1 (https://www. scipy. org/; scipy. stats. ttest_rel()). An uncorrected p-value of <0. 05 and a p-value corrected for multiple comparisons at a threshold of <0.
, 2014). A modified Euler algorithm was used  (Dell'Acqua et al. , 2013 ). An angle threshold of 35°, a step size of 0. 85mm, and a minimum length of 20mm were chosen. Diffusion tensor images were registered to the MNI152 standard space and, then, into the "functional space" applying the affine and diffeomorphic deformation generated in 2. 2 and in 2. 2. 2. , using the tool tractmath as part of the software package Tract Querier  (Wassermann et al. , 2016). Regions of interest were defined according to the previous anatomical models of the DMN  (Andrews-Hanna et al. , 2010; Buckner et al. , 2008). Additional regions of interest were defined in the novel subcortical regions identified in our new model. The command tckedit of MRtrix toolbox (http://www. mrtrix. org/; Tournier et al. , 2012)  was used to isolate the tracts of interest.
Only the streamlines that ended in both regions of interest were considered  (Shu et al. , 2011). The matrices were binarised (no threshold) for structural connection or no structural connection between regions. Brain Connectivity Toolbox for Python (https://pypi. python. org/pypi/bctpy) was used to obtain the network measures. The functions degrees_und and betweenness_bin were run, respectively, for each individual matrix  (Rubinov and Sporns, 2010). The median values for each measure were obtained. The illustration of the network was made using Surf Ice (https://www. nitrc. org/projects/surfice/) 3. Results.
Acknowledgement: PNA salary was supported by the  Research Experience Fellowship  grant of the  European Academy of Neurology. The research leading to these results received funding from the  "Agence Nationale de la Recherche " [grant number  ANR-13-JSV4-0001-01 ], the  European Research Council (ERC)   Consolidator Grant  " DISCONNECTOME" [ERC-2018-COG 818521 ] and from the  Fondation pour la Recherche Médicale  ( FRM DEQ20150331725 ). Additional financial support comes from the program " Investissements d'avenir "  ANR-10-IAIHU-06.
Besides that, the main limitation of our studies are the input data of macronutrient availabilities. The possible bias related to the values we have used (SI Dataset S1) is difficult to judge. For most of the macronutrients we used a 5 g step in our calculator, which seems to be a reasonable estimation of the errors of data presented in Table  1. We have assumed that the consumption within the population follows the normal Gaussian distribution and the average consumption per-capita covers the majority of population and all age groups and both sexes. We did not consider the medical conditions of the population since, it would be extremely difficult, if not impossible, to do it for the whole population data for the USA. We assume that our predictions are valid for the healthy part of the population.
Methods The graphical representation of input data variability in time is presented in Fig. 2  The flow charts of the "Calculator" Flow chart 1. General procedure. Flow chart 2. Global optimization procedure. Flow chart 3. Procedure of calculation of optimal diet in regard to macronutrients proportions. The calculator from our previous papers  12, 13  was modified and optimized to obtain precedence periods for carbohydrates, protein, saturated, monounsaturated, and polyunsaturated fat. We used global de novo optimization for all 5 nutrients within GAM analysis procedure instead of multiple regression. This resulted in 4084101 variants of sets of precedence periods when using back steps up to -20 years. From them the one variant with the highest R was chosen as the optimal one (Roptimal). The results from GAM analysis with Roptimal for each period studied were chosen as calculator equations and are presented in SI Dataset S2.
We obtained four equations corresponding to four periods of life: youth, early middle age, late middle age and late age. These equations were then used to generate a set of tables with new proportions of macronutrients to minimize Rpredicted and optimal proportions (optimal diet) were chosen as in our previous papers  12, 13  except that the corresponding mean availability was calculated without back shift by precedence periods. The codes in R program for optimization, calculation of Rpredicted and minimum energy difference together with the manual are available in a public repositories at https://bitbucket. org/seventm/fsproj_alzheimeroptimaldiet and https://github. com/seventm/ fsproj_alzheimeroptimaldiet/releases/tag/v1. 0, https://doi. org/10. 5281/zenodo. 3574598. The computations were performed on the computer cluster with 96 cores and 1TB of RAM.
Data availability The input data and results of calculations are available in the Supplementary Information files. The software written for the purpose of calculations employed in this project is deposited at public repositories https://bitbucket. org/ seventm/fsproj_alzheimeroptimaldiet and https://github. com/seventm/fsproj_alzheimeroptimaldiet/releases/ tag/v1. 0, https://doi. org/10. 5281/zenodo. 3574598. All other data are available from authors on request.
To validate DamMet, we extended gargammel, a sequence simulator for ancient DNA data, by introducing methylation-dependent features of post-mortem DNA decay. This new simulator provides direct validation of DamMet predictions. Additionally, the methylation levels inferred by DamMet were found to be correlated to those inferred by epiPALEOMIX and both on par and directly comparable to those measured from whole-genome bisulphite sequencing experiments of fresh tissues. Conclusions: DamMet provides genuine estimates for local DNA methylation levels in ancient individual genomes. The returned estimates are directly cross-sample comparable, and the software is available as an open-source C++ program hosted at https://gitlab. com/KHanghoj/DamMet along with a manual and tutorial.
Introduction Recent studies in ancient DNA (aDNA) research have demonstrated that osseous methylomes can be mapped from the high-throughput DNA sequencing (HTS) data underlying ancient genomes  [1] [2] [3]. This paves the way for identifying potentially evolutionary-relevant epigenetic changes during major environmental and societal transitions  [4]. Although aDNA methylation states can be inferred following methods usually applied to fresh tissues such as bisulfite DNA sequencing  [5]  and methyl-binding domains enrichment  [6] , the degraded nature of aDNA molecules generally limits methylome mapping to indirect computational proxies exploiting post-mortem DNA deamination (PMD) footprints at CpG dinucleotides. Two available software programs, epiPALEOMIX  [1]  and ROAM  [2] , have been recently developed to map DNA methylation levels at the regional scale.
Last, the regional methylation scores returned are neither readily crosssample comparable nor directly comparable to methylation data generated using methods applied to fresh tissues. Herein, we present DamMet, a software program returning regional maximum likelihood estimates (MLEs) of CpG methylation from HTS data obtained from individual ancient specimens. The underlying algorithm follows a 2-step procedure, the first step of which aims at obtaining MLEs of PMD rates in a positionspecific manner at both methylated and unmethylated CpG dinucleotides. To disentangle PMD events at such sites, we assume that the expected fraction of methylated cytosines genome-wide is known. In mammals, the fraction of methylated CpG dinucleotides in somatic tissues is 70-80%  [9] , implying that 20-30% of unmethylated states can be expected at CpG sites.
More specifically, we applied the first step of the model implemented in DamMet, which is aimed at estimating positionspecific PMD rates at both methylated and unmethylated sites, to the 3 simulated datasets across a wide range of genome coverage. To obtain the MLE of methylation position-specific PMD rates, the likelihood function makes use of chromosome-wide read observations covering a cytosine located in a CpG context in the reference genome and an equal number of observations of cytosines located outside a CpG context. We find that the MLEs of position-specific PMD rates at both methylated and unmethylated cytosines (D) are highly accurate, at least down to 5-fold coverage, regardless of the PMD profile and library protocol considered (Fig. 1  and Supplementary Results Section 2. 1. 3).
These results validate the first step of the algorithm. Next, we analyzed the accuracy of the second step, estimating local methylation levels (f), across a wide range of genomic window sizes and sequencing efforts using the same 3 simulated datasets (Supplementary Results 2. 1). Here, the likelihood function is maximized to obtain f, and includes all dinucleotide read observations covering CpGs in a given genomic window. First, we investigated the accuracy of f across a range of sequencing efforts using the root mean squared deviation of the estimates as measures of accuracy (Fig. 2a ) (see Supplementary Results 2. 1. 4 for a wide range of combinations of diverse sequencing efforts and window sizes). We found that the DamMet accuracy increases with sequencing depth (and/or window size) in all 3 scenarios.
Additionally, we found that the accuracy is positively correlated with PMD levels. Both observations are in line with the expectations of our likelihood model. We also compared f with the true methylation levels within a CpG island (chr20:324243-327679; GRCh38) for the same simulated datasets and sequencing depths. This specific CpG island was selected to illustrate the abrupt decline in methylation levels often observed in CpG islands. We found that the accuracy of the MLE of f increased with higher sequencing depths (Fig. 2B ) and that in all simulated scenarios, confident estimates of f require ≥20× coverage. Further information about the trade-off between accuracy of f and resolution by permuting a range of window sizes and sequencing efforts both locally and chromosome-wide can be found in Supplementary Results 2.
2. 2). In the latter libraries, both methylated and unmethylated cytosines are sequenced as thymines, which confounds the methylation estimate if not properly accounted for. By analyzing Vi33, we can thus test the ability of DamMet to obtain reliable estimates of f despite the presence of similar conversion signals at both methylated and unmethylated cytosine residues. We found that our chromosome-wide MLEs of f are comparable (Ust'Ishim: 0. 768, Vi33: 0. 756) to the methylation levels measured in modern samples generated with WGBS (Modern: 0. 748). The same holds true at the regional level, where DamMet obtains methylation estimates highly similar to those found in WGBS data from a modern sample (Fig. 3 ). Both samples display a minor underestimate (RMSD: 0. 04-0. 05) due to the relatively small genomic window sizes.
Implementation Details DamMet software is implemented in C++ and can be found at [21] https://gitlab. com/KHanghoj/DamMet. It takes a BAM file  [22]  as input, together with the reference genome used for read alignment. Three canonical filters for HTS data, namely, base quality, mapping quality, and minimum DNA fragment length, are implemented in DamMet  [23]. These can be modified by the user. Moreover, DamMet ignores "N" nucleotides present in either the reference genome or sequencing data. The reconstruction of the entire methylome of Ust-Ishim and Vi33 took 11 and 8 hours on a single CPU (E5-2683 v4 at 2. 10 GHz) with memory usages peaking at 10 and 9GB, respectively. Because DamMet analyzes each chromosome individually, it can easily be parallelized per chromosome to speed up the computation time.
Regional methylation levels can be recovered using either a sliding window procedure along the chromosome or within genomic regions based on a user-provided BED file. Low-mappability regions can be masked prior to estimating the regional methylation level by providing the regions in a BED format file. Particular genomic sites can also be excluded if needed. Along with DamMet, 2 dependencies will be installed, namely, nlopt and htslib. The new sequence simulator, implemented as a novel feature in gargammel  [11] , is available at https://github. com/grenaud/g argammel, together with a manual and running examples.
Finally, when the graph to be shown is a tree, we can also make use of implicit layouts, such as icicle plots  [10] , sunburst plots  [11, 12] , or treemaps  [13, 14]. Implicit layouts encode the relationships between parents and children by adjacency, and the size of the leaves is commonly scaled according to an attribute. S. Ribecca's Data Visualisation Catalogue (datavizcatalogue. com) provides a wide although nonexhaustive array of possible representations.
Choosing an appropriate basic representation (node-link, matrix, etc. ) and layout of the data comes next (Rule 2 and Rule 3), along with the appropriate labels and clarifying text (Rule 4). Gradual data preprocessing through aggregation (Rule 5), appropriate color mappings (Rule 6), the use An important aspect of network visualization that we have shown implicitly, although not discussed directly, is the power of network images to support the integration of a wide variety of data and to encode that data in a number of ways (for example, mapping expression fold change onto node fill color). This is an important and powerful feature of network visualization, particularly for exploring the results of multiple experiments in a single visualization in order to find new hypotheses or to confirm hypotheses, as often done in environments such as Cytoscape.
Many of the illustrations in this manuscript have been generated using the open-source software platform Cytoscape. Wherever possible, we provided references to the software packages, as well as specific instructions. In an effort to make the application of these rules more accessible, we also provide, wherever possible, the session files for generating these images in a public repository (http://github. com/uic-evl/10RulesBionets). Whereas obviously there are many other software tools for network visualizations, we hope that knowing how to implement these rules in one tool might help the reader more easily transfer that knowledge to another tool. Beyond the basic "how-to" mechanics of the rules, we further that recommend biology researchers contact the biological data visualization community (e.
The goal of TexNet is to provide authenticated data to evaluate the location, frequency, and likely causes of natural and induced earthquakes. TexNet, as of December 2018, had installed 68 new broadband seismic stations in Texas. Of these new stations, 26 are permanent and form, along with 18 existing broadband stations, a 70-120 km spaced, backbone seismic network in the state; 42 of the new stations are portable and have been deployed in five priority areas of the state having recent seismicity and which are of high socioeconomic importance. Implementation of TexNet has reduced the magnitude of completeness (Mc) for west Texas from 2. 7 to 1. 3, along with a large decrease in uncertainties of earthquake-source parameters  (Savvaidis et al. , 2019). High-accuracy, absolute location of seismic events with realistic uncertainty estimates is critical to TexNet monitoring, especially when seismic sources must be precisely associated with geographic locations of human activities.
Hydraulic Fracturing and Saltwater-Disposal Data sets The HF data set contains HF activity reported through IHS (ihsmarkit. com/products/us-well-data. html) and FracFocus (fracfocus. org) for wells in and around the Delaware Basin study area over the period September 2011 through May 2019. In Texas, HF reporting can be delayed by as much as 5 months. The dataset includes information on HF jobs, consisting of one or more HF stages, describing each job with a well identifier (UWI_API) and the times and spatial positions of the first (toe) and last (heel) limits in the horizontal production well of the stages in the job. We set job start and end times to the first and last stage times, respectively, although for the IHS data there is no reported end time for most jobs. For the FracFocus data, most jobs have reported end times; the difference between the last and first stage times gives a mean job duration of 14 days with most durations being between 5 and 20 days.
We also had information on SWD well activity in the study area over the period of 1983 through January 2019  (Lemons et al. , 2019) ; this activity may have caused some of the observed seismicity. However, temporal association of this SWD activity with seismic events provides little information on causality because of a lack of start and end time precision and very long durations (up to decades) of operation. Also SWD reporting can lag by as much as 1 year and a possible 3 months for processing gives total delay up to 15 months. SWD well activity may be causing some seismicity, which would add noise to our analyses using HF wells only; to mitigate adverse effects of this type of noise, we use a probabilistic association procedure. The SWD dataset also adds information for interpreting seismic event depths.