dataesr/scientific-paragraphs-categorization huggingface dataset:
Extremophiles Average nucleotide identity (ANI) values between the genome of isolate PT9 T and those of the type strains of Promicromonospora species were calculated using best hits and reciprocal best hits between two genomic datasets as described by  Yoon et al. (2017)  available at (https ://www. ezbio cloud. net/tools /ani). The top hits of the 16S rDNArelated strains of PT9 T were identified via the in silico calculation of digital DNA-DNA hybridization (dDDH) values between strains determination, using the Genometo-Genome Distance Calculator (GGDC) web server (https ://ggdc. dsmz. de/distc alc2. php) (Goris et al. 2007; Meier-Kolthof et al. 2013).
The package lethal (https ://githu b. com/hofne rb/letha l) was used also to estimate the survival of PT9 T following exposure to desiccation. Growth of isolate PT9 T with various concentrations of H 2 O 2 (v/v) was also performed as described by  Gholami et al. (2015). E. coli DH5α was used as a proteome-oxidation-sensitive bacterium (Krisko et al. 2010) for comparative analyses. Fifty µL of bacterial suspensions were added to 100 µL of TSB supplemented with 0-5% (v/v) of H 2 O 2 using 96-well microplates. Plates were kept in the dark at 30 °C for 48 h with agitation at 120 rpm. The optical density was determined at 630 nm using a microplate reader TECAN (Ininite ® 200 PRO, Germany).
Nucleotide sequence accession number The whole-genome shotgun sequence of isolate PT9 T was submitted to GenBank/ENA under BioProject accession number PRJEB29453 (ERP111755), BioSample number SAMEA5056529 and SRA accession number ERS2867704. Contig sequences of the de novo assembly were deposited at EMBL under the accession numbers UWYY01000000-UWYY01000056.
Description of The major fatty acids are anteiso-C15:0 and iso-C15:0. The polar lipid pattern is diphosphatidylglycerol (DPG), phosphatidylglycerol (PG), phosphatidylinositol (PI), phosphoglycolipid (PGL), unidentiied glycolipids (GL 1-3 ), lipid (L) and phospholipid (PL). Whole-organism hydrolysates contain DL-2,6-diaminopimelic acid (DL-DAP), galactose, glucose and ribose as whole cell sugars. The genome of the type strain is 6. 5 Mb with a GC content of 71. 2 mol%. The GenBank/ENA accession number for the draft genome sequence of isolate PT9 T is PRJEB29453. The type strain PT9 T (LMG 31103 T = DSM 108613 T ) was isolated from irradiated roots of P. turgidum collected from the Ksar Ghilane oasis, southern Tunisia.
Acknowledgement This work was performed under the auspices of  Université Lyon 1, CNRS UMR 5557, Écologie Microbienne (France)  and the  National Center for Nuclear Sciences and Technology CNSTN (Tunisia)  and in the ambit of the  BIODESERT research program  of the  LR11-ES31  ( BVBGR, ISBST, University of Manouba ). The  LAB-GeM  ( CEA/Genoscope & CNRS UMR8030 ), the  France Génomique and French Bioinformatics Institute  national infrastructures (funded as part of  Investissement d'Avenir program  managed by  Agence Nationale pour la Recherche , contracts  ANR-10-INBS-09  and  ANR-11-INBS-0013 ) are acknowledged for support within the MicroScope annotation platform. We thank  Betty Bigai ,  Corinne Sannaire  and platform  Genomique Environmental PGE ,  UMR CNRS 5557  for technical support.
Methods Data from eight healthy, right-handed, volunteers with normal or corrected-to-normal vision and no history of neurological or psychiatric disorders was used for our analyses (six male, aged 28. 5 AE 8. 52 years;  Bonaiuto et al. , 2018a; Little et al. , 2018). The study protocol was in accordance with the Declaration of Helsinki, and all participants gave written informed consent which was approved by the UCL Research Ethics Committee (reference number 5833/001). All analysis code is available at https://github. com/jbonaiuto/dipole_orientation.
The flip angle was 6 for the PD-and MT-weighted volumes and 21 for the T1 weighted acquisition. MT-weighting was achieved through the application of a Gaussian RF pulse 2 kHz off resonance with 4 ms duration and a nominal flip angle of 220 prior to each excitation. The field of view was 256 mm head-foot, 224 mm anterior-posterior (AP), and 179 mm right-left (RL). Gradient echoes were acquired with alternating readout gradient polarity at eight equidistant echo times ranging from 2. 34 to 18. 44 ms in steps of 2. 30 ms using a readout bandwidth of 488 Hz/pixel. Only six echoes were acquired for the MT-weighted acquisition in order to maintain a repetition time (TR) of 25 ms for all FLASH volumes. To accelerate the data acquisition, partially parallel imaging using the GRAPPA algorithm was employed with a speed-up factor of 2 in each phase-encoded direction (AP and RL) with forty integrated reference lines.
To maximize the accuracy of the measurements, inhomogeneity in the transmit field was mapped by acquiring spin echoes and stimulated echoes across a range of nominal flip angles following the approach described in  Lutti et al. (2010) , including correcting for geometric distortions of the EPI data due to B0 field inhomogeneity. Total acquisition time for all MRI scans was less than 30 min. Quantitative maps of proton density (PD), longitudinal relaxation rate (R1 ¼ 1/T1), MT and effective transverse relaxation rate (R2* ¼ 1/T2*) were subsequently calculated according to the procedure described in  Weiskopf et al. (2013). (v5. 3. 0; Fischl, 2012)  was used to extract cortical surfaces from the MPMs for MEG source localization. We used a custom Free-Surfer surface reconstruction procedure to process MPM volumes, using the PD and T1 volumes as inputs  (Carey et al.
This involved using MATLAB's reducepatch function to remove vertices from, and re-tesselate the pial surface, and then removing the same vertices from the white matter surface and copying the edge structure from the pial surface. This yielded two meshes of the same size (same number of vertices and edges), comprising about 30,000 vertices each (M ¼ 30, 094. 75, SD ¼ 2,665. 45 over participants).
Simulations All simulations were based on a single dataset acquired from one human participant. This dataset was only used to determine the sensor layout, sampling rate (1200 Hz, downsampled to 250 Hz), number of trials (  515 ), and number of samples (251) for the simulations. All simulations and analyses were implemented using the SPM12 software package (http://www. fil. ion. ucl. ac. uk/spm/software/spm12/). In each simulation, we specified spatially distributed source activity centered at a single vertex on the pial surface. We simulated a Gaussian activity time course in this vertex, centered within the epoch, with a width of 25 ms and a magnitude of 10 nA m. We then spatially smoothed this simulated dipole time course with a Gaussian kernel (FWHM ¼ 5 mm), to obtain a patch of spatially distributed activity.
Within this patch, the orientation of each vertex differed, but was specified by the same rule using the link vectors method. We then used a single shell forward model  (Nolte, 2003)  to generate a synthetic dataset from the simulated source activity. We simulated sources at 100 random vertices on the pial surface, and ran two sets of simulations: one varying the level of noise in the simulated data and the other varying the magnitude of co-registration error. Typical per-trial SNR levels for MEG data range from À40 to À20 dB  (Goldenholz et al. , 2009) , and therefore Gaussian white noise was added to the simulated data and scaled in order to yield per-trial amplitude SNR levels (averaged over all sensors) of À50, À40, À30, À20, À10, or 0 dB to generate synthetic datasets across a range of realistic SNRs.
For a full description of the paradigm and task structure, see  Bonaiuto et al. (2018a). Each block contained 180 trials in total. Participants completed three blocks per session, and 1-5 sessions on different days, resulting in 540-2700 trials per participant (M ¼ 1822. 5, SD ¼ 813. 21). The task was implemented in MATLAB (The MathWorks, Inc. , Natick, MA) using the Cogent 2000 toolbox (http://www. vislab. ucl. ac. uk/cogent. php).
MEG acquisition and preprocessing MEG data were acquired using a 275-channel Canadian Thin Films (CTF) MEG system with superconducting quantum interference device (SQUID)-based axial gradiometers (VSM MedTech, Vancouver, Canada) in a magnetically shielded room. A projector was used to display visual stimuli on a screen (~50 cm from the participant), and a button box was used for participant responses. The data collected were digitized continuously at a sampling rate of 1200 Hz. MEG data preprocessing and analyses were performed using SPM12 (http://www. fil. ion. ucl. ac. uk/sp m/) using MATLAB R2014a. The data were filtered (5th order Butterworth bandpass filter: 2-100 Hz, Notch filter: 50 Hz) and downsampled to 250 Hz. Eye blink artifacts were removed using multiple source eye correction  (Berg and Scherg, 1994).
Trials were then epoched from 1s before RDK onset to 1. 5s after instruction cue onset for analysis of visual responses, and from 2s before the participant's response to 2s after for analysis of movement-evoked responses. Blocks within each session were merged, and trials whose variance exceeded 2. 5 standard deviations from the mean were excluded from analysis. The epoched data were then averaged over trials using robust averaging, a form of general linear modeling  (Wager et al. , 2005)  used to reduce the influence of outliers on the mean by iteratively computing a weighting factor for each sample according to how far it is from the mean. Preprocessing code is available at http://github. com/jbonaiuto/meg-laminar.
With high precision MEG data, getting the orientation right matters Having established that each method yields substantially different orientation vectors, we next sought to determine the minimum angular difference between dipole orientations distinguishable by source inversion model comparison, and how this is affected by typical levels of SNR and co-registration error. We therefore simulated dipoles at 100 random source locations on the pial surface and created synthetic datasets with varying SNR and co-registration error levels. We then performed source inversion on the synthetic datasets, using a reference model in which the dipole orientations exactly match those of the simulated dipole, and 9 other models in which the dipole orientations were rotated with respect to simulated dipole orientation.
By contrast, methods that utilize link vectors between pial and white matter surfaces constructed from higher resolution structural images perform significantly better in explaining observed evoked responses. The average angular differences between the five methods compared here were substantial, with means of 18-30 , both in high-resolution MPMs and in commonly used T1-weighted structural images with 1 mm 3 spatial resolution. We do not know the ground truth of current flow orientation in the brain, but we show here that the average angular difference between methods is within the range distinguishable in simulated data with SNR and co-registration error levels achievable with high precision MEG. We were therefore able to compare these methods in terms of how well they fit human MEG data, leveraging the free energy metric, in order to determine which method best estimates true dipole orientations.
, 2018a). Future extensions of this work could utilize source inversion in successive time bins to address this limitation and generate temporally resolved estimates of laminar activity. We assumed the sensor level covariance matrix to be diagonal. However, an independent sensor dataset recorded during a similar time period in an empty room, showed off-diagonal structure (Figure  S5 ). Importantly, the same pattern of model comparison results was obtained when using a sensor covariance matrix based on these noise measurements  (Figure S6, S7). In this work, we used free energy as our metric of model fit but we would expect these findings to generalize across other metrics. For example, we have previously shown that for model comparison problems of the type utilized in this study, free energy is very highly correlated with nonparametric cross validation error measures of model fit  (Bonaiuto et al.
Acknowledgements FA, MF, and MB were supported by the  European Union 's  Seventh Framework Programme  (FP7/2007-2013)/ ERC  starting grant agreement number  716862  to  M. Bonnefond. KW was supported by the  Wellcome Trust  ( 215901/Z/19/Z ). The  Wellcome Centre for Human Neuroimaging  is supported by a centre award from the  Wellcome Trust  ( 203147/Z/16/ Z ). The funders had no role in the preparation of the manuscript.
, 2005; Noda and Lapusta, 2013; Lui and Lapusta, 2016) , and the relation between the earthquake source and seismological and geodetic observations  (Kaneko et al, 2010; Thomas et al. , 2017; Barbot, 2019; Ulrich et al. , 2019). However, a major challenge that remains is relating laboratory data on RSF parameters to fault rheology at depth in the Earth's crust. Physics-based models for fault slip such as the CNS model provide a transparent origin of the constitutive parameters used, so when employed in numerical simulations a substantial portion of epistemic uncertainty is eliminated. Conveniently, in the case of the CNS model, its numerical implementation into a seismic cycle simulator (QDYN in the study of  Van den Ende et al. , 2018; see Luo et al. , 2017)  is similar to that of the RSF equations, implying that it is compatible with existing codes for seismic cycle and dynamic rupture simulations.
g. Meyers et al. , 2006; Hochella et al. , 2019)  -the room-temperature ductile nanofibres encountered in calcite gouge being an example of this (Fig. 5c , inset)  (Verberne et al. , 2014b (Verberne et al. , , 2019)). The implication is that extrapolation of data from compaction experiments using micron-sized crystals or larger, which are used to constrain parameter values appearing in the CNS model, may lead to large errors when applied to nanogranular or (partly) amorphous fault rock. Even when coarser-grained fault rocks are considered, frictional sliding on the contact between two grains is ultimately governed by nanometre-scale processes. This can be envisioned as lattice-scale solid-solid interactions for "dry" contacts or contacts with incomplete coatings of adsorbed species, such as water (used in the CNS model;  Chen and Spiers, 2016) , or as interactions arising from the unique properties of fully developed, adsorbed water or hydration layers  (Leng and Cummings, 2006; Sakuma et al.
Acknowledgements. The research work described in this paper would have been impossible without the support of  Magda Mathot-Martens ,  Eimert de Graaff ,  Gert Kastelein , Thony van der  Gon-Netscher ,  Peter van Krieken ,  Otto Stiekema ,  Leonard Bik , Floris van Oort, and  Gerard Kuijpers. Colin Peach  is thanked for his major scientific contributions throughout the years, as well as for developing, improving, and maintaining experimental equipment. Miki Takahashi  is thanked for providing the photo in Fig. 2a. Financial support. This work has been supported by the  Japan Society for the Promotion of Science (KAKENHI  grant no. 19K14823 ), the  Government of France UCAJEDI  (grant no. ANR-15-IDEX-01 ), and the  European Research Council  (grant no. 335915 ). Review statement. This paper was edited by  Jean Sulem  and reviewed by two anonymous referees.
Herein, we propose a systematic approach to producing chronologies for sediment cores using short-lived radionuclides ( 210 Pbex, 137 Cs and 241 Am) and different types of 210 Pbex models, based on the free and open-source software R. We first describe the different hypotheses for 210 Pbex decay and the resulting models; we then introduce the argument of the R function we developed, before applying the serac code to six complex case studies. We wish our code to supplement clam for chronologies for the past century.
R code We developed a package on the open-source software R (R Core Team, 2014). All input files must be saved in a tab separated '. txt' format, with periods as decimal delimiters. The function serac_input_formatting('MyCore') can be used to help format the input file. To use it, place the raw input file (column names in first row, data starting from the second row) in the folder as described above. This function asks the user to identify columns, rename them, and replace the input data file automatically. The package can be downloaded from the GitHub repository https://github. com/rosalieb/serac, or with the package devtools  (Wickham et al. , 2018)  and the code: library(devtools) devtools::install_github("rosalieb/serac", build_vignettes = TRUE) library(serac).
205'N; 10°4. 340'E) is a large lowland lake in Northern Italy 25 km long and 60. 9 km² in surface area. This core is a sample from the Monte Isola plateau at approximately 70 m depth and contains evidence for a recent eutrophication  (Rapuc et al. , 2018). From short-lived radionuclides data on this core (Table  1 ), SAR = 3. 157 mm. y - 1. In the script below, note that we request to visualise all three 210 Pbex models, 137 Cs and 241 Am peaks and varve counting (Figure  3 ) and we use a 5 mm resolution for our interpolated model. serac(name="Iseo",coring_yr=2010,model=c("CFCS","CIC","CRS"),plotphoto=TRUE,minp hoto=c(0),maxphoto=c(320),plot_Pb=T,plot_Am=T,plot_Cs=T,Cher=c(70,75),Hemispher e=c("NH"),NWT=c(130,140),  FF=c(164, 173), varves=TRUE, plotpdf=T, stepout=5)  The comparison between varve counting, artificial radionuclides and the 210 Pbex model shows that the CFCS model is preferable for this core and that there is evidence for the "too-old" age error described first by  Binford (1990)  for the CRS model in the deeper core sections and now widely observed  (Abril, 2019; Tylmann et al.
Metadata Every time the code is run, a metadata file is automatically generated in the folder. The metadata file summarises the main decisions made by the user (e. g. , presence/absence of instantaneous deposit, type of model chosen) but also other general information on the user (ORCID, affiliation, email) and the core (ISGN: International Geo Sample Number (IGSN)/System for Earth Sample Registration Database (www. geosamples. org, measurement laboratory, measurement method, date of measurement). These data are entered independently from the exploration phase of the model through the function user_infos() and core_metadata(). The former function theoretically needs to be used only once by each new user the first time the library serac is used. The new user will be required to answer several questions (affiliation, ORCID number, etc.
The overall aim of this study was to produce conclusive data on the presence or absence of mtDNA CpG methylation (5mCpG) using a novel technique, and to determine its conservation across different biological conditions. Three cellular settings known to influence mitochondrial dynamics were employed: a model of cellular differentiation, cancer and a model of oxidative stress. After enrichment, mitochondria were sequenced using a ONT Minion device and mtDNA methylation status was directly obtained from the raw signals. We observed low levels of strand specific DNA methylation in hepatocytes with consistent changes related to sample origin.
1) target regions. Both, EPIC and Nanopore data are able to capture the expected dip in methylation associated with active regulatory regions (Figure  1B , top panels)  (Lawson et al. , 2018). In contrast, non-active transcription factor binding sites produce a flat methylation profile after aggregation of a similar number of genomic regions in both EPIC and Nanopore data (Figure  1B , bottom panels). Both techniques were highly correlated when aggregated data from all transcription factor binding sites was taken together (Figure  1C ). Next, we tested different available tools to detect DNA methylation from Nanopore sequencing data. We used the well-established tool, Nanopolish  (Simpson et al. , 2017)  which uses a hidden markov model to detect DNA methylation and compared it to the novel tool Guppy + Medaka which has been trained to basecall for modified human CpG dinucleotides using a recurrent neural network  (Wick et al.
Bioinformatic analyses Basecalling was performed with Guppy version 4. 0. 15 (ONT). We first determined the methylation status of each CpG site on every read by using the widely used tool, nanopolish  (Simpson et al. , 2017)  used recently by  (Gigante et al. , 2019). For validation, we also called DNA methylation using novel tool, Medaka (git repository reference). Medaka is a tool to create a consensus sequence from nanopore sequencing data. This task is performed using neural networks applied from a pileup of individual sequencing reads against a draft assembly. It outperforms graph-based methods operating on basecalled data, and can be competitive with state-of-the-art signal-based methods, whilst being much faster. PycoQC was used for data inspection and quality control (https://github.
com/a-slide/pycoQC), and methplotlib (https://github. com/wdecoster/methplotlib) for read-level visualizations. Called CpG sites in the FU control were used to determine a baseline of methylation. The following calculation was utilised: FalsePositiveRate=[#called methylated cytosines in FU/#called cytosines in FU]. For differential methylation analyses we used DSS (Dispersion shrinkage for sequencing data) (Park and Wu, 2016) adapted for nanopore sequencing  (Gigante et al. , 2019).
These sexsensitive lesion pattern effects could be discovered and subsequently robustly replicated in two large independent, multisite lesion datasets. The constellation of findings has several important conceptual and clinical implications: 1) suggesting sex-specific functional cerebral asymmetries, and 2) motivating a sex-stratified approach to management of acute ischemic stroke. To go beyond sex-averaged stroke research, future studies should explicitly test whether acute therapies administered on the basis of sex-specific cutoff volumes of salvageable tissue will lead to improved outcomes in women after acute ischemic stroke.
Replication analyses Similar main findings were noticed when repeating analyses in a completely independent, multisite dataset. 26 Once again, we extracted ten lesion atoms that captured typical stroke patterns in the left and right hemisphere in low dimensions (Supplementary Figure  4 ). Lesion atoms of the development and replication dataset were highly correlated, indicating our unsupervised approach facilitated the recovery of similar lesion topography embeddings in both independent datasets (Supplementary Table  3 ). The most relevant regions explaining stroke severity were also located subcortically in the left and right hemisphere as well as in bilateral pre-and postcentral gyri and left-hemispheric insular and opercular regions (Supplementary Figure  5A ). Women, once again, presented more widespread eloquent lesion patterns compared to men (Supplementary Figure  5B&C ).
This difference was stably observable across datasets and did not appear to represent an artifact of slightly greater stroke severity in women or imbalanced numbers of men and women in our sample. It is important to note that the strongest sex differences, that we observed, were strictly lateralized to the left hemisphere. Previous research suggests that male or female sex and respective sex hormones contribute to induce functional cerebral asymmetries. 27 Men appear to have a stronger hemispheric asymmetry; however, while robustly replicated, determined effect sizes have been very small. 28 Such an enhanced asymmetry in men was also found in some early lesion studies on intelligence. 29 However, further early lesion studies suggest that lateralization differences between the sexes might be even more complex: Inglis and colleagues underline that female brains may be asymmetric to a comparable degree, yet in different ways.