dataesr/scientific-paragraphs-categorization huggingface dataset:
DSB-RS tend to be conserved at the species level, but CRISPR-Cas systems sometimes vary between strains. Hence, we analysed the patterns of co-occurrence at the genome level, i. e. we analysed all genomes even when they were from the same species. This increased the size of the datasets, at the cost of increasing phylogenetic dependence. To control for the latter, we inferred the phylogenetic trees for the Firmicutes and for the Proteobacteria, and used them to test the co-occurrence of systems with BayesTraits  [46]. This revealed 53 positive and negative significant associations out of a total of 233 possible (figure  2 ). We first concentrated on testing if previously reported interactions were retrieved by our method. We detected a positive association between the presence of RecBCD and.
It remains unclear, however, which, if any, modulation arises from expectations of the specific stimulus condition. A drift-diffusion model analysis of the present two datasets (see Supplementary Information; see also  28  ) suggests that participants adjusted their decision threshold for each condition. We tested whether this behavior was also reflected in the CPP amplitude, a neural marker of evidence-to-bound accumulation. In a first step, a repeated measures ANOVA with factors of CONDITION (  4  Single dots show single-subject data, bars display mean of the distribution (solid line), 95% confidence interval (dark grey shaded area), and 1 standard deviation (light grey shaded area). Amplitude differences across conditions show little variation by RT, however, RT does modulate differences in slope across conditions.
Methods All methods were carried out in accordance with the relevant guidelines and regulations pertaining to non-clinical human research, as instructed by the institutional review board. Methods and procedures were approved by the Medical Sciences Inter-Divisional Research Ethics Committee, Medical Sciences Divisional Office, Oxford. All data was collected at the Department of Experimental Psychology in Oxford. All participants provided written informed consent. Participants. We tested 16 participants in the behavioral experiment (Experiment 1) and 20 participants in the EEG experiment (Experiment 2). Participants took part in only one of the experiments. Two participants were post-hoc excluded from Experiment 2, one because of excessive noise in electrodes crucial to the analysis, the other because the staircasing procedure failed and the participant displayed behavior substantively different from any other of the 35 participants (see Supplementary Information for a separate analysis of this dataset).
Statistical analysis. All statistical analysis was conducted in Matlab using the Measures of Effect Size (MES) Toolbox  53. Where possible, we report point-biserial correlation coefficients (r equivalent). In addition to standard RT and accuracy measures, we compare performance across conditions as inverse efficiency, calculated as median correct RT divided by accuracy (inverse efficiency score IES  26  ). Moreover, our design included two conditions of medium difficulty, which our previous studies had found to be matched in performance  7, 19. We therefore report Bayes Factors (BFs) for all non-significant t-tests to be able to assess the probability with which the null hypothesis is true given the data, using the R package BayesFactor by Morey, Rouder and colleagues  54, 55. We report the BF as support for the null hypothesis over the alternative hypothesis as BF 01.
EEG recording and pre-processing. A Neuroscan Synamps2 system (10 GΩ input impedance; 29. 8 nV resolution; Neuroscan, El Paso, TX, USA) 57 was used to record EEG data from 32 Ag/AgCl electrodes mounted in an elastic cap at locations FP1, FPZ, FP2, F7, F3, FZ, F4, F8, FT7, FC3, FCZ, FC4, FT8, T7, C3, CZ, C4, T8, TP7, CP3, CPZ, CP4, TP8, P7, P3, PZ, P4, P8, POZ, O1, OZ, and O2. Additional six external electrodes were attached: to the outer canthi of the left and right eyes, above and below the right eye to measure electro-oculograms (EOGs), and to the left and right mastoids. Electrode recordings were referenced to the right mastoid. All electrode impedances were kept below 50 kΩ. EEG data were recorded at a sampling rate of 1000 Hz. Data were online high-pass filtered at 0. 1 Hz to avoid slow-wave drifts.
The data were low-pass filtered at 24 Hz with a Hamming-windowed sinc finite impulse response function, as implemented in EEGLab  [58] [59] [60]  prior to epoch extraction to avoid noise in the recording to inflate peak-measures of the components of interest. All analyses were performed on data down-sampled to 250 Hz. Auto-detected noisy channels were removed, and interpolated using the EEGLab pop_ rejchan function  58. We extracted cue-epoched data from 300 ms pre cue to 2800 ms post cue. These epochs were baselined to a window 100 to 200 ms post-cue, to prevent differences in the visual response to the stimuli affecting the baseline. Epochs with noise in the component-specific target electrodes were identified using first a probabilistic (pop_jointprob) followed by a thresholding (pop_eegthresh) approach (both implemented in EEGLab  58  ).
We used this setup to test for expectation-driven modulations of CPP amplitude by comparing identical conditions pre-and post-switch; we expected an interaction between the effects of condition and switch on CPP amplitude. A post-cue baseline avoided contamination of the measurement by cue-driven variation (see below). Using trial-by-trial reaction time recordings the data were realigned to the individual responses. We measured CPP slope by fitting a linear regression to each participant's individual data in a window ranging from 400 ms to 100 ms before the response. CPP amplitude was measured as the peak in a time-window ranging from 180 ms to 80 ms before the response 21 in an electrode cluster containing the electrodes CZ-CPZ-PZ  21. For the CPP analysis of pre-switch blocks, the lowest number of trials entered per participant was 44, the average was 93.
Acknowledgements This work was supported by the  Biotechnology and Biological Sciences Research Council (BBSRC)  grant number " BB/I019847/1 ", awarded to N. Y. and F. W. A. B. was supported by her  Economic and Social Research Council  PhD studentship and is supported by a  Sir Henry Wellcome Postdoctoral Fellowship. The authors are grateful to  Verity Smith  and  Anna Katharina Hauperich  for their help in data collection. The funders had no role in designing the study, decision to publish, and preparation of the publication.
Methods: The study was conducted at 170 sites across 31 countries. Data from patients of any age with a documented clinical visit for TSC in the 12 months preceding enrollment or those newly diagnosed with TSC were entered. Results: SEGA were reported in 554 of 2,216 patients (25%). Median age at diagnosis of SEGA was 8 years (range, <1-51), with 18. 1% diagnosed after age 18 years. SEGA growth occurred in 22. 7% of patients aged ≤ 18 years and in 11. 6% of patients aged > 18 years. SEGA were symptomatic in 42. 1% of patients. Symptoms included increased.
Although substantial progress has been made in our understanding of the biological and genetic basis of TSC in the past decade, several questions, especially those related to the natural history of the disease, remain unanswered. To address this gap, the TOSCA (TuberOus SClerosis registry to increase disease Awareness) registry was designed with the aim of providing deeper insights into the manifestations of TSC and its management. The baseline core data of the TOSCA registry published previously provided understanding of the overall manifestations and natural history of TSC  (26). Here, we present the clinical characteristics of SEGA in children and adults.
The data were recorded on an electronic case report form (eCRF) that was accessed via a secure web portal hosted by a contract research organization. Input of data was carried out by local investigators or their deputies, and then independently checked by a network of clinical research associates for accuracy and consistency using the original local case records. The web portal has an explanatory manual to guide the investigators. Data collected specific to SEGA included tumour characteristics such as presence of single or multiple SEGA, clinical signs and symptoms associated with SEGA, and management. Characteristics of SEGA according to the age at consent were evaluated. The study also assessed the association between genotype (TSC1 vs. TSC2) and SEGA characteristics using Chi-square test or fisher exact test, and median test.
Since baseline data were collected prior to the 2013 international consensus on SEGA definition, no specific inclusion criteria were defined. The TOSCA cohort therefore reflects worldwide clinical practice. Given that the natural history study is exploratory in nature, background and clinical parameters were reported with descriptive statistics only. All eligible patients enrolled in the TOSCA registry were considered for analysis. Categorical data were reported as frequencies and percentages, and continuous variables were expressed as mean (± standard deviation) or as median (range), unless stated otherwise. TOSCA was designed and conducted according to the Guidelines for Good Clinical Practice and ethical principles outlined in the Declaration of Helsinki  (28, 29). After appropriate approval by central and all local human research ethics committees, written informed consent was obtained from all patients, parents, or guardians prior to enrollment.
Acknowledgements The work presented in this paper has been carried out under a PhD sponsored by  TWI Ltd. and  University of Edinburgh  at  NSRIC  ( National Structural Integrity Research Centre ). The main author would like to thank these institutions for this opportunity. Moreover, part of this work was done as a visiting research student at  LMS ,  CNRS Ecole Polytechnique (France ) partially founded by an  ERASMUS Grant. Finally, the experimental data used for the validation of the computational fatigue assessment was kindly provided by a Joint Industry Project lead by  TWI Ltd. whose sponsors are:  American Bureau of Shipping (ABS) ,  BP Exploration Operating Co Ltd. , Shell Projects and Technology  (former BG group), Equinir,  Ramnäs Bruk  (in kind) and  Vicinay Cadenas  (in kind). The authors are grateful to these companies for the permission to publish these results.
Gamma-ray Observations and Analysis The Fermi LAT  (Atwood et al. 2009 ) is a pair conversion telescope sensitive to gamma-ray photons with energies between ∼20 MeV to > 300 GeV. The LAT data used in this paper were obtained between 2008 August 5 and 2019 March 13  (MJD 54, 683 to 58, 555). During this time, Fermi was primarily operated in a sky survey mode where the LAT pointing position is alternately rocked away from the zenith to the orbit north for one spacecraft orbit, then towards the orbit south for one orbit. In this way, the entire sky is observed every two spacecraft orbits, approximately every three hours. For LAT analysis we used the fermitools version 1. 0. 1. We used the updated Pass 8 LAT data files ("P8R3",  Bruel et al. 2018 ) and the weekly photon files provided by the Fermi Science Support Center which include precomputed diffuse response columns.
Thus, when a source is brighter than the model predicts, the probability of a photon coming from the source is underestimated, and, when the source is fainter than the model, the probability is overestimated. This results in a decrease of the apparent modulated amplitude. In addition, there is an energy-dependent effect as the smaller point spread function (PSF) of the LAT at higher energies results in higher-energy photons having higher weights. Time bins of 500s were used for all sources. Power spectra of these probability-weighted aperture photometry LAT light curves were calculated weighting each data point's contribution by its relative exposure, after first subtracting the mean count rate. This is beneficial because of the substantial exposure changes from time bin to time bin  (Fermi LAT Collaboration et al.
The exposures of these archival XRT observations ranged from ∼90 s to ∼4. 6 ks. 4FGL J1405. 1-6119 was observed in Photon Counting (PC;  Hill et al. 2004 ) mode with a readout time of 2. 5 s adopting the standard grade filtering (0-12 for PC). We reduced and analyzed the data using the Swift XRT product generator  (Evans et al. 2007 ) and the HEAsoft v. 6. 20 package and calibration files dated 2017 May 1, following the procedures defined in the XRT Data Reduction Guide  (Capalbi et al. 2005). The data were reprocessed with the XRTDAS standard data pipeline package xrtpipeline using the standard filtering procedure to apply the newest calibration and default screening criteria. We find the background-subtracted count rates of our TOO observations to be between 1. 0 +1. 8 -0. 9 ×10 -3 and 7 ± 2×10 -3 counts s -1.
For this we only used XRT data from the TOO observations. Due to the sparse coverage of the X-ray light curve, we did not bin this but instead linearly interpolated to cover observation gaps. From this we find the X-ray light curve leads the LAT light curve by a phase of 0. 586. We also used a simple sine wave fit to the entire XRT light curve to determine maximum of the X-ray light curve and find this occurs at a phase of 0. 59 ± 0. 03. This is thus consistent with the results of the cross-correlation analysis. However, this result should be treated with caution since we only have good coverage of two cycles, the individual flux measurements have relatively large errors, and the shape of the modulation is hence not yet well defined.
We thank an anonymous referee for useful comments. This work was partially supported by  NASA   Fermi grant   NNX15AU83G. The Australia Telescope Compact Array is part of the  Australia Telescope National Facility  which is funded by the  Australian Government  for operation as a National Facility managed by  CSIRO. J. Strader  acknowledges support from a  Packard Fellowship. This work was partially supported by  NASA  grant  80NSSC17K0507  and  NSF  grant  AST-1714825. This paper was partially based on observations obtained at the  Gemini Observatory , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under a cooperative agreement with the NSF on behalf of the Gemini partnership: the  National Science Foundation  ( United States ), the  National Research Council (Canada) ,  CONICYT (Chile) ,  Ministerio de Ciencia, Tecnología e Innovación Productiva (Argentina) , and  Ministério da Ciência, Tecnologia e Inovação (Brazil).
Acquisition of all signals generated on wires and scintillators within a time window of about 1 µs. The drift time will be measured by multi-hit TDC. Pulse height will be measured in parallel with ADC. Two alternative options are also considered: (i) obtaining pulse height information from TDC data using the improved time-over-threshold method and (ii) obtaining both the drift time and pulse height information from signals sampled at frequency of about 500 MHz. 3. Signals delivered by SiPM sensors serving "proton" detectors will be acquired by dedicated electronic channels. Acquired information will allow to deduce: (i) electron energy from pulse height in the scintillator, (ii) proton hit position from the pulse height distribution of light signals registered by SiPM sensor close to hit position, (iii) electron tracks from drift times and pulse height asymmetry on both wire ends in MWDC, (iv) proton energy from measured time-of-flight, hit position and 3body kinematics.
Simulations Transport of protons and electrons (both primary and secondary originating from the pe converter foil) was investigated with the help of COMSOL  [35]  simulation package. By applying special configuration of electrodes, recoil protons are focused on a small area of the converter foil. Similarly, the secondary electrons are perfectly collected by "proton" detectors. As a result, the distributions of the deflection angle were obtained. Figure  5  presents a few results from that simulation. The worst case detection efficiency of protons (including pe conversion losses) is higher than 90%. The detection efficiency of primary electrons from neutron decay is essentially defined by the overall geometrical transparency of the vacuum window and should be close to 50%. In the first approach, rotationally symmetric structure for 216 module windows arranged in 36 rows at 10 o angular step (cf.
Fig. 6a ) and featuring 50% transparency was proposed. In order to check mechanical deformations and stability of such a vacuum chamber made of 10 mm thick aluminum tube and having the outer diameter of 350 mm and the total length of 1500 mm the ANSYS engineering simulation software [36] was used. The calculations show that this design can sustain external overpressure of 1. 3 bar with a high safety factor. The maximum stress obtained was 86 MPa which is much smaller than the yield strength value of 276 MPa for aluminum alloy used in this simulation. The same conclusion follows from the calculated load multiplier for buckling which in all cases was larger than 5. The maximum deformation was only about 0. 3 mm (cf. Fig. 6b ).
ATPS Sulfurylase (ATPS) is the first of three enzymes in the sulfate reduction pathway -one of the oldest metabolic pathways on Earth, utilized by Sulfate Reducing Bacteria (SRB). Due to the low redox potential of the sulfate ion, its reduction requires activation via formation of adenosine 5′-phosphosulfate (APS), which is catalyzed by ATPS. Dispersion-corrected hybrid density functional theory (DFT/B3LYP-D3) was used to test three reaction mechanisms proposed for conversion of ATP to APS: two-step SN-1 reaction running through AMP anhydride intermediate, two-step reaction involving cyclic AMP intermediate and direct SN-2 conversion of ATP to APS molecule. The study employed five different cluster models of the ATPS active site: one containing magnesium cation and four without it, constructed based on the crystal structure (PDB code: 1G8H) solved for ATPS from Saccharomyces cerevisiae in complex with APS and pyrophosphate (PPi), where Mg 2+ was not detected.
e. ATP Sulfurylase (ATPS). ATPS is an enzyme ubiquitous in Nature, catalyzing the reversible reaction of sulfate activation with the employment of ATP, which leads to APS and pyrophosphate anion products. ATPS from SRB, yeast and plants are characterized as monomers or homo-oligomeric structures  [1, [17] [18] [19]. There are crystal structures available for ATPS in complex with intermediates of the native catalytic reaction as well as with its inhibitors. The crystals with the following PDB codes: 4MAF, 1I2D, 1G8H, 1G8G, 1V47, 1JEE  [9, [20] [21] [22] [23]  were solved for the enzyme-APS complex, whereas 1G8F, 1JHD are structures for enzyme-sulfate complexes  [22, 24]. The structures of ATPS-APS complex are available for the following organisms: soybean (PDB code: 4MAF), Penicillium chrysogenum (PDB code: 1I2D), Saccharomyces cerevisiae (PDB codes: 1G8H, 1G8G, 1JEE) and Thermus thermophillus (PDB code: 1V47).
However, there is only one structure available with both reaction products of ATPS -APS and pyrophosphate (PPi) molecules: 1G8H  [22]. The quaternary structure of ATPS from Saccharomyces cerevisiae (PDB code: 1G8H), similarly to the one from Penicillium chrysogenum (PDB code: 1I2D), was determined as a homohexameric assembly arranged as two trimeric rings stacked with a twist of 60 ∘ (D3 symmetric arrangement  [21, 22] , Fig. 1 ). In the active site of ATP Sulfurylase, four conserved motifs are observed in the available ATPS sequences and crystal structures: QXRN (Gln195-Thr196-Arg197-Asn198), HXXH (His201-Arg202-Ala203-His204), VGRDHAG (Val288-Gly289-Arg290-Asp291-His292-Ala293-Gly294) and PFR (Pro327-Phe328-Arg329) motifs presented in Fig. 1  [1, 21, 22]. Based on the crystal structures solved for the ATP Sulfurylase from Saccharomyces cerevisiae and Penicillium chrysogenum, the role of the mentioned conserved motifs were described in the works published by Ullrich et al.
and MacRae et al. [21, 22]. The QXRN module is hypothesized to be involved in anchoring of the phosphosulfate group of adenosine 5′-phosphosulfate -the product of the ATPS enzymatic reaction. The side chain of Thr196, which is not strictly conserved in all sequenced ATPS, has been observed to form a hydrogen bond with one of phosphate oxygen. In the structure of ATPS-APS-PPi complex from Saccharomyces cerevisiae, Gln195 is within hydrogen bond distance with the sulfate group of APS, the side chains of Thr196 and Asn198 are involved in hydrogen bonds with the phosphate non-bridging oxygen atoms  [22]. Very similar interactions between QXRN motif and phosphosulfate moiety of APS are observed in the ATPS-APS crystal structure from Penicillium chrysogenum  [21]. According to the solved crystal structure of ATPS-APS-PPi complex (PDB code:1G8H), His204 from the HXXH motif is within hydrogen bonding distance with pyrophosphate non-bridging oxygen and is positioned between pyrophosphate anion and phosphate group of APS  [22].
It is worth to mention, that ATP binding enzymes commonly bind ATP in complex with Mg 2+ coordinating to the β and γ phosphates  [27] , while in the mentioned crystal structure of the ATPS-APS-PPi (PDB code: 1G8H) and derived from it ATPS-ATP modeled complex, magnesium ion has not been observed  [22]. The site directed mutagenesis studies preformed with the murine and human bifunctional ATP Sulfurylase/APS Kinase revealed that replacement of one of histidines or arginine from the HRAH motif by alanine inhibits sulfurylase activity, while all mutants retain their kinase activity  [28] [29] [30]. The sequence VGRDHAG seems to be a part of a common adenyl binding motif, called "PP loop", observed in enzymes catalyzing cleavage of the bond between α and β phosphates in ATP molecule  [28, 31]. The highly conserved VGRDHAG motif, known as GRD-loop, is responsible for substrate binding.
QM Models The present study is focused on the enzyme-products (E-P) ATPS complexes and modeling of the backward reaction, since such a crystal structure is available in Protein Data Bank. Moreover, MD simulations performed for the hexameric enzyme-substrate (E-S) complex containing six ATP, six SO 4 2-molecules and six magnesium ions provided very diverse geometries of active site in each of six ATPS subunits. Five cluster models of the active site were considered. Model MG for the E-P complex containing magnesium ion was constructed based on the results obtained from Molecular Dynamics (MD) simulations performed for the hexameric ATPS-APS-Mg 2+ -PPi complex. The stable conformation of the active site binding APS, PPi and Mg 2+ was chosen based on the clustering analysis of MD trajectory, described in the Supplementary Data.
ATPS-APS-Pyrophosphate Model Four QM cluster models of active site with no magnesium ion were constructed based on the crystal structure solved for the ATPS-APS-pyrophosphate complex from Saccharomyces cerevisiae (PDB code:1G8H, Fig. 1B ). This is the only structure available for ATPS with both products, but Mg 2+ was not observed within the active site  [22]. All four models presented in Figs. 7  and 8 , labeled as model 1-4 consist of APS, pyrophosphate and the following nine protein residues: Gln195, Thr196, Arg197, Asn198 (QXRN motif) with N backbone atom of Gln195 and carbonyl backbone group of Asn198 replaced by hydrogen atoms, His204 (HXXH motif) truncated to 5-methylimidazole, Arg290 (VGRDHAG motif) and Asn355 with NH and CO backbone moieties replaced by hydrogens, and His292-Ala293 protein fragment (VGRDHAG motif), where His292 amino and Ala293 carbonyl groups of protein backbone are replaced by hydrogen atoms.