dataesr/scientific-paragraphs-categorization huggingface dataset:
Assembly and annotation Genome assembly, scaffolding, and gap-closing of the Illumina sequence reads obtained in this study and from public databases were performed using the Platanus assembler  (Kajitani et al. 2014). Original assemblies were used if assembled sequences were publicly available. Annotation was performed with the DNA Data Bank of Japan (DDBJ) Fast Annotation and Submission Tool (DFAST)  (Tanizawa et al. 2018). ST, serotype, and phylogroup determinations and stx and eaeA subtyping ST determination and stx1 and stx2 subtyping were performed by a read mapping-based strategy using the SRST2 program (maximum 1% divergence)  (Inouye et al. 2014 ). In the public database sequences, for which raw read data were not available, the reads were simulated with the wgsim version 0. 3. 2 (https://github.
com/lh3/wgsim) using the default parameters. In silico serotyping was conducted by BLASTN search (>85% identity and >60% coverage) of scaffold sequences of each strain against the database file EcOH. fasta that is distributed with SRST2. Phylogroup was determined by ClermonTyping  (Beghain et al. 2018). Subtypes of eaeA genes were determined by BLASTN search (>96% identity with 96% coverage). Reference sequences of each subtype of stx and eae have been described elsewhere  (Ooka et al. 2012; Scheutz et al. 2012).
The ML phylogenetic trees were displayed and annotated using iTOL  (Letunic and Bork 2016). Clustering analysis was performed using the hierarchical Bayesian Analysis of Population Structure (hierBAPS) program  (Cheng et al. 2013). Of the strains sequenced in this study, strains with low sequence coverage (<×25) were excluded (n = 54). Strains that were found to belong to cryptic Escherichia lineages or species (n = 35) and those that showed five or less of SNP difference to one of the other strains (n = 176) were also excluded from the analyses. Finally, 884 strains were used for further analyses and are listed in Supplemental Table  S1.
Co-occurrence network analysis of virulence genes To analyze the co-occurrence of virulence genes among bovine and human commensal E. coli strains and visualize it in the network interface, we constructed a pairwise co-occurrence matrix for each gene (Supplemental Table  S8 ). Only one co-occurrence between genes was filtered out. Network visualization and hierarchical community clustering was conducted using the linkcomm package  (Kalinka and Tomancak 2011)  in the R software (R Core Team 2018). The network was weighted by the number of co-occurrence between strains.
Acknowledgments This work was funded by  JSPS KAKENHI  under grant numbers  16H06279 ,  16K15278 , and  17H04077  to Y. O. and  16H05190  to T. H. and by  AMED  under grant numbers  JP17efk0108127h0002  to Y. O. and 18fk0108065h0801 to T. H. We thank  A. Yoshida ,  H. Iguchi ,  Y. Inoue ,  M. Shimbara ,  M. Horiguchi , and  Y. Sato  for providing technical assistance. We also thank  H. Funakura ,  Y. Inoue ,  K. Toda ,  S. Terayama ,  Z. Nakamura ,  H. Hasunuma ,  D. Matsumoto ,  Y. Iwatani ,  A. Kunisawa , and  I. Kobayashi  for collecting cattle stool samples. Author contributions: Y. O. and T. H. designed the study. Y. O. , Y. K. , K. U. , T. S. , S. Y. , T. O, A. I. , T. M. -I. , M. O. , F. A. , H. B. , E. O. , J. G. M. , K. S. A. , and D. D. collected the samples. Y. A. , Y. O. , M. P.
It is important to notice that |∆C i /C F | should be minimized in order to ensure that the nominal step size of the ramp is larger than the contribution of the mismatch in  (14). That is, for a practical ramp generator it has to be verified that C i2 -C i1 C F √ 2 ∆C i C F. ( 15 ) Fulfilling the condition in (15) represents a feasibility limit for the practical implementation of the proposed ramp generator in a given technology. Under this feasibility condition, Fig. 2  shows the evolution of the worst-case resolution for the generated ramp stimulus as a function of the maximum relative mismatch, ∆C i /C i , affecting the input capacitors, for a ramp generator with a 15-bit ideal resolution. These results were obtained by simulating a Matlab behavioral model of the generator in Fig. 1  under capacitor mismatch conditions, based on the analytical behavior in equation (  14 ).
Thus, Figure  3  shows the evolution of the static ENOB of the generated ramp stimulus as a function of the amplifier gain, for a ramp generator with a 15-bit ideal resolution. These results were obtained by simulating in Matlab a behavioral model of the generator in Fig. 1  under finite gain conditions, based on the analytical behavior in equations (  16 )-  (18). As it can be seen, reducing the gain of the amplifier in the integrator yields a significant degradation of the linearity of the generated ramp. 3) Amplifier input offset: In the proposed generator architecture depicted in Fig. 1 , an uncompensated offset V off at the input of the amplifier would be integrated together with the reference voltage, in such a way that the output of the integrator at time t = nT clk would be given by V out (n) = V out (n-1)+ C i2 -C i1 C F V ref + 1 + C i2 + C i1 C F V off (19) The integration of the offset can significantly affect the step size of the generated ramp signal.
Similarly to the effect of capacitor mismatch, the offset of the amplifier affects the resolution of the generated ramp, but not its linearity. Nevertheless, taking into account that C i1 and C i2 would be approximately equal to assure a small step size under nominal conditions, an uncompensated input offset may even become the dominant contribution to the evolution of the output. In order to avoid this issue, for a practical implementation of the proposed generator it has to be verified that, C i2 -C i1 C F V ref 1 + C i2 + C i1 C F |V off |. ( 20 ) Figure  4  shows the results of a Matlab behavioral simulation of the proposed generator taking into account the effect of a positive input offset, for a generator with a 15-bit ideal resolution and assuming Ci1 C F ∼ = Ci2 C F = 0. 1. As it can be seen, a positive input offset can degrade significantly the resolution of the generated ramp stimulus.
Our test setup is depicted in Fig. 10. It makes use of two National Instrument USB-6361 Data Acquisition Cards (DAQs) controlled by a conventional computer. Two sets of experiments are carried out to validate the functionality and performance of the fabricated ramp generator. The first set of experiments is aimed at characterizing the linearity performance of the fabricated ramp generator as a stand-alone circuit. The second set of experiments is aimed at validating the functionality of the fabricated ramp generator in a proofof-concept static test scenario. In this proof-of-concept we characterize the linearity of an 11-bit ADC by using the proposed step-wise ramp generator and the static linearity measurement strategy described in section III. B.
A. Performance of the on-chip step-wise ramp generator Our first set of experiments is focused on validating the performance of the proposed ramp signal generator as a standalone device, for the 15 fabricated samples. The instrumentation cards provide the master clock signal for the generator and capture the generated differential analog ramp at the output of the generator with a 16-bit quantizer. The captured signals are then processed in Matlab to evaluate the resolution and linearity of the generated step-wise ramp signals. Figures  11  and 12  show the histograms of the measured resolution and static ENOB, respectively, for the 15 fabricated ramp signal generator samples. These performance metrics were evaluated considering a ±2 V differential output range for the generated ramp signal, while the generator operates at a clock frequency f clk = 200 kHz and with a reference input voltage V ref = 1 V.
B. Static linearity measurements using step-wise ramp stimuli Our second set of experiments is focused on validating the functionality of the generator in a static test scenario, using the proposed static linearity measurement strategy described in section III. B. For that purpose, we employ the fabricated step-wise ramp generator to provide the test stimulus for characterizing the static linearity of an off-the-shelf 11-bit ADC. The selected ADC under test is a 10 MSamples/s 11bit ADC with a differential input range of ±1 V. In this set of experiments, the generated ramp stimulus is fed to an evaluation board containing the ADC under test, while the output codes generated by the ADC are captured by a Data Acquisition Card. The ramp stimulus generator works in the same operating conditions as in the previous set of experiments.
The captured output codes are then processed in Matlab using the classical histogram test algorithm to extract the INL of the ADC under test  [3]. For validating the obtained results, the ADC is also characterized by using the arbitrary waveform generator in one Data Acquisition Card to excite the ADC with a slow 16-bit analog ramp as test stimulus. The INL is then extracted for each ADC output code using the histogram test algorithm and the obtained values are stored as a reference for comparison. It is important to remark that this is a proof-of-concept experiment devised to show the equivalence between the proposed on-chip ramp stimulus generator and dedicated external test equipment. In an actual on-chip test application, both the ramp stimulus generator and the ADC under test would be integrated in the same die.
Image Acquisition and Analysis Image acquisition was performed using the INCell 2000 automated widefield system (GE Healthcare, United States) at a 20X magnification (Nikon 20X/0. 45, Plan Apo, CFI/60), using the same exposure time for all plates in the experiment and across replicate experiments. Plates were loaded onto the microscope system with a Kinedx robotic arm (PAA, United Kingdom). Images of four different positions in each well were acquired, each containing channels for Hoechst 33242, ManII-SBP-EGFP and GM130. The total number of cells measured in a well was typically around 300. Computational image processing operations were performed using the dual area object analysis in the INCell Analyzer Workstation 3. 7 software (GE Healthcare). Nuclei were defined based on DNA staining and cell region was segmented using top-hat and collar segmentation, respectively.
ManII-SBP-EGFP in the Golgi area was identified as inclusions in the collar cell area using multiscale top-hat segmentation, and quantified by the average intensity of pixels within the defined inclusion region. Individual cells were then gated to single phenotypes using CellProfiler Analyst software  (Jones et al. , 2008). Briefly, cell samples from all replicate experiments were sorted into four-defined classes (1. Golgi-disrupted, 2. ER + Golgi, 3. ER-retained and 4. Golgi) using DMSO, brefeldin A and nocodazole-treated wells as a training set for the classifier active learning module. Obtained data was normalized using the robust Z-score method under the assumption that most compounds are inactive and can serve as controls  (Malo et al. , 2006; Birmingham et al. , 2009). Plate positional effects were corrected using median polishing  (Mosteller and Tukey, 1977; Birmingham et al.
FUNDING Work performed in the F. Perez laboratory was funded by  Centre Nationale de la Recherche Scientifique , the  Fondation pour la Recherche Medicale  ( FRM DEQ20120323723 ), the  Labex CellTisPhyBio , and the  Agence Nationale de la Recherche  ( ANR-12-BSV2-0003-01 ). The lab of F. Perez is part of  Labex CelTisPhyBio  ( 11-LBX-0038 ) and  Idex Paris Sciences et Lettres  ( ANR-10-IDEX-0001-02 PSL ). GK is supported by the  Ligue contre le Cancer (équipe labellisée) ;  Agence Nationale de la Recherche  ( ANR ) -Projets blancs;  ANR  under the frame of E-Rare-2, the  ERA-Net for Research on Rare Diseases; Association pour la recherche sur le cancer (ARC) ;  Cancéropôle Ile-de-France ;  Chancellerie des Universités de Paris.
Results Database assembly and compound selection. Following the same synthetic route developed for chrysin  (1)  in a previous study  [11] , we were now interested in the base-catalysed Claisen-Schmidt aldol condensation reaction for introducing structural diversity into the new flavone analogues. On the basis of the commercially available aldehydes, we then generated a database collection of 98 compounds (the aglycones), which were submitted to a selection process using the Central Nervous System (CNS)-MultiParameter Optimization (MPO) algorithm  [22]. This mathematical tool enables the alignment of six key drug-like attributes: partition coefficient (ClogP), distribution coefficient (ClogD), acidity constant (pKa), molecular weight (MW), topological polar surface area (TPSA), and the number of hydrogen bond donors (HBD).
Once estimated, these parameters are processed by the algorithm, which generates a desirability score in a scale from 1 to 6. This process allows the selection of compounds with chemical features that make them the most suitable to enter the central nervous system, while displaying favorable permeability, P-gp efflux, metabolic stability, and safety  [22]. Using the Molecular Operating Environment (MOE) software, the required physicochemical parameters were calculated for each database compound and were subsequently processed through the CNS-MPO algorithm to generate a set of output scores. According to the creators of this tool, only molecules having a CNS-MPO desirability score above 4 are adequate for further development. Following this restriction, a diverse group of flavone analogues with different alkyl, aryl, and heteroaryl substituents at C-2 was selected (see Supplementary Materials, Table  S1 ) and synthesized.
Preparative HPLC was performed in a Gilson apparatus using either Phenomenex Gemini NX, C18, 5 µm 30 × 100 mm or Phenomenex Gemini NX, C18, 10 µm 50 × 150 mm columns. NMR spectra for compound characterization were recorded on a Bruker AV III HD Nanobay spectrometer running at 400. 13  MHz equipped with a room temperature 5 mm BBO Smartprobe. Chemical shifts are expressed in δ (ppm) and the proton coupling constants J in Hertz (Hz). NMR data were assigned using appropriate COSY, DEPT, HMQC, and HMBC spectra (representative examples are provided in the Supporting information appendix). Optical rotations were measured with a Perkin-Elmer 343 polarimeter. Melting points were measured using a Stuart SMP30 melting point apparatus. High-resolution mass spectra of final compounds were acquired on a Bruker Daltonics HR QqTOF Impact II mass spectrometer (Billerica, MA, USA).
The sandwich was assembled, placed in the GutBox TM , and left undisturbed for 16 h. Then, it was disassembled and samples (150 µL) were transferred from each donor and acceptor well to UV-plates for determination of the UV/Vis spectra. Effective permeability (log Pe) was calculated from the compound flux deduced from the spectra, the filter area, and the initial sample concentration in the donor well with the aid of the PAMPA Explorer Software (pIon, version 3. 5). LC-MS measurements. Analyses were performed using a 1100/1200 Series HPLC System coupled to a 6410 Triple Quadrupole mass detector (Agilent Technologies, Inc. , Santa Clara, CA, USA) equipped with electrospray ionization. The system was controlled with the Agilent MassHunter Workstation Data Acquisition software (version B. 01.
Supplementary Materials: The following are available online at http://www. mdpi. com/1424-8247/12/2/98/s1, Table  S1 : Structure, physicochemical properties and CNS-MPO score of the flavone analogues selected for synthesis. All physicochemical properties were calculated using MOE software; Table  S2 : Cytotoxic activity of each compound assessed in SH-SY5Y neuroblastoma cells via an MTT cell viability assay; Experimental procedures for cholinesterase inhibition assays; Table  S3 : Acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibitory efficacy of chrysin (1), 8-glucosylgenistein (2) and some of the synthesized flavone analogues at 100 µM; Synthetic approaches for chromones, flavones and the corresponding C-glucosyl derivatives; Scheme S1: Synthesis of selected flavones and analogues (A) via chalcone formation and (B) via flavanone formation; Scheme S2: Synthesis of (A) C-glucosyl flavones and analogues and (B) the 6-glucosyl-5,7-dihydroxychromen-4-one (41); Preparation procedures, physical and LCMS data, and NMR spectra of intermediate compounds.
Acknowledgments: The authors thank  Fundação para a Ciência e a Tecnologia  for financial support through the project  UID/Multi/0612/2019  and for the PhD grant attributed to  Ana Marta de Matos  ( SFRH/BD/93170/2013 ). The  European Union  is also gratefully acknowledged for the support of the project entitled " Diagnostic and Drug Discovery Initiative for Alzheimer's Disease " ( D3i4AD ),  FP7-PEOPLE-2013-IAPP ,  GA 612347.
Location of seismic events We use the SeisComp3 software for our events database  (Hanka et al. , 2010). To automatically detect the seismic events on the stations records, we use a new application, Seismic Data Playback code (SDP), developed at the OVSM-IPGP (Observatoire Volcanologique et Sismologique de Martinique, Institut de Physique du Globe de Paris) (SDP is available on GitHub, https://github. com/ovsmdev/sdp). One has to note that the SDP interface is still under development and this is the first time that it has been used on real data (Supplemental File 1). SDP is a semi-automatic interface for the post-campaign seismic data processing. It enables fast reading of the data and the automatic extraction of potential seismic events that are detected with a STA/LTA (Short Time Average/Long Time Average) filter.
It generates Python ObsPy code for the offline analysis of seismic data  (Beyreuther et al. , 2010). When two distinct pulses are identified (P and S waves) on at least three stations, a seismic event is considered on the related portion of the seismogram. Trans-Haiti data are relatively noisy and the specific north-south distribution of the seismic stations result in a high dependency of the triggering events on the choice of the STA/LTA parameters. Typically, we ran SDP with the set of STA/LTA parameters of 2. 8s and 22. 5s respectively (Supplemental File 1). Triggered events are then visually validated and sent to the SeisComP3 interface scolv. The P and S phases are then manually picked and earthquake locations are computed with HYPO71  (Lee and Lahr, 1972) , NonLinLoc  (Lomax et al. , 2000)  and LOCSAT.
Focal mechanisms calculations To compute focal mechanisms by full waveform inversion we use the software package ISOLA-GUI  (Sokos and Zahradnik, 2008). The methodology is based on the iterative deconvolution technique, adjusted for regional and local distances. Prior to the inversion process, we inspect all the data to choose only the events where P and S waves are well identified. We use ISOLA to pre-process the data (integration, instrument correction, origin alignment, signal to noise ratio). Stations with inconsistent results are removed from the data set. For the waveform inversion, we use the same velocity model as for the location of the seismic events (see section 3. 2). Density values are from  Nafe and Drake (1960)  and attenuation quality factors Qp and Qs are from  Graves and Pitarka (2010) , with Qs = 50 Vs and Qp = 2 Qs.
ACKNOWLEDGMENTS We thank  SEIS-UK  for use of the instruments and their computing facilities. The facilities of SEIS-UK are supported by the  Natural Environment Research Council (NERC)  under agreement  R8/H10/64. This research was partially supported by the  European Interreg Caraïbe V program  through the  PREST  project. We are grateful to the team of the  OVSM-IPGP  for the software provided for the data management. We thank the associate editor  Laura Wallace  and two anonymous reviewers for helpful comments that improved the manuscript.
Model parameters The parameters considered in this paper are drawn from the literature and are summarized in Table  1. Specifically, the physical properties of the blood are obtained from the virtual population database from the IT IS foundation  [27]. The electrode and thermistor physical properties were found in the literature  [15, 17]. We assume the electrical conductivity of the external factors board σ b to be constant and we tune it to match the initial impedance of the experimental setup, as detailed in Section 3. 1. 2. We choose the remaining parameters for the board to be the same as the initial tissue state; they appear to have no impact on the results of our numerical simulations. Considering that in our in-vitro experiments RFA was performed on a porcine myocardium, we Parameter Blood Tissue Electrode Thermistor Board ρ (kg m -3 ) 1050 1076 21500 32 1076 c (J kg -1 K -1 ) 3617 c 0 = 3017 132 835 3017 k (W m -1 K -1 ) 0.
As a consequence of the different time steps employed, the two models are synchronized at the larger time step, where also the potential equation is solved. Let t n = n∆t be the n-th time step of the bioheat equation, and let (u n h , p n h , Φ n h , T n h ) be the numerical solution at time t n. We can briefly summarize the procedure to advance from t n to t n+1 as follows. We refer the reader to the supplementary material section for a more detailed description of the numerical discretization. Finally, the lesion dimensions are calculated in a post-processing step in Paraview  [42]  with self-developed Python-coded filters. Remark. Since the stress tensor σ(u, p) is independent of the temperature, equations (10a) and (10b) are in a one-way coupling with the rest of the system. Thus, already in our current situation, but even should the geometry change in time due to a variable pressure during the ablation (as it is the case in clinical RFA procedures), it would still be possible to solve the Navier-Stokes equation independently as a first step and then use the solution to solve equations (10d) and (10c), potentially for a variety of different protocols.
On the other hand, the presence of the board allows us to use as much information as possible from the   RFA system: by properly tuning the board conductivity, we can match both the appropriate power delivered to the tissue and the overall system impedance, without the need to modify the tissue conductivity. Finally, the amount of power dissipated in the tissue is computed as a function of the surface of contact between the electrode and the tissue itself. The computational model relies on open-source software, from geometry construction to numerical simulations, and postprocessing. In particular, Salome (http://www. salome-platform. org) was used for mesh generation, FEniCS-HPC (http://www. fenics-hpc. org) for the numerical solution of the PDE system and Paraview (http://www. paraview. org) for the postprocessing of the numerical results and the visualizations.
Acknowledgements This research was supported by the  Basque Government  through the  BERC 2014-2017 and BERC 2018-2021 program  and by  Spanish Ministry of Economy and Competitiveness MINECO  through  BCAM Severo Ochoa  excellence accreditations  SEV-2013-0323  and  SEV-2017-0718 , and through projects  MTM2015-69992-R  and  MTM2016-76016-R. JJ  acknowledges support from project  EU   H2020 MSO4SC. ML acknowledges the "LaCaixa 2016" PhD Grant. The experimental protocol was supported by  Abbott  through a non-conditional grant to JG. We acknowledge  Esther Jorge-Vizuete  for her valuable help during the in vitro experiments.