dataesr/scientific-paragraphs-categorization huggingface dataset:
Putative identification of peaks was made if the observed m/z matched the predicted m/z within 10 p. p. m. error when checking against the Reaxys database (version 2. 20770. 1, Elsevier Information Systems GmbH, Frankfurt, Germany) limited to secondary metabolites of A. nidulans or A. fumigatus depending on the experiment and an in-house database of secondary metabolites for these two fungal species. Putative identifications were discarded if proposed adducts were incompatible with those present in the spectrum. For adducts annotated as the same compound, retention times agreed within 45 s. Each peak was scaled to have unit variance and additional scaling by surface area was performed for experiments using devices with varying diameters. PCA was done using the PCA function in MATLAB with the singular value decomposition algorithm.
Acknowledgements We thank  Neda Jasemi ,  Andrew Siedschlag ,  Benjamin Horman ,  Kelsie Harris  and  Aniket Biswas  for assistance with device fabrication. We also thank  Guillaume Delapierre  for his support,  Erin Gemperline  for the helpful discussions and  Sumit Kar  for help with photography. This work was supported in part by the  National Science Foundation-Emerging Frontiers in Research and Innovation-MIKS : Grant  1136903  (C. C. C. W. , D. J. B. , N. P. K. and E. B. ); in part by the  National Institute of Health :  R01 AI065728  (N. P. K. ),  P30 CA014520  (D. J. B. ),  K12 DK100022  (A. B. T. ),  T32 ES007015  (A. B. T. ); and in part by the  National Library of Medicine :  5T15LM007359  (L. B. ). L. B. is a student in the  UW-MSTP  ( T32 GM008692 ). We thank the  NIH West Coast Metabolomics Center at the University of California, Davis  for helpful discussions and training in metabolomics data collection and analysis ( U24 DK097154 ).
1% for each layer  [26]. This system allows in-situ monitoring at a single wavelength of the transmission of a thin film filter during its fabrication and integrates turning point and trigger point monitoring strategies  [27]. However, achieving such high precision on the thickness of each of the layers requires defining an optimal monitoring strategy. Actually, we used trigger point (TP) strategy combined with percent of optical extrema monitoring (POEM)  [27]. While TP strategy uses precalculated transmission level at a given wavelength to stop the deposition of each layer, POEM uses the same strategy but partially corrects manufacturing errors by recalculating, during the deposition, the next TP based on the error between the previous theoretical and experimental extremum. To define the optimal strategy, we used a commercial simulation software from Bühler (mcalc) in order to identify the optimal monitoring wavelengths and ran an associated Virtual Deposition Process (VDP) software, also provided by Bühler, in order to test whether the determined strategy is valid or not.
Prp43-his was purified from the soluble fraction on Ni-NTA resin and eluted with increasing imidazole concentrations. The nickel affinity chromatography was followed by a gel filtration step on a Superdex 200 16/60 column connected to an ÄKTA pure (GE Healthcare) using buffer A + 10 mM ␤mercaptoethanol. Proteins were purified for ITC measurement in buffer A + 2. 5 mM MgCl 2. The Prp43 R159A and F357A mutants were purified as the wild-type and the absence of major conformational change in these mutants was checked by CD spectroscopy (data not shown). Pfa1 was expressed from pSL20 Pfa1  (25) , and Prp22 was expressed from pET-16 Prp22 (38) with the same protocol described above for Prp43 and its variants. Lysis was performed in buffer B (500 mM NaCl, 20 mM Tris-HCl pH 8), 1 mM MgCl 2 , 5 mM ␤-mercaptoethanol.
Crystallization, data collection and processing Prp43 (6 mg/ml) crystallized at 4 • C by the hanging drop vapor diffusion method from a 1:1 mixture of protein, 1 mM CDP, 1 mM MgCl 2 and precipitant containing 6-7. 25% PEG 4000, 100 mM ammonium acetate and 50 mM sodium cacodylate pH 6. 5. Crystals were transferred in the mother liquor containing 7. 5%, 15% and 30% glycerol before flash freezing. X-ray diffraction data were all collected on the Proxima 1 beamline at SOLEIL synchrotron (Gifsur-Yvette, France). Diffraction data were processed using MOSFLM  (39)  and SCALA  (40)  and the XDS package  (41). The crystal belongs to the P3 2 21 space group with two molecules per asymmetric unit. The cell parameters and data collection statistics are reported in Table  1.
Isothermal titration calorimetry ITC experiments were performed on a MicroCal iTC200 (Malvern) with a 40 M protein solution in the cell and a 0. 8-2 mM NDP solution in the syringe. We typically performed 30 injections of 0. 8-1. 5 l and monitored heat release after injection. Data processing was performed on Origin 7. 0 (OriginLab). Thermodynamic parameters H, G and S were determined with the binding isotherm, from these parameters, K d was calculated. All measurements were performed in triplicate at least. The measurement of CDP isotherms, due to its low binding affinity, was performed by increasing the concentration of the protein to the 40 M solubility limit of Prp43. A precise affinity from the CDP isotherms could not be measured in these conditions but a minimum K d value could be estimated.
Helicase activity assays In a final volume of 10 l, the helicase substrate (1 nM) was pre-incubated with 100 nM Prp43 and/or 500 nM Pfa1, 10 min on ice, in the reaction buffer (20 mM HEPES pH8, 5 mM Mg(CH 3 COO) 2 , 0. 2 mM DTT, 200 g/ml BSA, 20 nM trap oligonucleotide). The reaction mixtures were complemented or not with 1 mM nucleotide, and incubated at 30 • C for 30 min. The reactions were stopped by addition of 3 l of a solution containing 1 mg/ml proteinase K, 1. 25% SDS, 10 mM Tris-HCl pH8, 0. 06% bromophenol blue, 0. 06% xylene cyanol and 40% glycerol in the presence of 600 nM of the trap oligonucleotide. The samples were analyzed by native electrophoresis on 12% polyacrylamide, 5% glycerol gels using 1× TBE, 5% glycerol as running buffer, at 4 • C. Gels were scanned with a Licor Odyssey, and analysed with Image Studio Lite software.
4 s -1 ). Although the cationpi interaction of R159 to puric or pyrimidic bases could also contribute to the observed differences, these results seem to indicate that the main contribution comes from the reduced stacking to the nucleotide base by either the removal of F357 or by the use of a pyrimidine base, which have similar effects on catalytic activity. To determine if other members of the DEAH/RHA helicases also have the same enzymatic properties as Prp43, we measured ATPase and CTPase activities of Prp22 in the presence of 0. 2 mM polyA and 1 mM ATP or CTP (data not shown). The ATP hydrolysis rate is 3. 7 s -1 and the CTP hydrolysis rate is 9. 3 s -1. The NTPase activity with a pyrimidic base is therefore twice that of a puric base. The activity of a DHX9 construct containing only the two RecA-like domains of the helicase in the presence of pyrimidic NTP was previously found to be twice to five times that of DHX9 in the presence of puric NTP  (48).
Affinity of Prp43 for the nucleotide is impaired without base stacking In order to further characterize Prp43 enzymatic activity and to determine what is the influence of base stacking on the affinity of the enzyme for its ligand, we measured binding affinities using ITC. The wild-type protein displays a K d of 1. 45 M for ADP (Figure  3A ). The K d of Prp43 for CDP is superior to 25 M (Figure  3B ). We next performed the same experiment with Prp43 F357A. The Prp43-F357A mutant is strongly impaired in ADP binding, with a K d superior to 45 M (Figure  3C ). No detectable binding of Prp43-F357A to CDP is measurable in our conditions (Figure  3D ). No detectable binding of either ADP or CDP is observed with the Prp43-R159A mutant (data not shown). These results show the R-motif is essential for NDP binding.
ACKNOWLEDGEMENTS We thank the beamline scientists on the PROXIMA I and II beamlines. Author contributions: J. R. P. and S. R. expressed and purified, crystallized the Prp43/CDP complex and solved the crystal structure. J. R. P. and F. C. performed the NTPase assays. J. R. P. and L. D. performed the ITC experiments. F. C. and M. B. performed the helicase assays. S. L. performed RNA protein binding assays. M. H. constructed yeast expression vectors, generated yeast strains, performed yeast complementation assays (growth curves, serial dilutions, westerns) and pre-rRNA processing analyses by northern blot. M. H. and A. H. performed the sedimentations on sucrose gradients and downstream analyses. R. C. expressed and purified Prp43 proteins and O. H. performed helicase assays for Supplementary Figure S3.
FUNDING Work at UPD was supported by  CNRS, University Paris Descartes ; Nanogears grant from  USPC ;  RNPGenesis grant  from the  Agence Nationale de la Recherche  ( ANR-11-JSV8-0004 ), the  Institut Universitaire de France  (to N. L. ). Work in the Henry/Henras group is supported by  CNRS, University of Toulouse III (Paul Sabatier)  and  Agence Nationale de la Recherche  grant  RIBOPRE40S  ( ANR-10-BLAN-1224 ). J. R. P. and M. H. were supported by  Ph. D. fellowships from the  Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche. Funding for open access charge:  University Paris Descartes.
Objective: To describe the incidence and mortality rates from colon and rectal cancer in Midwestern Brazil. Methods: Data for the incidence rates were obtained from the Population-Based Cancer Registry (PBCR) according to the available period. Mortality data were obtained from the Mortality Information System (SIM) for the period between 1996 and 2008. Incidence and mortality rates were calculated by gender and age groups. Mortality trends were analyzed by the Joinpoint software. The age-period-cohort effects were calculated by the R software. Results: The incidence rates for colon cancer vary from 4. 49 to 23. 19/100,000, while mortality rates vary from 2. 85 to 14. 54/100,000. For rectal cancer, the incidence rates range from 1. 25 to 11. 18/100,000 and mortality rates range between 0. 30 and 7.
RESUMO: Objetivo: Descrever o perfil do câncer de cólon e reto no Centro-Oeste do Brasil. Métodos: Os dados de incidência foram obtidos dos Registros de Câncer de Base Populacional (RCBP) de acordo com o período disponível. Dados sobre os óbitos foram obtidos do Sistema de Informação em Mortalidade (SIM). As taxas de incidência e mortalidade foram calculadas por gênero e grupos etários. As tendências de mortalidade foram analisadas pelo software Joinpoint. Os efeitos de idade-período-coorte foram calculados no software R. Resultados: As taxas de incidência do câncer de cólon variaram de 4,49 a 23,19/100. 000, e a mortalidade, de 2,85 a 14,54/100. 000. A incidência do câncer de reto variou de 1,25 a 11,18/100. 000; a mortalidade, de 0,30 a 7,90/100. 000. As tendências de mortalidade por câncer de cólon mostraram um aumento entre homens em Cuiabá, Campo Grande e Goiânia.
The cohort was calculated as the difference between the year of death and the age of the individual, grouped in six-by ten-year age groups and four birth groups with 20-year overlaps in each. The statistically significant results were those with p-values inferior to 0. 05, and all computations were made with R software (2. 12. 2 version). Input of the mortality data (deaths and population) by age and period followed. The results included age-specific effects, and cohort-period effects (RRS -Relative Risk and 95% confidence interval with RR = 1 for reference group, 1966). The analysis was carried out and deviation tables were built, comparing the four classic models (age, age-group, age-period, and age-period-cohort)  19, 20.
We present the TRIQS/DFTTools package, an application based on the TRIQS library that connects this toolbox to realistic materials calculations based on density functional theory (DFT). In particular, TRIQS/DFTTools together with TRIQS allows an efficient implementation of DFT plus dynamical mean-field theory (DMFT) calculations. It supplies tools and methods to construct Wannier functions and to perform the DMFT selfconsistency cycle in this basis set. Post-processing tools, such as band-structure plotting or the calculation of transport properties are also implemented. The package comes with a fully charge self-consistent interface to the Wien2k band structure code, as well as a generic interface that allows to use TRIQS/DFTTools together with a large variety of DFT codes.
In this paper we present version 1. 3 of the TRIQS/DFTTools application, which is based on the TRIQS library  [21]. The purpose of this application is to provide a complete set of tools to perform fully charge self-consistent ab initio calculations for correlated materials, and it enables researchers in the field of correlated materials to do these calculations without dedicating time to code their own implementation. The TRIQS/DFTTools package provides a fully charge self-consistent interface to the Wien2k  [22]  band structure program, as well as an interface that is usable with many other DFT or tight-binding codes, however, without the possibility of full charge self-consistency. Physical properties that can be calculated range from spectral functions and (projected) densities of states (DOS), charges and magnetic moments, to more involved quantities like optical conductivity and thermopower.
The package can handle spin-polarised cases, as well as spin-orbit coupled systems  [23]. It is released under the Free Software GPLv3 license. The paper is organised as follows. We start with a brief general discussion of the structure of TRIQS/DFTTools in Sec. 2. In Sec. 3 we show how the interface between DFT and DMFT is set up, in particular for the Wien2k DFT package. We will also introduce the projective Wannier functions that are used as the basis set for the DMFT calculations. 3 We continue in Sec. 4 with a simple example of a DFT+DMFT calculation, without charge self-consistency. In Sec. 5 the concept of full charge self-consistency is introduced, followed by a discussion of post-processing tools in Sec. 6. Information on how to obtain and install the code is given in Sec. 7, and we finally conclude in Sec.
The difference in the latter two projections is minor, and consists primarily in the small transfer of weight to large energies around 7 eV. In Fig. 3  we show the real-space representation of the Wannier charge density. The left plot shows a t 2g -like Wannier function for a projection of t 2g bands only, which corresponds to the black line in Fig. 2. The right plot is the corresponding Wannier function when all bands are included in the projection. The effect of increasing the projection window is obvious: it results in a better localisation of the resulting Wannier functions. All necessary steps to construct the orthonormalised set of projection operators from Wien2k DFT calculations is contained in the program dmftproj that is shipped with TRIQS/DFTTools. In Wien2k the Bloch functions are expanded in the LAPW basis set as ψ σ kν (r) = NP W G c ν,σ G (k) lm A α,k+G lm u α,σ l (r, E α 1l )Y l m (r) + N lo n lo =1 c ν,σ lo A α,lo lm u α,σ l (r, E α 1l ) + B α,lo lm uα,σ l (r, E α 1l ) Y l m (r)+ + NLO nLO=1 c ν,σ LO A α,LO lm u α,σ l (r, E α 1l ) + C α,LO lm u α,σ l (r, E α 2l ) Y l m (r), (7) where N P W is the total number of plane waves in the basis set, N lo is the number of local orbitals, and N LO the corresponding number of auxiliary orbitals for semi-core states.
Usage As a prerequisite a self-consistent Wien2k DFT calculation has to be performed. At this point, the further steps for the interface are • writing of necessary data from the Wien2k calculation into files, which is done using the Wien2k command x lapw2 -almd, • running dmftproj to calculate the projective Wannier functions, • conversion of the text output files into the hdf5 file format using the Python modules provided. After these steps, all necessary information for a DMFT calculation is stored in a single hdf5 file. For the details of these steps, and all input/output parameters we refer the reader to the extensive online documentation of TRIQS/DFTTools and the example below.
General interface for non-Wien2k users The TRIQS/DFTTools package also contains an interface layer that can be used in many situations, albeit without the possibility of full charge self-consistency. In fact, as an input it takes the correlated subspace Hamiltonian H mn (k), where m and n are orbital indices, and the k-mesh spans the first BZ. Unlike the Wien2k converter, the calculation is done exclusively in Wannier orbital space. The converter takes this H mn (k), together with information such as the quantum numbers of the correlated orbitals and the required electron density, and produces a hdf5 file that can be used for the DMFT calculations. We note that this interface is completely independent of how the H mn (k) is produced. It can use, for instance, output from Wannier90  [28, 29] , which is available for many DFT codes, from NMTO calculations  [30] , or from any other tight-binding model representing the non-interacting electronic structure of the system.
One-shot DFT+DMFT calculations Listing 1 Sketch of the Python script for the DMFT self-consistency loop in one-shot DFT+DMFT calculations. Note that the solver imports, construction and execution must be appropriately added and modified. As mentioned in the introduction, TRIQS/DFTTools provides simple modules that allow the compact and efficient implementation of the DMFT self-consistency cycle. We will illustrate how to use the package by giving a one-to-one correspondence of the code statements to the necessary equations. In this section we give a simple Python script for so-called 'one-shot' DFT+DMFT calculations without full charge self-consistency. Note that the construction of the Wannier functions and the conversion procedures discussed in the previous section have already been performed.
We assume that the hdf5 archive for the calculation, where everything is stored, is called YourDFTDMFTcalculation. h5. Furthermore, we assume that a suitable method to solve the quantum impurity problem is available, such as the TRIQS/CTHYB solver  [18]. Note that TRIQS/DFTTools itself does not provide any impurity solver. The DMFT calculation as given in Lst. 1 consists of the following steps. First, we initialise our calculation as done in lines 2 to 6 of Lst. 1. The SumkDFT module is loaded and initialised with the previously generated hdf5 archive YourDFTDMFTcalculation. h5. We initialise a solver module, which is used for the solution of the Anderson impurity model, for instance, the TRIQS/CTHYB package  [18]. It is important that the user is familiar with the details of this solver initialisation, including set-up of the interaction matrix, definition of the interaction parameters U and J, etc.
TRIQS/DFTTools offers three different DC corrections: The flag use_dc_formula=0 corresponds to the fullylocalised limit (FLL) correction, use_dc_formula=1 is a variant of FLL that is used for Kanamori-type Hamiltonians  [31] , and use_dc_formula=2 is the around-mean-field (AMF) correction. This step completes the self-consistency loop. Steps 1 to 6 are now iterated until convergence. After that, we can save the final solution into the hdf5 archive, as done in lines 27 to 32. We want to note here that it is sometimes useful to save all data produced by the user in a separate subgroup, e. g. dmft results, in order to have a clean structure in the hdf5 archive. For details how to handle the hdf5 archive we refer the reader to Ref. [21]  and the online TRIQS documentation. We want to stress here that this simple script of about 30 lines 2  of Python code is sufficient for a full DFT+DMFT calculation, which is possible only thanks to the modular structure of TRIQS/DFTTools and TRIQS.
Full charge self-consistency The one-shot DFT+DMFT calculation can be extended to a fully charge self-consistent calculation with only marginal additional effort. The basic concept is general, but the implementation will depend of course on the DFT code that you wish to use for the calculations. We are presenting the interface to the Wien2k DFT package, which is the first interface implemented in TRIQS/DFTTools. We describe this interface below. Work is in progress to include more DFT packages, in particular VASP  [32, 33]. First of all, we want to stress that the fully charge self-consistent calculation is controlled by Wien2k scripts. That is why a sound knowledge of Wien2k and how it is used is absolutely necessary for the considerations in this section. As shown in Fig. 4 , the idea is to extend the existing Wien2k self-consistency loop by including a DMFT step.
Instead of simply calculating the charge density from the Kohn-Sham orbitals as in standard DFT (lapw2 step in Wien2k), we supplement this charge density with correlation effects. Therefore, the Python scripts that control the DMFT iteration have to be modified: • We do not start each DMFT iteration from scratch, but instead from the previous iteration. This is most conveniently done by storing all necessary data in the hdf5 archive. • One DMFT iteration (n loops = 1) per full self-consistency cycle is usually sufficient. In cases where convergence is problematic, more iterations may be needed. • In order to calculate the correction to the charge density, one has to calculate the density matrix from the correlated Green's function at the end of the DMFT step, as shown in Lst. 2. The first two lines set the chemical potential more accurately.
The conductivity and the Seebeck coefficient for the combination of directions αα ′ are defined as σ αα ′ = βe 2 K αα ′ 0 and S αα ′ = - k B |e| K αα ′ 1 K αα ′ 0 (18) with β the inverse temperature. The kinetic coefficients K αα ′ n are given by K αα ′ n = N sp π dω (βω) n f (ω) f (-ω) Γ αα ′ (ω, ω). (19) Here N sp is the spin factor and f (ω) is the Fermi function. The transport distribution Γ αα ′ (ω 1 , ω 2 ) is defined as Γ αα ′ (ω 1 , ω 2 ) = 1 V k Tr v α (k)A(k, ω 1 )v α ′ (k)A(k, ω 2 ) , (20) where V is the unit cell volume. In multi-band systems the velocities v α (k) and the spectral function A(k, ω) are Hermitian matrices in the band indices ν and ν ′. The frequency-dependent optical conductivity is given by σ αα ′ (Ω) = N sp πe 2 dωΓ αα ′ (ω + Ω, ω) f (ω) -f (ω + Ω) Ω. (21) A related implementation, also using full dipole matrix elements but in the framework of maximally localised Wannier functions, has recently been published as the woptic package  [41].
Obtaining TRIQS/DFTTools The TRIQS/DFTTools source code is available publicly and can be obtained by cloning the repository on the GitHub website at https://github. com/TRIQS/dft tools. As the TRIQS project, including its applications, is continuously improving, we strongly recommend users to obtain TRIQS and its applications, including TRIQS/DFTTools, from GitHub. Bugfixes to possible issues are also applied to the GitHub source. For users that wish to have a more stable version of the code, without the latest bugfixes and functionalities, we suggest downloading the latest tagged version from the GitHub releases page https://github. com/TRIQS/dft tools/releases.
Installation Once the TRIQS library has been installed properly, the installation of TRIQS/DFTTools is straightforward. As for TRIQS we use the cmake tool to configure, build and test the application. With the TRIQS library installed in /path/to/TRIQS/install/dir, the TRIQS/DFTTools application can be installed by $ git clone https://github. com/TRIQS/dft_tools. git src $ mkdir build_dft_tools && cd build_dft_tools $ cmake -DTRIQS_PATH=/path/to/TRIQS/install/dir. /src $ make $ make test $ make install This will first download the code to the src directory, then build, test, and install the application in the same location as the TRIQS library.