dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, a newly available statistical algorithm offers to derive clock gene mRNA circadian rhythms from a single-time-point measurement of 10 clock genes  [68]. Such methodology could potentially be used to predict clock gene variations when only one time point is available, which is often the case for the tumour. Our combined model could then be used to infer irinotecan personalized best timing from clockgene expression. As such chronoPK-PD models could be developed for any other drug, optimal timing could be derived for multiple compounds from a single dataset of clock gene mRNA circadian variations. In addition, instead of simplifying a patient's complex circadian profile by an estimate of a value associated with their circadian time, our model has the potential to fit the circadian rhythms of the patient based on their personal gene expression data from peripheral tissues (e.
Acknowledgments The work in A. R. 's group was supported by the  German Federal Ministry of Education and Research (BMBF) -eBio-CIRSPLICE -[FKZ031A316] and by the  Dr. Rolf M. Schwiete Stiftung. O. A. was additionally funded by the  I4H program  of the  Charité -Universitätsmedizin Berlin. We acknowledge support from the  German Research Foundation (DFG)  and the  Open Access Publication Fund of the Charité -Universitätsmedizin Berlin. The work in the group of A. B. was supported by  INSERM, Inria , the  ATIP-Avenir program  and the  French Plan Cancer.
For the Refrex1/DC16HA ligand, cells were labeled beforehand for 30 minutes with an anti HA antibody (3F10, Roche 11867423001) diluted at 1:200 in PBA. For the mouse anti-CTR1 antibody, a 30 minute incubation was performed at a 1:200 dilution before the secondary antibody staining. Cells were then washed in PBA and analyzed on a Novocyte flow cytometer (Acea, Biosciences, Inc). Data analysis was performed using FlowJo software. When indicated, 293T were incubated in FEA or CRFK cells conditioned medium, or incubated overnight with 100 µM CuCl2 (307483, Sigma) or MgCl2 (208367, Sigma).
Cells were resuspended and incubated for 15 min at room temperature in the dark. After one wash in binding buffer, cells were resuspended in 150 µL of binding buffer containing PI diluted 1:20 and directly analyzed using a Novocyte flow cytometer (Acea, Biosciences, Inc). Data analysis was performed using FlowJo software. One well was not stained with Annexin V and stained with PI only to be used as a gating control.
ROS staining Cells were transfected with siRNA, incubated and collected as described above. Cells were then distributed into 3 or 4 technical replicates in a round-bottom 96-well plate and washed twice with PBS. Cells were resuspended in 50 µL of PBS containing 2 µM of DCFDA and incubated at 37°C in the incubator for 30 min to allow for dye loading. After 1 wash in PBS, cells were resuspended in 50 µL PBS+SYTOX Red diluted 1:1000 and incubated for 15 min at room temperature in the dark. 100 µL of PBS was then added per well and cells resuspended before analysis using a Novocyte flow cytometer (Acea, Biosciences, Inc). Data analysis was performed using FlowJo software. One well was not stained with DCFDA to be used as a control of background cell fluorescence.
ACKNOWLEDGEMENTS The authors are grateful to the  Centre Hospitalier Vétérinaire Languedocia, the clinique vétérinaire de l'Aiguelongue, the clinique vétérinaire du Corum , and the clinique vétérinaire d'Alco in  Montpellier France  for providing blood samples, to the  OSU OREME platform of Montpellier University  for metal dosage, and to  Andrea Cimarelli  ( CIRI, Lyon, France ) for CRFK cells. We also thank all the members of our team for technical help and advice. This work was supported by the  French National Research agency (ANR CALCIPHOS ,  ANR-17-CE14-0008-01  to JLB) and by the  French SIDACTION  ( 19-2-AEQ-12560  to JLB). ST was supported by the  ANR CALCIPHOS , LC by the  French AIDS National Research ANRS , VC by  CNRS  and JLB by INSERM.
Simulations of thermal kinetics using Eq. (19) Next, consider Eq. (  19 ) for simulating the evolution of the x, y, and z components of the magnetization of a "thermal magnetization" (non-hyperpolarized) sample. We seek the NMR spectrum that results from a two-site exchange reaction between solutes A and B, Fig. 1a , as conventionally observed in room temperature NMR experiments. Simulations were performed in MatLab with equilibrium z magnetizations M A z,eq = 1. 0 and M B z,eq = 0. 8 and an initial magnetization vector given by M 0 = [0, 0, 1. 0, 0, 0, 0. 8]. Chemical shift offsets were A = 10 × 2π rad s -1 and B = -10 × 2π rad s -1. Relaxation rate constants were R A 1 = R B 1 = 1 s -1 and R A 2 = R B 2 = 1 s -1. The influence of an RF y pulse was then calculated with ω x = -γ B 1 cos (π/2) and ω y = -γ B 1 sin (π/2) and with a field strength of 1.
Acknowledgements. This article is dedicated to  Geoffrey Bodenhausen  on the occasion of his 70th birthday. Financial support. The work was supported by the  NIHR Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust  and KCL; the  Centre of Excellence in Medical Engineering  funded by the  Wellcome Trust  and  EPSRC  ( WT 203148/Z/16/Z ); and the  BHF Centre of Research Excellence  ( RE/18/2/34213 ). PWK's work was supported by an  Australian Research Council   Discovery Project Grant ,  DP190100510. SJE was supported by  ENS-Lyon , the  French CNRS, Lyon 1 University , and the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  ( ERC grant  agreement no. 714519/HP4all ). Review statement. This paper was edited by  Daniel Abergel  and reviewed by  Paul Vasos  and one anonymous referee.
T. R. Eykyn et al. : Extended Bloch-McConnell equations    Code and data availability. All Matlab codes are reproduced in the Supplement and are free to adapt for personal use. For any further clarification please contact the corresponding author directly. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/mr-2-421-2021-supplement. Author contributions. All the authors planned the research, conducted the research and wrote the paper.
Catalyst characterization The XRD patterns were recorded using a Bruker D8 Advance X-ray diffractometer with Ni-filter CuK α radiation. The actual amount of Pt and Co loadings in the samples were analyzed by AAS. The N 2 physisorption was conducted by using a Micrometrics ASAP 2020 instrument with the Brunauer-Emmett-Teller (BET) method. The TEM observations were performed with a JEOL JEM 2010 transmission electron microscope operated at 200 kV and equipped with LaB 6 thermoionic electrongun, an UHR pole piece (point resolution 0. 196 nm) and a Pentafet Link-INCA EDX spectrometer (Oxford Instruments). The percentages of Pt dispersion were measured by CO pulse chemisorption using a Micromeritics ChemiSorb 2750 equipped with ChemiSoft TPx software. Prior to chemisorption, the catalyst was reduced with hydrogen (25 cm 3 /min) at 500 • C for 2 h and then cooled down to the room temperature under helium flow (25 cm 3 /min).
Acknowledgments The financial supports from the  Rachadapisek Sompote Endowment Fund  for the Postdoctoral Fellowship,  Chulalongkorn University  for W. T. and the  Thailand Science Research and Innovation for the Basic Research  grant  BRG6180001 ,  IRN62W0001 , and  CAT-REAC   Industrial  Project  RDG6250033  are gratefully acknowledged. The authors also would like to thank the  Research Team Promotion grant  from the  National Research Council of Thailand (NRCT).
The summary of our calculations is shown in Fig. 1  (for the full data tables for this and other derivatives see ESI †). Only two methods approach the pink rectangular (showing the experimental errors): HF with relatively small basis and B3LYP with aug-cc-pVXZ, X ¼ D, T. Unexpected is poor performance of APFD, M062X and, especially, MP2 methods. The C-N equilibrium bond length is reasonably reproduced by B3LYP, MP2FC/aug-cc-pVTZ, APFD and M062X with aug-cc-pVDZ, whereas HF and MP2AE apparently failed. The DM of pyridine (2) in the gas phase determined by the Stark effect at À20 to 25 C (ref. The experimental augmented by B3LYP/6-311+G(3df,2pd) equilibrium C-N bond length r e 1. 3362(5) A (semiempirical) is well reproduced by B3LYP functional, underestimated by HF, and overestimated by MP2FC and CCSD.
IR spectra from samples in neat form were measured with a Thermo Scientific Nicolet iS10 FT-IR spectrophotometer (Thermo Fisher Scientific Inc. , Waltham, MA, USA) and Bruker Tensor 27 (Bruker Optic GmbH, Ettlingen, Germany) (KBr pellets, for compounds 3c, 3e, 3f). IR absorption frequencies are given in cm -1. Lowresolution mass spectra were recorded with Perkin-Elmer Sciex API 300 spectrometer (PerkinElmer Inc. , Waltham, MA, USA) and Shimadzu LCMS-2020 (Shimadzu Corporation, Kyoto, Japan) (for compounds 3c, 3e, 3f). High-resolution mass spectra (HRMS) were recorded with a Bruker MaXis spectrometer (Bruker Daltonik GmbH, Bremen, Germany) in the electrospray ionization (ESI) mode. 1 H-, 13 C-, 19 F-, DEPT, and some COSY, HSQC NMR spectra, and HRMS data of all new compounds are provided in Supplementary Materials as Figures  S1-S61.
Routines for minimization and molecular dynamics (MD) simulations followed previous reported protocols  [7]  and are summarized in the supplementary materials. Improvements include using a time step of 2 fs and performing five replicas of 10 ns MD production for each model structure. 2. 9. Data Analysis. Data were fit and shown using SigmaPlot (Systat. Software Inc. Richmond, CA, USA), Origin 7. 0 (Ori-ginLab Corporation, Northampton, MA), and Pro-K (Applied Photophysics Ltd. , Surrey, UK). VMD  [25]  and PyMol  [26]  were used to analyze and visualize structural data, as well as to produce structural figures.
Altogether, these observations suggest that (i)   P N  , from 1 to 0), with their fits to a two-transition unfolding model (continuous and dashed lines for oxidized and reduced states, respectively). Decrease in FAD bound fraction was experimentally followed by the increase in its fluorescence upon release from the holoprotein along a 20 to 85 °C temperature ramp. Data were obtained in 50 mM potassium phosphate at pH 7. 4 and at a final ionic strength of 150 mM. Protein concentration was ~2 μM. The CTC forms were obtained by premixing hAIF Δ1-101ox and NADH at a 1 : 100 ratio. 14 Oxidative Medicine and Cellular Longevity the competent interaction of hAIF with CHCHD4 relies on the adequate CTC architecture to favor the assembly of potentially disordered regions from both proteins to achieve a specific conformation.
Curves for FAD thermal release in oxidized variants (closed symbols) and its CTC state (open symbols), as monitored by increase in FAD fluorescence emission upon protein denaturation. The WT, W196A, W196L, and W196Y hAIF Δ1-101 are in black circle, black square, black diamond, and black triangle, respectively. The curves are roughly normalized to the change in fluorescence signal of the FAD bound fraction (P N , from 1 to 0), with their fits to a two-transition unfolding model (continuous and dashed lines for oxidized and reduced states, respectively). Decrease in FAD bound fraction was experimentally followed by the increase in its fluorescence upon release from the holoprotein along a 20 to 85 °C temperature ramp. Data were obtained in 50 mM potassium phosphate at pH 7. 4 and at a final ionic strength of 150 mM.
Acknowledgments This work was supported by the  Spanish Ministry of Economy, Industry and Competitiveness-State Research Agency (MINECO ,  BIO2016-75183-P AEI/FEDER ),  Spanish Ministry of Science and Innovation-State Research Agency (MICINN)  (Grant  PID2019-103901GB-I00 ), and the  Gobierno de Aragón-FEDER [Grupo de Referencia Biología Estructural  ( E35_20R )]. A. V-C. thanks  ARAID  for financial support.
Introduction A mass spectrum is a beam of abundances of ions represented by their m/z values  [3]. Within each mass spectrum, the regions of m/z values with high abundances, or signals, form mass peaks, or peaks. HRMS-high resolution mass spectrometry consists in liquid chromatography coupled to mass spectrometry. The analysis of a sample in HRMS yields a MS data, which is a series of mass spectra, each acquired at a different retention time (RT). A collection of several MS data will form a MS dataset. Within each MS data, a feature is defined when a same peak is identified in several mass spectra which are consecutive by their RTs. A feature is a triplet containing a m/z value, a retention time and a signal intensity. Extraction of features, performed by proprietary or free softwares, is the beginning of HRMS preprocessing.
It can be summarized as follow for a MS1 acquisition (full scan). Let ms i be the mass spectrum corresponding to the ith retention time. Each ms i is a matrix of two columns, m/z values and signal intensities, which form peaks with the profile option of the mass spectrometer, or which have been converted to centroids with the centroid option. Successive ms i , e. g. ms i , ms iþ1 and ms iþ2 , may contain the same m/z value. From a MS1 data and a given m/z value, an Extracted Ion Chromatogram or EIC is obtained by collecting the signals associated to a given m/z value (with a tolerance) along all the RTs. The next process is the identification of features or chromatographic peaks, sometimes referred to as peak picking. But the calculation is tricky, and several softwares, or even the same software with different parameters, do not yield the same solution.
Benchmarks give clues on their respective performances, according to different figures of merit. A first approach was to compare the features obtained by different softwares, using their distances calculated with their m/z and RT values. The figure of merit was the number of features identified by several softwares, considered to be true positives. Venn diagrams were used by Tautenhahn et al. [8]  to compare Centwave, MZMine and Matchfilter algorithms, and by Myers et al. [4]  to compare XCMS  [7]  and MZmine2  [6]. But an outlier on Venn diagrams is simply an algorithm which performs differently; its performances can be worse, or better. Then, another and more reliable approach was based on a ground truth, reference values to which experimental results could be compared. Li et al. [2]  compared several softwares on samples for which hundreds of compounds had been manually quantified.
The EIC þ algorithm Contrary to ADAP  [5]  or Centwave  [8]  which process each data once, our algorithm perfoms several processings of the original centroid data. The goal was to determine more accurately the experimental m/z values leading to the future EICs. At each loop, m/ z values were recalculated, converging towards stable values. The algorithm called EIC þ is schematized in Fig. 1. The three tuning parameters were: mztol, eicsig and dw, i. e. a tolerance around a given m/z value, a minimum of signal and a Durbin-Watson threshold. The starting point was a centroid high resolution MS data. C Identification of a first m/z list All the signals of the MS data were gathered into the same matrix of two columns: m/z values and signal intensities. The m/z values were sorted in ascending order, identical m/z values were merged, signal intensities added.
C Filling a first EIC matrix The first EIC matrix was initiated as a matrix of zeros; the rows were the retention times, and the columns the list of mz max previously identified. The MS data was scanned again, each m/z value was compared to the m/z of the EIC matrix. If an absolute difference was lower than mztol, the signal was added into the EIC matrix at the corresponding retention time and m/z; otherwise it was discarded. Some statistics were also collected.
The MS datasets Two MS datasets were considered. MS dataset 1 consisted in a single Orbitrap MS data, named VI2016_AC_4A. raw acquired at the University of Barcelona in 2016. The sample was a red wine spiked with acetaldehyde prior to its analysis. Polyphenols had been targeted, 117 compounds had been previously manually identified in this wine sample, following a previous work on model solutions  [9]. Nevertheless, as several chemical compounds had exactly the same raw formulae, the number of different m/z values was 109. MS dataset 2 consisted in ten Orbitrap MS datasets described by Li et al. [2] , and available with their metadata thanks to the Metabolights project at: https://www. ebi. ac. uk/metabolights/MTBLS733. The spectra were named SA1. raw to SA5. raw and SB1. raw to SB5. raw. The metadata consisted in a list of 836 chemical compounds manually identified by Li et al.
Tuning the EICþ with dataset 1 The workflow concerning dataset 1 involved EIC extraction only, features were not considered yet. The development and the very first application of EIC þ was based on dataset 1 and the figures of merit described hereafter. Preliminary experiments had concluded that the minimum of signal eicsig and the Durbin-Watson threshold dw could be fairly set to 30000 and 1 respectively. Variations around these values did not strongly modify the results (not shown). Our attention was focused on mztol. Values were chosen from 0. 0012 to 0. 0060 by steps of 0. 0012. The algorithm was run with each of these values, yielding for each mztol a list of experimental m/z values. Then, each reference m/z value was seeked within these lists with m/z tolerances set to 0. 0005, 0. 0100, 0.
Step 1: Extracting the EICs with EICþ The workflow concerning MS dataset 2 is described in Fig. 2. Compression was a means to save computational resources and time. It consisted in merging 5 successive RTs. When EICs had been extracted from each of the 5 datasets, they were averaged, yielding a single EIC file from SA and a single EIC file from SB. Then, features could be extracted.
The figures of merit Five figures of merit were calculated. The EICs m/z recovery and the EICs m/z unicity ratio were applied to dataset 1 for tuning the parameters, and to dataset 2 for assessing the quality of EICs extraction. The true features, the accurately quantified true features and the RMSEP-root mean square error of prediction of the true features m/z values were applied to dataset 2 after features extraction.
Results and discussion Results concerning the tuning of the parameters for dataset 1 were gathered into Table  1. The best solution should present the highest recovery percent and the lowest unicity ratio, while mztol being as low as possible: mztol ¼ 0. 0024 was chosen. Then a processing of dataset 2 was performed with the parameters defined above, i. e. mztol ¼ 0. 0024, eicsig ¼ 30000 and dw ¼ 1, which are also the default parameters. This choice was supported by similar results previously obtained on two other datasets, not reported here. Using the EIC þ algorithm, two EIC matrices X sa and X sb of dimensions (2484 Â 19958) and (2475 Â 18773) were obtained from the five SA and the five SB MS data of Li et al. respectively. The first value was the number of retention times, the second the number of EICs.
Manual integration would have recovered as accurately quantified 11 features which failed the automatic integration. However this result was not considered any more, Li et al. based their benchmark on automatic integration and so did we. All the results concerning the features were made available as a spreadsheet file in the supplementary material. A summary is presented in Table  3. The numbers of true features and accurately quantified true features were 820 and 787 respectively. These scores (820 À 787) were good compared to the results obtained by Li et al. : 748 À 482 for Compound Discoverer, 799 À 654 for MS-Dial, 820 À 731 for XCMS and 769 À 761 for MZMine2. Then it is important to point out that Li et al. tuned their algorithms with the same datasets analyzed in their paper, while we used another dataset.
Their approach led to the best performance of each software, relevant for their benchmark but not in most non-targeted analysis, the reference compounds being unknown. Its simplicity of use constitutes a real added-value for the EIC þ algorithm, even if the calculation takes time. Some attention was also paid to the errors of prediction for the RTs and for the m/z values. RTs showed a shift of 0. 30 min between our estimations and the reference RTs, see Fig. 5 (a), certainly due to a different calculation; our estimations were based on the median RTs. The RMSEP on m/z values were 0. 00056 and 0. 00052 for SA and SB respectively, but errors of prediction were lower for a majority of samples. From Fig. 5 (b) corresponding to SA, more than Fig. 4. Dataset 2, diversity of features shapes for SA and SB in blue and red, respectively.
Conclusion Our EIC þ algorithm presented a good recovery of true features and a good accuracy of the calculated m/z values, standing the comparison with XCMS, MZMine2 or Xcalibur. But its most interesting characteristic relies on a few (3) and self-explanatory tuning parameters, a threshold on the m/z values, a threshold on the signal intensities and a threshold on the noise estimated by the Durbin-Watson value. Less parameters means easier and more reproducible tunings. To give access to Galaxy users, the algorithm was deposited in the Galaxy Test Toolshed (https://testtoolshed. g2. bx. psu. edu/) where it forms a suite of 9 tools. The Galaxy Test Toolshed eases the installation in a Galaxy web application. The code of the 9 tools can also be downloaded via a link to a Mercurial git managed by Galaxy.
Acknowledgement This work was part of the  Phenoval PRRI program  funded by the  Region Occitanie, France. Thanks to  Emmanuelle Meudec ,  Pierre-Marie Allard ,  Arnaud Garcia ,  Yann Guitton  and  David Ropartz  for useful discussions and/or providing datasets during the preliminary tests of this algorithm, to  Virginie Rossard  for support about Galaxy and to the reviewers for their relevant remarks.
The data reduction was performed with the ATHENA  [Ravel and Newville, 2005]  and the CHERO-KEE softwares  [Michalowicz et al. , 2013]. Metallic tungsten foil, WO 2 , WO 3 minerals and amorphous H 2 WO 4 were used as reference compounds. The oxidation states of W in samples were quantified using linear combination fit (LCF) of XANES and EXAFS (Extended X-ray Absorption Fine Structure) spectra of the experimental samples using the reference compounds as database. XANES LCF are detailed in Supplementary Information.
Acknowledgments The Nano-ID transmission electron microscope was funded by the  EQUIPEX  project  ANR-10-EQPX-39-01. The project leading to this publication has received funding from  Excellence Initiative of Aix-Marseille University -A*MIDEX, a  French "Investissements d'Avenir" program , through its associated  PASSIV-ITER  project. The authors acknowledge the  European Synchrotron Radiation Facility  for provision of synchrotron radiation beamtime and especially the scientists in charge of the FAME (BM30b) beamline financially supported by  CNRS  and  CEA.
Since we consider only phonons from the Γ-point of the Brillouin zone (for conventional, cubic cell q = 0), then the last term in eq 2 is equal to unity, and the deformation vector can be rewritten as = α α m Q u e 1 i (3) In this paper, the eigenvectors e α are extracted from VASP output file (OUTCAR) and deformed structures are prepared with our in-house python script. 18 The deformation parameter Q was varied from -1. 5 to 1. 5 [in units of Å•amu 1/2 ] with step of 0. 1. The value Q = 0 corresponds to an undistorted (optimized) ZIF-8 structure, whereas for the limiting value Q = 1. 5 we observe full opening of the gates in a previously reported gate opening mode  [4] [5] [6]  (with linkers in a four-member ring arranged in parallel to each other). DFT Calculations. All phonon calculations have been performed on optimized structures for which the internal energy was found to be minimal.