dataesr/scientific-paragraphs-categorization huggingface dataset:
Target prediction The Tarbase v8  [47]  and Diana mirPath v3  [48]  software programs were used to predict genes targeted by known miRNA with at least 100 reads per million mapped reads (RPM) in one milk fraction and to identify metabolic pathways affected by the regulation of these targeted genes. Default options in miRPath were used, the pathways union option and FDR correction have been applied for the pathway analysis, and the enrichment analysis method used the Fisher's exact test with a p-value threshold of 0. 05. • fast, convenient online submission • thorough peer review by experienced researchers in your field • rapid publication on acceptance • support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year.
Acknowledgements The authors are very grateful to the staff at the  Herbipôle (INRAE, UE1414, Marcenat, France)  and  IEPL (INRAE ,  UMR PEGASE, Le Rheu, France ) experimental farms for animal care and sampling. Exclione ( Elancourt, France ), and particularly  Zuzana Krupova , are thanked for the preparation of milk extracellular vesicles. Sandra Wiart  ( INRAE, UMR PEGASE, France ) and  Sebastien Bes  ( INRAE, UMR H, France ) are thanked for their assistance with milk mammary epithelial cell purification and milk fat RNA preparation.
In our contribution, the Suzuki-Miyaura arylation of odihaloarene, challenging in terms of selectivity, illustrates this potential. ( 31 P NMR). FTIR spectra were recorded on a Nicolet 6700 spectrometer in the range 400-4000 cm À 1. Elemental analysis was performed on an Analyzer CHNS/O Thermo Electron Flash EA 1112 Series and ICP-AES iCAP Thermo. ESI mass spectra were obtained with a Bruker Compact Q-TOF spectrometer. The precursors 1 a-b were prepared from reported procedures. [11] Full synthetic methods and analytical data for ligands and complexes are detailed in the Supporting Information.
X-Ray crystallography. Full-sphere diffraction data for 2 a, 6 c, Pd-8 a, Pd-8 b and Pd-8 c were collected with a Bruker D8 VENTURE Kappa Duo PHOTON100 instrument equipped with a IμS microfocus X-ray tube. The data for other compound 8 a were recorded with a Bruker D8 Venture diffractometer. The diffractometers were equipped with Cryostream Cooler (Oxford Cryosystems) and Mo Kα radiation was used in all cases. The structures were solved by direct methods (SHELXT)  [24]  and then refined using a full-matrix least squares routine based on F 2 with SHELXL-2014/2018. [25] All nonhydrogen atoms were refined with anisotropic displacement parameters. Relevant crystallographic data, data collection and structure refinement parameters are presented in Supporting Information. a] Entry Ligand Haloarene Products 1 / no reaction 2  [b]  PPh 3 27 % 11 a; 14 % 12 a; 2 % 13 a 3  [c]  60 % 11 a; 16 % 12 a; traces 13 a 4 no reaction 5 / no reaction 6 no reaction 7  [d]  PPh 3 11 % 13 a 8  [b]  30 % 11 b; 7 % 12 a; traces 13 a.
Deposition [a] Conditions: dihalide (0. 25 mmol, 1 equiv. ), aryl boronic acid (0. 25 mmol, 1 equiv. ), potassium phosphate (0. 75 mmol, 3 equiv. ), ligand (0. 025 mmol, 0. 1 equiv. ), 3 mL THF tris(dibenzylideneacetone)dipalladium(0) (5. 7 mg, 0. 00625 mmol, 0. 025 equiv. ). [b] Full conversion of boronic acids with > 55 % of unidentified side-products. [c] Full conversion of boronic acid with < 25 % of unidentified side-products. [d] Full conversion of boronic acids with > 88 % of unidentified side-products. Yields determined by GC with an external standard from duplicated experiments. paper. These data are provided free of charge by the joint Cambridge Crystallographic Data Centre and Fachinformationszentrum Karlsruhe Access Structures service.
Acknowledgements This work was supported by the  ANR-PRC 2016 program  ( ALCATRAS ,  ANR-16-CE07-0001-01 ), the  CNRS ,  Université de Bourgogne ,  Conseil Régional de Bourgogne  through the plan d'actions régional pour l'innovation (PARI) and the  fonds européen de développement regional (FEDER). Thanks are due to the PACSMUB platform for analyses (SATT SAYENS) especially  M. -J. Penouilh ,  Dr. Q. Bonnin.
ADP is no clearly visible after molecular replacement in Fo-Fc difference map but could be refined with good density (Final 2Fo-Fc map). Density for ADP was verified with calculation of a composite omit map (Fig. S3 ), meanwhile, comparison of ADP site with DDX17 structure (Protein Data Bank 6uv3) showed a good conservation of the nucleotide-binding site (Fig  S4B ). In the crystal structure of Dbp2-apo, two RecA-like domains adopted an open state, with the NTE subdomain located on the exterior surface of RecA1 and the CTE subdomain inserted between two RecA-like domains (Fig. 2B ). To estimate the position and intrinsic mobility of the two RecA-like domains, we calculated the distance between the centers of mass of the RecA1 and RecA2. This distance was 42. 9 Å in the open state, showing that the two domains were far apart, as shown in Figure  2B.
X-ray diffraction data were collected on beamline BL18U1 beamline at Shanghai Synchrotron Radiation Facility (SSRF) and were processed using the XDS package (http://xds. mpimf-heidelberg. mpg. de/) (49). The Dbp2 structure was solved by molecular replacement. Phenix was used for refined  (50)  and Coot was used for model building  (51). For twinning problems of Dbp2-ADP-ssDNA crystal, we made a lot of efforts and optimally selected P2 1 2 1 2 1 space group. Crystals had unit cell 168. 84 168. 84 145. 93 90. 0 90. 0 90. 0 and could be apparently indexed in space group P4 3 2 1 2 with three molecules in the asymmetric unit. Though data collection statistics were good, solving the structure in this space group by molecular replacement are impossible with no solution found. Apparent P4 3 2 1 2 could be due to perfect merohedral twinning and possible space groups can be P4 3 or P2 1 2 1 2 1 as described in  (52).
Small-angle X-ray scattering The full-length Dbp2 was prepared at 5. 0 mg/ml in 25 mM Hepes pH 7. 5, 500 mM NaCl, and 20% glycerol buffer. SAXS data were collected at the BL19U2 beamline of the Shanghai Synchrotron Radiation Facility. The SAXS data for Dbp2 53-496  with ADP was collected at beamline SWING (SOLEIL Synchrotron, Saint-Aubin). The scattering processing was measured as described  (53). The SAXS data were analyzed using ATSAS  (54)  and model building was done with Ensemble Optimization Method  (30). All SAXS data are shown in Table  S3.
CD spectroscopy CD spectra measurements were performed using a spectropolarimeter (Applied Photophysics). Purified Dbp2, NTE, and CTE mutants contained N-terminal MBP tag dialyzed in sodium phosphate buffer (Na 2 HPO 4 /NaH 2 PO 4 , pH 7. 5) at 25 C with a protein concentration of 3 μM. CD spectra were collected in the wavelength range of 190 to 260 nm. CD results were analyzed using the CAPITO software (https://capito. software/)  (55).
Acknowledgments-We thank  Xia Ai  and  Hai-Hong Li  ( Northwest A & F University ) for experiment assistance. We thank the beamlines scientists from  BL18B/BL18U1  and  BL19U1  of the  National Center for Protein Sciences, Shanghai , for assistance during data collection. We are also grateful for access to the SOLEL (SWING) synchrotron radiation facility for SAXS data collection. This study was supported by the  National Natural Science Foundation of China  ( 32071291 ,  32201042 ,  32071225 , and  31870788 ) and  CNRS LIA  ("Helicase-mediated G-quadruplex DNA unwinding and genome stability"). Author contributions-Q. -X. S. , Z. -X. L. , Y. -Z. Z. , and S. R. investigation; Q. -X. S. data curation; N. -N. L. , S. R. , X. M. H. , and X. -G. X. formal analysis; N. -N. L. , X. M. H. , and X. -G.
RTTOV coefficients are evaluated using a large, independent dataset of 25,000 atmospheric model profiles as a robust test of the diverse 83 training profiles typically used. The study is carried out for nine historic satellite instruments: IRIS-D, SIRS-B, MRIR and HRIR from the infrared part of the spectrum, and MSU, SSM/I, SSM/T2, SMMR and SSMI/S from the microwave. Simulated channel brightness temperatures show similar statistics between both the independent and the 83 profile datasets, confirming that it is acceptable to validate the RTTOV coefficients with the same profiles used to generate the coefficients. Differences between RTTOV and the line-by-line models are highest in water vapour channels, where mean values can reach up to 0. 4 ±0. 2 K for the infrared, and 0. 04 ±0. 13 K for the microwave.
Though highly accurate, this parameterisation introduces a small error in the calculated radiance, which can be quantified by comparison with the equivalent results obtained from a high-resolution line-by-line (LBL) model  (Saunders et al. , 2007). The evaluation of RTTOV's performance presented in this study was carried out as one of the radiative transfer objectives for a project within the European Union's Copernicus Climate Change Service 1  (C3S), entitled 'C3S 311c: Support for climate atmosphere database on Extension to the Standard Atmosphere  (1976) , is used for the other 22 molecules and Chlorofluorocarbons (CFCs), though not every molecule is included for every instruments, depending on its spectral absorption coverage. The atmospheric profiles for the first six molecules were selected from a large database originally on 91 levels generated by the experimental suite (cycle 30R2) of the ECMWF forecasting system, as described in  Chevallier et al.
(2006). The 81, 82 and 83 profiles are the minimum, maximum and mean, respectively, of the initial database. We refer the reader to the RTTOV science and validation reports for a complete description of the training dataset, listed in  Saunders et al. (2017). The larger independent set of atmospheric profiles used is described in  Eresmaa and McNally (2014). Only values for water vapour, temperature and ozone profiles were used in the present evaluation. This dataset includes 25,000 profiles divided into five subsets each with 5,000 profiles. The profile is split into 137 levels from the surface to 0. 01 hPa, which is the resolution currently used by the Integrated Forecasting System (IFS) developed at ECMWF. The dataset is selected from the shortrange IFS forecast over one year and is available from the NWPSAF website 1.
Each of the 5,000 profile subsets represents maximum diversity of one of five different variables: temperature (t), specific humidity (q), ozone (o3), cloud condensate  (ccol)  or precipitation (rcol). Figure  1  shows the temperature profiles (grey lines) for the 25,000 profiles and 83 training profiles (Fig. 1a  and 1b, respectively). The mean profile for the full 25,000 dataset (green lines) and for the 83 profiles (red lines) are also plotted on each panel. The vertical distributions of the maximum and minimum values are similar between the subsets and the training profiles, however, the mean stratopause height around 1 hPa is noticeably higher in the training set. This is probably due to the different vertical resolutions of the initial data from the ECMWF model, where the 137 levels of the larger dataset will better resolve the upper atmosphere than the 91 levels that comprise the training profiles.
The maximum (minimum) temperature of the independent profiles is 319. 39 K (160. 29 K) and for the training profiles it is 318. 25 K (159. 61 K). Figures 1c and 1d also show the water vapour profiles of the 25,000 profiles subsets and the training profiles, respectively. In the upper troposphere the profiles from the independent profiles generally have less water vapour than in the training profiles. The water vapour mean profiles (green and red lines) are similar. The minimum values of the two datasets are the same (0. 016 ppmv) and the maximum values are 41. 881 ppmv in the independent profiles and 42,868 ppmv in the training profiles. The vertical distributions of ozone profiles are shown in Fig. 1e  and 1f. The training profiles consist of less ozone in the stratosphere when compared to the independent dataset, which can be seen in the mean values.
The mean ozone value around the peak level at 10 hPa is smaller in the training profiles relative to the independent profiles (7 ppmv versus 8 ppmv) and at this altitude the ozone variability is larger in the training profiles (Fig. 1f ). The small values of ozone in the training profiles could be due to a profile located at the ozone hole. Ozone values vary between 0 and 10. 65 ppmv in both datasets. The ozone distribution for all five subsets contains some profiles with a second peak of ozone above 1 hPa (only the ozone subset is shown in the Fig. 2 ), whereas this behaviour is not present in the training profiles. These profiles are located mainly in the polar regions. Figure  2a  shows the spatial distribution of the profiles with a second peak taken from the ozone subset (the same behaviour is observed in the double peak profiles from the other four subsets) and Fig.
Infrared instruments The LBL simulation of the radiances at the top of atmosphere (TOA) were performed with the Line-by-Line Radiative Transfer Model (LBLRTM) version 12. 2  (Clough et al. , 2005). LBLRTM v12. 2 combines the Atmospheric and Environmental Research (AER) v3. 2 molecular line database and the MT-CKD absorption continuum version 2. 5. 2. The AER v3. 2 molecular database is based on High-resolution Transmission molecular Absorption database (HITRAN) 2008  (Rothman et al. , 2009)  with many improvements concerning positions and intensities of some molecules and line-mixing effects 2. The 101 (54) level profiles were used to simulate the hyperspectral (other) sensors. As previously mentioned, the independent profiles only contain temperature, water vapour and ozone information, and profiles relating to the molecules: CO 2 , CH 4 , N 2 O and CO are the mean training profile set (profile 83), and one US76 standard profile for other the 22 other molecules.
Independent profile dataset versus training profile dataset A description of each of the sensors used in this study follows. The evaluation was performed separately for each subset of 5,000 profiles. However, the statistics were almost the same for each subset and for the ensemble of 25,000 profiles. For this reason, only the statistics for the 25,000 profiles (together) are shown. Note that maximum difference can be positive or negative with respect to the order of the subtraction between datasets.
IRIS-D The InfraRed Interferometer Spectrometer (IRIS-D) was a hyper-spectral IR sensor, which had 862 channels (from 400. 47 cm -1 to 1597. 71 cm -1 ), 94 km of spatial resolution at nadir and flew on Nimbus 4  (Hanel et al. , 1971). Figure  3  shows the TOA Brightness Temperature (BT) as mean (a), standard deviation (b) and maximum value (c) of the differences between RTTOV and LBLRTM for all IRIS-D channels. Overall, the mean differences between the independent profiles (blue lines) and the training profiles (red lines) are very similar, and vary between -0. 238 and 0. 247 K. The differences between the two datasets are more evident in the CO 2 band (between 600 and 800 cm -1 ) and in the ozone band (near 1000 cm -1 ). A similar behaviour is found in the standard deviation of differences, which varies between 0.
006 K to 0. 235 K. The highest standard deviation, and mean differences between datasets, are in the H2O channels below 600 cm -1 and above 1300 cm -1. The variability of water vapour profiles in the independent profiles is greater than in the training profiles, which could explain why they are higher. Conversely, the variability of ozone profiles from the training profiles is higher in the ozone peak, which could explain the higher values of standard deviation of mean differences in the ozone band. The statistics from the independent profiles have higher maximum values than the ones from the training profiles (up to 6 K in some channels but mostly below 2 K against 0. 5 K in the training profiles) which is to be expected because they have more variability. The standard deviation of the differences present a small value when compare against the instrument noise.
95 µm and 22. 91 µm, and for all other channels the reverse is found. The statistics for the training dataset are very good for channels below 15 µm as compared with the independent dataset but overall the mean differences are between -0. 05 and 0. 2 K for all channels and for both datasets. The standard deviations are slightly larger in the independent profiles but the two datasets have similar values, and for both datasets the standard deviation increases with The first eight SIRS-B channels show statistics similar to the SIRS-A channels. The mean differences for the training profiles (red bars) are larger than the independent profiles (blue bars) only in the channels centred at 14 µm and 14. 95 µm, and for all other channels the reverse is found. The statistics for the training dataset are very good for channels below 14.
94 µm as compared with the independent dataset but overall the mean differences are between -0. 05 and 0. 1 K for all channels and for both datasets (figure not shown). The standard deviations are slightly larger in the independent profiles but the two datasets have similar values, and for both datasets the standard deviation increases with wavelength, which is more evident in the independent profiles. As for the IRIS-D channels, the maximum values for the SIRS-A are higher for the independent profiles, with values up to 1 K against 0. 2 K for the training dataset. These statistics of SIRS-A were also compare against the instrument noise. The channel 14 µm presents the highest value (0. 7 K) and in the other channels the noise varies between 0. 1 and 0. 25 K which are higher when compared against the standard deviation of the differences (figure not shown).
Other two sensors were also analysed. The High Resolution Infrared Radiometer (HRIR) which had only one channel (3. 76 µm), spatial resolution of 8 km and flew on Nimbus 1, 2 and 3. The statistics are very similar for both profile datasets (figure not shown). The other sensor is the Medium Resolution Infrared Radiometer (MRIR), this sensor had four channels (centred between 6. 62 µm and 17. 06 µm), flew on Nimbus 2 and 3 and presented a spectral resolution of 55 and 45 km. The statistics of this sensor is similar to the other two sensors, except in the channel centred at 17. 06 µm which presents the highest mean differences, standard deviation and maximum value. The main cause probably being the fact that this channel has a very large bandwidth (between 5 and 30 µm).
MSU The Microwave Sounding Unit (MSU) was a Dicke-type cross-track radiometer with a 110 km footprint (at nadir) that successfully flew on nine NOAA satellites between TIROS-N and NOAA-14 (not NOAA-13),  Spencer and Christy (1990). It had four temperature sounding double passband channels in the 50-60 GHz oxygen region, each with a bandwidth of 200 MHz and NE∆T (Noise Equivalent ∆ Temperature) of 0. 3 K. Temperature channels tend to perform well in RTTOV compared with the underlying line-by-line model and this instrument is no exception (Fig. 5 ). In general the differences between the two profile datasets are small, with a slight decrease in the mean and standard deviation but increase in the maximum value when using the larger dataset -all of which would be expected. The maximum difference in any channel is below 0.
15 K. Variation with satellite zenith angle varies depending on channel, but the pattern of variation is similar between the two datasets. Channel 1 is more like a window channel than the other three and shows more dependency on SZA in general. Mean biases reduce and then become negative with increasing angle, giving an overall six angle mean of near zero. Standard deviations increase slightly and so does the value of maximum difference, with bigger values in the larger dataset, up to 0. 5 K. The other three channels show relatively little angular dependence in the mean, a slight increase (or decrease in the case of channel 3) in the standard deviation, and little dependence in the maximum differences, apart from channel 4 in the larger dataset, where values increase with satellite zenith angle up to 0.
6 ). The pattern of mean statistics look quite similar between datasets. As with MSU, the maximum differences increase in the 25,000 profile set, however, the standard deviation increases instead of decreases. As SSM/T-2 is a humidity sensitive instrument this is likely due to the broader range of water vapour profiles in a far larger dataset relative to the possible dry air profiles. Channel 3, which is the channel closest to the centre of 183. 31 GHz line shows the largest increase in standard deviation and maximum difference, with the maximum changing from 0. 2 to nearly -2 K. There is a similar variation of satellite zenith angle dependency in the mean bias of all channels, however the standard deviation of the bias is relatively independent of angle. Both nadir and the total angular mean are very close in value indicating one or the other could be used in bias correction schemes.
SSM/I The Special Sensor Microwave Imager (SSM/I) was a conical scanner that flew onboard seven of the DMSP satellites from F8 -F15 (not F9),  Hollinger et al. (1990). It comprised seven double passband channels with four frequencies, three of which had vertical and horizontal polarisation, and the remaining channel vertically polarised and centred directly on the 22. 235 GHz water vapour line. The other frequencies are in semi-window regions centred at 19. 35, 37 and 85 GHz. Nadir footprint sizes vary between around 43 km and 13 km depending on channel. NE∆T values vary between 0. 37 and 0. 73 K. There are similarities in the difference patterns to SSM/T-2 (not shown) as they are both window/water vapour sensing instruments and there are slightly larger difference statistics in the 25,000 profile dataset for all channels, however, the magnitudes for SSM/I are about 10-fold less than those for SSM/T-2 as they are at lower frequencies.
Channel 3 at 22. 235 GHz shows some similarities to the 183. 31 GHz channels on SSM/T-2, however, the mean differences in the 25,000 profile dataset are larger in the 85. 5 GHz channels. The maximum difference is -0. 3 K in channel 3. When all six zenith angles are included mean differences reduce quite significantly for both profile datasets, however, standard deviations increase. The maximum difference increases to -0. 6 K in channel 3 though the other channels only reduce slightly.
SMMR The Scanning Multichannel Microwave Radiometer (SMMR) was an early conical scanning instrument that flew on Nimbus 7  (Gloersen and Barath, 1977) , and on a demonstrator mission on SeaSat but this is not considered in this project. SMMR comprises ten single passband channels with five frequencies -one channel each for either vertical or horizontal polarisation. The frequencies are all between 6. 6 and 37 GHz and are primarily window channels with some influence from the 22. 235 GHz line. The footprint sizes varied from 148 km by 95 km at 6. 6 GHz to 27 km by 18 km at 37 GHz. Values of NE∆T vary from 0. 9 K for the lower frequency channels to 1. 5 K at the highest frequency measured. SMMR shares similarities with equivalent low frequency channels on SSM/I and the patterns are the same, with an increase of all statistics for the 25,000 profile dataset with respect to the training dataset (not shown).
Channels 7 and 8 at 21 GHz show the largest differences as they are in close proximity with the 22. 235 GHz water vapour line, with a maximum bias of -0. 12 K but with a mean value of just above 0. 003 K. When all six satellite zenith angles are included mean differences significantly reduce, whereas standard deviations and maximum biases increase for both profile datasets, with a maximum bias of -0. 27 K in channels 7 and 8. Brightness temperatures differences are overall low as this is quite a flat part of the spectrum.