dataesr/scientific-paragraphs-categorization huggingface dataset:
Factors involved in gluconeogenesis, acquisition of natural immunity, and MAPK signaling were highly represented in the DAVID pathway analysis (v 6. 7; https://david. ncifcrf. gov/) (Table  3  and S1 Dataset. Supplementary Dataset GSE57590. pdf). In summary, the Table  3. Kyoto encyclopedia of genes and genomes pathway analysis. Data from renal tissue genomic arrays of wild-type and CXCL7-null mice were analyzed using DAVID software.
Influence of sex differences in OPLL Based on these data, we hypothesized that the ubiquitin-proteasome induces chemokine degradation in OPLL. Given the known sex differences in the development of OPLL, the expression of the ring-finger protein (Rnf)216 (https://www. genenames. org/cgi-bin/genefamilies/set/58), which encodes a ubiquitin E3 ligase (http://www. ncbi. nlm. nih. gov/gene/54476) [35, 48] , was examined in the testes and ovaries of CXCL7-null mice. A significant increase in Rnf216 expression was detected in the ovaries and testes of CXCL7-null mice compared to that in tissues from wild-type mice (Fig  4C ). These data suggest a relationship between sex and OPLL.
Acknowledgments We dedicate this article to the late  K. Uchida  ( Department of Orthopaedics and Rehabilitation Medicine, Faculty of Medical Sciences, University of Fukui ). The authors thank  Y. Toyama  ( Department of Orthopaedic Surgery, Keio University ),  T. Soejima  ( Department of Orthopaedic Surgery, Kurume University ),  M. Shiwa  ( Life Science Division, Bio-Rad Laboratories K. K. , Tokyo, Japan ),  K. Yasuda  ( Cell Innovator Co. , Ltd. , Fukuoka, Japan ),  A. Itou  ( Ito Bone Histomorphometry Institute Co. , Ltd. , Niigata, Japan ),  K. Kimura  ( Department of Cardiology, Kurume University Medical Center ),  I. Sagawa  ( Support Center for Advanced Medical Sciences, Institute of Biomedical Sciences, Tokushima University Graduate School ),  R. Fujikawa  ( Trans Genic ),  A. Kuwano  and  Y.
The data from the fast CMOS camera is processed offline while the 80 Hz camera is used for real-time monitoring. These techniques allow parallelized imaging and have been demonstrated for in vivo human retinal imaging; they have been made possible by technological progress made by the camera industry which now allows for higher acquisition speed, and they seem to have opened the way for new possibilities especially in the realm of offline numerical processing  [26, 27]. The laser Doppler holography technique we present here is conceptually close to laser Doppler flowmetry, but like the aforementioned methods it is derived from holography which allows for full-field imaging. In laser Doppler holography, the optical field serving as non-Doppler shifted light is a separate reference beam beating against the Doppler shifted optical field backscattered by the retina.
Acousto-optics modulators (not shown on the schematic) are used in conjuction with the slow camera to introduce a 40 Hz frequency shift in order to perform stroboscopic and narrowband signal measurements in time-averaging conditions  [31]. However due to eye motion the quality of the signal is limited and this channel is mostly used for alignment purposes. As both methods are based on holography, the slow camera gives indications on the quality of the holographic reconstruction that are also valid for the fast camera; moreover it is able to yield a contrast related to blood flow that allows visualizing the retinal area being imaged during the acquisition, which is not possible on the fast camera. The real-time hologram rendering is done with our software (Holovibes) that runs calculations on graphics processing units; for this purpose a personal computer equipped with a Nvidia GeForce GTX Titan graphics card is used.
The fast camera is used for wideband measurements of the Doppler spectral broadening. Interferograms from this camera are recorded on its on-board memory and the data are processed offline using Matlab. Interferograms are recorded on the 512×512 pixel sensor with an acquisition rate of f S = 39 kHz (except in section 4 where experiments are made to explore the effect of the sampling frequency), and a frame exposure time of 25. 6 µs. Image rendering of complexvalued holograms H(x, y) is performed using the angular spectrum propagation of the recorded interferograms I(x, y), appropriate for near-field reconstructions  [32] : H(x, y, z) ≈ ∬ Î(k x , k y ) exp (ik z z) exp ik x x + ik y y dk x dk y (1) where Î(k x , k y ) is the two-dimensional Fourier transform of the interferogram I(x, y). The optical axis is z, and k x , k y , and k z = k 2 -k 2 x -k 2 y are the wave vectors projections along x, y, and z, respectively.
Data processing Data processing consists of measuring the local optical fluctuations of the holograms recorded with the high-speed CMOS camera. A Short-Time Fourier Transform (STFT) method is used to analyze the changes in blood flow over time through the changes in the Doppler broadened spectrum. The variations over time of the Doppler Power Spectrum Density (DPSD) are resolved with a sliding short time window constituted of j win consecutive holograms H. Given a sampling frequency f S , the duration of the short-time window is thus: σ win = j win f S (2) For each short-time window, a single image with a blood flow contrast is formed using the process illustrated in Fig. 2. First, the short-time window is fast Fourier transformed along the temporal dimension, then the power spectral density is estimated from the squared magnitude of the Fourier transform: S(x, y, t n , ω) = ∫ t n +σ win t n H(x, y, τ) exp (-iωτ) dτ 2 (3) Finally the quantity M 0 ± is calculated from the integration of the DPSD: M 0 ± (x, y, t n ) = ∫ ω 2 ω 1 S(x, y, t n , ω) ± S(x, y, t n , -ω) dω (4) M 0 + is the zeroth moment of the bandpass filtered DPSD; it is a single image corresponding to the area under the curve of the DPSD over the frequency interval [ f 1 , f 2 ] = (2π) -1 ×[ω 1 , ω 2 ].
STFT parameter σ win The chosen width of the short-time window σ win results from a trade-off between temporal resolution and Signal-to-Noise Ratio (SNR). Integrating the signal over a longer time period improves the SNR insofar as the amplitude of lateral displacements stays limited during the duration of the short-time window. For excessively long short-time windows, involuntary eye movements limit the improvement in SNR. More importantly, as two events happening during the same short-time window cannot be separated, the sliding window duration σ win defines the temporal resolution of the instrument. In Fig. 5  and Fig. 6 , a comparison is made to evaluate the importance of σ win on the SNR. The STFT analysis was carried out two times on the same dataset by using different parameters with an approximate tenfold increase in temporal resolution; j win = 64 (σ win = 1.
Funding This work was supported by  LABEX WIFI (Laboratory of Excellence   ANR-10-LABX-24 ) within the  French Program Investments for the Future under Reference   ANR-10-IDEX-0001-02 PSL , and  European Research Council  ( ERC Synergy HELMHOLTZ , grant agreement # 610110 ). The Titan Xp used for this research was donated by the  NVIDIA Corporation.
Species Abundance Distributions (SADs) bear the imprint of ecological processes that shape biological communities, and are therefore used to discriminate among different scenarios of community assembly. Even though empirical distributions appear to follow a handful of qualitative laws, it is still unclear if and how quantitative variation in SADs reflects peculiar features of the communities and their environmental context. Here, we use the extensive dataset generated by the Tara Oceans expedition for marine microbial eukaryotes (protists) and an adaptive algorithm to explore how SADs vary across plankton communities in the global ocean. We show that the decay in abundance of non-dominant OTUs, comprising over 99% of local richness, is commonly governed by a power-law. The power-law exponent varies by less than 10% across locations and shows no biogeographical signature, but is weakly modulated by cell size.
One possible solution to the failure of demonstrating universality based only on fitting SADs is to try to fit other ecological patterns, such as the variation of taxonomic abundances in space and time  27  and body-size distribution  26  , in order to increase the discriminating power of the models. However, these extensions require a spatiotemporal and/or morphological resolution in community analyses that is seldom available in molecular datasets. The identification of universal laws by fitting empirical distributions with one of the many available theoretical models may also be hampered by limitations of model selection approaches. First, by focusing on the best-fitting theoretical distribution, these approaches do not examine the parameters of the best fit. Since different models can provide very similar distributions (for instance, the Poissonlognormal can yield a power-law behaviour) and models based on distinct assumptions predict, for appropriate parameter values, effectively indistinguishable distribution  26  , different families of laws can fit equally well an empirical distribution.
Results We exploited the extensive molecular dataset generated by the Tara Oceans expedition, which comprises millions of protist metabarcode sequences from 121 different locations worldwide  30  , constituting a comprehensive picture of planktonic protist communities. By using different proxies for protist 'taxa', as defined by swarm  31  (hereafter swarms or species) and UCLUST for 95% and 97% sequence identities (hereafter OTUs), we aimed to minimize possible intraspecific variability and PCR/sequencing errors. We analyzed abundances of pelagic protist taxa spanning more than three orders of magnitude in cell size (from 0. 8 µm -2 mm, subdivided in 4 size classes: pico-nano, nano, micro, meso; Methods), hence assessing communities that are subjected, other than to environmental heterogeneity, to different physiological and ecological constraints in terms of their metabolic rates, population densities, dispersal and diversification potential  32 33 34.
The similarity of the SADs power-law exponent to 3/2, a value obtained, for instance, in temporal spectra of intermittently varying ecosystems  45  , might indicate that local abundances bear a signature of spatio-temporal variability  27. All in all, understanding the interplay between ocean transport and community structure would require a resolution in space and time that is still unattained at a broad geographical scale. However, the combination of different observation platforms  46  , and the genomic characterization of samples in a given -usually coastal -location  3 47 48  opens new perspectives for a dynamic characterization of drifting communities, which is not possible with the dataset we used. Our interpretation of the observed ubiquitous trends in the plankton protist SADs, is based on features proper to the plankton lifestyle.
Sequence data Sequence data were retrieved from Richter et al. 30. Briefly, seawater samples were collected in 121 stations distributed worldwide. Each sample was filtered using different mesh sizes so as to isolate different classes of the planktonic community: the pico-nanoplankton (0. 8-5 µm), the nanoplankton (5-20 µm), the microplankton (20-180 µm), and the mesoplankton (180-2000 µm). Total DNA was extracted from each size class and the V9 region of the 18S rRNA gene  50  was PCR-amplified and subjected to Illumina sequencing in order to describe eukaryotic planktonic communities. The retrieved paired-end sequencing reads were assembled using the fastx software (http://hannonlab. cshl. edu/fastx_toolkit/index. html) and dereplicated. To remove potential artefacts, we excluded sequences (i) that did not contain both forward and reverse primers, and (ii) identified as chimeras by the usearch programme (version 4.
2; Edgar et al. 51  ), using either the V9_PR2 reference database as reference (database W2; Guillou et al. 52  ), or the de novo approach. The resulting sequences were clustered based on their similarity. First, the swarm algorithm  26  was used to minimize the amount of PCR/sequencing errors, as well as intraspecific variability. Because we used it to cluster sequences distant from one mismatch, swarms represent the Operational Taxonomic Units (OTUs) at the finest taxonomic resolution. Second, swarms were clustered at 97% and 95% similarity using the UCLUST algorithm  53  so as to obtain descriptions of the community structure at coarser taxonomic levels. All OTUs were then assigned to a taxon using the ggsearch program (Fasta package, http://faculty. virginia. edu/wrpearson/fasta/CURRENT/) against the V9_PR2 and the SILVA databases (http://www.
Indeed, the intrinsically discrete structure of the distributions does not allow the use of critical values tables for statistical tests, so that a p-value cannot be directly assigned from the Anderson-Darling test. We adopt thus the bootstrapping approach followed by Clauset et al. 36  (step 3). The rationale is that that best fits to the data obtained in step 1 shall be discarded if their distance from the empirical data is unlikely due to random fluctuations. Such random deviations are quantified by the statistics of the distances between random samples of the best fitting model distribution and their relative maximum likelihood fits. For each fit, we generated 200 synthetic datasets, whose K abundances match the length of the vector ñ , by sampling the model distribution p (n) for the best fitting parameters (r,α,β).
We then measure the Anderson-Darling distance of each such synthetic dataset from the distribution that fits them with highest likelihood. Finally, we assign a p-value to the original fit as the fraction of those distances that are larger than the Anderson-Darling distance between the original data and its best fitting distribution. Hence, fits are considered significant if the empirical distance falls within 'typical' values that we would expect to obtain by random sampling. In this case, the fraction p is large. Instead, p is small if the samples are systematically less distant from their fit than the empirical distribution is from its own fit. By setting a minimum threshold on p (in our analysis, 0. 1), we aim to exclude the latter case. We repeated the maximum-likelihood fit and p-value calculation for sub-communities of growing size (step 4), by progressively increasing n cut (starting from n cut =100 ).
Acknowledgements The authors are very grateful to  Francesco d'Ovidio ,  Valentin Anjou  and  Stéphane Audic , who participated in the early stages of this work, to  Olivier Missa ,  Guilhem Sommeria-Klein  and  Erik van Nimwegen  for discussions on neutral models and model fitting, to the  Tara Oceans consortium , and to three anonymous referees for their constructive comments. The support of the informatics platform of IBENS has been essential for the computational part of this study. This work has received support under the programmes  «Investissements d'Avenir» launched  by the  French Government  and implemented by  ANR  with the references  ANR-10-LABX-54   MEMOLIFE ,  ANR-10-IDEX-0001-02 PSL * Research University and OCEANOMICS, as well as from the  EU  project MicroB3. CB additionally acknowledges funding from the  ERC Advanced Award ''Diatomite' ', the  Louis D Foundation of the Institut de France , and the  Radcliffe Institute of Advanced Study at Harvard University  for a scholars fellowship during the 2016-2017 academic year.
RyhB represses nuo and sdh expression RyhB inhibition of Sdh and Nuo activities may be due to the repression of the synthesis and / or of the maturation of the complexes. Expression of sdh has already been shown to be repressed by RyhB  (20, 30). In contrast, although pointed out in global approaches, RyhB regulation of nuo genes expression still awaited investigation  (17, (31) (32) (33). Using the RNA-fold software (http://unafold. rna. albany. edu), we could predict a base-pairing in between RyhB and the 5' un-translated region of the first gene of operon, nuoA  (34). This base-pairing involves 21 nucleotides (nt) of RyhB and includes the ribosome-binding site (RBS) and the start codon of nuoA (Fig. 4A ). Overexpression of ryhB on a plasmid decreased the activity of a P BAD -nuoA-lacZ fusion of about 4-fold, as compared to cells transformed with an empty vector (Fig.
Acknowledgements We would like to thank  A. Battesti  for precious help with strain constructions and A. Huguenot for critical guidance with enzymatic activities assays. P. M. , F. B. and S. C. work was funded by the  Centre National de la Recherche Scientifique (CNRS)  and  Aix Marseille Université (AMU). S. C. is a recipient of a  Fondation pour la Recherche Médicale  grant ( FDT20170436820 ).
The Coordinated Regional Climate Downscaling Experiment (CORDEX) is a scientific effort of the World Climate Research Program (WRCP) for the coordination of regional climate initiatives. In order to accept an experiment, CORDEX provides experiment guidelines, specifications of regional domains, and data access and archiving. CORDEX experiments are important to study climate at the regional scale, and at the same time, they also have a very prominent role in providing regional climate data of high quality. Data requirements are intended to cover all the possible needs of stakeholders and scientists working on climate change mitigation and adaptation policies in various scientific communities. The required data and diagnostics are grouped into different levels of frequency and priority, and some of them even have to be provided as statistics (minimum, maximum, mean) over different time periods.
org/, last access: 18 March 2019), which come from the Coupled Model Intercomparison Project (CMIP) exercises. The list of variables required by CORDEX consists of standard model fields and some diagnostics in certain frequencies, as well as statistical aggregations such as minimum, maximum, or mean, for a given period. These variables are grouped into different priority levels (Core, Tier 1, and Tier 2), with Core being the mandatory list of variables (see Appendix A for more details). The production of these datasets is not a simple task and usually represents a big issue for the modeling community. Regional climate experiments tend to produce large amounts of data, since scientists simulate long time periods at high resolutions. Modelers have to code software at least capable of (1) computing a series of diagnostics, (2) concatenating model output, (3) performing statistical temporal computations, and (4) producing data following  CF-compliant (i.
e. , cmorization)  criteria in NetCDF format (NetCDF stands for Network Common Data Form, a binary self-describing and machine-independent file format; https://www. unidata. ucar. edu/software/netcdf/, last access: 18 March 2019). Aside from being time-consuming due to complexity and process management, this codification also implies certain duplication of huge datasets and additional consumption of computational resources. Several tools (e. g. , NetCDF operators -NCOs, climate data operators -CDOs) exist to facilitate the manipulation of NetCDF files  (extract, concatenate, average, join, etc. ) , and there are also other post-processing initiatives that have been made available, especially to the Weather Research and Forecasting (WRF; http://www. mmm. ucar. edu/wrf/users/, last access: 18 March 2019;  Skamarock et al.
However, in this version, they do not cover all the previously mentioned aspects of the task, such as computation of statistics and the cmorization of the data. New variables and diagnostics will be provided at the user-selected output frequency. The user still needs to postprocess the data in order to obtain the different statistics required by CORDEX at daily, monthly, and seasonal periods. The data cmorization can be defined as a series of processes that need to be applied to the model output in order to meet the standards provided under the CF guidelines (which follows the CMOR standard; https://pcmdi. github. io/cmor-site/, last access: 18 March 2019). These guidelines are designed to facilitate comparison between climate models, and they represent the standard for the Coupled Model Intercomparison Project (CMIP; https://cmip.
Default values for all the options are provided in order to facilitate the use of the module. This module has been tested under different highperformance computing (HPC) environments and compilations. It has been compiled with two compilers: GFortran and IFORT. Different parallelization paradigms include serial, distributed memory, and hybrid (distributed and shared), as well as the parallelized version of the NetCDF libraries. The tests have been performed mainly using two-nested domains with the second one being at convection-permitting resolution (no cumulus scheme activated). Under all these circumstances the module worked as expected. Since the current version (v1. 3) of the module, a text message with the version of the module has been printed in the standard output at the first time step of the model run in order to facilitate the detection of the module version that is being used.
-Fixed: values that do not have an evolution in time. These fields are fixed over the simulation. The WRF I/O file managing system provides an infrastructure for more than 20 different output files (called streams) at the same time. Each file is independently managed, and therefore in the namelist a user has to set up mainly two different options for each output stream: the frequency of an output (frequency of writing out the variables to an output file in minutes, e. g. , 30, 60) and the number of frames per file (e. g. , for 3-hourly frequency eight frames per file will give a daily output). Variables can be written in multiple streams (selected via the registry + files). During the model integration, at the given time step corresponding to the defined output frequency, data will be written out to the output file.
A domain as shown in Fig. 17  has been set up to perform short runs (5 days) to check the changes in performance of the WRF model when the module is activated in its different possible configurations. In order to avoid nonhomogeneous communication among the cluster nodes (which would affect the analysis), all the simulations were executed on a single node and with the WRF model compiled with Intel and GNU Fortran compilers. Tests are performed at the HPC Fram (https://www. sigma2. no/Fram, last access: 18 March 2019) from the Norwegian academic HPC infrastructure. Fram is based on Lenovo NeXtScale nx360, constituted by the CPU types Intel E5-2683v4 2. 1 GHz and Intel E7-4850v4 2. 1 GHz. The execution time is calculated as the mean elapsed time used during the 5-day model integration. The elapsed time necessary for each simulation step is available from the standard output of the model run (rsl.
< t step > GCC CDXWRF2_01 / < t step > GCC CDXWRF2_00 ) , of the mean time step when only statistics for extreme convective indices are activated (CDXWRF2_01, < t>= 4. 2388, 5. 4120 s) and 27, or 19 % (< t step > CDXWRF2_10 / < t step > CDXWRF2_00 | IFORT,GCC ), when only the water budget terms are included (CDXWRF2_10, < t>= 4. 8510, 5. 7534 s). Reduction of the time step for CDXWRF1 with GCC must be related to a moment during which HPC Fram experiences a period of very low working load. These results are not conclusive (optimization of the module should be tested in other HPC resources, domains, and compilers), but they provide a first insight on how the number of variables included during integration (in the derived grid) has an important effect on model performance by reducing or increasing the required amount of memory.