dataesr/scientific-paragraphs-categorization huggingface dataset:
As explained in the introduction, such a model splits the execution of critical tasks into two distinct phases: a memory phase where the task context is pre-fetched into local memory, and an execution phase where the task executes with no memory contention. The work was focused on cache-based management of PREM-compatible tasks, showing how to enforce a predictable scheduling of memory and computing resources. It also showed how to automatically re-factor the task code at compile time, provided a set of restrictions is satisfied. Such restrictions are in line with those typically imposed by state-of-the-art tools for static timing analysis. An automatic tool for code refactoring is presented in  [26] , making the adoption of the M/C model transparent to the programmer. Alternatively, M/C-compliant code may be written using programming models commonly adopted for heterogeneous computing systems (e.
In humans, CXCR1 and CXCR2 are two homologous proteins that bind ELR+ chemokines. Both receptors play fundamental roles in neutrophil functions such as migration and reactive oxygen species production. Mouse Cxcr1 and Cxcr2 genes are located in an insulindependent diabetes genetic susceptibility locus. The non obese diabetic (NOD) mouse is a spontaneous well-described animal model for insulin-dependent type 1 diabetes. In this disease, insulin deficiency results from the destruction of insulin-producing beta cells by autoreactive T lymphocytes. This slow-progressing disease is dependent on both environmental and genetic factors. Here, we report descriptive data about the Cxcr1 gene in NOD mice. We demonstrate decreased expression of mRNA for Cxcr1 in neutrophils and CD4+ lymphocytes isolated from NOD mice compared to other strains, related to reduced NOD Cxcr1 gene promoter activity.
Looking for Cxcr1 protein, we next analyze the membrane proteome of murine neutrophils by mass spectrometry. Although Cxcr2 protein is clearly found in murine neutrophils, we did not find evidence of Cxcr1 peptides using this method. Nevertheless, in view of recently-published experimental data obtained in NOD mice, we argue for possible Cxcr1 involvement in type 1 diabetes pathogenesis.
Real-time quantitative RT-PCR mRNAs were extracted from 4X10 6 cells using Dynabeads mRNA DIRECT kit (Fisher Scientific, Illkirch, France) and converted into cDNA using M-MLV Reverse Transcriptase (Promega, Charbonnières, France). Primer (obtained from Eurogentec) sequences were designed using Primer3Plus software (Table  1 ). Quantitative real-time PCR mRNA analyses (RT-qPCR) were performed on an ABI Prism 7300 Sequence Detection instrument (Applied Biosystems, Courtaboeuf, France) using EvaGreen fluorescence system (Solis BioDyne, Tartu, Estonia) for quantification of amplicons. All cDNA samples were run in duplicate in 96-well optical reaction plates. In each run, control cDNA dilution series were created for each primer pair to establish relative standard curves. Melting curve analysis was performed (65°C-95°C) in order to confirm the presence of unique amplified products.
Sequencing of mCXCR1 gene and 5'-flanking region The Ensembl genome browser (http://www. ensembl. org/) was used to obtain genomic Cxcr1 sequence  [23]. The Genomatix database (http://www. genomatix. de/) allowed the in silico identification of the putative Cxcr1 promoter region  [24]. Genomic DNA was extracted from tail fragments by phenol-chloroform protocol. Primers were designed to amplify the exons and intron/exon junctions as well as the 5' flanking region (Table  1 ). Amplified fragments were sequenced by GATC Biotech (Mulhouse, France). The NODShi/Ltj mouse Cxcr1 promoter sequence has been deposited in the Genbank database  [25]  under number KT156754.
Targeted methods were further used in which only peptides with a m/z ratio similar to selected Cxcr1 and Cxcr2 peptides were subjected to MS/MS fragmentation during the run (S1 Table ). Protein identification was performed with X!Tandem search engine  [26]. Mass data were confronted to UniprotKB restricted to "Mus" taxonomy (taxon id 862507; 83192 sequences, release from March 12, 2014)  [27] , or searched against a custom databank made of Cxcr1 and Cxcr2 sequences. Parameters of the databank search were as follows: mass accuracy for peptides was set at ± 5ppm, cysteines were modified by iodoacetamide (this modification was introduced during the sample preparation), and possible oxidation of methionines and sulfation of tyrosines were considered. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.
We also found 26 already described variations, including 19 variations not mentioned as validated in the SNP database (http://www. ncbi. nlm. nih. gov/SNP/). The only SNP found in the coding sequence did not affect protein sequence since it encodes a synonymous amino acid. Of note, polymorphisms were concentrated into the 5'-flanking region and around the first non coding exon. This region matches the 601 bp-long putative promoter region which spreads from 500 bp upstream from the first exon (40 bp-long) to 61 bp downstream (Figs  2  and 3 ). A TTATTCATC sequence, described as the transcription initiation site in human CXCR1 sequence  [29]  is logically found 54 bp upstream from the first exon. Polymorphisms may affect transcription factors binding efficiency, thus we assess whether polymorphisms of the NOD Cxcr1 promoter affect its transcriptional efficiency.
Mass spectrometry for Cxcr1 protein identification The first evidence of mouse Cxcr1 expression at the protein level was provided by Lubec and coworkers who identified Cxcr1 protein in hippocampal protein extracts into a 2D electrophoresis-spot using MS analysis  [38]. Other evidence was also reported in several proteomic assays available in the PRIDE database  [39]. Browsing the PRIDE archives for the Uniprot accession number of mouse Cxcr1 (#Q810W6) results in five studies reporting Cxcr1 detection (accessed on january  19, 2015). However, these assays were not primarily focused on Cxcr1 identification and they do not bring a convincing evidence that the protein was unequivocally identified. In fact, a few numbers of peptides were retrieved (one peptide per study, except for the PRD000458 one in which three peptides were detected), and in all cases, a poor correlation of their MS/MS spectra with the expected peptide sequences was obtained.
Likewise, until recently, none of the MS studies performed on human neutrophil proteomes had reported CXCR1 expression  [40] [41] [42] [43] [44]. In 2012, Bleijerveld et al. appended the PRIDE database following an in-depth analysis of human neutrophil proteome by 2-dimensional LC MS/MS  [45]. In this study, more than 3000 proteins were identified, with relative abundances spanning over 5 orders of magnitude. Human CXCR1 (UniprotKB #P25024) and CXCR2 (UniprotKB #P25025) were respectively identified with 3 and 2 peptides. Their similar relative abundance suggests that both have close expression levels in human neutrophils. In the present work, we attempted to detect mouse Cxcr1 in membrane-enriched neutrophil protein extracts by performing mass spectrometry experiments. Noteworthy, we demonstrated unambiguously Cxcr2 while no convincing indication of Cxcr1 presence was found.
restriction enzyme sites used for cloning. b See Figs 2 and 3 for position in genomic sequence. c GenBank accession numbers are as follows: NM_178241 for mCxcr1, NM_009909 for mCxcr2, NM_007393 for ß-actin and U37500 for Rpb1. doi:10. 1371/journal. pone. 0134365. t001.
Acknowledgments The authors are grateful to  A. Geairon ,  M. Hamon ,  D. Jégou ,  M. Joint , and  M. Ouary  for their technical contribution at some point in this project. They also thank  B. Blanchet ,  D. Boucher  and  F. Poirier  for animal care. The biomolecular platform (PFBM, Oniris, France) provided facilities for RT-qPCR.
Forward restriction enzyme sites used for cloning. b See Figs 2 and 3 for position in genomic sequence. c GenBank accession numbers are as follows: NM_178241 for mCxcr1, NM_009909 for mCxcr2, NM_007393 for ß-actin and U37500 for Rpb1. doi:10. 1371/journal. pone. 0134365. t001.
Ethics statement The IBLOMAVED study was registered in a clinical database (ClinicalTrials. gov NCT01024049) and conform to the ethical guidelines of the 1975 Declaration of Helsinki. The protocol was approved by the institution's human research (COSSEC) and regional ethics committee (Comite de Protection des Personnes (CPP) # DC 2008-452). Written informed consent was obtained from all participants and/or their legally authorized representatives.
Baseline demographic data, admission vital signs, clinical history, assessment of cardiovascular risk factors and admission medication were recorded. Biochemical data including BNP levels were obtained at admission. Venous blood samples were obtained during the morning within 24 hours following admission. In this cohort, 427 patients had a medical history of HF at admission. This cohort comprised patients from all AHA/ACC HF classification groups to provide a wide spectrum of patients for HF biomarkers studies. All the 686 patients underwent transthoracic echocardiography for left ventricular ejection fraction (LVEF) assessment. We used a threshold value of LVEF< 45% to select stable systolic chronic heart failure (CHF) patients (n = 427). Because of the multivariate filtering (propensity score), patients with missing data were discarded (n = 74).
Statistical analysis Analyses were performed using Statistical R (version 3. 0. 1; http://www. r-project. org) or Medcalc (version 12. 7. 7; Medcalc Software bvba, Ostend, Belgium). Continuous variables were expressed as means ± standard deviation for normally distributed data or as medians with 95% confidence interval. Propensity scores were generated from logistic regression analysis (MatchIt package version 2. 4. 21) [25]  with MRA treatment as dependent variable and included age, gender, obesity, diabetes, arterial hypertension (AHT), dyslipidemia, ischemic cardiomyopathy (ICM), left ventricular ejection fraction (LVEF), heart rate (HR), blood creatinine concentration (Creat) as predictors. Pairs of MRA-Neg and MRA-Plus patients were formed with propensity scores differing by at most 0.
Methods and Results A set of 399 poly(AC) markers was used to perform a linkage analysis which peaked at a 2. 98 lod score on the long arm of chromosome 15. The haplotype analysis delineated a 7. 7 meganucleotides genomic interval which included the alpha-cardiac actin gene (ACTC1) among 36 other protein coding genes. A heterozygous missense mutation was found (c. 251T>C, p. (Met84Thr)) in the ACTC1 gene which changed a methionine residue conserved up to yeast. This mutation was absent from 1000 genomes and exome variant server database but segregated perfectly in this family with the affection status. This mutation and 2 other ACTC1 mutations (p. (Glu101Lys) and p. (Met125Val)) which result also in congenital heart defects are located in a region in close apposition to a myosin heavy chain head region by contrast to 3 other alpha-cardiac actin mutations (p.
Genotyping and linkage analysis Genotyping from all available DNA samples was performed at the Genotyping National Center (CNG, Evry). A panel of 399 microsatellites was tested (Life Technologies, Evry, France). Multipoint linkage analysis was prepared with easyLINKAGE  [14]  and performed with GeneHunter v2. 1r5  [15]  initially with a disease allele frequency of 0. 01%, a fully penetrant disease and a 0% phenocopy rate and then with decreasing penetrance values to 85%. Haplotypes of the region of interest were prepared with GeneHunter. Genehunter output files were used to visualize haplotypes with the Haplopainter 1. 043 software  [16].
Variants prediction methods In order to evaluate the deleterious consequence of putative variants, the following prediction software were used: Align GVGD  [17] , SIFT  [18] , Mutation Taster  [19]  and PolyPhen2  [20]. Grantham score  [21]  was obtained. The interspecies conservation of amino-acid was evaluated and the presence of variants was searched in 1000 genomes (http://www. 1000genomes. org/) and Exome Variant Server (http://evs. gs. washington. edu/EVS/).
Structural interpretation of the actin and myosin variants The interpretation of the structural consequences of specific actin and myosin variants was performed by analyzing the recently published Rigor Actin-Tropomyosin-Myosin complex (PDB accession codes 4A7F, 4A7H, 4A7L and 4A7N), reported as the first subnanometer-resolution structure of the actin-tropomyosin-myosin complex in the rigor (nucleotide-free) state determined by cryo-EM  [22]. Images were prepared using the Molmol software  [23].
Acknowledgments We are very indebted to family members who participated to this study. PB is very grateful to  Jean-Louis FEBVRE  and foundation  Renaud FEBVRE  for their longstanding and very efficient support. PB dedicates this study to  Prof. Edouard Stephan  who passed away some years ago. He is the cardiologist who contributed to uncover this family. Maxime BOUVAGNET  is warmly thanked for improving the edition of this manuscript.
Here, a series of analyses that enable the observation of growth dynamics by automatic imaging are described. It will be shown that top-view imaging of Arabidopsis plants in combination with high-throughput image analysis can be used to follow rosette growth over time in a large and diverse population of natural accessions. It will further be shown that comparison of accessions demonstrating a large variation in developmental rate and in plant size can be done by modelling of growth. In addition, GWA mapping on temporal plant size data was performed using univariate and multivariate mapping approaches. Time-specific growth QTLs were detected by performing univariate GWA mapping for each time point separately, whereas general QTLs related to growth rate during the course of the whole experiment were identified by performing univariate and multivariate GWA mapping on the growth model parameters.
When more than one plant was present in a pot before thinning, the largest one close to the middle of the pot was taken for analysis. For each individual plant, the growth, based on PLA, was modelled using three functions: two functions describing indeterminate growth by an exponential curve, Expo1 and Expo2; and one function describing determinate growth by a S-curve, Gom (Table  2 ). The optimal parameter values were estimated using the Growth Fitting Toolbox TM of MATLAB with the following settings. Expo1: optimization algorithm, 'Trust-Region'; fitting method, non-linear least square; bounds, r [0, Inf]. Expo2: optimization algorithm, 'Trust-region'; fitting method, nonlinear least square; bounds, A 0 [0,Inf], r[0,Inf]. Gompertz: optimization algorithm: 'Levenberg-Marquardt'; fitting method, robust linear least square, using bisquare weights; bounds, A max  [0, 20000] , b[0,60], r  [0, Inf].
Goodness of fit indicators (SSE, r 2 , and RMSE) and 95% confidence intervals of the parameters of all three models were calculated in MATLAB (Supplementary Table  S2 ). Based on these data and the principle of parsimony, Expo2 was chosen as the best model and was therefore used for further analysis; fits with r 2 <0. 9 (11 out of 965) were excluded from further analysis. The removal of the largest leaf from day 24 onwards was not corrected for when the fresh weight of the rosette and the model parameters were determined because plants rapidly compensated for this loss.
GWA mapping was performed on the predicted means using the EmmaX software package for R, which is based on  Kang et al. (2010). A mixed model was used that corrects for population structure, based on the kinship matrix of all SNPs. SNPs with a minor allele frequency <0. 05 were excluded from the analysis. The parameters 'A 0 ' and 'r' of model Expo2 were also mapped together using a Multi Trait Mixed Model (MTMM) approach  (Korte et al. , 2012; El-Soda et al. , 2015). Pearson correlations were used to determine correlations between data series. To calculate the correlation between traits, the corrected means were used. Broad-sense heritabilities were obtained with GenStat by fitting the same model as above. This time the term Check was assumed fixed and all the remaining terms were considered random effects.
For much smaller sets of accessions, similar experiments have previously been performed, but to be able to use the phenotypes in mapping studies much larger populations need to be screened  (El-Lithy et al. , 2004; Granier et al. , 2006). Growth was determined not only by differences in PLA over time, but also at the end of the experiment by measuring the FW of the rosettes. For PLA and FW, large natural variation was observed, 28-70% of which could be explained by genetic differences (Table  1 ). Broad-sense heritability (H 2 ) of PLA increased over time (Table  1 ), most probably because determination of the PLA of small plants was less accurate than that of larger plants. These data demonstrate that top-view imaging of Arabidopsis is a powerful method to compare plant size and growth rate in large panels of plants which differ not only in size but also in developmental traits such as flowering time  (Li et al.
It is shown that even if the inflection point 'K' is reached, model Expo1 and Expo2, which both assume indeterminate growth, resulted in good fits. (C) Data and curve fits for line CS76308 (ZdrI2-25), representative for 56% of the plants for which the growth curve did not reach 's1'. The data were fitted to the three models. (D) Magnification of (C) allowing better comparison of the various models for the time window of the experiment. these growth characteristics are determined partly by the genotype. The parameter 'r' of Expo2 is weakly correlated with plant size in week 2 and week 3, but no correlation was observed with plant size at the end of the experiment (Fig. 2 ). This indicates that the growth characteristic represented by this parameter goes beyond simple information about plant size.
To identify the genetic basis of growth, GWA mapping was performed on PLA data (12 different dates) and FW data (end-point), and on the parameters derived from the growth model Expo2. This resulted in the detection of 22 growth QTLs which were highly associated [-log(P)>5] with the growth traits. Many of these QTLs would not have been identified if growth had only been evaluated at a single time point. Eight candidate genes were identified for which a mutant or overexpression phenotype related to growth has previously been reported, suggesting that the identified QTLs are true positives. For some QTLs, no obvious candidates were found, opening up the way to identify new functions for underlying genes or to annotate unknown underlying genes by performing followup experiments.
Acknowledgements We would like to thank  Myriam Dauzat  for operating and maintaining PHENOPSIS,  Gaelle Rolland  and  Crispulo Balsera  for their assistance during sowing and harvesting, and  Marcos Malosetti  for statistical assistance. This research is part of the ' Learning from Nature ' programme, supported by the  Dutch Technology Foundation  (STW grant no. 1099 ), which is part of the  Netherlands Organisation for Scientific Research (NWO). Access to the PHENOPSIS platform was possible thanks to funds from the  European Plant Phenotyping Network (EPPN , grant agreement no. 284443 ) funded by the  FP7 Research Infrastructures Programme  of the  European Union.
The unique isoenergetic circular path described by the 3 vectors was used to modulate local contour orientation cyclically. As a result, the original image was rendered into a sequence of its wavelet-scrambled versions, which conserved the principal physical features of the original image (local luminance modulation, global contrast and local spatial frequency). We created the SWIFT movies for the experiments reported here in MATLAB (The MathWorks Inc. , Natick, MA) using a function (available in  [10] ). We used the following parameters: independent scrambling cycles = 3, number of harmonics = 8, frames per cycle = 200, wavelet decomposition levels = 9. One cycle of a SWIFT movie goes from the original image, through its fully scrambled version and to the original image again. The transitions are smooth, minimizing transient effects (see S1 Movie).
Phase-encoded retinotopic mapping To determine the boundaries of visual areas from V1 to V4 for each participant, we used the phase-encoding method  [23, 24]. Double wedges containing dynamic colored patterns cycled through 10 rotations in 10min (retinotopic stimulation frequency = 0. 033 Hz). To ensure deployment of attention to the stimulus during the mapping, participants performed the same detection task as used in SWIFT 3 (i. e. , pressing a button upon seeing a gray dot anywhere on the wedges). The program for this experiment was downloaded from Samuel Schwarzkopf's tutorial (http://sampendu. wordpress. com/retinotopy-tutorial/). One run was performed for each participant.
Time series (9min, 216 volumes) from the SWIFT experiments were transformed into the frequency domain using a Fast-Fourier Transform (FFT) algorithm (fft. m) implemented in MATLAB. For every voxel, the power at the tagging-frequencies (signal) was then compared to the power at the neighboring frequencies (noise). The noise consisted of 22 frequencies, sparing the tagging-frequencies (0. 0464, 0. 0483, 0. 0501, 0. 0520, 0. 0538, 0. 0650, 0. 0668, 0. 0687, 0. 0705, 0. 0724, 0. 0742, 0. 0854, 0. 0872, 0. 0891, 0. 0910, 0. 0928, 0. 0947, 0. 1058, 0. 1077, 0. 1095, 0. 1114 and 0. 1132 Hz). A p-value for each voxel was obtained by comparing the power at the taggingfrequency (0. 1, 0. 08 or 0. 06 Hz) with the power at the noise frequencies using a two-tailed twosample t-test. Signal-to-noise ratio (SNR) for each voxel was defined as the ratio of the power at a given frequency divided by the average power in a 0.
Retinotopic ROIs Phase-encoded retinotopic mapping data was analyzed using the Fast-Fourier Transform (FFT) in MATLAB. The FFT was applied voxel-wise across time points. The complex output of the FFT contained both the amplitude and phase information of sinusoidal components of the BOLD signal. The phase information at the frequency of stimulation (0. 033Hz) was extracted, using its amplitude as threshold (!2 SNR, calculated as in the analysis of SWIFT data explained above) and overlaid them on each participant's cortical surface reconstruction obtained using Freesurfer  [32, 33]. We manually delineated boundaries between retinotopic areas on the flattened surface around the occipital pole by identifying the voxels showing phase reversals in the polar angle map, which represents the horizontal and vertical visual meridians.
SWIFT tags by frequency high-level areas, but not low-level visual areas Fig  2B  shows the same BOLD signals in the SWIFT experiments now represented in the frequency domain. For visualization purposes, here we present the average spectra of a subset of for each category (e. g. , FFA for faces) and in the remaining ROIs which are not selective for a given category (e. g. , PPA and LOC for faces. See main text for details). Orange, red, and black lines indicate the ROC curves constructed using the data in SWIFT 2, 3 and the localizer, respectively. Squares on the curves represent p = 0. 001. The squares always locate with hits = 1 for the localizer because we defined ROIs as such. (C) Area under the curves (AUC). Asterisks above the bars indicate either significant differences from AUC = 0.
Acknowledgments This research was supported by  Monash Biomedical Imaging-Psychology Grant  (to NT, RK, RV),  Scientific Mobility Program-Australian Academy of Science/French Embassy  (to RK),  JST PRESTO  fellowship (Japan, to NT),  ARC Future Fellowship and Discovery Project (Australia , to NT). We particularly thank  Gary Egan  for his support,  Janelle Giling  and  Miao Cao  for the help in the participant's testing, and  Lisandro Kaunitz ,  Dror Cohen ,  Benoit Cottereau  and  Leila Reddy  for useful comments on the manuscript.