dataesr/scientific-paragraphs-categorization huggingface dataset:
With a similar presence in the compiled literature, the use of ground data and satellite estimates is commonly used to address intra-day forecasting; only a residual amount of applications address longer-term horizons through the incorporation of NWP outputs. We believe that the spatio-temporal characteristic will be extended to longer horizons, as there is an increasing demand in the exploration of long-term horizons as well as advancements in the field toward the addressing of this task. We also perceived the emergence of novel techniques for representing spatially distributed solar time series datasets in forecasting methods with data with a wider spatial scope (such as matrices, graphs, irradiance maps). These techniques enable a better processing of large amounts of data with deep learning and the conversion of time series data to image data mages.
For example, irradiance maps allow us to work with a time-varying number of stations due to their use of interpolated values and not of values from a particular station (nonetheless, they are dependent on the amount of data points from the considered region). In another line, graphs allow the grouping and integration of features as part of the data structure. They are also capable of improving performance and handling large volumes of information. When it comes to choosing one representation over another, there is no set formula-the decision depends largely on the particular dataset the user is going to work with, as both proved to be a more effective prediction of regions. In our literature review, we also found that the most commonly used baselines are Persistence and Smart Persistence. However, there are others such as Climatology, Probabilistic Persistence, Ramp Persistence, and Gap Persistence.
In this line, it is observed that using more than one processing method could drastically improve the results, as hybrid methodologies leverage the strengths of different model for accomplishing a common objective. Lastly, it is considered that solar forecasting is an important and constantly evolving research field, having moved from univariate to multivariate time series forecasting and from using single data sources to the combination of several datasets. In the past, the spatio-temporal methods used on ground measurements were less explored due to the lack of data; however, these are beginning to stand out toward the improvement of results from state-of-the-art methods. In this review work, it is observed that classical physical and statistical methods have been reinforced with contributions from other research areas such as artificial intelligence.
Literature search Five databases including Pubmed, Embase, Web of Science, Cochrane and Scopus were searched using the following terms: ("muscle damage" OR "muscle injury" OR "EIMD") AND (contraction OR exercise) AND (strength OR MVC OR torque OR force OR "muscle function" OR "neuromuscular function" OR "maximal voluntary contraction") NOT ("mouse" OR "Mice" OR "rat" OR "animal") for "English-language" papers from start of records until 29/10/ 2021. In addition, the literature search was completed with SPORTDiscus that contains theses and dissertations and thereby included "gray literature" (i. e. , literature that is difficult to locate or retrieve  [14] ). This led to the identification of 4,897 potential studies for the initial inclusion for this study (Fig  1 ). The review was not registered and all necessary information is provided in the present manuscript.
Data collection and quality assessment Included studies (n = 141) were randomly distributed to nine reviewers and data were independently extracted from text or tables using a standardized assessment spreadsheet. The following criteria were collected: sample size, sex, age, training level, exercise type, MVC torque and other indirect markers of EIMD values at all available time points. When incomplete or missing data were found, the corresponding authors of the studies were contacted or the required data was extrapolated from figures using ImageJ software (v1. 51j8;  [17] ). Only EIMD markers present in at least five studies were included in the analysis: jump height, ROM, DOMS assessed actively and passively, pain pressure threshold (PPT), limb circumference, mechanical response to peripheral stimulation (evoked torque response), rate of torque development (RTD), voluntary activation level (VAL), CK, Mb, lactate dehydrogenase (LDH), interleukin-6 (IL-6), and transverse relaxation time (T 2 ) in magnetic resonance imaging (MRI).
R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www. R-project. org) and the optimal number of clusters was determined using the NbClust R  [23]. The script used to perform the analysis is available at: https://github. com/SirJul/Chalchat-2022-Meta-analysis. This clustering analysis was used to classify 141 experimental groups into 3 categories (clusters) based on the largest reduction in SMD torque at 24-48 h post-exercise (MVC loss 24-48h ) of each group. Thus, experimental groups were classified as low responders (cluster 1; LOW), moderate responders (cluster 2; MOD) and high responders (cluster 3; HIGH) according to the magnitude of MVC loss 24-48h (i. e. , SMD). Once allocated in the clusters, their respective overall effect (i.
Meta-regressions were performed using Comprehensive Meta-analysis software to investigate the association between the changes in MVC loss 24-48h and changes in indirect markers at each time-point (<6, 24, 48, 72, >96 h) (Table  2 ). A correlation between MVC loss 24-48h and an EIMD marker measured within 6 h post-exercise indicates that this marker could partly predict the MVC loss 24-48h , whereas a correlation between MVC loss 24-48h and an EIMD marker measured between 24 h and 48 h indicates that this marker could be used as a surrogate at these time points. Moreover, correlation between MVC loss 24-48h and an EIMD marker measured later than 48 h post-exercise indicates that this marker is able to retrospectively estimate MVC loss 24-48h.
Discussion This study was the first to quantitatively review the magnitude and time-course of the most common indirect markers of EIMD and cluster them based on levels of MVC loss 24-48h following lower limb eccentric-biased exercises. This approach was applied to an extensive dataset from 141 studies employing various exercise modalities and participant characteristics. Firstly, three different time-courses were identified, corresponding to three levels of MVC loss 24-48h (LOW, MOD, HIGH) for commonly used EIMD markers (e. g. , CK activity, Mb concentration, DOMS, ROM). For instance, magnitude of changes is greater and recovery slower in HIGH cluster compare to other clusters for MVC torque, evoked torque response, jump height, Mb concentration and the peak may also be reached later in HIGH cluster compare to other clusters for DOMS and CK activity.
Amplification of the nodC Gene The nodC gene from the Rhizobium isolates was amplified by PCR by using forward primer nodCf (5 -GCTGCCTATGCAGACGATG-3 ) and reverse primer nodCr (5 GGTTACTGGCTTTCATTTGGC-3 )  [39] , obtained from Rhizobium leguminosarum bv. viciae strain USDA 2478 (GeneBank accession number D28960). PCRs were realized out in 25 µL reaction mixtures according to Moschetti et al. [40]  following the conditions below: 1 cycle at 94 • C for 5 min, followed by 35 cycles of 94 • C for 1 min, 55 • C for 1 min, 2 min at 72 • C and a final cycle at 72 • C for 7 min. PCR products stained with ethidium bromide were parted on a 1% (p/v) agarose gel at 100 V for 20 min in TBA buffer and visualized under UV light.
PCR Amplification of the 16S rRNA Gene Bacteria were identified on the basis of their 16S rDNA gene sequences. Primers fD1 and rD1  [41]  were utilized to amplify 16S rRNA genes  [41]. Amplified products resulting from PCR reactions were purified in agarose gels using the EZ-10 spin column DNA gel extraction kit (Bio Basic Inc. Markham, ON, Canada). Purified fragments were sequenced on an ABI3130 genetic analyzer (Applied Biosystems). Sequences were assembled by the CAP program (http://doua. prabi. fr/software/cap3, accessed on 17 January 2022) and checked manually. The accession numbers are given in Table  2. The BLAST program (http://blast. ncbi. nlm. nih. gov/Blast. cgi, accessed on 17 January 2022) was used to search for sequence similarities in DNA databases. -  ], root dry mass RDM [mg plant -1 ], shoots length SL [cm plant -1 ], roots length RL [cm plant -1 ] and number of nodules NN [N plant -1 ] in six Vicia faba genotypes.
Statistical Analysis Multivariate analysis and Two-way ANOVA variance analysis were performed using the SPSS software ver. 20, Inc. , Chicago, IL, USA program. The differences between treatment data were estimated using the STATISTICA software and the means of comparison by HSD (higher significant difference) Duncan's test (p ≤ 0. 05). Results for growth parameters (DM, FM, nodule number, Chl, A, gs, E and Ci) are means of six replicates for each treatment. Three replicates were performed for ARA, sugars, lipid peroxidation measurements and electrolytes leakage. The statistical analysis "Principal Component Analysis (PCA)" was performed using the overall analyzed parameters in the study. The PCA analysis was performed to (1) classify the genotype groups, (2) identify genotypes with opposing behavior, and (3) fix the axes and important characters contributing to the diversification.
Based on their salinity tolerance limit (greater than or equal to 600 mM NaCl) and their nitrogen fixation capacity (Table  5 ), four lines (S12, S21, S23 and S26) were considered both efficient for plant growth and tolerant to salinity, and subsequently sequenced. The nucleotide sequence of the amplified DNA was determined and compared with available 16S rRNA sequences in Gen Bank using the BLAST program (http://blast. ncbi. nlm. nih. gov/Blast. cgi, accessed on 17 January 2022). The subsequent BLAST analysis show that the strain S21 presents 99% homology with EF549399. 1 Rhizobium sp. CCBAU 83480, the strains S23 and S26 present 98% and 99% homology, respectively, with JN105993. 1 Rhizobium leguminosarum strain 35-1, and the strain S12 presents 98. 8% homology with EU074196. 1 Rhizobium leguminosarum strain CCBAU 03285.
To identify the strain Marseille-Q2903, a MALDI-TOF mass spectrometry (MS) protein analysis was carried out in triplicate using a Microflex spectrometer (Bruker Daltonics, Bremen, Germany) but failed, suggesting that the generated spectra were not in the database. Strain spectra were imported into the MALDI BioTyper software (version 3. 0, Bruker, Bremen, Germany) and analyzed by standard pattern matching with default parameters. Our database (https://www. mediterraneeinfection. com/access-resources/base-de-donnees/urms-database/) was then incremented with the spectrum of this new bacterial species.
Fatty acid methyl esters were prepared as described by Sasser  [6]. GC/MS analyses were carried out as described before  [7]. Briefly, fatty acid methyl esters were separated using an Elite 5-MS column and monitored by mass spectrometry (Clarus 500-SQ, 8S, Perkin Elmer, Courtaboeuf, France). Spectral database search was performed using MS Search 2. 0 operated on the Standard Reference Database 1A (NIST, Gaithersburg, USA) and the FAMEs mass spectral database (Wiley, Chichester, UK).
Adapters were ligated to both ends of genomic DNA. After purification on AMPure XP beads (Beckman Coulter Inc, Fullerton, CA, USA), the library was quantified by a Qubit assay with the high sensitivity kit (Life technologies, Carlsbad, CA, USA). e workflow WIMP was chosen for bioinformatic analysis in live. Genome annotation was obtained through the NCBI prokaryotic genome annotation pipeline  [8]. e genome sequence data were uploaded to the Type (Strain) Genome Server (TYGS), a free bioinformatics platform available under https://tygs. dsmz. de, for whole genome-based taxonomic analysis  [9]. Determination of the closest type strain genomes was conducted in two complementary ways: first, all user genomes were compared against all type strain genomes available in the TYGS database via the MASH algorithm, a fast approximation of intergenomic relatedness,  [10]  and the ten type strains with the smallest MASH distances chosen per user genome.
Second, an additional set of ten closely related strains was determined via the 16S rDNA gene sequences. ese were extracted from the user genomes using RNAmmer  [11] , and each sequence was subsequently BLASTed  [12]  against the 16S rDNA gene sequence of each of the currently 12983 strains available in the TYGS database. is was used as a proxy to find the best 50 matching strains (according to the bitscore) for each user genome and to subsequently calculate precise distances using the Genome BLAST Distance Phylogeny (GBDP) approach under the algorithm "coverage" and distance formula d5. [13]. ese distances were finally used to determine the 10 closest type strain genomes for each of the user genomes. All pairwise comparisons among the set of genomes were conducted using GBDP and accurate intergenomic distances inferred under the algorithm "trimming" and distance formula d5.
100 distance replicates were calculated each. Digital DDH values and confidence intervals were calculated using the recommended settings of the GGDC2. Complementarily, the degree of genomic similarity of interest strains with closely related species was estimated using the orthologous average nucleotide identity (OrthoANI) software with default parameters,  [14]  the closest species were determined with the DDH basis. Trees were inferred with FastME 2. 1. 6. 1  [15]  from GBDP distances calculated from 16S rDNA gene sequences or whole-genome sequence. e branch lengths are scaled in terms of GBDP distance formula d5. e numbers above branches are GBDP pseudobootstrap support values >60% from 100 replications, with an average branch support of 84. 3%. e tree was rooted at the midpoint and regenerated with the iTOL Tool v5.
[16]. Antibiotic resistance genes and presence of pathogenesisrelated proteins were investigated using the ABRicate tools v1. 0. 1 against ARG-ANNOT [17], EcOH  [18] , NCBI Bacterial Antimicrobial Resistance Reference Gene Database  [19] , PlasmidFinder  [20] , ResFinder  [21] , CARD  [22] , and VFDB  [23]  using the Online Galaxy platform. [24].
Strain Marseille-Q2903 Identification. Strain Marseille-Q2903 exhibited a 99. 5% 16S rRNA sequence similarity with Brachybacterium muris T (accession number: NR_024571. 1) but with 92% of coverage (Figure  1(a) ). e closest species based on a 100% coverage of the 16S rRNA sequence is Brachybacterium timonense T with a sequence similarity of 97. 63% (accession number LT962482. 1). Furthermore, digital DNA-DNA hybridization revealed an identity percentage of 31. 5% (Table  S1 ). OrthoANI parameter provided a value of 86. 95% (Figure  2 ) between the new bacterial strain and Brachybacterium muris T. Taken altogether, these results confirm the status of this strain as a new member of the Brachybacterium genus for which the name of Brachybacterium epidermidis Marseille-Q2903T is proposed.
Acknowledgments e authors are indebted to  Ludivine Brechard  for sequencing the genome and  Nicholas Armstrong  and  Céline Charterfor  performing the fatty acid analyses. is study was supported by the  French State  managed by the  National Research Agency  under the " Investissements de'avenir (Investments for the Future)" program  under the reference  ANR-10-IAHU-03  ( Méditerranée Infection ) and by the  Région Provence-Alpes-Côte-d'Azur and the European funding FEDER PRIMI.
Scanning procedure Participants were asked to attend two imaging sessions separated by a minimum of 3 days, during which simultaneous PET-MRI scans were acquired. One session was performed under placebo condition (101 mg of lactose/sucrose tablets), the other after oral dexamphetamine administration (0. 5 mg/kg). Scanning was initiated 3 h after placebo or amphetamine administration  [20, 25]. Placebo/amphetamine order was determined by prior randomization. Participants and staff involved in the scan sessions were blind to the condition, the number of administered placebo tablets were matched to the number of amphetamine tablets, and blinding was maintained throughout the scanning sessions. Researchers were also blind to drug status during initial image analysis. PET-MRI data were acquired simultaneously using a General Electric (Milwaukee, WI, USA) SIGNA 3 Tesla scanner.
Image analysis Positron emission tomography. The Molecular Imaging And Kinetic Analysis Toolbox (MIAKAT) version 4. 3. 13 in MATLAB 8. 2 (MathWorks, Natick, MA, USA) was used for PET data analysis  [34]. The motion was corrected by registering each frame of the dynamic sequence on a single frame of reference, which was the sixteenth frame acquired between 13-15 min after injection  [35]. Each subject's PET scan was then coregistered to their T1 structural scan. The Montreal Neurological Institute (MNI) structural template underwent a non-linear transformation to allow co-registration to each subject's T1 structural scan. An in-house MNI-based atlas including the whole striatum, its limbic, associative, and sensorimotor functional subdivisions (as defined by Martinez et al. [36] ) underwent the same transformation.
Voxel-based morphometry Voxel-Based Morphometry (VBM) analysis was performed using SPM12 in MATLAB 8. 2. This analysis examined relationships between gray matter volumes and (1) D 2/3 receptor availability (BP NDplacebo ), (2) amphetamineinduced dopamine release (ΔBP ND ), (3) ACC Glx concentrations, and (4) the interaction between dopamine and glutamate measures. MR data acquired during the placebo sessions were used for all analyses. All T1 structural scans were checked for artefacts, gross abnormalities or poor quality, and the origin was set to the anterior commissure. Images were segmented into gray, white matter and cerebrospinal fluid using the SPM12 probability maps. The Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) pipeline was used to improve intersubject registration, especially of small structures  [40] , and generated gray matter images were subsequently normalized to MNI space, modulated, resliced (1.
44 toolbox (http:// marsbar. sourceforge. net) in SPM12. We used the GraphPad Prism 9 software (https://www. graphpad. com) to draw scatter plots. The MRIcroGL software (https://www. nitrc. org/projects/mricrogl) was used for visualization of results and brain rendering  [46]. Exploratory analyses on the whole brain, using glutamate (Glu) and linear regressions between other variables were thereafter performed and can be found in the Supplementary Information (Supplementary Tables  S1-3 ).
In addition, the use of voxel-based morphometry in SPM12, a free and widespread software, allows for replicability of these findings and robust comparability with other findings based on the same methods. While the use of [ 11 C]-( + )-PHNO BP ND to estimate D 2/3 receptor availability and dopamine release is well validated, our cross-over design could lead to carry-over effects for subjects who received amphetamine first. A 3-day washout period should, however, allow for good elimination of a product with a 10 h half-life  [59]. Despite blinding in the present study, subjects experienced greater subjective drug effects when receiving amphetamine which may have led to some subjects being effectively unblinded  [24]. Despite this as analyses were focused on neuroimaging variables rather than subjective report, and volunteers were blind to hypotheses, any inadvertent unblinding is unlikely to have significantly influenced results.
Bioinformatic analysis We predicted the presence of intrinsically disordered regions in TPD54 using the following servers. PrDOS (https:// prdos. hgc. jp/cgi-bin/top. cgi) predicts natively disordered regions from the amino acid sequence  (60). PONDR-Fit (http:// original. disprot. org/pondr-fit. php) combines the outputs of several individual disorder predictors  (61). IUPred3 (https:// iupred. elte. hu/) is based on an energy estimation method  (62). flDPnn (http://biomine. cs. vcu. edu/servers/flDPnn/) predicts disorder using a neural network  (63). We used the software coils (https://embnet. vital-it. ch/software/COILS_form. html) for the presence of coiled-coil regions  (64)  and Alpha-Fold (https://alphafold. ebi. ac. uk/) for the overall structure of TPD54  (40).
MS analysis-μHPLC-Q-exactive plus The sample was analyzed by liquid chromatography coupled to a mass spectrometer equipped with a heated electrospray ionization probe. HPLC was performed using a Dionex U3000 RSCL instrument. The injection volume was fixed at 5 μl (Ultimate 3000, Thermo Fisher Scientific). Protein analysis was performed on a 2. 1 mm i. d. × 150 mm, 5 μm, Syncronis C18 column at a flow rate of 250 μl/min. The elution program was based on water (solvent A) and acetonitrile (solvent B) both containing 0. 1% formic acid (v/v): 0 min 5% B, 18 min 80% B, 19 min 90% B. The Q-exactive plus spectrometer completely controlled by the Xcalibur software (Thermo Scientific) was operating in electrospray positive mode. Typical electrospray ionization conditions were as follows: electrospray voltage 4 kV; capillary temperature 320 C, probe temperature 325 C, sheath gas flow 40 U, and auxiliary gas 10 U.
After 6 h, the cells were fixed with paraformaldehyde 4% for 20 min at RT. Golgi staining was performed using sheep polyclonal antibody against TGN46 (AHP500G, Bio-Rad). Live cell observations for FRAP experiments were done using a confocal microscope (Zeiss LSM-780). Images were analyzed using Fiji J software (Fiji). To determine the fraction of GFP-TPD54 and mutants at the Golgi, we outlined the Golgi area from the TGN46 signal carried out in the red channel. Then, we applied the corresponding mask to the green channel to define a region of interest (ROI) at the Golgi and a ROI of the same area in the cytosol. Once the background was subtracted, we determined the average fluorescence in each ROI and calculated the Golgi/cytosol ratio. All images were acquired with a confocal microscope and were analyzed using Volocity software (Volocity).
For STED experiments, TPD-54 and ALPS(GMAP-210)-ACC1 were cloned in the halo-tag vectors HTN and HTC, respectively. After transfection, these constructs were revealed using a Janelia Fluor 549 ligand (Promega). Endogeneous proteins were labeled with Rb polyclonal antibody anti-TPD52L2 (ProteinTech), Rb polyclonal antibody anti-GMAP-210 (ProteinTech), or Ms monoclonal anti-Trip230 human (Invitrogen). Images were acquired on a Leica confocal TCS SP8 STED microscope. Deconvolution was performed using Huygens Professional software (Scientific Volume Imaging). TPD54 recognizes membrane curvature via an ALPS motif.
The AR5 concluded that total column water vapour very likely increased since the 1970s at a rate that is overall consistent with the CC relationship (about 7%/°C), and that an anthropogenic contribution to increases in specific humidity was found with medium confidence at and near the surface. The AR6  7  was even more cautious, stating only that (it is likely that) human influence has contributed to moistening in the upper troposphere since 1979  15. Here, and for the first time to the best of our knowledge, we use a novel qualitycontrolled dataset of in situ measurements to constrain the projections of GTPW in two generations of global climate models (GCMs). Since the early 1990s, ground-based measurements from the Global Navigation Satellite Systems (GNSS) have been shown to enable the estimation of total-column water vapour by applying the technique of accurate positioning  16  (see Methods).