dataesr/scientific-paragraphs-categorization huggingface dataset:
Significant (p < 0. 01) improvements occurred in anthropometry and Yo-Yo IR1 running performance in forwards and backs. Total distance (p < 0. 01) and impact (p < 0. 05) during the second block were meaningfully higher than the other two blocks, with backs showing higher values than forwards. As expected, WL decreased significantly (p < 0. 01) during the last training block. The WL variations were correlated with changes in biochemical markers over the preseason period. The collected data can be used for i) profiling French Pro D2 rugby championships players, ii) establishing effective training strategies, and iii) setting preseason WL expectations.
External workload The on-field training EWL was collected via 10 Hz GPS and Global Navigation Satellite System (GLONASS) enabled units with an embedded 100 Hz tri-axial piezoelectric accelerometer technology (Sport tracker V4, McLlyod, France). The micro-technology (44 × 77 × 20 mm, weighing 43 g) was activated 30 min before each training session to ensure a clear satellite reception. Units were placed on the upper thoracic spine, between the scapulae, in a customized vest. Each subject was assigned the same unit throughout the preseason period. The strength of a GNSS signal (over 60%) received during data acquisition and the horizontal dilution of precision (HDOP) (less than 3) was satisfactory  [33]. The GPS information was transmitted and processed using the associated Sports tracking software (McLlyod, France).
Statistical analysis Statistical analyses were completed using SigmaStat 4. 0 program (Jandel Scientific, San Rafael, CA, USA). Data are presented as mean ± SD. Prior to analysis, assumptions of normality in the data were made using the Shapiro-Wilk test and visualization of normality plots. The changes (from pre to post) in body composition and Yo-Yo IR1 results were examined using paired t-test. Differences in blood parameters during the preseason were evaluated with one-way ANOVA on repeated measurements. Changes in GPS, HI, training volume, and s-RPE parameters for each positional group were analyzed using mixed-model ANOVA, with the timing phase as the within-subject factor and playing position as the between-subject factor. Differences between forwards and backs were compared using an independent t-test.
Significance was set as p < 0. 05. Confidence intervals (95% CI) were calculated for all measures analyzing positional differences. When significant differences were observed, data was subsequently analyzed using the effect size (ES)   [34]. The G-Power statistical software (version 3. 1. 9. 7; University of Düsseldorf, Düsseldorf, Germany) was utilized to determine that the minimum sample size of n = 15 was required for a statistical power (1β) ≥ 0. 80 at an alpha level of p ≤ 0. 05  [35].
Study design and population The HELENA-cross-sectional and multicentric study was conducted in 10 European cities between 2006 and 2007 (Athens, Greece; Dortmund, Germany; Ghent, Belgium; Heraklion, Crete; Lille, France; Pécs, Hungary; Rome, Italy; Stockholm, Sweden; Vienna, Austria; and Zaragoza, Spain) with large enough sample (N = 3,528) to represent population diversity  (26). The main objective of the HELENA study was to obtain representative data of European adolescents in terms of nutrition, lifestyle and health-related parameters. The HELENA study was implemented following the Declaration of Helsinki 1964 recommendations (revision of 2000) and with the approval of the Ethics Committees of each of the ten participating countries  (27). The protocol was approved by the Ethical Committee (Comité de Ética de la Investigación de la Comunidad Autónoma de Aragón: CEICA).
SNPs were excluded if imputation quality was <0. 3. Based on the literature, SNPs related to blood pressure in populations (adults and adolescents) with Caucasian ancestry were selected. The selection criteria for those SNPs found from GWAS was a p-value <5 × 10 -8 (33). All HTN related genes (n = 57) encoding these SNPs were acquired from the HELENA genetic dataset and all available SNPs, within these genes, were used to create the GRS. Among these genes, 13,428 SNPs are available in the HELENA genetic dataset. Unfortunately, the gene ACE2 was not available due to its location on the X chromosome which was not analyzed in the HELENA study. SNPs quality control Linkage Disequilibrium was calculated via PLINK, in the HELENA genetic dataset, and a r 2 > 0. 8 was applied, leading to 4,770 independent SNPs (572 genotyped SNPs and 4,198 imputed) (Supplementary Figure  S2 ).
Development of the genetic risk score After quality control check, a total of 4,770 SNPs related to BP were obtained from the HELENA GWAS analyses. The SNPAssoc R package was used to establish genetic filters: a Hardy-Weinberg equilibrium (HWE) < 0. 05 and minor allele frequency (MAF) > 0. 1. A total of 1,534 SNPs were candidates to be included in the GRS. Each SNP was recoded as 0, 1 or 2, depending on the number of risk alleles defined in previous literature, and the additive genetic model was used. Then, using univariate logistic regression analysis with a cut-off p-value of 0. 1, 166 SNPs were found. Next, a backward/forward stepwise logistic regression algorithm was carried out to select the significant SNPs at p < 0. 05 (n = 16). From the selected SNPs, two methodological approaches were used to create the multivariable GRS, the unweighted method (uGRS) and the weighted method (wGRS).
Validation of the genetic risk score Receiver operating characteristics (ROC) curves analysis was applied to test the diagnostic accuracy of the GRS to classify potential participants with risk of predisposition to HTN or not. For both uGRS and wGRS an area under the curve (AUC) was obtained considering BP as a binary variable. A principal component analysis (PCAs) were calculated on the total sample of SNPs available in order to explain the variability related to ethnicity and center. Additionally, both GRSs were adjusted by PCAs, sex and age, we, from now on we refer to this as GRS baseline. The models developed were validated internally by 10-fold cross validation. The validation process divided the initial dataset in 10 groups, 9 of them were used to build the predictive model and the last one to validate this model, thus ensuring the validation of models with data not used in their construction.
Dietary assessment Dietary intake was determined from two no-consecutive selfadministered 24 h recalls at any point in the week. Then, was computerized by a tool previously validated in Flemish adolescents  (34) : the HELENA Dietary Assessment Tool (HELENA-DIAT). This tool allows participants to select all foods and beverages consumed in six meals (breakfast, morning snacks, lunch, afternoon snacks, dinner and evening snacks). Then, the multiple source method (MSM) was used to calculated individual usual dietary intake. Thus allowing to correct the variability of dietary data between and within participants  (35). Additionally, based on the meals salt content reported in the 24 h recalls sodium intake was calculated. Regarding the assessment of daily fructose intake, the variable created by Béghin et al.
Other covariates Moderate-to-vigorous intensity physical activity (MVPA) was assessed using an ActiGraph GT1M accelerometer, which has been extensively validated in youth  (37). The device methodology was fully described in a previous study  (38). Adolescents wore the accelerometer on their lower back for 7 consecutive days, except during water activities. Subsequently, the accelerometry data were downloaded using the Actilife software (Actigraph) with 15 s epochs. The time spent in moderate and vigorous physical activity (measured in minutes/day) was calculated using on cutoffs of 500 and 1,000 counts per 15 s, respectively. These cut-off point were similar to those used in other study to define activity intensity categories  (39). Birth weight information was obtained through a selfadministered questionnaire completed by parents.
Supplementary Table The predictABEL R package  (40)  was used to create the uGRS and a wGRS from the multivariate model aiming to predict the risk of HTN. Figure  1  shows the ROC curve and the AUC obtained by comparing the probability of risk of HTN and the real outcome in both GRSs adjusted by PCAs, sex and age (uGRS: 0. 834, wGRS: 0. 84). The discrimination ability of wGRS and uGRS, adjusted by PCAs, sex and age, was internally validated by 10-fold crossvalidation. Both GRSs gives robust predictions (AUC = 0. 802 uGRS and AUC = 0. 777 wGRS). In addition, a boxplot was created to show the uGRS and wGRS distribution in the different groups, adolescents with normal BP and with risk o HTN (Figure  2 ). Both GRSs showed differences between the groups. The uGRS showed a possible cut-off point that would differentiate those adolescents with normal BP levels from those with above or equal to the cut-off points established by AAP CPG for HTN.
Funding The HELENA Study was financial support of the European Community Sixth RTD Framework Programme (contract FOOD-CT-2005-007034). GP-G, has receiving a predoctoral fellowship from the Government of Aragón. DFS-T was supported by a Marie S. Curie Global Fellowship within the European Union research and innovation framework programme (2014-2020; ClimAHealth: 101030971). MS-C has received funding from the Iberus Talent Pre-doctoral fellowships 2018, under the European Union's H2020 research and innovation programme under Marie Sklodowska-Curie grant agreement No. 801586. This study has been funded by Instituto de Salud Carlos III (ISCIII) through the project "PI20/00988" and co-funded by the European Union.
| SIMULATIONS RESULTS Simulations have been performed with matlab to evaluate the performance of the suggested OCDM-CS and OCDM-CDCS methods in terms of PAPR and BER, as well as the generalisation to multi-user OCDM. To this end, signals with N ∈ {256, 512} subcarriers has been used, and the results have been averaged over 20000 simulations runs. Furthermore, a CP of length N CP ¼ N 16 has been considered in all simulations.
ACKNOWLEDGEMENTS There is no funding. F I G U R E 1 3 Bit error rate (BER)  versus  SNR (dB)  for clipped OFDM, clipped orthogonal chirp division multiplexing (OCDM), and clipped OCDM-CDCS, 256-QAM, and 256-PSK constellations and P t ∈  {0. 01, 0. 001}. F I G U E 1 4 CCDF (λ) versus threshold λ for orthogonal chirp division multiplexing (OCDM), OCDM-CS with 1 band (1 user), and OCDM-CS with 2 sub-bands (or 2 users). SAVAUX.
PCR products (5 μL of each PCR content) were electrophoretically separated on a 0. 8% agarose gel, in TAE buffer 1 × (40 mM Tris, 20 mM acetate and 1 mM EDTA) with GelRed® (1:10,0 0 0 ratio), at 10 0 V for 1 h and visualized by UV transillumination using Gel Doc (Bio-Rad, Hercules, CA, USA). PCR products were then purified using the Nucleospin gel and PCR clean up Kit (Macherey-Nagel) and sequenced. Amplified 16S rRNA fragments were sequenced by Eurofins genomics (Nantes, France). The obtained sequences were compared with 16S ribosomal RNA sequences (bacteria and archaea) and Genebank databases, respectively, using the Nucleotide Basic Local Alignment Tool (BLASTn) software provided online by the National Center of Biotechnology and Information (NCBI, Bethedsa, MD, U. S. A. ). Sequence alignment and phylogenetic tree construction were performed on the basis of 16S rRNA using MEGA 11  [22].
Lipopeptides quantification by HPLC-MS The analyses of HPLC coupled to mass spectrometry were performed on an Accela UHPLC system acquired from Fisher Scientific (Thermo Fisher Scientific, Bremen, Germany) that was composed of an auto-sampler equipped with a column oven, a tray compartment cooler and a quaternary pump with a built-in solvent degasser, all piloted by Xcalibur software. The chromatographic separation was performed with the same column used for standard HPLC analysis. Samples elution was performed with a constant flow rate of 600 μL. min -1 and the eluent was a gradient composed of solvent A (water, 0. 1% formic acid) and solvent B (acetonitrile, 0. 1% formic acid). The gradients for the separation of each lipopeptide used solvents A and B to compose eluents as first described, whereas trifluoroacetic acid is replaced by formic acid for better compatibility with the MS.
Results of the BLASTn Bioinformatics Analysis of the Nucleotide Sequences of the 16S rRNA Genes of Isolates Having antimicrobial Activity We used Blast results for the identification via NCBI / GenBank isolates of Kawal for the 16S rRNA genes ( https: //www. ncbi. nlm. nih. gov/Genbank/update. html ). Different parameters of Blast such as Max score, Total Score, E. value and the percentage of similarity were used to identify the equivalent of the query strain in Gen Bank ( Table  2  ). Forty-three bacteria of Bacillus -genus have been identified with a similarity between 98. 12 and 99. 86% against 16S rRNA genes of known species among the 45 isolated strains. The sequences determined in the present study have been deposited under Genbank NCBI and will be available from the 31/01/2023 (SUB12243493).
Phylogenetics of studied strains The Fig. 5  shows the phylogenetic tree constructed from the nucleotide sequences of the 16S rRNA encoding genes of the Bacillus strains isolated from kawal. Alignment of all sequences (query sequences and homologs) was performed by clustalW ( http://www. ebi. ac. uk/clustalw )  [24]. The phylogenetic tree was produced using MEGA 11 software  [22]. We used Escherichia coli strain U 5/41 (NR_024570. 1) as an out-group. Phylogenetic tree analysis revealed that strains B. subtilis (ka10-2, ka3-4, ka9-1, ka23-4, ka11-1, ka1-5, ka26-5, ka23-2), B. spizizenii (ka4-5, ka4-3, ka1-4), B. tequilensis (ka4-4), B. velezensis (ka23-3, ka19-3), B. amyloliquefaciens (ka26-1, ka18-1); B. haynesii (ka7-3, ka13-3), and B. licheniformis (ka17-2, ka12-2, ka10-3, ka21-4) are closely related to Bacillus subtilis ( Fig.
ACKNOWLEDGMENTS H. O. , T. O. , and Y. Y. were supported by  JSPS KAKENHI  (Grant Nos. JP21J20580 ,  JP18K03929 ,  JP22H01400  and  JP23H01346 ), Japan, respectively. Y. Y. was also supported by  JST CREST  (Grant No. JPMJCR18I1 ),  Japan.
Data set preparation In order to establish a database of DP sequences that would ultimately fuel our model, we focussed our attention on publications within which depolymerase activity had been experimentally demonstrated. A comprehensive literature search was conducted, and a database established consisting of 50 depolymerase sequences. Additional file 1: Table  S1  presents an overview of this sequence database including the phages and references from which they were found. 28 of the sequences exclusively state CPS as an enzymatic target, 20 target EPS, and the remaining two target LPS and a combination of targets. The majority of sequences were Podoviridae derived and the database concerned phages infecting Gram-negative bacteria. The size range of sequences varied from 150 amino acids to 1267 amino acids in length.
To complete this dataset, we required 50 sequences that would serve as the negative non-depolymerase set and thus provide a 1:1 positive to negative sequence set. To do this, we randomly extracted 50 sequences from a soil metagenome (SRR15048733) that were sampled across the size distribution of sequences so as to avoid the introduction of sequence size biases. BLAST searches were conducted with these sequences against the positive depolymerases to ensure the absence of homology followed by HHPred analysis to confirm the absence of domains known to be associated with depolymerase activity  [24]. To highlight the dissimilarity in the dataset, we calculated pairwise similarity scores across the entire dataset and represented this as a heatmap (Fig. 1 ).
Model selection, training, and evaluation With respect to the appropriate choice of machine learning algorithm, we decided to test both support vector machine (SVM) and random forest (RF) approaches  [26, 27]. This was due to the fact that our data set constituted a small number of samples exhibiting a high feature space. In both cases, we leveraged a grid search with a nested validation scheme in order to assess the hyperparameter space and find the best model configuration for both algorithms. We conducted 20 independent trials with inner and outer cross-validation loops defined with 4 splits on a randomly shuffled dataset using the trial number as the random state. This was conducted using the scikit-learn library (version 0. 23. 2)  [28]. To evaluate final model performance, we particularly focussed on the overall accuracy and recall on the defined as follows: (1) Accuracy = TP + TN TP + FP + TN + FN where TP FP, TN, and FN correspond to true positive, false positive, true negative, and false negative respectively regarding the classification performed on the test data.
Software package DePolymerase Predictor Once optimal parameters were determined for the model following evaluation, the final RF-based version of the DePolymerase Predictor (DePP) tool was created incorporating the entirety of the training set. Both the source code of the tool and a standalone readyto-use version of the application are available as detailed in the "Availability of data and materials" section. A simple user-friendly GUI has been developed through which users can step-by-step upload their sequences, generate the feature vector, and carry out predictions and view the output. Finally, our tool is also available as a web application. We continue actively working in the field of phage depolymerase research and will be regularly updating, expanding and improving our tool as new experimentally verified depolymerases become available.
Model evaluation and final selection The SVM approach was initially applied to the dataset with no hyperparameter tuning, with the application of a linear kernel. This resulted in a model exhibiting an overall accuracy score of 0. 70 across all folds as presented in the normalised confusion matrix in Fig. 2. This model performed extremely poorly with respect to true and false positives but handled negative cases well. Indeed, hyperparameter tuning did little to resolve this problem. The overall accuracy remained unchanged, but the model improved in its ability to correctly identify non-depolymerase sequences with 100% success rate. This was at the cost of decreased performance on positive cases with only 45% of true depolymerases being correctly identified as such. (2) Recall = TP TP + FN Subsequent application of an RF approach yielded more promising results.
Benchmarking DePP against other approaches Phage research, like other domains, is starting to reap the benefits of widespread AI application. This is no less true of depolymerase prediction and it is therefore necessary to benchmark our tool against existing approaches, notably PhageDPO  [22]. In order to do this, we generated PhageDPO predictions across the same genome set from Pires et al. [11]  using both SVM and ANN approaches and noting the result which ranked the depolymerase highest amongst the list of gene products for each phage (Additional file 3: Table  S3 ). Globally across the 155 genomes, PhageDPO outperformed DePP on 36% of the dataset, was equivalent to our approach on 34% of the sequences, and was outperformed by our model on the remaining 30% of the sequences. Much of the superior performance of PhageDPO is attributable to better predictions on phages infecting Gram-positive bacteria, notably Bacillus.
This is not wholly surprising considering the fact that the training dataset used by DePP consists of only of sequences of experimentally verified depolymerases and thus is limited in size and coverage. The lack of depolymerases of Bacillus phages in our dataset clearly represents an opportunity for further work involving isolation, purification and biochemical characterisation ofthese putative depolymerases. Indeed, this knowledge gap has been already recognised and being addressed; for instance, two novel Bacillus pumilus phages and their experimentally characterised depolymerases were described in a recently published study  [31]. Despite the better performance of PhageDPO on phages of Gram-positive bacteria, our model displayed a noteworthy advantage in its ability to predict depolymerases possessing infrequently encountered domains such as Glyco_hydro_32N/Glyco_hydro_32C and Peptidase_S74 for which PhageDPO showed greater difficulty.
16, 27 Thus, SI was assessed more as a PPH detection tool than a predictor. 16, 27 evertheless, guidelines for PPH management have incorporated the SI as an 'obstetric early warning' tool of maternal haemodynamic instability after delivery. 30 he 'TRAAP trial' assessed the prophylactic administration of tranexamic acid versus placebo in addition to prophylactic oxytocin for PPH prevention. Its data thus offer the opportunity to explore the performance of SI measured in the immediate postpartum period for predicting PPH after vaginal delivery with documentation of the temporal sequence between SI measurement and PPH occurrence. 31, 32 The protocol of this trial called for collecting information on maternal heart rate and systolic blood pressure at six prespecified time points within 2 hours after vaginal delivery (0, 15, 30, 45, 60 and 120 minutes).
| Population The source population included women who participated in the TRAAP randomised controlled double-blinded trial (Clini calTr ials. gov number NCT02302456) approved by the West II Committee for the Protection of Research Subjects on 21 August 2014 (2014/09), and by the French Health Products Safety Agency on 6 August 2014 (2014-001748-39). 31, 32 All women provided written informed consent to participate in the trial. This multicentre trial conducted in 2015-2016 aimed to compare prophylactic administration of tranexamic acid versus placebo in addition to prophylactic oxytocin for the prevention of PPH after vaginal delivery. It found that the use of tranexamic acid, compared with the placebo, did not result in a significantly lower rate of blood loss of at least 500 ml (primary outcome).
| Strengths and limitations The main strength of our study is the description of the SI distribution with six pre-established and standardised post-delivery time points for measuring SI in a large population of women who delivered vaginally with characteristics similar to women receiving care in similar facilities, including PPH rate. 31 The particular focus on collection of the time of SI measurement and the time of diagnosis of our blood loss-related outcomes in the TRAAP trial enabled us to take into account the necessary time sequence between SI (the predictor) and PPH (the predicted outcome). In the TRAAP trial, the quality of the blood loss data was checked at each centre in a random sample of 10% of the participants and in all the women who had PPH. 31  This study has several limitations.
Median DoR was 14. 4 months, 8. 9 months, not reached, 7. 7 months, not reached, 31. 2 months, not reached and 11. 1 months, respectively. Median PFS was 6. 7 months, 9. 0 months, not reached, not evaluable, 9. 5 months, 5. 5 months, not evaluable and 6. 3 months, respectively. Median OS was 14. 5 months, 13. 5 months, not reached, 21. 8 months, not evaluable, 17. 6 months, not evaluable and 33. 9 months, respectively. The most frequent (≥20% of patients) treatment-related adverse events were pyrexia (40. 8%), fatigue (25. 7%), chills (25. 7%), nausea (23. 8%) and rash (20. 4%). The encouraging tumor-agnostic activity of dabrafenib plus trametinib suggests that this could be a promising treatment approach for some patients with BRAFV600E-mutated advanced rare cancers. ClinicalTrials. gov registration: NCT02034110.