dataesr/scientific-paragraphs-categorization huggingface dataset:
The same model is used for every hour of the day, and the hour of the day is not explicitly passed to the model as a predictor (somehow considering that the other predictors provide enough information). When designing a daily model, a selection procedure is achieved before the training in order to choose (between the daily mean, daily min and daily max of each physical quantity) the one which is best correlated with the output variable. For both the local and global approaches, this correlation is calculated based on the full training dataset, meaning typically with 365 records for a local model built with a 1-year training dataset and 3000 records for a global model based on 1000 monitoring stations spread over the domain and a 3 d training period. Set1 is the base set of predictors. It includes the ensemble forecasts (including the forecasts of the targeted pollutant and the three others), a first selection of surface meteorological variables (namely the temperature, relative humidity, zonal and meridional wind speed, and boundary layer's height) and observations of the previous day.
Sensitivity of the MOS to training data and predictors Specific local approach simulations have been carried out to evaluate O 3 daily max and PM 10 daily mean predictions performances with various input data configurations. Only O 3 daily max and PM 10 daily mean predictions have been considered in this preliminary analysis in order to limit the number of simulations. These forecasts are critical in Europe because of the frequent exceedances of the regulatory threshold values that determine pollution peaks (180 µg m -3 for O 3 daily max and 50 µg m -3 for PM 10 daily mean). Each pollutant was tested with two configurations regarding the size of the training dataset. For O 3 , one summer (June-September 2018) or two summers (June-September 2017 and 2018) have been used as training datasets.
Performances have been evaluated with 2019 data, over the summer season (June-September) for O 3 and the whole year for PM 10. As expected, the RMSE of the local MOS score average over all the monitoring stations, shown in Fig. 1 , is significantly reduced in comparison to that of the raw ensemble model. The MOS allows us to greatly reduce the bias (see also Fig. B1 ) and thus to significantly decrease the RMSE. The use of larger datasets is beneficial for all the machine learning algorithms tested and is particularly interesting for O 3 daily max predictions (RMSE strongly decreases when using 8 months of summer data instead of 4). Results also suggest being very careful with the choice of predictors, using more predictors as in Set2 generally leads to no improvement or even a loss in performance, especially if the algorithm is not designed to handle overfitting and if the training period is too short (see the deterioration of the O 3 RMSE when using the larger set of predictors, Set2, in the standard linear model).
Exceedance of these thresholds requires authorities to inform the exposed population and the set-up of mitigation actions by local authorities to reduce the adverse effects of the pollution. We therefore paid special attention to the ability of the models to detect such threshold exceedances. The threshold value of 180 µg m -3 for O 3 daily max concentration and 50 µg m -3 for PM 10 daily mean are regularly exceeded in Europe. These exceedances events remain relatively rare. In our 2019 testing dataset (only summer months for O 3 ), the base rate is 1. 3 % and 2 %, respectively, for O 3 and PM 10 exceedances. On average, the duration of these episodes of exceedances at a station is 1. 6 d for O 3 , with 30 % of the episodes lasting 2 d or more and 4 % lasting 5 d or more. For PM 10 , the episodes tend to be a little To assess the ability of a model to detect these exceedances, we use the so-called contingency table which counts the number of good detections (predicted and observed exceedances), missed (observed but not predicted) and false alarms (predicted but not observed) over the whole set of monitoring stations.
Conclusion This work allows us to compare the performances of two model output statistics (MOS) approaches for the correction of the Copernicus Atmosphere Monitoring Service (CAMS) forecasts for four regulated pollutants for the upcoming day at daily and hourly timescales, at monitoring sites covering the European territory. Both approaches (local and global) are implemented with five distinct machine learning algorithms ranging from simple linear regressions to more sophisticated tree-based models. The construction of optimized local MOS needs relatively long periods of data available for training individual models at each site. It was therefore tested with a reasonable scenario, where a full year of training data was available for PM 10 , PM 2. 5 and NO 2 pollutants. For O 3 , we focused on summer predictions, and the MOS was trained with 4 months of summer data.
In addition to its operational flexibility, the global approach shows performances that compete with those of the local approach. For this global approach, the random forest algorithm gives the best RMSE scores whatever the pollutant and timescale considered. However, if the MOS is designed for hourly prediction, the gradient boosting machine (GBM) algorithm is more adapted than random forest to detect O 3 daily max threshold exceedances. We would therefore recommend the GBM model in that situation. But one might also consider using a MOS specifically designed for daily maximum predictions to further improve detection skills. As mentioned above, the local approach was performed in this study with a relatively large training dataset. Interestingly, such a local approach was tested with CAMS O 3 forecasts by  Petetin et al.
In the future, such a global approach could also be used with a gradually expanding training dataset as in  Petetin et al. (2022) , being mindful, however, of the computing demand of automated learning of such a MOS in an operational set-up. Because of its flexibility, we also expect that this global approach is prone to adapt in real time to rapid changes in pollutant emissions as experimented with during the COVID crisis. Further investigation could be made using 2020 data to test this approach in such a situation.
Introduction Type 2 diabetes mellitus (T2DM) is a major risk factor for atherosclerosis, and patients with diabetes and documented atherosclerosis have a high risk of ischemic events  [1, 2] , comparable to that of patients with prior myocardial infarction (MI). Antiplatelet therapy is used to mitigate that risk. In patients with diabetes in primary prevention, the most recent data support aspirin use in carefully selected patients  [3] [4] [5] [6]. For patients with documented coronary artery disease, aspirin is recommended regardless of the presence of diabetes mellitus  [7] [8] [9]. The THEMIS (Effect of Ticagrelor on Health Outcomes in Diabetes Mellitus Patients Intervention Study, NCT01991795) trial is the first randomized controlled trial designed to test a strategy of a dual antiplatelet therapy with aspirin and ticagrelor in patients treated for diabetes mellitus with known coronary artery disease but without prior MI or stroke  [10].
These higher event rates suggest that in routine clinical practice, with a similar relative treatment effect, 'THEMIS eligible' patients may derive an even greater absolute benefit from ticagrelor than the more selected trial participants in terms of ischemic outcomes reduction, but a higher bleeding risk might in part counterbalance some of this benefit. Recently, the US Food and Drug Administration updated the label of ticagrelor based on the THEMIS results. The label states ticagrelor is now additionally approved "to reduce the risk of a first MI or stroke in patients with coronary artery disease at high risk for such events. " [28]  Based on the label, the vast majority of patients with CAD (with or without diabetes) in REACH would be eligible for therapy with ticagrelor. A limitation of the present analysis is that REACH participants were enrolled in 2003-2004 and followed up until 2008-2009; the data therefore antedate the start of THEMIS by approximately 10 years.
The evolution of treatment strategies and drug therapies that have occurred between inclusion in the REACH registry and THEMIS trial might have influenced the categorization of the patients. In addition, the higher use of secondary prevention drug therapies in the placebo group of THEMIS compared to the patients from the REACH registry may have affected the outcomes. Finally, the bleeding definitions used in the REACH registry and in the THEMIS trial differed such that no direct comparison can be made. BARC ≥2 is a more sensitive definition of bleeding, but these data were not captured in the REACH registry. In conclusion, in the REACH registry, over a quarter of patients with diabetes and stable coronary artery disease were 'THEMIS eligible' -a sizeable population. Given their higher event rates compared with actual trial participants, they may derive a greater absolute benefit from ticagrelor than the more selected trial participants.
Disclosures Dr. Jeremie Abtan  discloses the following relationships: research grant from  Federation Française de Cardiologie , research grant from  Institut Servier. Dr. Bhatt  served as co-Chair of THEMIS and  THEMIS-PCI , with funding from  AstraZeneca  paid to  Brigham and Women's Hospital. Dr. Bhatt  discloses the following relationships -Advisory Board:  Angio-Wave ,  Bayer ,  Boehringer Ingelheim, Cardax, CellProthera, Cereno Scientific, Elsevier Practice Update Cardiology, High Enroll, Janssen, Level Ex, McKinsey, Medscape Cardiology, Merck, MyoKardia, NirvaMed, Novo Nordisk, PhaseBio, PLx Pharma, Regado Biosciences, Stasys ; Board of Directors: AngioWave (stock options),  Boston VA Research Institute, Bristol Myers Squibb  (stock),  DRS. LINQ (stock options),  High Enroll  (stock), Society of Cardiovascular Patient Care, TobeSoft; Chair: Inaugural Chair, American Heart Association Quality Oversight Committee; Consultant: Broadview Ventures; Data Monitoring Committees:  Acesion Pharma ,  Assistance Publique-Hôpitaux de Paris, Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute , for the PORTICO trial, funded by  St.
Transformed cells were grown in Kp medium for 8-10 vegetative divisions and their genomic DNA was extracted using the NucleoSpin Tissue kit ( Macherey-Nagel ). Transgene injection levels ( copy number per haploid genome, cphg ) were determined using real-time qPCR ( see detailed information in qPCR_MIQE_checklist. xlsx file deposited in Zenodo ). Oligonucleotide primers used to amplify PGM and GFP , with KU80c as a genomic reference, are listed in Supplementary Table  S1. Since there is no endogenous GFP gene in Paramecium , we used genomic DNA extracted from cells transformed by the PGM-GFP fusion as a standard to quantify GFP DNA amounts, with a cphg = 6 based on Southern blot hybridization  ( 30 )  and qPCR using PGM primers. For all GFP transformants, we calculated the apparent cphg from the Cp value provided by real-time qPCR: cphg ap p arent = 2 ( C p genomic re f -C p GFP ) In each experiment a correction factor ( f ) was calculated from the Cp value obtained with the GFP standard: cphg standard × f = 6 ⇔ f = 6 cphg standard The correction factor f was used to correct the value of the GFP injection level ( cphg ) of transformed cells.
In all autogamy time-courses, the T0 time-point is defined as the time when 50% of cells have a fragmented MAC. Three days after autogamy has started, the ability of sexual progeny to develop a functional new MAC was monitored by performing a survival assay from 30 individual autogamous cells, as described ( 30 ) ( Supplementary Figure  S1 , see Numerical_data for Figures. xlsx file in Zenodo ). Control experiments were performed using plasmid p0ND7c, which targets RNAi against the non-essential ND7 gene  ( 42 ). The plasmids used for PGM , KU80c , XRCC4 and CtIPa + CtIPb ( = CtIP ) RNAi were pPBL49HN ( 29 ) , pL4440-KU80C-2 ( 34 ) , pXRCC4-R ( 33 ) , pL4440-PtCtIPa and pL4440-PtCtIPb  ( 43 )  , respectively. Plasmids used to target DCL2 and DCL3 were from  ( 23 )  , the sequence of plasmid p0474_L4440_Dcl5 used for DCL5 RNAi has been deposited in Zenodo.
Primary antibodies were revealed using species-appropriate HRP-conjugated secondary antibodies ( anti-rabbit or anti-mouse IgG ( 1:5000, Promega ) , or anti-guinea pig IgG ( 1:5000, Thermo Scientific ) ). For each antibody, the signal was acquired using the Chemi-Doc Touch Imaging System ( Bio-Rad ) and quantified with the Image Lab software ( Bio-Rad ) , and GFP or Pgm signals were normalized relative to alpha tubulin. Unless otherwise specified, the normalized intensity at T6 was set to 1 as a reference to quantify protein amounts in each time-course. All numerical data can be found in Zenodo ( Numerical_data for Figures. xlsx file ).
05 default parameters )  ( 46 )  , normalized by total read coverage per Mbp of the MAC + IES genome ( CPM, counts per million ) ( Supplementary Table  S3  ). As a control, a sequencing dataset with 100% IES retention was simulated using ART_Illumina ( v2. 5. 8 -l 80 -ss HS10 -p -qs 90 -qs2 90 -m 250 -s 50 ) on the MAC + IES genome, with the same sequencing depth as the XRCC4 KD dataset, and IES boundary coverage was estimated from the simulated data using the method described above ( Supplementary Table  S3 , Supplementary Figure  S2B  ).
Differential expression ( DE ) analysis For each RNAseq dataset, raw fragment counts were determined for each gene using htseq-count ( v0. 11. 2, -stranded = yes -mode = intersection-nonempty ) on filtered BAM files ( samtools v1. 3. 1 ) as described  ( 13 ). Version 4 of R  ( 47 )  was used for data analyses and to generate images ( ggplot2 v3. 3. 6; VennDiagram v1. 7. 3 )  ( 48 ,49 ). Batch effects due to differences in RNA extraction conditions and sequencing protocols ( HiSeq or NextSeq ) were corrected using ComBat-seq ( sva v3. 38. 0, R package, with no group condition )  ( 50 ). The relevance of this procedure was verified taking advantage of available technical replicates from the EZL1 and ICL7 time courses that were sequenced twice, using either HiSeq  ( 45 )  or NextSeq  ( 27 )  ( Supplementary Figure  S3 , Supplementary Table  S4  ).
We then corrected all RNAseq datasets ( except for the EZL1 time course, see DESeq2_metadata. xlsx file in Zenodo ) as the first step of our analysis workflow. For the ICL7 time course, the time points sequenced both by HiSeq and NextSeq were collapsed, as recommended by DESeq2 for technical replicates, before completing a first round of DESeq2 analysis ( DESeq2 v1. 30. 1, R package )  ( 51 ). This first round allowed us to obtain normalized counts for all samples, using a blind variance stabilizing transformation ( vst ) ( Part 1 of Supplementary Figure  S4 , see Expression_data. xlsx file in Zenodo ). Identification of differentially expressed genes could not be performed at this stage of the analysis, because 'real' replicates ( technical or biological ) were available only for control KDs ( ND7 and ICL7 time-courses ).
Hierarchical clustering ( derived from a Pearson correlation matrix, stats v4. 0. 3, R package ) and the first two dimensions of principal component analysis ( PCA, FactoMiner v2. 4, R package ) were used to identify groups of similar samples corresponding to 4 autogamy stages for each RNAi condition, in an unsupervised way  ( 45 ). The resulting groups were manually curated based on cytological observations of autogamous cells ( Supplementary Figure  S5  ). Our dataset, therefore, was organized in different types of replicates ( Supplementary Figure  S6  ). In each time-course, the different time-points that were attributed to the same autogamy stage were considered as 'pseudo-replicates': this means that, even if they are not rigorously biological replicates, they share a high level of similarity, which can be used for the identification of differentially expressed genes.
In addition, for the four biological replicates of the control time courses ( ICL7 and ND7 ) , all samples ( biological and pseudo-replicates ) corresponding to the same autogamy stage were grouped together for DE analysis. The results of DE analysis were extracted using both RNAi conditions and autogamy stages as contrast ( Part 2 of Supplementary Figure  S4  ). We considered a gene to be deregulated if its fold-change reached at least 2 compared to controls with an adjusted P -value ( padj, calculated by DESeq2 ) lower than 0. 05 ( Supplementary Table  S5 , see Deregulation_all_genes. xlsx file in Zenodo ).
Identification of a conserved motif We used STREME 5. 4. 1  ( 52 )  to search for 5 to 20-bp sequence motifs ( with a P -value < 0. 05 ) that are enriched in the promoters ( defined as the 150-bp window upstream of the annotated transcription start site ( TSS ) , see (  13 ) ) of the 180 genes that are coregulated with PGM ( Co-PGM genes: intermediate peak genes, upregulated in XRCC4 , PGM and KU80c KDs and downregulated in a CtIP KD ). The search was repeated 5 times against different control sets of the same number of randomly-picked gene promoters. The. meme files corresponding to the first hits of each STREME round were aligned and trimmed to the shortest motif size ( 18 bp ) ( trim_motifs, universalmotif v1. 12. 3, R package ). A consensus motif was defined by merging the five motifs ( merge_motif, universalmotif v1.
Analysis of the motif in gene promoters To identify all the genes that carry the motif in their promoter, we used the FIMO 5. 4. 1 software ( 53 ) with default settings. We first extracted the data obtained for the set of 180 Co-PGM genes and defined a cut-off p-value that matches the significance threshold calculated from STREME enrichment factors ( defined above ). We then applied the resulting cut-off to the full list of motifs identified by FIMO. To compare the position of the motifs in their respective promoter, we considered the position of the first 5 nucleotide of the motif relative to the TSS and counted the number of motifs found in successive 5-bp windows within gene promoters.
To gain broader insight into how many genes are upregulated at the genome-wide level in the three KDs used in this study to inhibit DNA cleavage or repair during IES excision, we performed a large-scale differential gene expression analysis. We used RNA sequencing data from independent autogamy timecourse experiments performed on cells subjected to KU80c , PGM  ( 34 )  or XRCC4 KDs ( 33 ) compared to control timecourses. Because of the heterogeneity of the collected datasets, we set up a dedicated bioinformatics workflow to identify differentially expressed genes ( Figure  2  and Supplementary Figure  S4  ). We corrected the batch effects and applied principal component analysis and hierarchical clustering to the normalized datasets to group the samples in four developmental stages ( Supplementary Figure  S6  ).
We observed that each developmental stage ( until the LATE stage ) included similar autogamy time-points from different conditions, indicating that KU80c , PGM or XRCC4 KDs do not affect the progression of autogamy ( see also the cytological stages in Supplementary Figure  S5  ). The EARLY, INTERMEDIATE and LATE stages coincide with the time-windows when genes from the early, in-Figure  2. Main steps in RNA-seq data analysis. RNA-seq reads were obtained using Hi-seq or Next-seq sequencing technologies, depending on the collected dataset. Data analysis was divided in five steps: ( A ) read counts were extracted from each dataset using standard protocols ( quality control, read mapping and counting ) ; ( B ) batch effects due to the specificities of each sequencing technique were corrected using ComBat-seq ( 50 ) ; ( C ) corrected read counts were normalized using the DESeq2 vst ( variance stabilizing transformation ) procedure; ( D ) pseudo-replicate groups corresponding to different autogamy stages were defined for each RNAi condition using PCA and hierarchical clustering ( Supplementary Figure  S6  ) ; ( E ) differentially expressed genes were identified using RNAi conditions and autogamy stages as DEseq2 contrasts.
In this context, we searched for similarity and dissimilarity patterns between critical environmental variables affecting the growth and survival of the first life stages, which are the most sensitive to the environment. These variables include water temperature variables covering the development of eggs, larvae, and early juveniles, which are key parameters for survival rates; zooplankton abundance variables, which are the main prey for each early stage; water level and wind intensity variables during egg development, both suspected to emerge or move the eggs out of the spawning ground. We used the R package "distantia" to calculate Euclidean distances between the ecological multivariate time series of each lake to compare the five lake environments between 2000 and 2020. The fishing effort, which reflects the intensity of the fishing activity on the whitefish populations, was also included to check for inter-lake similarities.
Defining environmental variables Environmental parameters that could directly impact the abundance of European whitefish stocks were selected based on their predicted effect on the early life stages of the target populations (eggs, larvae, and early juvenile stages). These parameters included temperature, zooplankton abundance, water level, and wind as identified by  Straile et al. (2007)  and  Myers et al. (2015)  and are detailed in Tab. 2. The data used in this study were collected from ongoing long-term monitoring programs for the five lakes, covering the longest available common period from 2000 to 2020 (Tab. 2). Approximately 4% of the collected data were missing during the studied period. Temperature and zooplankton data were collected once or twice per month, depending on the season and weather conditions, at the deepest point of each lake, following the usual monitoring protocols for deep lakes  (Pourriot and Meybeck, 1995; Jacquet et al.
, 2014). Zooplankton was collected in the first 50 m of the water column from the surface at the same coordinates as the temperature. However, zooplankton data were not available for Lake Aiguebelette as this variable was not monitored over the study period. The water level was recorded daily, while daily mean wind speed was obtained from the closest automatic meteorological station for each lake. Water temperature is a known factor affecting the growth and survival rates of all life stages of European whitefish. Each life stage, from the egg to the juvenile, has an optimum temperature value and a tolerance temperature range for the development  (Eckmann and Pusch, 1989; Stewart et al. , 2021). Warmer water temperatures within the species' tolerance range can increase growth and survival rates  (Perrier et al.
Fishery and management data Data on annual landings of whitefish, stocking (Tab. 1; Appendix A) and fishing efforts (Fig. 2 ) were obtained from the fisheries management organizations of each lake. For Lake Aiguebelette, the data was retrieved from the local recreational fishermen association (AAPPMA Aiguebelette); for the French territory of Lake Geneva, Lake Tab. 2. Defined variables. Period: period covered by the environmental variable (DEC, December; JAN, January; FEB, February; MAR, March; APR, April; MAY, May; JUN, June; JUL, July; AUG, August; SEP, September; OCT, October). Life-cycle stages detail the corresponding stages that are expected to be affected during this period. Expected effect details whether the impact of increasing values from this variable is expected to be positive [+; positive correlation between this variable and the abundance of European whitefish (Coregonus spp.