dataesr/scientific-paragraphs-categorization huggingface dataset:
Abbreviations: AH, antihistamines; AR, allergic rhinitis; AzeFlu, Azelastine-fluticasone; ICS, inhaled corticosteroids; INCS, intranasal corticosteroids; IQR, interquartile range; LABA, long-acting betaagonist; OCS, oral corticosteroids; SABA, short-acting beta-agonist; SD, standard deviation; VAS, visual analogue scale. a Effect size for the largest difference between clusters. All comparisons have a p-value <. 001 using classical inferential hypothesis tests (chi-square test for categorical variables and Kruskal-Wallis test for continuous variables). In linear discriminant analysis, the first linear discriminant function achieved a proportion of trace (percentage of separation) of 91. 3%. A linear discriminant analysis model based on daily VASs in a training dataset (80% of the assessed sample) was able to correctly predict the cluster for 95.
4% of the weeks in a test dataset. bConsidering only the weeks reported by European patients, and whose total value is of 1724 for cluster A, 1644 for cluster B, 1143 for cluster C, 626 for cluster D, 441 for cluster E, 356 for cluster F, 315 for cluster G, 277 for cluster H, 233 for cluster I, 36 for cluster J.
has been supported by EU grants (POLLAR, EIT Health; Structural and Development Funds, Twinning, EIP on AHA and H2020) and educational grants from Mylan-Viatris, ALK, GSK, Novartis and Uriach. AUTH O R CO NTR I B UTI O N B Sousa-Pinto and J Bousquet proposed the concept of the hypothesis and wrote the paper. B Sousa-Pinto performed the analyses. HJ Schünemann, JM Anto, T Zuberbier and JA Fonseca worked on the concept of the paper and the analysis of the results and reviewed the paper. A Sá-Sousa, RJ Vieira and Rita Amaral participated in the analysis of the data and reviewed the paper. L Klimek, W Czarlewski, J Mullol, O Pfaar and A Bedbrook discussed the analyses and reviewed the paper. L Brussino, V Kvedariene, DE Larenas-Linnemann, Y Okamoto, MT Ventura, KC Bergmann, S Bosnic-Anticevich, V Cardona, P Carreiro-Martins, L Cecchi, C Cingi, AA Cruz, WJ Fokkens, B Gemicioglu, JC Ivancevich, P Kuna, H Kraxner, M Makris, R Monti, M Morais-Almeida, R Mösges, M Niedoszytko, NG Papadopoulos, V Patella, N Pham-Thi, FS Regateiro, S Reitsma, PW Rouadi, B Samolinski, M Sova, A Todo-Bom, L Taborda-Barata, S Toppila-Salmi, J Sastre and I Tsiligianni recruited patients.
Clusters obtained when considering full weeks with no medication for allergic rhinitis Abbreviations: AR, allergic rhinitis; ICS, inhaled corticosteroids; IQR, interquartile range; LABA, long-acting beta-agonist; SABA, short-acting betaagonist; SD, standard deviation; VAS, visual analogue scale. a Effect size for the largest difference between clusters. All comparisons have a p-value <. 001 using classical inferential hypothesis tests (chi-square test for categorical variables and Kruskal-Wallis test for continuous variables). In the linear discriminant analysis, the first linear discriminant function achieved a proportion of trace (percentage of separation) of 94. 2%. A linear discriminant analysis model based on daily VASs in a training dataset (80% of the assessed sample) was able to correctly predict the cluster for 94.
FUF I G U R E 3 3 has been supported by EU grants (POLLAR, EIT Health; Structural and Development Funds, Twinning, EIP on AHA and H2020) and educational grants from Mylan-Viatris, ALK, GSK, Novartis and Uriach. AUTH O R CO NTR I B UTI O N B Sousa-Pinto and J Bousquet proposed the concept of the hypothesis and wrote the paper. B Sousa-Pinto performed the analyses. HJ Schünemann, JM Anto, T Zuberbier and JA Fonseca worked on the concept of the paper and the analysis of the results and reviewed the paper. A Sá-Sousa, RJ Vieira and Rita Amaral participated in the analysis of the data and reviewed the paper. L Klimek, W Czarlewski, J Mullol, O Pfaar and A Bedbrook discussed the analyses and reviewed the paper. L Brussino, V Kvedariene, DE Larenas-Linnemann, Y Okamoto, MT Ventura, KC Bergmann, S Bosnic-Anticevich, V Cardona, P Carreiro-Martins, L Cecchi, C Cingi, AA Cruz, WJ Fokkens, B Gemicioglu, JC Ivancevich, P Kuna, H Kraxner, M Makris, R Monti, M Morais-Almeida, R Mösges, M Niedoszytko, NG Papadopoulos, V Patella, N Pham-Thi, FS Regateiro, S Reitsma, PW Rouadi, B Samolinski, M Sova, A Todo-Bom, L Taborda-Barata, S Toppila-Salmi, J Sastre and I Tsiligianni recruited patients.
1% of the weeks in a test dataset. b Considering only the weeks reported by European patients, and whose total value is of 2600 for cluster AA, 1071 for cluster BB, 463 for cluster CC, Cluster Cluster Effect Cluster NC1 Cluster NC2 NPC1 NPC2 Cluster NU Cluster NV size a N weeks (%) 3623 (50. 3) 1777 (24. 7) 824 (11. 5) 466 (6. 5) 175 (2. 4) 331 (4. 6) - N days 25,361 12,439 5768 3262 1225 2317 - N users (average N weeks per user) 556 (6. 5) 538 (3. 3) 291 (2. 8) 206 (2. 3) 100 (1. 8) 234 (1. 4) - Females -N (%) 1602 (44. 2) 869 (48. 9) 448 (54. 4) 332 (71. 2) 116 (66. 3) 196 (59. 2) 0. 6 Age -mean (SD) 38. 9 (13. 9) 40. 0 (14. 8) 42. 3 (15. 8) 39. 2 (14. 4) 39. 4 (15. 1) 32. 6 (11. 9) 0. 7 VAS global allergy symptoms Maximum week value -median (IQR) 1 (6) 18 (10) 31 (11) 55 (16) 78 (23) 61 (20) 6.
Required auxiliary data In order to correctly derive the H 2 O column and ILS parameters, pressure and temperature need to be measured, both inside the instrument and outside in the laboratory. The temperature inside the spectrometer is recorded using the sensor built into the spectrometer by the manufacturer. The temperahttps://doi. org/10. 5194/amt-15-2433-2022 Atmos. Meas. Tech. , 15, 2433-2463, 2022  ture of the laboratory air is recorded using digital thermometers offering 0. 8 • C accuracy (Lutron MHB-382SD data loggers or thermo hygrometer barometers of type PCE-THB 40 were used). While in the previous data analysis the pressure readings of the aforementioned portable sensors were used, we migrated for the analysis of new measurements as well as for the re-analysis of previous measurements to the pressure record from the nearby meteorological tall tower.
, 2018)  to learn more local features. It results in training CNN efficiently on more local areas and learning the attention of each region's features extracted by LSTM, which helps classify them using fully connected layer (FCL) and outperforms state-of-the-art performance on 3 MiE datasets. The principal contribution of this study concerns extracting more local characteristics of each ROI, identified by  Aouayeb et al. (2019) , using CNN and SE. By training the CNN with very local regions (patches), the model focuses on learning more local features avoiding unnecessary ones for MER (e. g. , edges, shapes, and textures). However, it could augment the redundancy of the extracted spatial features from different patches and harm the model's training. To alleviate this issue, we employ SE as an attention block to learn the active patches.
Deep Learning Solutions Deep learning has been widely used for computer vision tasks such as face recognition, object detection, image segmentation, and tracking. Recently, deep learning architectures have been proposed to classify MiE videos/clips. Patel et al. (2016)  used a pre-trained model on the ImageNet dataset and then fine-tuned its weights to classify macro-and micro-expressions. Reddy et al. (2019)  proposed a 3D-CNN for spatiotemporal features extraction and then performed the classification using a FCL. Quang et al. (2019)  adapted CapsuleNet  (Sabour et al. , 2017)  for MER. Furthermore, Choi and Song (2020) created a 2D feature map based on the time variation of distance between facial landmarks. Then, they fed the sequence of 2D feature maps to a combined architecture of CNN and LSTM to extract spatiotemporal features and classify them.
The main challenge for deep learning solutions in MiE analysis is not only that the provided datasets of spontaneous MiE sequences are limited but also the imbalance between classes. To overcome these problems,  Yu et al. (2020)  used an improved architecture of conditional Generative Adversarial Nets (cGAN)  (Mirza and Osindero, 2014)  called Identity-aware and Capsule-Enhanced GAN (ICE-GAN) to synthesize and augment data. The proposed solution consists of a conditional encoder-decoder to generate synthesized MiE and a discriminator based on CapsuleNet  (Sabour et al. , 2017)  to discriminate the real from the fake and identify the corresponding MiE class. Considering the results of different deep learning solutions, we can notice the improvement compared to handcrafted solutions. However, the performance is still insufficient compared to other computer vision tasks.
Then, their size is normalized to a predefined size for each region. Table  2  shows the size by region on each dataset and the average size among the different databases used in our experiments. Next, each region is divided into m equal patches. One shall notice that our method differs from those of  Zhao and Xu (2018)  and  Zhao and Xu (2019)  in that we get the patches from the six ROIs, not from the entire face. Precisely, we have mp6 patches, and we have different sizes for patches depending on the size of the region. Thus, a reshape is applied to fit the CNN input architecture. An ablation study of the number of patches is presented in Table  3. It tests the performance of the model using a different number of patches on the mixed dataset of SAMM and CASME II for five AU classification tasks.
EXPERIMENTS AND COMPARISON In this section, we experimentally evaluate our contributions. We start with a brief introduction of the datasets and the evaluation methodology used in the 2nd Micro-expression Grand Challenge (MEGC) (4. 1). Then, we ablate the various design choices in the proposed architecture to assess the comprehension of each (see Section 4. 1. 6). Finally, we compare our solution to state-of-the-art solutions (Section 4. 2).
SMIC The Spontaneous Micro-Expression (SMIC) dataset contains three versions using three different cameras: a high-speed (HS) camera at 100 frames per second (fps) and two cameras at 25 fps of both visual (VIS) and near-infrared (NIR) light range. In all experiments, we used the SMIC-HS version that features 164 clips from 16 distinct persons. SMIC-HS generates sequences with a face resolution of (190 × 230) that fall into only three categories: negative, positive, and surprise.
SAMM The Spontaneous Micro-Facial Movement (SAMM) has the most ethnic diversity (13 ethnicities) and the most diverse age range. Disgust, surprise, happiness, fear, anger, contempt, and sadness are the seven main types of emotion depicted in the video sequences, captured with a high-resolution camera at 200 fps. A total of 159 spontaneous facial MiE sequences from 32 people are included in the database. Among these three datasets, it has the highest spatial resolution (400 × 400 pixels). Furthermore, the focus of this dataset is on the objective AUs labels rather than the    4.
Evaluation Methodology For the evaluation of the proposed solution, the Leave-One-Subject-Out Cross-Validation (LOSO-CV) is used as a protocol to split data into train and test sets. Data are divided per subject following this protocol. At each time, the training is conducted on Z-1 subjects and the test is run on the remaining subject (Z is the total number of subjects). The metrics applied to evaluate the system are the accuracy, the Unweighted Average Recall (UAR), and the Unweighted F1-score (UF1). The UF1 and UAR are computed by Eq. 11 and Eq. 10, respectively. Both metrics are used with LOSO-CV as they are more convenient for an imbalanced classification problem  (See et al. , 2019) : UF1 1 C C c 1 F1 c 1 C C c 1 2TP c 2TP c + FP c + FN c. ( 10 ) where TP c , FP c , and FN c are, respectively, true positive, false positive, and false negative of class c and C is the number of classes: UAR 1 C C c 1 ACC c 1 C C c 1 TP c N c.
Impact of Learning With ROI Labels Aouayeb et al. (  2019 ) suggested using a customized label for each region to train the spatial model. To demonstrate the effectiveness of this contribution, we test the proposed model with the provided labels for the whole face with the label given for each region based on  Aouayeb et al. (2019). Table  6  shows that the solution with customized labels for each region performs better because it helps the spatial model to train more efficiently by focusing on a local region. 7  shows that the proposed model improves the basic architecture in UAR and UF1 by almost 4% in the FULL database. By taking a closer look, one can find that the SAMM part is the most improved, with 8% in UAR and 14% in UF1. As shown in Table  8 , the proposed solution outperforms all state-of-the-art works, particularly handcrafted solutions where the UAR and UF1 metrics are improved in most cases by 40%, and one can also observe a slight improvement compared to recent deep learning-based solutions.
CONCLUSION In this study, we have proposed a region-based solution for MER. The proposed solution extracts spatiotemporal features using a combined architecture of CNN and LSTM supported by a SE fusion unit in space and time. The effectiveness of the architecture, the use of the SE, and the ROI labels are validated. Experiments on different databases demonstrate the potential of this model. It outperforms the first solution in the MEGC and other recently proposed solutions. In future work, we will explore less complex architecture for MER that addresses the locality character with an automatic system.
Primer design, PCR amplification and sequencing of OeFAD2-2 PCR primers (FAD2F: 5 0 GAATTGAAGGGCGAGCAGT 3 0 and FAD2R: 5 0 TGGAATGTAATGCAAACACTGA 3 0 ) were designed using the Primer3 program  [31]  based on the 5 0 -and 3 0 -UTR regions of the OeFAD2 mRNA sequence available at NCBI (http://www. ncbi. nlm. nih. gov) (accession number AY083163. 1) and the OeFAD2 gene was PCR amplified from the 19 olive varieties. PCRs were performed in 30 mL reaction volumes consisting of 100 ng of olive genomic DNA, 2. 5 mL of 25 mM MgC1 2 , 2 lL of 10 mM dNTPs, 1 lL of each primer (10 lM), 1. 25U of GoTaq Flexi DNA polymerase (Promega, Wisconsin, USA), 10 mL of GoTaq buffer (5X) and 3. 25 lL of distilled water. PCR amplifications were performed on a 96-well Verity Thermal Cycler (Applied Biosystems, Massachusetts, USA) with a starting denaturation at 95 °C for 5 min, followed by 35 cycles of 94 °C for 30 s, 57 °C for 30 s, 72 °C for 30 s, with a final elongation at 72 °C for 10 min.
Statistical methods The descriptive statistical analysis of the background data on quantitative variables was performed using R packages. Genotyping quality check and association analysis of alleles were done using the ''Genetics" package of R software  [36]. Genotypephenotype association analyses using logistic regression assuming different genetic models-dominant, codominant, recessive, over dominant, and log-additive were performed using the ''SNPassoc" R package  [37]. The relationship between the fatty acids composition and SNPs was analyzed and determined through various statistical tools and techniques. One-way ANOVA and the t-test were applied for the fatty acids composition to assess the significant differences between the means of variety groups for each SNP. A variance multi-way analysis was performed to examine the association of the studied SNPs simultaneously with fatty acids composition.
Three-dimensional protein modeling Three-dimensional (3D) protein models were predicted and proposed for the four desaturases. Homology modeling was performed using the (PS)2-v2 Protein Structure Prediction Server (http://ps2v2. life. nctu. edu. tw/) [42, 43] , where the reported crystal structure of a mammalian stearoyl-CoA desaturase (PDB ID 4YMK) was used as a template (42% identity and 98. 7% alignment with the targeted protein sequence). The four low-energy modeled structures were further validated by performing Ramachandran plot analysis (model structure with the maximum number of amino acids in allowed regions) using the PROCHECK suite of programs  [44]. The models were viewed using The PyMol Molecular Visualization System (https://pymol. org/2/) [45].
Protein docking Protein docking was performed using GRAMM-X online server (http://vakser. compbio. ku. edu/resources/gramm/grammx/)  [46]. The Arabidopsis ferredoxin structure (PDB ID 4ZHO) was used as the ligand protein. The structure provided by the NCBI data bank was prepared for docking using Discovery Studio 4. 1 Visualizer (https://discover. 3ds. com/discovery-studio-visualizer-download) [47]. The 4ZHO molecule was opened using Discovery Studio; after removing water molecules and other bound ligand molecules, the file was saved as a PDB file. The Discovery Studio software was also used to visualize hydrophobic surfaces of predicted models. Finally, Verify3D allowed the validation of the obtained models (http://nihserver. mbi. ucla. edu/Verify_3D/) [48]. Molecular modeling and docking analyses were combined to study the structurefunction relationship of the poorly structurally characterized integral membranes of FAD2.
FAD2 gene sequencing, SNP discovery, and molecular characterization To discover a large number of genic SNP markers across the olive tree genome, we sequenced the FAD2 gene from 19 olive oil varieties. All the obtained FAD2 sequences were submitted to NCBI GenBank (accession numbers are provided in Supplementary Table  3 ). FAD2 sequences of the studied olive varieties revealed intronless genes (IG) and were identified as mono-exonic genes containing full-length ORFs  [49]. FAD2 CDS is made of 1152 bases and encodes a protein of 383 amino acids. The multiple sequence alignment of the FAD2 nucleotide sequences showed 15 variations (Table  1 ), of which only six corresponded to non-synonymous substitutions in the FAD2 protein sequence, namely (Q26H, S82C, H213N, E286G, H309R, and G341W) in all 19 varieties, representing new haplotypes and protein isoforms.
We studied the important genetic function by analyzing the linkage disequilibrium (LD). To perform haplotype analysis for polymorphisms in the FAD2 gene, we used the Haploview software that provided a single haplotype block including the five SNPs (Supplementary Fig. 1 ) and having an average Lewontin's D' = 0. 88. The interaction between the five SNPs generated eight different haplotypes (Supplementary Table  4 ). On the other hand and according to Gabriel et al. [35] , the lower limit of a 95% confidence interval exceeded 0. 7, and the status of linkage disequilibrium was reached. Fig. 3  shows only three SNPs (SNP 307, SNP 699 and SNP 919) with a strong degree of linkage disequilibrium, and forming a single block with an average of Lewontin's D 0 = 1. 0 (D 0 = 1. 0 value demonstrates the strongest linkage disequilibrium between two given polymorphisms).
Structure prediction The 3D structures of FAD2 proteins were predicted by homology modeling, based on the 42% sequence identity with a mammalian stearoyl-CoA desaturase (PDB ID 4YMK). The quality of the produced models was assessed by Ramchandran plot analysis. The latter shows 97. 2% of the residues located in the allowed U and W dihedral angles, while only 2. 8% of the residues were outliers. All the FAD2 generated models consisted of 13 a-helices, among which, four longer a-helices (TM1, TM2, TM3 and TM4) with 29, 23, 21 and 20 residues, respectively were present, which together displayed a conical shape (Fig. 6A ). These helices are most likely organized in transmembrane domains, since they match with the large red peaks obtained by the TMHMM server (Fig. 6B ). To confirm this observation, a hydrophobicity surface representation was generated.
| Bioinformatics analyses The resulting sequences were analysed using R  (Team, 2019)  workflow combining dada2 v. 1. 16 (Callahan et al. , 2016)  and FROGS 3. 2. 2 (Escudié et al. , 2018)  software. Reads were filtered, merged and assigned to ASVs with dada2 and the ASVs were assigned to species using the FROGS affiliation tool. Adapters were first removed using cutadapt v. 1. 12 with Python 2. 7. 13. Reads were then filtered using the dada2 filterAndTrim function, with a truncation length of 250 bp for ITS1 forward and reverse reads and 275 and 200 bp for 16S forward and reverse reads, respectively. This truncation reduced the error rate while still allowing the merging of most reads. The error model was then calculated using the learnErrors function. Reads were dereplicated using derepFastq and the dada2 core sample inference algorithm was executed.
This may have reflected biases in the metabarcoding analysis (van der Loos & Nijland, 2020). DNA could have been poorly extracted or amplified, thus leading to a low number of reads. The reads might also not have passed the quality filtering or merging steps in the bioinformatics analysis, particularly if the ITS region was too long. This is a limitation of the dada2 software, where reads that are too long to be merged are lost. However, this does not concern the ITS database, as in this case the ASV would have been found but not assigned to a species.
DATA AVA I L A B I L I T Y S TAT E M E N T Raw data and scripts are available at: https://data. inrae. fr/datas et. xhtml ?persi stent Id=doi:10. 15454/ DF0BRL. The sequencing data generated in this study were deposited in the European Nucleotide Archive (ENA) under accession numbers PRJEB52423 for the 46 collected sourdoughs [dataset]  von Gastrow (2021) and.
Herein, we used different mixed ratios of Delta and Omicron cell culture isolates in order to assess the performances of four different primer sets for the detection of Delta/Omicron coinfections. A co-infection score was determined to warn about probable co-infection. The prevalence of Delta/Omicron and BA. 1/BA. 2 co-infections were then estimated on a large data set of sequences obtained from random surveillance of out-patients and systematic sequencing of hospitalized patients.
Bioinformatics Reads were processed using the in-house bioinformatic pipeline seqmet (available at https://github. com/genepii/seqmet, software versions provided in Table  S1 ). Paired reads were trimmed with cutadapt to remove sequencing adapters and low-quality ends, only keeping reads longer than 30 bp. Alignment to the SARS-CoV-2 reference genome MN908947 was performed by Minimap2. Mapped reads were processed to remove duplicates tagged by picard, then realigned by abra2 to improve indel detection sensitivity and finally clipped with samtools ampliconclip to remove read ends containing primer sequences. Variants present at frequencies of 5% or above were called using freebayes, then decomposed and normalized with vt and filtered with bcftools to eliminate false positives. To detect coinfection, obtained vcf files were compared to a lineage variant database, both developed internally to this end.
The database consists of vcf files listing variants found in 50% or more of 100 randomly selected sequences for a given pangolin lineage in the full GISAID dataset available (extracted on 02 February 2022). The database is available at https://github. com/genepii/seqmet. The co-infection detection script searches for each lineage any major or minor variant matching expected variants of the putative main lineage and then searches for any minor variant matching any other lineage excluding variants in common with the main lineage. This approach provides putative main and secondary lineages contained in a sequenced sample read, along with a ratio of observed and expected variants in each case. Variants were expected when they occurred on a position covered with at least 100 reads. Secondary lineages, considered indicative of a putative co-infection, required at least 6 lineage-specific variants to be called.
Data availability The GISAID accession numbers of the Delta and Omicron virus isolates used for experimental mixes are EPI_ISL_11171170 and EPI_ISL_11171169, respectively. Sequencing data of the Delta:Omicron mixes were deposited on the SRA database under accession PRJNA817870, and dehosted sequencing data of NPS with co-infections were deposited under accession PRJNA817806.