dataesr/scientific-paragraphs-categorization huggingface dataset:
62 CRAM files were downloaded from iRODs Wellcome Sanger Institute core pipeline. CRAM files were converted to FASTQ format using Biobambam2 (v2. 0. 37)  65  (bamtofastq exclude= SECONDARY,SUPPLEMENTARY,QCFAIL). Nextera adaptor sequences were trimmed using Trim Galore (v0. 4. 3) 66 (trim_galore -q 20 -a CTGTCTCTTATACACATCT -paired -stringency 3 -length 50 -e 0. 1). Trimmed FASTQ files were then mapped using HISAT2 (v2. 1. 0)  67 and indexes were produced using the P. berghei v3 genome sequences,  68  downloaded from GeneDB  69  (October 2016). Trimmed reads were then mapped using default parameters (hisat2 -max-intronlen 5000 -p 12 -q -x). GFF files were downloaded from GeneDB (October 2016) and converted to GTF files using an in-house script. All feature types (mRNA, rRNA, tRNA, snRNA, SnoRNA, pseudogenic_transcript and ncRNA) were conserved, with their individual 'coding' regions labelled as CDS in every case for convenience.
Where multiple transcripts were annotated for an individual gene, only the primary transcript was considered. Reads were summed against genes using HTSeq (v0. 11. 2)  47  (htseq-count -f bam -r pos -s no -t CDS). HTSeq excludes multimapping reads by default (-a 10). This means that reads mapping ambiguously to similar genes from the same family are not considered in our analysis. 10x data alignment, cell barcode assignment, and UMI counting The sequencing reads in CRAM format were downloaded from iRODs Wellcome Sanger Institute core pipeline. CRAM files were converted to FASTQ format using the samtools fastq command. 63 Cell Ranger (version 2. 1. 1) was used to create a reference file from the P. berghei v3 genome (obtained from: www. sanger. ac. uk/resources/downloads/protozoa/). 68 using standard parameters (cellranger mkref -genome=.
-fasta=. -genes=. ). The gene PBANKA_0713500 was manually corrected to include two exons with missing ids. FASTQ files were passed into the Cell Ranger 2. 1. 1 workflow to assign each read to a cell barcode and UMI using standard parameters  70  (cellranger count -id=. -transcriptome=. -fastq=. ). Filtering and normalization of scRNA-seq data R version 4. 0. 3 (2020-10-10) was used for all scRNA-seq analysis. 71 mart-seq2 filtering Count matrices and associated metadata (phenodata) were read into R (version 4. 0. 3) and no cell and 100 cell controls were removed from further analysis. Seurat (version 3. 2. 2) was used for pre-processing. 49 63/5245 genes were not detected in any cell and were also removed. Cells with genes per cell < 220, genes per cell > 3300, percentage of total counts mapping to mitochondrial genes > 20%, and number of counts per cell < 1000 were removed.
Normalization and doublet detection In both datasets cells were normalized (NormalizeData(x, normalization. method = "LogNormalize", scale. factor = 10000), variable genes were found (FindVariableFeatures(x, selection. method = "vst", nfeatures = 2000), and data was scaled (all. genes = all genes in the datset; i. e. ScaleData(x, features = all. genes)). For the 10x data, doublets (cell barcodes that are associated with a significant number of reads from multiple cells) were filtered out using DoubletFinder. v3  72 (doubletFinder_v3(pb_sex, PCs = 1:21, pN = 0. 25, pK = 0. 01, nExp = nExp_poi, reuse. pANN = FALSE, sct = FALSE). Doublet removal was not applied to the Smart-seq2 dataset as a singlet gate was applied during FACS.
Data integration Wild-type cells from the Smart-seq2 and 10x datasets were integrated using Seurat v3. 2. 2. 49 Each dataset was subsetted to only include genes that were present in both (5018 shared genes). Datasets were then individually normalised (NormalizeData()) and variable features were found (FindVariableFeatures(x, selection. method = "vst", nfeatures = 2000). Anchors were found (FindIntegratio-nAnchors(object. list = x, dims = 1:21) and integration was performed (IntegrateData(anchorset = x, dims = 1:21, features. to. integrate = shared. genes; where shared. genes is all 5018 genes).
Cell projection, clustering, and annotation To identify subpopulations of cells in the integrated dataset, PCA was performed (RunPCA(x, npcs = 30) and then cells were projected into two dimensions using the UMAP algorithm 54 using the first 10 principal components after inspection of an elbow plot to detect significant principal components in Seurat v3. 2. 2; the other parameters used were: n. neighbors = 150, min. dist = 0. 4, repulsion. strength = 0. 03, local. connectivity = 150. Cells were clustered in a ten dimension UMAP space using the Louvain algorithm with multilevel refinement at a resolution of 2. 73  Cells were manually annotated by assigning each Louvain cluster to either asexual, progenitor, male or female, based on established marker genes (Figure  S3 ). ] [53]  (learn_graph(x, learn_graph_control=list(ncenter=550, minimal_branch_len = 15), use_partition = FALSE; order_cells(x)).
The root cells were selected manually using the interactive feature. The median pseudotime value for each cluster was then used to order clusters within each lineage. At this point, two female clusters collapsed into one due to their similar median pseudotime. The progenitor cluster was analysed further (n = 206, all cells were from the 10x dataset). Variable features were calculated for this subset and the cells were projected into 30-dimension PCA space. The elbow plot of these PCs was inspected and seven of these dimensions were used to find neighbours. Clusters were generated using the Louvain algorithm with multilevel refinement at a resolution of 0. 5. (75) (Figure  S4A. ) Marker genes for these resulting two clusters were found using the MAST framework implemented in Seurat v. 3. 2.
2 (FindAllMarkers(x, only. pos = FALSE, min. pct = 0. 25, logfc. threshold = 0. 25, test. use = "MAST")  74  Pseudotime and gene module generation In order to analyse the sexual branch, the following clusters were subsetted for further analysis: Asexual_10, Asexual_11, Asexual_12, Progenitor, Male_1, Male_2, Female_1, Female_2, Female_3. 9 outlier cells were removed from analysis as they were located far from the clusters and would have affected pseudotime calculation. This resulted in a subset of 2817 branch cells (Figure  3C ). For the pseudotime analysis of the sex branch, the subsetted cells were further subsetted so that only wild-type cells generated using 10x were included in the analysis. The reason 10x cells were only used in following steps was because there is not a robust way to account for the batch effects between the Smart-seq2 and 10x dataset within Monocle and Monocle is not compatible with negative values generated during Seurat batch correction.
Data integration To compare mutant transcriptomes to wild-type ones, all cells from the Smart-seq2 and 10x datasets were integrated using Seurat v3. 2. 20. 49 Each dataset was subsetted to only include genes that were present in both (5018 shared genes). Datasets were then individually normalised (NormalizeData()) and variable features were found (FindVariableFeatures(x, selection. method = "vst", nfeatures = 2000). Anchors were found (FindIntegrationAnchors(object. list = x, dims = 1:21) and integration was performed (IntegrateData(anchorset = x, dims = 1:21, features. to. integrate = shared. genes; where shared. genes is all 5018 genes).
Cell projection, clustering, and sex branch isolation To identify subpopulations of cells in the integrated dataset, PCA was performed (RunPCA(x, npcs = 30) and then cells were projected into two dimensions using the UMAP algorithm  54  using the first 10 principal components after inspection of an elbow plot to detect significant principal components in Seurat v3. 20. 20; the other parameters used were: n. neighbors = 150, min. dist = 0. 4, repulsion. strength = 0. 03, local. connectivity = 150. Cells were clustered in a ten dimension UMAP space using the Louvain algorithm with multilevel refinement at a resolution of 0. 5 (75) (Figure  S5A ). In order to analyse the sexual branch, 11/26 clusters were subsetted for further analysis (Figure  S5D ). 8 outlier cells were removed from analysis as they were located far from the clusters and would have affected pseudotime calculation later.
6 Th (Thomson unit). Targeted precursors were dynamically excluded for further isolation and activation for 40 s with 10 ppm mass tolerance. Raw files were processed with Proteome Discoverer v. 2. 4 (Thermo Fisher). Database searches were performed with Sequest HT against the PlasmoDB-54 Plasmodium berghei ANKA annotated proteins (v. October 2021) appended with the cRAP database (www. thegpm. org/crap/). The search parameters were set to trypsin digestion, 2 missed cleavages, 10 ppm mass tolerance for MS, 0. 5 Da mass tolerance for MS/MS, with variable modifications of protein N-terminal acetylation, oxidation(M), and pyro-glu (N-term Q). Peptide false discovery rates (FDR) were estimated based on matches to reversed sequences in a concatenated target-decoy database using Percolator and set at 0.
01. Protein identification required at least one high-confidence peptide at FDR <1%. Protein hits from the cRAP database were removed from the lists prior to further analysis. To discriminate bait-specific interactions from background binding, protein lists from bait and control experiments were analysed with SAINTexpress. 55 Proteins with a SAINT probability score (SP) R 0. 99 were deemed high confidence interacting proteins. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  75  partner repository with the dataset identifier PXD033827. Fractionation of Plasmodium proteins into cytoplasmic, nuclear and nuclear insoluble extracts Fractionation of cytosolic and nuclear proteins was done as follows and largely followed the published procedure for P.
ACKNOWLEDGMENTS The authors would like to thank the staff of the  Illumina Bespoke Sequencing  and  Core Cytometry  teams at the  Wellcome Sanger Institute  for their contribution. Fernando Sialana  and Prashant Kaushal from the  Functional Proteomics team at the Institute of Cancer Research  are thanked for the MS analysis. Funding: the work at the  Wellcome Sanger Institute  was funded by  Wellcome  core grant  206194/Z/17/Z  awarded to O. B. , M. K. N. L. and  A. J. C. R. Work  at  Umea  ˚University received funding from the  Knut and Alice Wallenberg Foundation  and the  European Research Council  (grant agreement no. 788516 ). Work at the  University of Glasgow  (A. P. W. , A. B. R. , and R. S. K. ) was funded by the  Wellcome Trust  (refs:  083811 ,  104111 , and  107046  to A. P. W.
Pt[H-AHP]. Yield 36,3 mg (67%). UV-vis (CH2Cl2) λmax [nm, ε x 10 -4 (M  Diffraction data were collected using synchrotron radiation monochromated using silicon(111) to a wavelength of 0. 7293(1)Å. An approximate full-sphere of data was collected using 0. 6° w scans. The structure was solved by direct methods (SHELXT) and refined by full-matrix least squares on F 2 (SHELXL-2013). All non-hydrogen atoms were refined anisotropically. Hydrogen atoms were geometrically calculated and refined as riding atoms. Full details of the analyses are given in the crystallographic information files, which can be obtained free of charge from The Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/data_request/cif. Photophysical studies. Screening of the azahemiporphycenes for luminescent properties was done on a Fluorolog 3 fluorescence spectrometer from Horiba (Japan) equipped with a NIRsensitive photomultiplier R2658 from Hamamatsu (Japan).
Hydrogen atoms were geometrically calculated and refined as riding atoms. Full details of the analyses are given in the CIF files available from the Cambridge Structural Database. Selected crystal Data for H2[Me-AHP]. Chemical formula C40H31N5, Mr = 581. 70, orthorhombic, Pbca, a = 19. 2471(5) Å, b = 11. 2754(3) Å, c = 27. 7016(7) Å, a = b = g = 90 °, V = 6011. 8(3) Å 3 , T = 126. 0(1) K, Z = 8, Z' = 1, µ(Cu Ka) = 0. 596, 44172 reflections measured, 5312 unique (Rint = 0. 0670) which were used in all calculations. The final wR2 was 0. 1669 (all data) and R1 was 0. 0858 [I ≥ 2 s(I)]. Selected crystal Data for Pd[Me-AHP]. Chemical formula C40H20N5Pd, Mr = 686. 08, orthorhombic, Pbca, a = 19. 3019(8) Å, b = 11. 2284(5) Å, c = 27. 6885(12) Å, a = b = g = 90 °, V = 6000. 9(4) Å 3 , T = 100(2) K, Z = 8, Z' = 1, λ 0.
Excitation of the samples was carried out with the light of either a frequency double (532nm, 8 ns pulse duration; 30 mJ/pulse energy) or frequency triple (355nm, 8 ns pulse duration; 15 mJ/pulse energy) Spitlight Compact 100 (Innolas) Nd:YAG laser. Accession codes. The crystal structures reported in this paper have been deposited at the Cambridge Crystallographic Data Centre and assigned the deposition numbers CCDC 2255991 and 1002323. ASSOCIATED CONTENT Supporting Information. 1 H NMR and electrospray ionization mass spectra; spectra associated photophysical studies.
Our approach involves CTC-based modeling and an adaptation of the linear memory DTW to the CTC context. It offers several advantages: our end-to-end architecture avoids the complexity of legacy HMM-based solutions, and our linear memory CTC decoding avoids the necessity to segment long audio into imprecise shorter chunks before synchronization. Our contributions are:  (1)  we introduce an adaptation of the linear memory DTW to to the CTC context for step A, extending the use of CTC-based algorithms for the alignment of very long audio recordings; (2) we describe a CTC-based fully-convolutional neural architecture agnostic to audio duration for step E ; (3) we demonstrate that our system is able to synchronize several hours of audio with the corresponding text, yielding a mean absolute alignment error of 50 ms for speech and 120 ms for the singing voice, which corresponds to a median alignment error that is below 50 ms for both voice types; (4) we illustrate its robustness against deteriorated transcripts; (5) we demonstrate that, albeit being trained solely on unsegmented audio and text in English, it achieves similar performances for many other languages;  (6)  we compare it to the state-of-the art systems and demonstrate that our simple end-to-end approach rivals much more complex systems on publicly available datasets.
CTC-Based Modeling for the Encoding Step In our applicative context, the timestamped encoding E can be implemented as the output of a CTC-trained neural network trained on audio directly, which represents the per-frame character emission probabilities-referred to as posteriorgram. To the best of our knowledge, Stoller et al. [38]  were the first to exploit this strategy when they explicitly leveraged the time dimension of CTC posteriorgrams to align the lyrics and singing voice with background music. By extracting multi-scale representations from raw audio samples with a Wav-U-Net architecture  [41] , trained on a 40k+ song proprietary dataset, the authors proved the feasibility of aligning polyphonic music with an end-to-end CTC acoustic prediction framework, and demonstrated the interest of this approach for text-to-voice alignment in general.
Singing voice We used a 90-10 split of the DALI dataset (https://github. com/gabolsgabs/DALI (accessed on 31 January 2023)) for training and evaluation. The model was trained during 200 epochs (with 512 steps per epoch) via the classical CTC loss, Adam optimizer, and initial learning rate of 1 × 10 -4 that is reduced by a factor of 0. 9 each time the loss did not decrease on the evaluation set for 2 epochs. We used a batch size of 16 on 1 GPUs NVIDIA GeForce GTW 1080 Ti with 12 MB. The training lasted roughly 10 h.
Reference Corpora There is no publicly available corpora to assess the performances of systems addressing the very long audio alignment task. Therefore, we assembled such corpora for the spoken and singing voice, and released them publicly (https://ircam-anasynth. github. io/papers/ 2023/a-linear-memory-ctc-based-algorithm-for-text-to-voice-alignment-of-very-long-au dio-recordings (accessed on 31 January 2023)). We encourage the community to use them.
Singing Voice For singing, we leveraged the available annotations of the DALI dataset, in which all of the alignments have already been annotated at the word level  [45]. We selected 150 songs that were not used during our model training, and we gathered a playlist containing 50 of these songs. We simply concatenated both the audio and the corresponding annotated lyrics available in DALI. The playlist contains 18,094 words, and the corresponding audio has a duration of 2:50:24 (HH:MM:SS). The other 100 songs were kept for our experiments (see below). In the following, we will refer to this singing voice reference corpus as "Playlist 50".
Speech We selected from the Librivox corpus several audiobooks in other languages than English, listed in Table  3. For each language, the text corresponding to approximately 1 min of audio was manually aligned by native speakers. We publicly released the audio and the manually aligned transcript for each language (https://ircam-anasynth. github. io/papers/ 2023/a-linear-memory-ctc-based-algorithm-for-text-to-voice-alignment-of-very-long-au dio-recordings (accessed on 31 January 2023)). For Latin alphabet languages, diacritic characters are converted to plain characters with the python unidecode package (https://pypi. org/project/Unidecode (accessed on 31 January 2023)). For non-Latin alphabet languages, the text was first transliterated before the synchronisation: for Chinese, we used the python pinyin package (https://pypi.
org /project/pinyin (accessed on 31 January 2023)), for Greek, we used the python polyglot package (https://github. com/aboSamoor/polyglot (accessed on 31 January 2023)), and for Arabic, we used a human manual transliteration. We used our system to align the text to the audio, and summarized the results in Table  3. It appears that although the system was trained in English audio and transcripts only, it performs very well for other languages as well, including for languages that have a very distinct pronunciation, as compared to English. We interpret these results by the fact that the system encounters enough anchor characters (typically spaces, vowels, and consonants that are common to English) to align the full words correctly, even though it might not be able to recognize some particular phonemes perfectly.
The classical datasets used in the MIREX contest (https://www. music-ir. org/mir ex/wiki/2020:Automatic_Lyrics-to-Audio_Alignment_Results (accessed on 31 January 2023)), i. e. , Jamendo and Hansen's datasets, only include up to 20 songs of a few minutes. Although our system was designed to address the very long audio alignment use case, we compared our system with the current state-of-the-art systems for the sake of completeness, as reported in Table  6. For this experiment, our model was trained on the entire DALI dataset, with each epoch processing the whole database. The rest of the above-mentioned training procedure remains identical. Comparing first the results of the Jamendo dataset, we find that our system is second in MAAE, with a Q50% value equal to the best SOTA result. For the a cappella variant of the Hansen dataset, our system achieves results close to the second-best SOTA system.
Lyrics' alignment MAAE (ms) on the Hansen (a cappella variant) and Jamendo datasets and comparison to the latest alignment systems (MIREX 2020). ‡We indicate best performing version on each dataset, as reported on MIREX 2020 results (https://www. music-ir. org/mirex/wiki/2020: Automatic_Lyrics-to-Audio_Alignment_Results (accessed on 31 January 2023)).
Acknowledgments: The authors are very thankful to (1)  Christopher Tralie  for fruitful discussions about the linear memory DTW; and (2)  Jan Chab ,  Christina Chalastanis ,  Alice Cohen-Hadria ,  Paola Lumbroso ,  Klarissa Roebel ,  Mei-Hua Roebel ,  Léane Salais ,  Georgia Spiropoulos , and  Marc Wijnand , for manually aligning text-to-voice in their respective mother tongue.
Data Availability Statement: The Librispeech dataset can be found at https://www. openslr. org/12 (accessed on 31 January 2023). The DALI dataset can be found at (https://github. com/gabolsgabs/ DALI (accessed on 31 January 2023). The evaluation dataset (Chapter 10 and Playlist 50) are available at https://ircam-anasynth. github. io/papers/2023/a-linear-memory-ctc-based-algorithm-for-textto-voice-alignment-of-very-long-audio-recordings (accessed on 31 January 2023).
A digestion profile was developed from in vivo data and a review of in vivo data cited in the scientific literature to represent the piglet digestive system. The in vitro ileal digestate was previously compared to in vivo ileal digestates from piglets, using SEC-HPLC to validate protein/peptide profiles and sugar profiles. Protein/peptide profiles were very similar between the in vitro ileal digestate and the in vivo samples (data not shown). Carbohydrates available in the in vitro digestate were higher than in the in vivo samples, since the IViDiS model does not simulate absorption. Dilution studies were performed to determine the correct ratio of digestate to diluent to be used. Digestate was collected in sterile plastic bags (Whirl-pak, Madison, USA) immersed in iced water. Bags were changed every 30 min, during the digestion period and frozen at -20 °C until use.
Sequence analysis and diversity determination Raw sequencing reads were demultiplexed, quality-filtered, and analyzed using Mothur software  [34]  version 1. 43. 0, according to Mothur MiSeq SOP, with the following modifications: the method for pre-cluster was Deblur and the method for cluster was Unique  [35] , therefore ASV (Amplicon sequence variants) and not OTU were used. Mothur SOP was last accessed on April 27, 2021. Taxonomic assignation of the ASV was done using the Silva 132 Mothur-formatted database. The resulting files were further analysed in RStudio 1. 3. 1073 using R version 4. 0. 3. The shared and taxonomy files produced by Mothur were first imported into R. After inspection of raw data, controls were removed and remaining samples were rarefied to the lowest number of sequences within a sample prior to alpha and beta diversity analysis.
6  Graphical representation of the evolution of the log DNA copies of lactobacilli per ng of genomic DNA. N = 3, bar represents standard deviation. Lactobacilli levels were measured by qPCR. Graphical representation was generated using GraphPad. Student T-test showed a significant increase in the thymol 1000 samples, between 48 and 72 h of running time (P = 0. 001). Another significant increase was found for the thymol 1000 sample compared to the reactor without oil at T72 (P = 0. 001) is a dynamic in vitro digestion system which attempts to mimic appropriately the digestion process occurring in piglets by simulating the ingestion pattern, destructuration of feed, enzymatic activities of the various digestion organs, pH profiles, and flow rates, including gastric emptying rate, according to data obtained from the literature and in vivo data.
| Performing a systematic review (and meta- analysis) Each working group undertook at least one systematic review of the medical literature that was designed to form the basis for the evidence-based guidelines. Each systematic review was prepared according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines  17, 18  (http://www. prisma-statement. org). Each working group used the A MeaSurement Tool to Assess systematic Reviews tool to check that they were addressing the most important aspects of their systematic review (https://amstar. ca/Amstar_Checklist. php). Systematic reviews were prospectively registered in the PROSPERO database for systematic reviews before Depending on the number of papers found with this higher-level study design, working groups could also include lower-level designs, for example, non-RCTs, case-control studies, cohort studies (controlled) before-and-after studies, interrupted time series, prospective and retrospective non-controlled studies, cross-sectional studies and case series.