dataesr/scientific-paragraphs-categorization huggingface dataset:
To provide a reproducible approach in the microbiome research, we document software versions, description about each phase and the necessary code/scripts to perform experiments are available on Github (https://github. com/steppenwolf0/MicrobiomeREFS). An overview of the proposed methodology is illustrated in Fig. 4. The dataset selection criteria phase involves the selection, download, and extraction of relevant information from metadata (e. g. , samples labels). The datasets must meet the following conditions: • The databases should be 16s ribosomal RNA (rRNA) amplicon sequencing and belong to the same domain such as disease, disorder, or medication. • There should be a minimum of two groups such as a control group and a case group. • Each group should have a minimum of 10 samples. • The documentation, whether it be metadata or a scientific paper, should clearly specify which group each sample belongs to.
• Datasets should have the same source of samples such as tissue, feces, or mucosa. The raw data processing phase involves performing amplicon workflow on the raw data in the selected datasets and generate ASVs (features). We selected the DADA2 pipeline [1] [25] due to its clear documentation. The DADA2 open-source R package allows to implement the full amplicon workflow on 16s rRNA sequences: filtering, dereplication, sample inference, chimera identification, and merging of paired-end reads  [25]. We developed a DADA2-based script in R version 4. 1. 2, the code editor was RStudio version 2022. 07. 2 build 576, the DADA2 library version was 1. 22. 0, the DECIPHER library version was 2. 22. 0, the BiocManager library version was 1. 30. 19, and the taxonomy assignment was performed based on the SILVA SSU r138 2019 [2] reference database.
The feature selection phase aims to identify features (signatures) that are present in other datasets, so one dataset must be selected for discovery. The eligibility criteria for the discovery dataset is the one that contains the shortest sequence length after the raw data processing phase. Once the discovery dataset is selected, we have to perform two processes: 1 The Recursive Ensemble Feature Selection (REFS),which is an algorithm for identifying biomarkers by determining the features that are most effective in differentiating between groups in datasets achieving the highest accuracy with the fewest number of features  [48, 57, 58, 59, 60, 61, 62]. To minimize overfitting and bised performance, REFS employs a nested approach within a 10-fold cross-validation scheme, which is a proven solution to yield more accurate and unbiased results, even with a small sample size  [42].
The testing phase involves testing the features selected by using REFS in a minimum of two separate datasets. The selected features must be searched on each testing dataset. Features can be repeated in the testing datasets, so we must follow the next process: if Feature x is present n-times in the testing dataset, the final abundance of Feature x will be the sum of the abundance of those n-occurrences. To validate the features found in each testing dataset, the validation module must be executed one time on each testing dataset using as input the samples labels, the found features, and the corresponding abundance. The AUC is employed as a measure of diagnostic accuracy. Additionally, we conducted a comparative analysis with two different feature selection methods: [1] DADA2 pipeline is available in https://benjjneb.
github. io/dada2/tutorial 1 8. html [2] Available in: http://www2. decipher. codes/Classification/TrainingSets/ • K-Best with F-score. This selection method will be applied to the discovery dataset instead of REFS. We used the SelectKbest algorithm from the scikitlearn toolbox which selects the K top-scoring features based on a user-defined metric, with the F-score  [63]. The value assigned to K is determined by the number of features obtained using REFS. For instance, if REFS selected 10 features, the value of K would be set to 10. • 10-time random selection. This method consists in randomly select a given number of features from all features in each testing dataset. This given number is determined by the number of features found in each testing dataset. For instance, if 8 out of 10 features selected by using REFS were identified in the testing dataset, then 8 features will be randomly selected each time.
Datasets We used a total of nine datasets, with three datasets for each experiment: Autism Spectrum Disorder (ASD), Inflammatory Bowel disease (IBD), and Type 2 Diabetes (T2D), see Fig. 5. Each dataset adhered to the data selection criteria phase. We considered only two groups within each dataset: control and cases. The control group is made up of healthy people or people in remission, the case group is made up of people diagnosed with the medical condition. The datasets related with ASD are: 1) David et al [49] [3] , it has 117 samples of which 57 belong to the control group and 60 to the case group, 2) PRJNA589343  [64]  downloaded from the NCBI public repository [4] , it has 127 samples of which 50 belong to the control group and 77 to the case group, and 3) PRJNA578223  [65]  downloaded from the NCBI public repository, it has 96 samples of which 48 belong to the control group and 48 to the case group.
The datasets related with IBD are: 1) PRJEB21504  [66]  downloaded from the NCBI public repository, it has 95 samples of which 66 belong to the control group and 29 to the case group, 2) DRA006094  [67]  downloaded from the NCBI public repository, it has 70 samples of which 15 belong to the control group and 55 to the case group, and 3) PRJNA68458  [68]  downloaded from the NCBI public repository, it has 103 samples of which 45 belong to the control group and 58 to the case group. The datasets related with T2D are: 1) PRJNA325931  [69]  downloaded from the NCBI public repository, it has 112 samples of which 84 belong to the control group and 28 to the case group, 2) PRJNA554535  [70]  downloaded from the NCBI public repository, it has 60 samples of which 20 belong to the control group and 40 to the case group, and 3) PRJEB53017  [71]  downloaded from the NCBI public repository, it has 94 samples of which 46 belong to the control group and 48 to the case group.
0800 0. 6510 0. 1300 0. 8000 0. 0800 KNeighbors 0. 6800 0. 0900 0. 6220 0. 1210 0. 6800 0. 1300 MLP 0. 7400 0. 1200 0. 6490 0. 1390 0. 7300 0. 1100 Lasso CV 0. 7100 0. 1000 0. 5710 0. 0660 0. 5400 0. 0800 Average 0. 7480 0. 0940 0. 6278 0. 1154 0. 7040 0. 1080 PRJNA578223 20 of 26 features (REFS) 10-time random selection SelectKbest (17 of 26 features) Classifier AUC std AUC std AUC std AdaBoostClassifier 0. 8300 0. 0800 0. 6530 0. 0640 0. 6700 0. 1400 Extra Trees 0. 8400 0. 1100 0. 6510 0. 0670 0. 6900 0. 1400 KNeighbors 0. 7000 0. 1500 0. 6370 0. 0570 0. 5900 0. 1500 MLP 0. 7200 0. 0800 0. 6120 0. 0890 0. 7300 0. 1100 Lasso CV 0. 6100 0. 1000 0. 6230 0. 0770 0. 7100 0. 1100 Average 0. 7400 0. 1040 0. 6352 0. 0708 0. 6780 0. 1300 * Discovery dataset.
1900 0. 5210 0. 0850 0. 7200 0. 2200 KNeighbors 0. 8300 0. 1500 0. 5260 0. 0730 0. 6800 0. 2300 MLP 0. 8300 0. 1600 0. 5510 0. 1140 0. 7200 0. 2200 Lasso CV 0. 7400 0. 1800 0. 5230 0. 0830 0. 7600 0. 2100 Average 0. 7780 0. 1860 0. 5280 0. 0888 0. 7320 0. 2140 PRJNA684584 48 of 53 features (REFS) 10-time random selection SelectKbest (52 of 53 features) Classifier AUC std AUC std AUC std AdaBoostClassifier 0. 7200 0. 1300 0. 5410 0. 1260 0. 6400 0. 1400 Extra Trees 0. 7500 0. 1000 0. 5600 0. 1220 0. 7400 0. 1200 KNeighbors 0. 7000 0. 1200 0. 5610 0. 1120 0. 5900 0. 1900 MLP 0. 6800 0. 1000 0. 5700 0. 1370 0. 6800 0. 1700 Lasso CV 0. 7000 0. 1100 0. 5590 0. 0900 0. 6100 0. 1400 Average 0. 7100 0. 1120 0. 5582 0. 1174 0. 6520 0. 1520.
2. 1. Systematic literature search The PubMed database was searched from January 1, 2020 to August 10, 2022 for English-language publications regarding molecular imaging and therapy of PCa. Systematic reviews, meta-analyses, evidence-based guidelines, and consensus statements were included. Given that the area is evolving rapidly, review papers prior to 2020 were considered to be outdated and therefore were not used. The quality of the retrieved systematic reviews was assessed using the AMSTAR 2 criteria (full search strategy and results are shown in the Supplementary material)  [6]. The papers retrieved in the literature search were made available to the expert panelists alongside tables summarising the quality assessment.
Acknowledgements: We would like to thank  Evelyn Mansutti ,  Susanne Koebe ,  Andrea Csismazia ,  Jutta Peter ,  Petra Neubauer ,  Andreas Felser , and  Henrik Silber  (all from  EANM ) for project management. We also acknowledge all who participated in EANM Focus 5 meeting and especially the patients who participated in clinical and research projects, making it possible to inform expert opinion.
al, 2014, accession number GSE60428) and PSC (modEncode, 3960: Psc;Oregon-R;Embryos 14-16 hr OR;ChIP-seq, D. melanogaster). Domains having peaks of at least two of these PRC1 subunits were kept. They were also visually inspected using Hi-C maps from  (Ogiyama et al. 2018). For active domains, they were re-selected based on the enrichment of H3K4me3 and H3K36me3 (modEncode, 5096: H3K4me3;Oregon-R;Embryos 14-16 hr OR;ChIP-seq; 4950: H3K36me3; Oregon-R; Embryos 14-16 hr OR ;ChIP-seq), and they were also visually inspected using Hi-C maps from  (Ogiyama et al. 2018). For inactive domains, they were selected based on the   (Bantignies, 2011). Polycomb domains located outside the selected ~15 Mb region, or smaller than 20 Kb were not labeled as they interacted with other Polycomb genes very infrequently.
Library synthesis and amplification The library synthesis method is based on the development by  (Beliveau et al. 2012; 2015). After selecting the genomic regions of interest, a database of genomically unique, non overlapping sequences was used to generate the Oligopaint primary probes (Oligopaints website, https://oligopaints. hms. harvard. edu/). They were mined using OligoArray  (Rouillard, Zuker, and Gulari 2003). Each Oligo of the library is made of 148 nucleotides (nt), and consists in (from 5' to 3'): a 22-nt forward universal primer region for library amplification, a 20-nt readout region, unique for each 15 kb targeted region, a 2-nt spacer, a 20-nt readout region, unique for each 15 kb targeted region, a 42-nt region of homology to chromosomal DNA, a 20-nt readout region, unique for each 15 kb targeted region, and a 22-nt unique reverse primer for library amplification.
2012 , https://github. com/ome/bioformats, (Linkert et al. 2010). Raw images were deconvolved using Huygens Professional version 20. 04 (Scientific Volume Imaging, the Netherlands,https://svi. nl/), via the CMLE algorithm (SNR:20, 40 iterations) with a custom-made script written in Tcl/Tk. The following analysis steps were performed with home-made scripts written in MATLAB 2019b (The MathWorks, Inc. , Natick, United States) and available in Mendeley Data. First, X-Y drift is corrected for each hybridization cycle. A global X-Y correction for each cycle is obtained by crosscorrelating the images of the fiducial of each cycle to that of the reference fiducial (reference cycle). This produces a single 3D vector for each barcode cycle that represents a global correction applied to the whole FOV.
G. Proximity frequency vs genomic distance for wild-type S15-16 embryos (blue circles), and for simulations of a self-interacting polymer in the globule regime (green curve, U=0. 9k B T), a polymer at the θ-like transition (gray dashed curve, U=0. 65k B T) and of a polymer in the swollen coil regime (solid gray curve, U=0k B T). H. Proximity frequency vs genomic distance, for wild-type S15-16 embryos (blue circles), Pc mutant embryos (gray triangles), and NC14 embryos (red squares). Solid and dashed lines represent a guide to the eye for the three datasets. A plateau in proximity frequency can be observed at genomic distances higher than 1 Mb.
Acknowledgements This project was funded by the  European Union 's  Horizon 2020 Research and Innovation Program  (Grant  ID 724429 ) (M. N. ). We acknowledge the  Bettencourt-Schueller Foundation  for their prize 'Coup d'élan pour la recherche Française', the  France-BioImaging  infrastructure supported by the  French National Research Agency  (grant ID  ANR-10-INBS-04 , '' Investments for the Future ''), and the  Drosophila facility  ( BioCampus Montpellier ,  CNRS ,  INSERM ,  Univ Montpellier , Montpellier, France). Author Contributions Conceptualization, J. G. , F. B. and M. N; Methodology, J. G. , C. H. , J-B. F, J-C. W. , and M. N. ; Investigation, J. G. , J-C. W. , F. B. , and M. N. ; Writing -Original Draft, J. G. and M. N. ; Writing -Review & Editing, J. G. , J-C. W. , J-B. F. , M.
Custom neutron reflectometry flow cells optimised for measurements in a horizontal sample geometry were used. Specular neutron reflectometry measurements were performed on the POLREF reflectometer at the ISIS Neutron and Muon Source (STFC Rutherford Appleton Laboratory, Didcot, UK) over q range 0. 009-0. 27 Å -1 and on the D17 reflectometer at the Institut Laue-Langevin (Grenoble, France) over q range 0. 007-0. 27 Å -1. Throughout the experiment, the temperature was maintained at 25 °C and for all non-manual sample or contrast changes, an HPLC pump with a flow rate of 1 mL min -1 (POLREF) and 2 mL min -1 (D17) were used. The raw data from POLREF was reduced using Mantid Workbench  36  and the data can be found at https://doi. org/10. 5286/ISIS. E. RB2010562. The raw data from D17 was reduced using LAMP  37  and the data can be found at https://doi.
The level of theory was B3LYP  45  and cc-pVTZ  46  was the basis set. Restrained electrostatic potential (RESP) approach in the R. E. D. 47  software was utilised to obtain more precise partial charges. The IEFPCM  48  solvent model with the dielectric constant of 78. 4 were used in order to mimic the solvent around the lipid head-groups since they were of particular concern here. The set of partial charges can be observed in Fig. S1  of the ESI. † Then the derived model was added to the SLipids force field database and the MD simulations were prepared according to the following protocol. Firstly, lipid bilayers containing DOPC and DLin-MC3-DMA were created where the ionisable lipids were placed randomly among the phospholipids in order to avoid any potential aggregation during the equilibration.
Berendsen barostat was employed for maintaining and keeping the pressure under semi-isotropic  50, 51  pressure coupling scheme. Velocity rescaling  52  thermostat was used for regulating the temperature. Newtonian equations of motion were integrated by leap-frog  53  algorithm with a step of 2 fs. The cutoff scheme was Verlet  54  with the type van der Waals and a radius of 1. 2 nm. LINCS  55, 56  algorithm was used for optimizing the bonds with 12 iterations. GROMACS-2019  57, 58  software was used for simulating all systems. After equilibration of 200 ns frames with lipid bilayers were extracted without water in order to create simulations with polyA and polyU. Due to its large size which would lead to extremely large systems, mRNA were not considered for MD simulations. PolyA and polyU were taken in their short versions (11 residues for polyA and 8 residues for polyU) from the existing structural solutions in the Protein Data Bank (PDB).
The same trends were observed already in the lipid layer alone, indicating a similar multilayer is formed in these conditions. A mixed area model of a bilayer and multilayer stack, where the stack consists of two alternating layers, could be successfully fitted to the experimental data (Fig. S29 -32 †). Judging from the SLD values of the layers, they contain a mixture of lipids and mRNA, with one less hydrated, lipid rich layer and one more hydrated, mRNA rich layer (Table  S12  †). Considering the thickness of the layer and the high degree of hydration, it does not appear that the mRNA is densely packed in the mRNA rich layer, however there is not sufficient sensitivity in the measurement to extract any structural details. NR measurements were additionally performed for 15% MC3 in pH 6 buffer with polyA and polyU, in which conditions case (iii) is observed for polyA (and EPO mRNA) and case (ii) for polyU in QCM-D.
Since the NA backbone is responsible for the secondary structure dihedrals were selected of the backbone. Distributions for four of them (α, δ, γ and χ) were chosen for analysis and computed by BARNABA software. 79  Fig. S44 -S59 in the ESI † show the dihedrals themselves as well as their average difference computed at different time points. In general, it can be observed (for 3 out of 4 dihedrals) that there are fewer conformational changes for polyA regardless of the system, indicating that it is more rigid. Out of presented data it can also be concluded that pH and amount of MC3 can affect the conformational distribution of NAs and their stability. For instance, in simulations with 15% MC3 the dihedrals vary over bigger degree intervals than in systems with 5% of the ionisable lipid. Fewer variations in conformation can be seen in systems with protonated DLin-MC3-DMA, likely due to a stronger association with the ionised lipid.
Acknowledgements We are grateful to the  ILL and ISIS  for allocating beamtime on D17 (https://doi. ill. fr/10. 5291/ ILL-DATA. 8-02-892 ) and POLREF (https://doi. org/10. 5286/ISIS. E. RB2010562) respectively and  Dr Sophie Ayscough  for the useful discussions and support. We would like to thank the  Swedish Research Council (Vetenskapsrådet)  for grants  2017-06716  and  2019-04020. The computations were performed on resources provided by the  Swedish National Infrastructure for Computing (SNIC). In National  Supercomputer Center (NSC)  Tetralith cluster was Nanoscale Paper This journal is © The  Royal Society of Chemistry 2024 Nanoscale , 2024, 16,  777-794 | 791  Open Access Article. Published on 07 December 2023. Downloaded on 6 /3/2024 11 :00:44 AM. This article is licensed under a Creative Commons Attribution-NonCommercial 3.
The grouping of non-coding RNAs into functional classes started in the 1950s with housekeeping RNAs. Since, multiple additional classes were described. The involvement of non-coding RNAs in biological processes and diseases has made their characterization crucial, creating a need for computational methods that can classify large sets of non-coding RNAs. In recent years, the success of deep learning in various domains led to its successful application to non-coding RNA classification. Multiple novel architectures have been developed, but these advancements are not covered by current literature reviews. We propose a comparison of the different approaches and of ncRNA datasets proposed in the state-of-the-art. Then, we perform experiments to fairly evaluate the performance of various tools for non-coding RNA classification.
Relative to the fact that most of the genome is non-coding, the number of well-known ncRNAs is low. This context creates a need for computational methods that can rapidly handle a large amount of ncRNAs. To this aim, different computational ncRNA classifiers have been developed over the years. The term 'classification' of ncRNAs can have different meanings in the literature. It can refer to the identification of ncRNAs, i. e. , separating coding from non-coding RNAs  [6, 7, 8, 9]. It can also refer to the identification of one specific class of ncRNA in a dataset, or class-specific prediction  [10, 11, 12]. A third use of the term is for multi-class classification, taking as input a set of ncRNAs and associating each one to a ncRNA class, which this review focuses on. There are many classes of ncRNAs, and multiple levels of description.
Other than the extensive feature preparation step, the method is based on a simple DL network of five FCLs. 1 A k-mer is a subsequence of k nucleotides. To summarize, as seen on Figure  1 , non-coding RNAs are often represented by their sequences. Their structures can also be used. Only one method uses another source of data, namely epigenetic marks. Two strategies emerge for input representation: the first is to encode data as a simple one-hot encoded matrix, while the other is to employ an RNN to learn a more complex representation. Classification is then usually performed with a CNN. Two state-of-the-art methods do not rely on CNNs or RNNs, and employ either a GNN or a simple succession of FCLs. 2 State-of-the-art datasets ncRNAs can be viewed through different lenses. Their sequences, secondary and tertiary structures, as well as their expressions, or epigenetic modifications, all carry information.
However, these different data types can be more or less accessible. Most recent datasets for ncRNA classification focus on the sequence. It has the advantage of being available in online databases in high quantity (e. g. , almost 35 million sequences in the non-coding RNA database RNAcentral  [38] ). It can also be used to obtain the secondary structure. Indeed, due to a lack of experimentally validated structures for ncRNAs, it is common to predict secondary structure from the sequence. This can be done using external algorithms  [39, 40]. Five datasets have been proposed in the literature to evaluate the different methods presented in the previous section. These are summarized in   1 : Summary of the different state-of-the-art datasets for ncRNA classification. Datasets are sorted in order of publication, with the name of the tool they were published with.
The number of instances and classes in each dataset is given. We indicate how many times the dataset has been used by state-of-the-art ncRNA classification methods. Finally, we give the range of sequence lengths for accessible datasets. The first dataset was proposed in 2017 by Fiannaca et al. [24] , along with the nRC tool. Sequences were downloaded from the 12th release of the Rfam database  [41]  and the CD-HIT tool  [42]  was used to remove redundant sequences. A random selection was carried out to obtain (almost) balanced classes, with a final size of 8,920 sncRNAs spanning 13 classes. Since its proposal, nRC's dataset has been used by other tools to assess and compare performance: in fact, all state-of-the-art methods from the previous section use nRC's dataset, except for RAG+DL. The appeal of this dataset is that classes are (almost) balanced, and there is a reasonable number of classes to represent a variety of sncRNAs while returning intelligible results.
However, this dataset presents a major flaw: 347 ncRNA are present in both the training and the test set (representing respectively 5. 5% and 13. 3% of the sets). This kind of data leakage casts doubt upon the performances reported by the various tools. Boukelia et al. [30]  constructed a second dataset in 2020, as their method imCnC does not solely rely on ncRNA sequences, but also includes other data sources that needed to be collected. This dataset contains 42,000 sequences of human ncRNAs downloaded from Rfam's 13th release  [43]. It also includes four types of epigenetic modifications: DNA methylation, histone methylation, histone acetylation, and binding factors and regulators related to histones (from DeepBlue  [44]  and HHMD  [45]  databases). In total, 16 sncRNA classes are represented, though no information is given for the balance between classes.
This dataset only includes human ncRNAs, while multiple species are represented in the other datasets presented in this section. The inclusion of epigenetic data is novel, and is a benefit: the authors' experiments show a gain in performance when including epigenetic information in addition to the sequence. This means that epigenetic data is relevant for ncRNA classification, and could be used to further characterize the different classes. The drawback of this dataset is the fact that it is not publicly available. The same year, Noviello et al. [27]  proposed a dataset in which ncRNAs are classified into 88 Rfam families. These families are defined as groups with RNAs that are evolutionarily conserved, have evidence for a secondary structure, and have some known function. They can sometimes be extremely specific.