dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset To explore a variety of data types from several species, we used six datasets. The sRNAseq data and the genomes were obtained from the NCBI. The annotations concerning the known (pre-)miRNAs were obtained from MIRBASE release 20  [6]. The datasets are organised as follows: • D1: the chromosomes of six metazoan species: -Chromosome 25 from Bos taurus (27 miRNAs) -Chromosome I from Caenorhabditis briggsae (14 miRNAs) -Chromosome 2R from Drosophila simulans (36 miRNAs) -Chromosome 25 from Gallus gallus (6 miRNAs) -Chromosome 22 from Gorilla gorilla (8 miRNAs) -Chromosome 19 from Mus musculus (60 miRNAs) • D2: the genomic sequence of length 5,000nt extracted from the human chromosome 1 and taken from the dataset used by  [7]. • D3: the artificial dataset compiled by  [8]. This dataset is composed of 168 pseudo pre-miRNAs and 163 true human pre-miRNAs.
The pseudo pre-miRNAs are sequences that form a hairpin structure; however, they are not functional because they are located in coding sequence regions (CDS). This dataset is available at http://bioinfo. au. tsinghua. edu. cn/ mirnasvm/. • D4: an artificial dataset compiled by us. For each of the three chromosomes listed below: -Chromosome III of Caenorhabditis elegans (44 miRNAs) -Chromosome 2R of Drosophila melanogaster (92 miRNAs) -Chromosome 19 of Homo sapiens (234 miRNAs) 10 miRNAs were randomly chosen together with 100nt both up and downstream. Each fragment (miRNA + extension) was flanked by sequences of the same length, which were generated based on the nucleotide distribution of the given chromosome. In the end, we obtained three different sequences of ∼ 4,265nt that were given as input to CSHMM, MIRENA, MIRINHO, and miRPara (see Section "Compared methods").
• D5: genomic data of three insects that are of special interest for our group: -Acyrthosiphon pisum genome assembly version 2 (123 miRNAs) -Culex quinquefasciatus genome assembly version 1 (120 miRNAs) -Heliconius melpomene genome assembly version 1. 1 (101 miRNAs) • D6: genomic and sRNAseq data of plants. We used the sequence of chromosome 4 of Arabidopsis thaliana (version 2. 0) and the whole genome of Arabidopsis lyrata (version 1. 0), as well as the high-throughput small RNA sequencing data from the same species with, respectively, NCBI GEO accession number GPL3968 and GSE18077/GSE20442. The chromosome 4 from A. thaliana and the genome of A. lyrata contain, respectively, 57 miRNAs and 384 miRNAs.
Notice that for bulges and helices, G ru = 0. 45 and is referred to as the penalty for an RU end (and not closure as for internal loops). For bulges and internal loops larger than 30 nucleotides (n > 30), Equation 3 should be applied instead: G n>30 = G i (30) + 1. 75 × RT × ln(n/30) (3) Finally, for a helix, one should apply Equation  4 : G helix = G stck + G sym + ( G ru * λ) (4) where G stck is the stacking energy of each two consecutive base pairs; G sym is the symmetry correction for self complementary duplexes; and G ru = 0. 45 is, as mentioned before, the RU end penalty. All the thermodynamic NN energies used in this work, as well as the equations described above, were obtained in the NEAREST NEIGHBOR DATABASE (NNDB)  [14, 15].
Setting the energy threshold To determine an appropriate energy threshold for the prediction of pre-miRNAs, three approaches were tested. The first two were ROC (Receiver Operating Characteristic) curves, one using the insect genomes from dataset D5 (Additional file 1: Figure  S3 ) and the other the pre-miRNA dataset D3 (see Additional file 1: Figure  S4 ). In the third approach, we used a set of random genomes generated according to a multinomial distribution and compared the pre-miRNAs predicted in such genomes against those predicted in the original metazoan genomes from dataset D1. The reasoning behind this strategy is that, if the energy model is robust enough, there should exist an energy threshold that is able to differentiate the stable hairpin structures from the randomly generated ones in which the base pairs would be established by chance.
Moreover, the threshold (e = -16. 3) established with this approach was leading to a very weak precision. We then proceeded to use the second and third approaches which produced more reasonable results, meaning having good sensitivity and precision. The thresholds set with the latter approaches are very similar, e = -21 kcal/mol for the second and e = -20. 6 kcal/mol for the third. The default energy threshold is set to -20. 6 kcal/mol, that is the mean of the energies mentioned in Table  1. Once the secondary structure is computed by MIR-INHO, we may verify if the reads aligned on the locus mimic the constraints consistent with Dicer processing by using an a posteriori script called CHECKDICER. We check the position of the reads within the hairpin (on the mature, star or terminal loop), allowing facultative read overlaps between these three regions.
MIRENA applies a series of consecutive filters to determine a pre-miRNA and does not require a training step  [18]. It provides different starting points for the prediction depending on the type of file given as input. We set the parameter to allow for a genomic input (-M option). The set of known mature miRNAs required was from the same metazoan kingdom, taken from MIRBASE release 20. MIRPARA (version 6. 2) is an SVM implementation trained with sequences from MIRBASE  [19]. It makes available a script to generate the model according to the MIRBASE release and to the desired organism(s)/clade. In our case, we chose the model trained with the metazoan pre-miRNAs of MIRBASE release 20. It is worth observing that among the compared methods, MIRPARA is the only one that also predicts the position of the miRNA within the pre-miRNA.
n} and n is the number of true miRNAs for a given species. For each predicted pre-miRNA, denoted by ppm, we verified whether one of its arms fully covers a control miRNA, denoted by cm j. If that was the case, we accounted for one TP. If the same ppm covered more than one control miRNA, we considered just the one with the best prediction score according to each method. It is worth observing that a large proportion of the miRNA sequences in MIRBASE are not supported by experimental evidence. However, it is considered as a reference database.
Regression analysis of the free energies To verify how close we get to the algorithms based on a secondary structure prediction, we present a regression analysis between the energies of the pre-miRNAs corresponding to the true positive pre-miRNAs predicted by MIRINHO and their energies when predicted by RNAFOLD  [23]. Figure  4  shows the relationship of the energies for the true positive pre-miRNAs of the chromosomes in dataset D1. We consider as the dependent variable the energies of MIRINHO and as the independent variable the energies of RNAFOLD. As we can see, the energies are quite close to each other with, in general, bigger energies predicted by MIRINHO. This provides reasonable evidence that our method approximates well the free energy of hairpins.
Time efficiency is even more evident when comparing our method to other predictors, such as CSHMM, MIRENA, and MIRPARA. Table  2  presents the computing times for the prediction of pre-miRNAs in a sequence of dataset D2, running under a Mac OS X 10. 6. 8, 2. 7GHz Intel. As one can see, our method is indeed much faster than the others, making the prediction of pre-miRNAs much more feasible. To show this, we compared the prediction time of MIR-INHO, CSHMM, and MIRPARA. To facilitate the comparison of the predicted pre-miRNAs, we used dataset D4, as did the authors of CSHMM. All three software were submitted to a cluster queue of 29 hours. MIRINHO finished its job after 5 hours, while the other two exceeded the 29 hours without finishing their prediction, with no reported result.
Pre-miRNA hairpin structures Information on the length of the stem-arms and terminal loop may produce higher quality pre-miRNA structures. Energy thresholds, obtained with the methodology mentioned in Section "Setting the energy threshold", and the GC% of the different chromosomes from dataset D1, including the ones for test from dataset D4 (three last lines). To choose the genomes, we mainly considered the GC content (varying from 37% to 54%), as this plays an important role in determining a hairpin structure. When the search is made at a genomic scale, such precise information is not available. However with sRNAseq data, the length of the stem-arms and terminal loop may be naturally inferred from the alignment of the reads against the genome (see Section "Pre-processing input sequences with small RNA sequencing data").
The way we screen the input sequence thus allows for a direct application of MIR-INHO to sRNAseq data, thus enriching the prediction and the quality of the hairpin structures. To demonstrate this, we first set the parameters l (length of the stem-arm) and n (length of the terminal loop) using dataset D6, as described in Section "Pre-processing input sequences with small RNA sequencing data". We then gave the pre-miRNA sequence and the latter parameters as input to MIRINHO, RNAFOLD, and MIRNAFOLD. For MIRINHO, we set the stem-arm length to l using option -a and the minimum and maximum length of the terminal loop to n using options -n and -x respectively. Given that RNAFOLD is a method for predicting the secondary structure of an RNA in general, we used the option -C to force the structure to be a hairpin.
We then required that the stem-arm regions, each of length l, were paired, and that the terminal loop region of length n was unpaired. For MIRNAFOLD, we established as the sliding window parameter the length of the whole pre-miRNA, that is, l+n+l. It is worth observing that the structures in MIRBASE are also predictions. We however use them as a standard because MIRBASE is considered as a reference database: it is being constantly updated leading to new releases and it incorporates criteria based on experimental (i. e. , sRNAseq) approaches to reinforce the evidence provided  [24]. In order to compare the structures predicted by the three methods, we considered four criteria: (i) the number of internal loops within the stem; (ii) the number of bulges within the stem; (iii) the length of the predicted stem-arm; (iv) the length of the predicted terminal loop.
Comparison with other methods To compare the performance of the methods, we used datasets D3, D4, D5, and D6. Specificity was computed only in the case that a true negative pre-miRNA was precisely defined: this happens for dataset D3 in which a true negative is represented by a pseudo pre-miRNA. At a genomic scale, it is not an easy task to define a true negative pre-miRNA; specificity values are thus often overestimated. For this reason, this measure was not included in the genomic scale experiments. It was not included either in the case of sRNAseq because other kinds of transcripts may be expressed, and one may not know a priori if they correspond to a true or to a false pre-miRNA.
D3: artificial pre-miRNA dataset compiled by Xue et al. Table  3  presents a comparison between the different methods when using as input the pre-miRNA dataset D3 compiled by  [8]. As one may observe, when considering the measures separately, CSHMM and MIRINHO obtain better sensitivity while for the other measures the overall performance of MIRPARA and MIRENA is better. These results reflect the underlying algorithms used by each of the methods. While CSHMM and MIRINHO are specialised in finding stable hairpin structures, MIRPARA and MIRENA use other characteristics during the prediction. However, at a genomic scale such characteristics do not enable to outperform the two other methods, as one may see in Table  4. Considering a practical aspect involving the tradeoff between time and accuracy, one could boost the prediction results in a feasible time by combining MIRINHO and one of the methods that uses specific pre-miRNA characteristics (namely MIRPARA or MIRENA).
D4: artificial pre-miRNA dataset compiled by us Table  5  presents a comparison between the different methods using as input the pre-miRNA dataset D4. As we can see, in humans, MIRINHO obtains the best sensitivity (70%) and precision (50%) together with CSHMM. As concerns D. melanogaster, MIRINHO also has the best sensitivity (80%), while MIRENA gets the best precision The first three lines contain the results when using the original dataset with 163 positive pre-miRNAs and 168 pseudo pre-miRNAs. The last two lines contain the results when using only the 102 positive pre-miRNAs, from the same dataset, annotated with high confidence according to MIRBASE. The energy threshold used in MIRINHO was -20. 6 kcal/mol. Values for the three measures are given in percentage. (75%). For C. elegans, CSHMM obtains the best sensitivity (70%), and MIRENA the best precision (44.
D5: insect genomes To analyse the sensitivity and precision at a genomic scale (dataset D5), we used the genomes of three insects, one of which, A. pisum, is of particular interest to us. The results are shown in Table  4. Notice that the prediction is often far from being perfect for all methods; in particular, there is as usual a delicate choice to be made between sensitivity and precision, in as much as we are currently capable of accurately measuring the latter. The low precision for all the methods may be due to two reasons. One is that the model used for predicting (pre-)miRNAs needs refinement. The other is that the precise definition of a FP miRNA is completely dependent on the known miRNAs, which could represent just a small fraction of those that really exist.
D6: plant sRNAseq data Although the approaches used to treat sRNAseq data are different between our method and MIRDEEP2, we included a comparison with the latter in terms of sensitivity and precision as concerns the sRNAseq data of A. thaliana (dataset D6). The computing time was also compared considering only the module MIRDEEP2. PL. The parameters given to MIRINHO were the following: energy threshold of e = -42. 8 kcal/mol (obtained as described in Section "Setting the energy threshold" using the genome of A. lyrata); stem length of 48nt (mean of the stem lengths in A. thaliana); and loop range from 5nt to 70nt (see Additional file 1: Figure  S10 ). For MIRDEEP2, the following files were given as input: one containing the pre-miRNA and mature miRNA sequences of A. thaliana and another with the mature miRNA sequences of A.
Acknowledgements This work was supported by the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7 /2007-2013) / ERC  grant agreement no. [247073]10; and it was also partially supported by the  CAMP  project,  PTDC/EIA-EIA/122534/2010 , funded by the " Fundação para a Ciência e a Tecnologia " from the  Portuguese Ministry of Education and Science. This work was performed using the computing facilities of the  CC LBBE/PRABI  and  CCIN2P3.
Nevertheless, for certain muscles, texture features did not show significant correlations with the histopathology indices. In the work  [14]  texture analysis of T2-weighted MR images was used to describe the structure of thigh soft tissues in five groups of athletes (representing differently loading sport types: repetitive non-impact, repetitive low impact, odd impact, high impact, and high magnitude) and non-athletes. Five thigh muscles at two anatomical levels of the dominant leg were characterized with co-occurrence matrix-based texture features. Statistical analyses showed differences in thigh muscle textures, especially between the athletes performing high impact and odd impact loading sports. Another study  [15] , performed on a similar database (five athlete groups and non-athletic controls), analyzed the differences in MRI texture properties of hip muscles (on T1-weighted images).
Texture-Based Classification of the Canine Hindlimb Muscles Two stages can be distinguished in a typical system for texture-based classification of medical images  [16]. The first stage is preparation of the learning set used to construct the classifiers. In this stage a database of images, as large as possible, is gathered and preprocessed (if necessary). Then the Regions of Interest (ROIs, the image regions subjected to the analysis) are delineated, either manually, or by (semi)automated segmentation methods. Each ROI is assigned a label that refers to a tissue class (e. g. , healthy or pathologically changed) and is based on a verified diagnosis. Afterwards various methods are applied in order to characterize each ROI. In our case, these methods are based on texture analysis and result in the creation of vectors of textural features.
Database Description The experiments were performed on the database provided by  [17]. Five GRMD dogs and five healthy controls were considered in the study. The dogs were bred in a dedicated gene therapy facility at the National Veterinary School of Alfort, France. All procedures were carried out in accordance with the Guide for the Care and the Use of Laboratory Animals and approved by the Institutional Animal Use and Care Committee, in accordance with European legislation regarding the use of laboratory animals. The image database was created at the Nuclear Magnetic Resonance Laboratory of the Institute of Myology, Pitie-Salpetriere University Hospital in Paris, France (its full description, as well as a description of all the acquisition protocols, can be found in  [17] ). Each acquisition was performed on a 3T Siemens Magnetom Trio TIM imager/spectrometer (Siemens Healthcare, Erlangen, Germany) with a standard circularly polarized extremity coil.
Classification Classification experiments were performed with Weka software  [27]. Five classifiers were used to compare the potential of different feature sets and the usefulness of the information extracted from each of the 4 considered muscles: -Support Vector Machines (SVM)  [28]  using a Sequential Minimal Optimization (SMO) algorithm  [29]  and a second degree polynomial kernel, -Neural Network (NN)  [30]  with a backpropagation and sigmoidal activating function, having a hidden layer wherein the number of neurons was equal to the average value of the number of features and the number of classes, -Logistic Regression (LR)  [31] , -Random Forest (RF)  [32]  comprising a set of 50 trees, -Adaptive Boosting (AB)  [33]  using the C4. 5 tree  [34]. Classification accuracies were estimated by 10-fold cross-validation, repeated 10 times.
Direct comparisons between phases could be more reliable if equally numerous datasets were used for each phase-problem. In our case, the original number of ROIs was small, so we preferred not to truncate the datasets. Finally, it could be seen that the best texture analysis methods were those based on the co-occurrence matrices and on the run length matrices. It is difficult to say which of these is better, as their application very often resulted in similar classification accuracies. The use of the COM method led to better results for the GasLat muscle type, whereas the RLM method was the most frequently selected for the TC muscle. In the case of the first-and second-phase classification problem, the sets composed of all the possible textural features (derived from the 8 classification methods) were also frequently selected as the best ones.
Conclusion The study investigated the possibility of using MRI texture analysis in the process of distinguishing between healthy and GRMD dogs at various stages of canine growth and/or disease development. Four types of muscle located in canine pelvic limbs were considered: EDL, GasLat, GasMed, and TC. Eight texture analysis methods were used for their characterization, including statistical, model-based, and filter-based methods. The experiments, while conducted on a small sample, showed that several texture analysis methods have great potential. The best ones proved to be those based on co-occurrence matrices and run-length matrices. Satisfactory results were also obtained with the sets comprising features provided by all of the eight considered texture analysis methods. In this case, the calculations required more memory to store data and more time for classifier construction.
Fluorescent tags allow scientists to observe specific proteins, cell boundaries, and interactions between individual cells. These imaging techniques can produce large sets of data that need to be analyzed using a computer and incorporated into computer simulations that predict how a tissue or organ within an organism grows to form its final shape. Currently, most computational models of morphogenesis work on 2D templates and focus on how tissues and organs form. However, many patterning events occur on surfaces that are curved or folded, so 2D models may lose important details. Developing 3D models would provide a more accurate picture, but these models are expensive and technically challenging to make. To address this problem, Barbier de Reuille, Routier-Kierzkowska et al. present an open-source, customizable software platform called MorphoGraphX.
This software extracts images from 3D data to recreate curved 2D surfaces. Barbier de Reuille, Routier-Kierkowska et al. have also developed algorithms to help analyze growth and gene activity in these curved images, and the data can be exported and used in computer simulations. Several scientists have already used this software in their studies, but Barbier de Reuille, Routier-Kierzkowska et al. have now made the software more widely available and have provided a full explanation of how it works. How scientists can extend and customize MorphoGraphX to answer their own unique research questions is also described. It is anticipated that MorphoGraphX will become a popular platform for the open sharing of computational tools to study morphogenesis. Even small amounts of tissue curvature can hinder the accurate imaging of a single cell layer over an entire sample.
MorphoGraphX. org, Box 1) to quantify the temporal evolution of cellular geometry and fluorescence signal on curved 2D surface layers of cells over multiple time points in both plants and animals. In addition to 2D curved surfaces, MorphoGraphX also possesses a rich set of tools for full 3D image processing and cell segmentation, and can be used to easily transfer realistic cell geometries and fluorescent marker data into computational modeling environments. MorphoGraphX is built from a collection of loadable modules (shared libraries), centered around an interactive visualization core that exploits the latest features of modern Graphics Processing Units (GPUs). This design allows the software to be easily adapted to changing research needs, and facilitates the integration of algorithms from other opensource imaging processing libraries into a custom work flow.
3D visualization of voxels and meshes Modern imaging technologies today provide us with an abundance of data from a variety of sources: Confocal Laser Scanning Microscopy, Magnetic Resonance Imaging and block-face SEM all provide full 3D volumetric data that can be rendered in MorphoGraphX (Figure  1 , Video 1). Our software can also process surfaces, which can be imported from 3D scanners, reconstructions from Stereo-SEM images  (Routier-Kierzkowska and Kwiatkowska, 2008) , focus stacking microscopes and scanning probe methods such as Cellular Force Microscopy (Routier-Kierzkowska et al. , 2012) (CFM), or extracted within MorphoGraphX from full 3D data sets (Figure  1 ). MorphoGraphX contains a highly optimized rendering engine that is capable of accurate rendering of both semi-transparent surfaces and volumetric data simultaneously.