dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA deep sequencing and data analysis. For deep sequencing analysis, plasma and liver total RNA extracts were isolated from fah -/-control mouse (NTBC-treated) and fah -/-withdrawn from NTBC during 4 weeks (HT1 mice) (Fig. 1a-c ). These two time points were chosen as representative of mice in healthy state (control) and mice suffering of acute pathophysiological manifestation of the disease (4 weeks after therapy interruption)  19. Total RNA extracts were submitted to LC Science (Huston, TX, USA) for further processing. Four pools, constituted each from total RNA from 4 different mice, were generated with equimolar RNA amounts by LC Science, following our instructions (i. e. plasma from control mice (P0), plasma from HT1 mice (P4), liver from control mice (L0) and liver from HT1 mice (L4)) (Fig.
1b, c ). Each sample was processed to generate a cDNA library using the Illumina TruSeqTM Small RNA Preparation kit according to Illumina's TruSeqTM Small RNA Sample Preparation Guide (see Supplementary File S1). The purified cDNA library was used for cluster generation on Illumina's Cluster Station and then sequenced on Illumina GAIIx. Raw sequencing reads (40 nts) were obtained using Illumina's Sequencing Control Studio software version 2. 8 (SCS v2. 8) following real time sequencing image analysis and base calling by Illumina's Real-Time Analysis version 1. 8. 70 (RTA v1. 8. 70). The extracted sequencing reads were analysed using a proprietary pipeline script, ACGT101-miR v4. 2 (LC Sciences), as described in the Supplementary material (Supplementary File S1).
RT products were diluted 1:5 and subjected to quantitative PCR (qPCR) in triplicate on a 7500 Fast Real-Time PCR System (Applied BioSystems, Foster City, CA). Data were analysed with 7500 Software version v2. 3 (Applied BioSystems), with the automatic setting for assigning baseline and C t (cycle threshold) determination. The miRNA expression levels were normalized against cel-miR-39 and calculated by the equation 2 -ΔCt , in which Δ C t = C t miRNA-C t cel-miR-39. Target gene prediction and enriched biological function analysis. The Ingenuity incorporated analysis of TarBase (http://diana. cslab. ece. ntua. gr/tarbase/)  35 , miRecords (http://mirecords. biolead. org/)  36 and TargetScan (http://www. targetscan. org/)  37 , were used to predict the potential target genes of detected miRNAs.
47v software. Values were normalized to loading control levels and finally compared to the fah -/-healthy control (NTBC-treated). Data in graphs represents three mice for each time point. Statistical analysis. The Student's t-test or ANOVA was used to analyse the differences between samples. Values of p < 0. 05 were considered statistically significant. RT-qPCR experiments were repeated at least twice with similar results. All experiments included triplicate samples for each treatment group. Statistical analyses were performed with GraphPad Prism software v6. 0b (La Jolla, CA, USA).
Acknowledgements We thank  Guillaume Côté  ( Dept Biology and IBIS ) for his help with the RT-qPCR setting and analysis. This work was supported by grants from the  Canadian Institutes of Health Research (CIHR)  and  Fondation du Grand Défi Pierre Lavoie  to RMT. We also thank  PROTEO  for a postdoctoral fellowship to FA and a studentship to VR as well as the Corporation de recherche et d'action sur les maladies héréditaires (CORAMH) for a studentship to VR.
Remarkably, when comparing the sequences of the resume codon among the various bacterial species, the more primitive the bacteria, the more ancestral GNC codons are recovered (Figure  7B-C ). The three conserved tmRNA alanine codons (-----A-AA) that follow are also crucial, allowing specific recognition of the tagged protein Sequence conservation is measured in bits, going from weakly conserved (0) to highly conserved (4). Alanine is in red and the other amino acids are in black. The alignment was obtained using 708 tag sequences from the UTHSCSA RNP database (http://rnp. uthscsa. edu/rnp/tmRDB/peptide/peptide. html) (66). The consensus logo was created using WebLogo (http://weblogo. berkeley. edu/logo. cgi) (75). (B) Analysis of the first MLD codon in different bacteria phyla. The first codon of the MLD is indexed by the ancestral codons (GNC) and the others.
, 2008; Brouard et al. , 2010; Lemieux et al. , 2015; Watanabe et al. , 2016). Within the latter clade, the Oedogoniales is sister to the Chaetophorales and Chaetopeltidales. Comparative analysis of chloroplast DNA (cpDNA) not only clarified the evolutionary relationships among and within the main groups of the Chlorophyceae, but also highlighted the remarkable plasticity of the chloroplast genome in this class and the poor conservation of ancestral structural features inherited from the bacterial progenitor of chloroplasts. Complete cpDNA sequences of 35 chlorophycean taxa are currently available in GenBank, but a number of these genomes have yet to be described in detail. Published studies include genomes from the five major lineages of the Chlorophyceae: Oedogoniales  (Brouard et al. , 2008) ;  Chaetophorales (Bélanger et al.
, 1990)  searches against a local database of cpDNA-encoded proteins or the nr database at the National Center for Biotechnology Information, and (3) consecutive 100-bp segments of the genome sequence were analyzed with  BlastN and BlastX (Altschul et al. , 1990)  to determine the approximate positions of RNA-coding genes, introns and exons. This pipeline produces a summary spreadsheet file listing the conserved genes and ORFs found in consecutive 100-bp segments, thus providing a quick portrait of the genome content. The precise positions of genes coding for rRNAs and tRNAs were identified using RNAmmer  (Lagesen et al. , 2007)  and tRNAscan-SE  (Lowe & Eddy, 1997) , respectively. Boundaries of introns were located by manual modelling of intron secondary structures  (Michel, Umesono & Ozeki, 1989; Michel & Westhof, 1990)  and by comparing the sequences of intron-containing genes with those of intron-less homologs using various programs of the FASTA package  (Pearson & Lipman, 1988).
Phylogenetic analyses The deduced amino acid sequences of individual chloroplast protein-coding genes shared by nine chlorophyceans (see Table  1  for the names of taxa and accession numbers of chloroplast genome sequences) and the ulvophycean Pseudendoclonium akinetum  (Pombert et al. , 2005)  were aligned using MUSCLE 3. 7  (Edgar, 2004). A total of 61 genes met this criterion: atpA, B,  E, F, H, I, ccsA, cemA, clpP, ftsH, petB, D, G, L, psaA, B, C, J, psbA, B, C, D, E, F, H, I, J, K, L, M, N, T, Z, rbcL, rpl2, 5, 14, 16, 20, 23, 36, rpoA, B, C1, C2, rps2, 3, 4, 7, 8, 9, 11, 12, 14, 18, 19, tufA, ycf1, 3, 4, 12. The amino acid alignments were converted into alignments of codons, and the poorly aligned and divergent regions in each codon alignment were removed using GBLOCKS 0. 91b  (Castresana, 2000)  and the option.
Analysis of group IIA introns in Bulbochaete cpDNA Positional homologs of six Oedocladium chloroplast group IIA introns were searched in Bulbochaete by PCR amplification of the cpDNA regions spanning these intron insertion sites. The reactions were performed using total cellular DNA and the following pairs of primers: 5 -TTCCGATTGGTCGTGGTCAACG and 5 -GATAAGCTTCACGTCCTGGAGG (atpA); 5 -AACCTCTTTCTTTAAGTTTCCG and 5 -TATACGCACCTGCTAATGTTGC (atpI ); 5 -CGTTGGTCTGCTAGTATGATGG and 5 -CAGAACCTCCACGTAATAATTC (petB); 5 -ATCAGGTGTTTATCAATGGTGG and 5 -CTGGATTTTGAGCATAAGCTGC (psaB); 5 -CTGTTGTATTAAATGACCCTGG and 5 -GCTGCTACTGTTTCATATGTCC (psbB); 5 -AACGTTTAGGTGCTAATGTAGC and 5 -CACGACGTTCTTGCCATGGTTG (psbC). The resulting PCR products were analyzed by agarose gel electrophoresis and subsequent Sanger DNA sequencing using internal primers was carried out to confirm the presence of a group IIA intron of 725 bp in the petB insertion site (GenBank accession KX507372).
For each pairwise comparison, the minimal number of reversals required to render the genomes colinear is given together with the number of reversals per 1,000 substitutions (in parentheses). Reversals were estimated using GRIMM  (Tesler, 2002) ; for the Chaetophorales and Chlamydomonadales, these estimates were taken from  (Brouard et al. , 2011). (Brouard et al. , 2011). To interpret these results, the evolutionary distances separating the taxa in each of these three algal pairs were estimated with CODEML using a data set of 61 concatenated protein-coding genes from nine chlorophyceans (total of 37,209 nucleotide positions) and the best RAxML tree inferred from this data set (Fig. 2 ). The evolutionary distance between Oedocladium and Oedogonium was found to be much smaller than those separating Stigeoclonium from Schizomeris and Chlamydomonas from Volvox.
Analysis was performed with R and Bioconductor (https://www. bioconductor. org/). Differentially expressed genes were selected by Linear Models for Microarray and RNA-seq Data (LIMMA; https://www. r-project. org/), having a fold change ≥ 2. 0 and a P value ≤ 0. 05. Data are publicly available in ArrayExpress (www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-5218. Flow cytometry analysis of lung infiltrating inflammatory cells. Lungs were perfused with 0. 02% EDTA-PBS until the tissue turned white. Lung tissue was then sliced into 1 to 2 mm 3 pieces and incubated for 45 min at 37 °C with collagenase (150 U/ml) and DNase (50 U/ml; Sigma, St Louis, MO) in RPMI 1640 (Gibco, Paisley, Scotland, UK) containing 10 mM Hepes (Gibco) and antibiotics (Penicillin 100 U/ml-Streptomycin 100 μ g/ml).
Acknowledgements The authors acknowledge the support from  CNRS  through  International Associated Laboratory « TB IMMUNITY » (LIA N°   236 ),  Region Centre, France  ( N° 2013-00085470 ;  N° 2015-00099232 ),  FEDER  ( N° 2016-00110366 ) and  Novartis Institutes for Biomedical Research, CH-4002 , Basel, Switzerland. We wish to thank  Timothy Wright MD PhD  for his scientific support, and  Federico Bolognani MD PhD  for his contribution to gene transcription analysis.
Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases. Their architecture is somewhat similar to that of the human visual system: both use restricted receptive fields, and a hierarchy of layers which progressively extract more and more abstracted features. Yet it is unknown whether DCNNs match human performance at the task of view-invariant object recognition, whether they make similar errors and use similar representations for this task, and whether the answers depend on the magnitude of the viewpoint variations. To investigate these issues, we benchmarked eight state-of-the-art DCNNs, the HMAX model, and a baseline shallow model and compared their results to those of humans with backward masking.
This is an important limitation: in the past, it has been shown that models performing well on apparently challenging image databases may fail to reach human-level performance when objects are varied in size, position, and most importantly 3-D transformations  [19, 20, 21, 22]. DCNNs are position invariant by construction, thanks to weight sharing. However, for other transformations such as scale, rotation in depth, rotation in plane, and 3-D transformations, there is no built-in invariance mechanism. Instead, these invariances are acquired through learning. Although the features extracted by DCNNs are significantly more powerful than their hand-designed counterparts like SIFT and HOG  [20, 23] , they may have difficulties to tackle 3-D transformations. To date, only a handful of studies have assessed the performance of DCNNs and their constituent layers in invariant object recognition  [24, 25, 26, 20, 27, 28].
In this study we systematically compared humans and DCNNs at view-invariant object recognition, using exactly the same images. The advantages of our work with respect to previous studies are:  (1)  we used a larger object database, divided into five categories; (2) most importantly, we controlled and varied the magnitude of the variations in size, position, in-depth and in-plane rotations; (3) we benchmarked eight state-of-the-art DCNNs, the HMAX model  [10]  (an early biologically in-spired shallow model), and a very simple shallow model that classifies directly from the pixel values ("Pixel"); (4) in our psychophysical experiments, the images were presented briefly and with backward masking, presumably blocking feedback;  (5)  we performed extensive comparisons between different layers of DCNNs and studied how invariance evolves through the layers;  (6)  we compared models and humans in terms of performance, error distributions, and representational geometry; and  (7)  to measure the influence of the background on the invariant object recognition problem our dataset included both segmented and unsegmented images.
Deep convolutional neural networks (DCNNs) The idea behind DCNNs is a combination of deep learning  [14]  with convolutional neural networks  [9]. DCNNs have a hierarchy of several consecutive feature detector layers. Lower layers are mainly selective to simple features while higher layers tend to detect more complex features. Convolution is the main process in each layer that is generally followed by complementary operations such as max pooling and output normalization. Up to now, various learning algorithms have been proposed for DCNNs, and among them the supervised learning methods have achieved stunning successes  [29]. Recent advances have led to the birth of supervised DCNNs with remarkable performances on extensively large and difficult object databases such as Imagenet  [29, 14]. We have selected the eight most recent, powerful, and supervised DCNNs and tested them in one of the most challenging visual recogni-tion task, i.
e. invariant object recognition. Below are short descriptions of all the DCNNs that we studied in this work. Krizhevsky et. al. 2012  This outstanding model reached an impressive performance on the Imagenet database and significantly defeated other competitors in the ILSVRC-2012 competition  [15]. The excellent performance of this model attracted attention towards the abilities of DCNNs and opened a new avenue for further investigations. Briefly, the model contains five convolutional (feature detector) and three fully connected (classification) layers. They used the Rectified Linear Units (ReLUs) for the neurons' activation function, which significantly speeds up the learning phase. The max pooling operation is performed in the first, second, and fifth convolutional layers. This model is trained using a stochastic gradient descent algorithm.
It has about 60 million free parameters; to avoid overfitting, they used some data augmentation techniques to enlarge the training set as well as the dropout technique in the learning procedure of the first two fully-connected layers. The structural details of this model are presented in Table 1. We used the pre-trained version of this model (on the Imagenet database) which is publicly released at http://caffe. berkeleyvision. org by Jia et. al  [30].
Overfeat has some important differences with other DCNNs: 1) there is no local response normalization, 2) the pooling regions are non-overlapping, and 3) the model has smaller convolution stride (= 2) in the first two layers. The specifications of the Accurate version of the Overfeat model, which we used in this study, are presented in Table  1. Similarly, we used the Imagenet pre-trained model which is publicly available at http://cilvr. nyu. edu/doku. php?id=software:Overfeat:start.
Hybrid-CNN 2014 The Hybrid-CNN model  [31]  has been designed to do a sceneunderstanding task. This model was trained on 3. 6 million images of 1183 categories including 205 scene categories from the place database and 978 object categories from the training data of the Imagenet database. The scene labeling, which consists of some fixed descriptions about the scene appearing in each image, was performed by a huge number of Amazon Mechanical Turk workers. The overall structure of Hybrid-CNN is similar to the Krizhevsky model (see Table  1 ), but it is trained on a different dataset to perform a scene understanding task. This model is publicly released at http://places. csail. mit. edu. Surprisingly, the hybrid-CNN significantly outperforms the Krizhevsky model in different scene-understanding benchmarks, while they perform similarly different object recognition benchmarks.
Chatfield CNNs Chatfield et. al. [18]  did an extensive comparison among the shallow and deep image representations. To this end, they proposed three different DCNNs with different architectural characteristics, each exploring a different accuracy/speed trade-off. All three models have five convolutional and three fully connected layers but with different structures. The Fast model (CNN-F) has smaller convolutional layers and the convolution stride in the first layer is four, versus 2 for CNN-M and -S, which leads to a higher processing speed in the CNN-F model. The stride and receptive field of the first convolutional layer is decreased in Medium model (CNN-M), which was shown to be effective for the Imagenet database  [16]. The CNN-M model also has a larger stride in the second convolutional layer to reduce the computation time.
The Slow model (CNN-S) uses 7 × 7 filters with stride of 2 in the first layer and larger max pooling window in the third and fifth convolutional layers. All these models were trained over the Imagenet database using a gradient descent learning algorithm. The training phase was performed over random crops sampled from the whole parts of the image rather than the central region. Based on the reported results, the performance of CNN-F model was close to the Zeiler and Fergus model while both CNN-M and CNN-S outperformed the Zeiler and Fergus model. The structural details of these three models are also presented in Table  1. All these models are available at http://www. robots. ox. ac. uk/ ~vgg/software/deep_eval.
Image generation All models were evaluated using an image database divided into five categories (airplane, animal, car, motorcycle, and ship) and seven levels of variations  [19]  (see Fig. 1 ). The process of image generation is similar to Ghodrati et. al. [19]. Briefly, Table  1 : The architecture and settings of different layers of DCNN models. Each row of the table refers to a DCNN model and each column contains the details of a layer. The details of convolutional layers (labeled as Conv) are given in three sub-rows: the first one indicates the number and the size of the convolution filters as N um × Size × Size; the convolution stride is given in the second sub-row; and the third one indicates the max pooling down-sampling rate, and if Linear Response Normalization (LRN) is used. The details of fully connected layers (labeled as Full) are presented in two sub-rows: the first one indicates the number of neurons; and the second one whether dropout or soft-max operations are applied.
This type of masking is well established to be used in rapid object recognition tasks  [37, 19, 38, 33, 39]. Finally, subjects had to select one out of five different categories using five keys, labeled on the keyboard. The next trial started immediately after the key press. Stimuli were presented using MATLAB Psychophysics Toolbox  [40]  in a 21" CRT monitor with a resolution of 1024 × 724 pixels, a frame rate of 80 Hz, and viewing distance of 60 cm. Each stimulus covered 10 • × 11 • of visual angle. Subjects were instructed to respond as fast and accurately as possible. All subjects voluntarily accepted to participate in the experiment and gave their written consent. The experimental procedure was approved by the local ethic committee. According to the "interruption theory"  [41, 39, 42] , the visual system processes stimuli sequentially, so processing of a new stimulus (the noise mask) will interrupt the processing of the previous stimulus (the object image) before it can be modulated by the feedback signals from higher ar-eas  [39].
Model evaluation Classification accuracy: To evaluate the classification accuracy of the models, we first randomly selected 600 images from each object category, variation level, and background condition (see Image generation section). Hence, we have 14 different datasets (7 variation levels × 2 background conditions), each of which consists of 3000 images (5 categories × 600 images). To compute the accuracy of each DCNN for a given variation level and background condition, we randomly selected two subsets of 1500 training (300 images per category) and 750 testing images (150 images per category) from the corresponding image dataset. We then fed the pre-trained DCNN with the training and testing images and calculated the corresponding feature vectors for all layers. Afterwards, we used these feature vectors to train the classifier and compute the recognition accuracy of each layer.
Results We tested the DCNNs in our invariant object categorization task including five object categories, seven variation levels, and two background conditions (see Materials and methods). The categorization accuracy of these models were compared with those of human subjects, performing rapid invariant object categorization tasks on the same images. For each model, variation level, and background condition, we randomly selected 300 training images and 150 testing ones per object category from the corresponding image dataset. The accuracy was then calculated over 15 random independent runs and the average and standard deviation were reported. We also analyzed the error distributions of all models and compared them to those of humans. Finally, we compared the representational geometry of models and humans, as a function of the variation levels.
One possible reason for the low accuracies in the final layers of DCNNs is that the fully connected layers are designed to per-form classification themselves, and not to provide input for a SVM classifier. Besides, the fully connected layers were optimized for Imagenet classification, but not for our dataset. A last reason could be that the convolutional layers have more features than the fully connected layers. Given the accuracies of all layers, it can be seen that the accuracies evolved across the layers. However, similar to Fig. 3 , layers 2, 3, and 4 of Krizhesvky, Zeiler and Fergus, and Hybrid-CNN contribute almost equally to the final accuracy. Again, CNN-F, CNN-M, and CNN-S showed a different trend in terms of the contribution of each layer to the final accuracy. Moreover, as shown in Fig.
Such information is attenuated at intermediate and high variation levels. In contrast, DCNNs demonstrate clear categorical information for different objects across almost all levels, for both background conditions. Categorical information is more evident when objects had uniform background, even at high variation levels, while this information almost disappears at intermediate levels when object had natural backgrounds. In addition, Overfeat did not clearly represent different object categories. The Overfeat model is one of the most powerful DCNNs with high accuracy on the Imagenet database, but it seems that the features are not suitable for our invariant object recognition task. It uses no fewer than 230400 features! This might be one reason for poor representational power: it probably leads to a nested and complex object representation.