dataesr/scientific-paragraphs-categorization huggingface dataset:
Restriction site Associated DNA Sequencing (RAD-Seq) is a technique characterized by the sequencing of specific loci along the genome that is widely employed in the field of evolutionary biology since it allows to exploit variants (mainly Single Nucleotide Polymorphism-SNPs) information from entire populations at a reduced cost. Common RAD dedicated tools, such as STACKS or IPyRAD, are based on all-vs-all read alignments, which require consequent time and computing resources. We present an original method, DiscoSnp-RAD, that avoids this pitfall since variants are detected by exploiting specific parts of the assembly graph built from the reads, hence preventing all-vs-all read alignments. We tested the implementation on simulated datasets of increasing size, up to 1,000 samples, and on real RAD-Seq data from 259 specimens of Chiastocheta flies, morphologically assigned to seven species.
All individuals were successfully assigned to their species using both STRUCTURE and Maximum Likelihood phylogenetic reconstruction. Moreover, identified variants succeeded to reveal a within-species genetic structure linked to the geographic distribution. Furthermore, our results show that DiscoSnp-RAD is significantly faster than state-of-the-art tools. The overall results show that DiscoSnp-RAD is suitable to identify variants from RAD-Seq data, it does not require time-consuming parameterization steps and it stands out from other tools due to its completely different principle, making it substantially faster, in particular on large datasets.
The key challenge is therefore the clustering part. To do so, the classical approach relies on all-vs-all alignments. To reduce the number of alignments to compute, the clustering is first performed within each sample independently, then sample consensuses are compared between samples. Nevertheless the number of alignments to perform remains very large in datasets composed of many large read sets. Importantly, analysis of RAD-Seq data is highly dependent on the chosen clustering method, the sequencing quality and the dataset composition, such as the presence of inter and/or intra-specific specimens or the number of individuals. Thus, existing tools allow customization of numerous parameters to fine-tune the analysis. Particularly, both methods have parameters controlling the granularity of clustering: the number of mismatches allowed between sequences of a same locus within and among samples for STACKS and the percentage of similarity for PyRAD.
These can be arbitrarily fixed by the user, but have a significant impact on downstream analyses  (Shafer et al. , 2017). We present here DiscoSnp-RAD, an utterly different approach to predict de novo small variants (SNPs and indels) from large RAD-Seq datasets, without performing any read clustering, avoiding all-vs-all read comparisons and without relying on a critical similarity threshold parameter. DiscoSnp-RAD takes advantage of the DiscoSnp++ approach  (Uricaru et al. , 2015; Peterlongo et al. , 2017) , that was initially designed for de novo prediction of small variants, from shotgun sequencing reads, without the need of a reference genome. The basic idea of the method is a careful analysis of the de Bruijn graph built from all the input read sets, to identify topological motifs, often called bubbles, generated by polymorphisms.
Notably, those bubbles arise whatever the global similarity level between homologous reads, explaining why DiscoSnp-RAD is free of similarity-related parameters. Note that STACKS2 also uses a de Bruijn graph approach, but in a different way, as it is used to build a so-called "RAD-locus" contig catalog on which reads are aligned for calling SNPs  (Rochette, Rivera-Colón & Catchen, 2019). After validation tests on simulated datasets of increasing size, we present an application of the DiscoSnp-RAD implementation on double-digest RAD-Seq data (ddRAD) from a genus-wide sampling of parasitic flies belonging to Chiastocheta genus. Using DiscoSnp-RAD, the 259 individuals analyzed could be assigned to their respective species. Moreover, within-species analyses focused on one of these species, identified variants revealing population structure congruent with sample geographic origins.
Various optional filtering options The output of DiscoSnp-RAD is a VCF file containing predicted variants along with various information, such as their genotypes and allele read counts in all samples, their rank value and the cluster ID (locus) they belong to. This enables to apply custom filters at the locus level, as well as any variant level classical RAD-Seq filters (such as the minimal read depth to call a genotype or the minimal minor allele frequency to keep a variant). Several such RAD-seq filtering scripts are provided along with the main program (https://github. com/GATB/DiscoSnp/tree/master/discoSnpRAD/post-processing_ scripts).
Comparison with other tools For comparisons, STACKS v2. 4 and IPyRAD v0. 7. 30 were run on the simulated datasets. STACKS stacks were generated de novo ( denovo_map. pl ), with a minimum of 3 reads to consider a stack (-m 3). On the simulated dataset composed of 100 samples, five values of the parameter -M governing stack merging (i. e. , 4, 6, 8, 10 and 12) were tested. On the remaining datasets, the parameter -M was fixed to 6 following r80 method  (Paris, Stevens & Catchen, 2017). All other parameters were set to default values. Similarly, IPyRAD was run using five values of clustering threshold on the dataset composed of 100 samples (i. e. , 0. 75, 0. 80, 0. 85, 0. 90 and 0. 95) and then fixed to 0. 80, following r80 method  (Paris, Stevens & Catchen, 2017) , for larger datasets. The other parameters have been kept at the default values.
Data origin Tests on real data were performed on ddRAD reads previously obtained for the phylogenetic study of seed parasitic pollinators from the genus Chiastocheta (Diptera: Anthomyiidae). The dataset corresponds to the sequencing of 259 individuals sampled from 51 European localities generated by Lausanne University, Switzerland  (Suchan et al. , 2017)  (https://www. ebi. ac. uk/ena/data/view/PRJEB23593). A total of 608,367,380 reads were used for the study with an average of 2. 3 Million reads per individual.
Population genomic analyses The species genetic structure was inferred using STRUCTURE  (Pritchard, Stephens & Donnelly, 2000) v2. 3. 4. This approach requires unlinked markers, thus only one variant by locus, randomly selected, has been kept. The STRUCTURE analysis was carried on the datasets generated by each tool. Simulations were performed with genetic cluster number (K) set from 1 to 10. Best K was identified using  Evanno, Regnaut & Goudet (2005)  method. We used 20,000 MCMC iterations after a burn-in period of 10,000. The output is the posterior probability of each sample to belong to each of the possible clusters. For C. lophota species, a multivariate analysis were used to investigate intra-specific genetic structure using adegenet R package  (Jombart, 2008).
Phylogenomic analyses Maximum likelihood (ML) phylogenetic reconstruction was performed on a whole concatenated SNP dataset using GTRGAMMA model with the acquisition bias correction  (Leaché et al. , 2015). We applied rapid Bootstrap analysis with the extended majority-rule consensus tree stopping criterion and search for best-scoring ML tree in one run, followed by ML search, as implemented in RAxML v8. 2. 11  (Stamatakis, 2014).
Results on simulated data DiscoSnp-RAD was first run on several simulated RAD-Seq datasets composed of an increasing number of samples (from 100 to 1,000) in order to validate the approach, to evaluate its speed and efficiency and to compare it with the other clustering approaches. This experiment shows that DiscoSnp-RAD predictions are accurate with a good compromise between recall and precision (see Fig. 2 ). On average, 84. 6% of the simulated variants are recovered with very few false positive calls, that is, reaching a precision of 98. 5% on average. Importantly, these performances are not impacted by the number of input samples in the dataset. For instance, recall varies from 84. 6% to 83. 3% between the smallest and the largest datasets (100 vs 1. 000 samples), and precision from 98.
1% to 98. 5%. By comparison with other tools, for each of the tested population sizes, recall and precision are comparable between tools, with typically a recall lower than STACKS and IPyRAD and an intermediate precision, lower than IPyRAD and higher than STACKS. The loss of recall may be explained by the fact that DiscoSnp-RAD voluntary does not detect the variants within 3 bp of each locus end (see "Methods"). The amount of predicted loci are similar between all tools (Table  S2 ). The main differences between the tools concern the run time and the disk space usage. These differences increase with the number of samples in the dataset. For instance, on the largest dataset composed of 1,000 samples DiscoSnp-RAD is more than 3 times faster than STACKS and more than 5 times faster than IPyRAD.
We enriched results presented in this figure with two additional simulated datasets, following the protocol presented in the "Simulation protocol" section, but introducing SNPs at rates of 0. 5% and 2%, instead of 1%. Results are presented in the Fig. S2. They also highlight the robustness of results and the rational for the default parameter choices with two times more and two times less simulated diversity. vcf file obtained with the 259 samples, the 40 C. lophota samples were extracted and the same filters, that is, MAF, missing etc. , were applied on this C. lophota dataset. We obtained 1,306 SNPs by selecting one variant per locus extracted from 4,364 variants identified in this species. The multivariate analysis of this dataset identify three populations comprising respectively 31, 5 and 4 samples (Fig.
Run-time The use of DiscoSnp-RAD dramatically decreased the overall time for discovering and selecting relevant variants, as compared to other tools. This is made possible thanks to the use of a unique indexing data structure, the dBG built from all the reads. To build this graph, reads do not need to be compared to each other. DiscoSnp-RAD speed depends on the graph size and at a lesser extend on the number of reads. Importantly, it is not expected to increase quadratically with the dataset size. This can likely be anticipated that with the drop of sequencing costs, RAD projects will grow in size, either by using higher frequency cutting enzymes to obtain a dense genome screening, by increasing the sequencing depth to compensate sequencing variation or by increasing the number of samples.
Easy parameter choice Another substantial advantage of using DiscoSnp-RAD is the fact that parameters are not directly linked to the level of expected divergence of the compared samples. In fact, they impact the number and type of detected variants, but are not related to the subsequent clustering step. As a result, same parameters can be used whatever the type of analysis (e. g. , intra or inter-specific), contrasting with classical tools in which parameters govern loci recovering. Indeed, in STACKS, the parameters governing the merge of the stacks can compromise the detection of relevant variants if they are not adapted to the studied dataset  (Shafer et al. , 2017). Therefore, the authors recommend to perform an exploration of the parameter space before downstream analyses  (Paris, Stevens & Catchen, 2017).
Sequencing error rate Our tests on simulated data sets, performed with 1% error rate, show that, in this worst case scenario, DiscoSnp-RAD can deal with a high error rate and can thus afford analyses of data not generated with the most recent sequencing technologies. With a lower error rate, the good performances of DiscoSnp-RAD are confirmed as shown by the results obtained on the real Chiastocheta dataset. The breakthrough in running time with respect to the other approaches is slightly reduced with the real dataset and this could be due to STACKS and IPyRAD being more impacted by sequencing errors in the data.
Conversely, variants detected from reads 1 are not related to variants detected from reads 2. This ability of DiscoSnp-RAD to handle reads that do not necessary start at the same genomic position makes it particularly well suited to analyze the datasets produced by another group of genome-reduction techniques, namely sequence capture approaches  (Grover, Salmon & Wendel, 2012). In these techniques, DNA shotgun libraries are subject to enrichment using short commercially-synthesized  (Faircloth et al. , 2012)  or in-house made  (Suchan et al. , 2016)  DNA or RNA fragments acting as "molecular baits", that hybridize and allow separation of homologous fragments from genomic libraries. One of such promising approaches is HyRAD, a RAD approach combining the molecular probes generated using ddRAD technique and targeted capture sequencing, designed for studying old and/or poor quality DNA, likely to be too fragmented for RAD-sequencing  (Suchan et al.
Results on real data In this section, we present an application of the DiscoSnp-RAD implementation on ddRAD sequences obtained from the anthomyiid flies from the Chiastocheta genus. In this genus, classical mitochondrial markers are not suitable for discriminating the morphologically described species  (Espíndola, Buerki & Alvarez, 2012). Although RAD-sequencing dataset phylogenies supported the species assignment  (Suchan et al. , 2017) , the interspecific relationships between the taxa could not be resolved with high confidence due to high levels of incongruences in gene trees  (Gori et al. , 2016; Suchan et al. , 2017). The dataset is composed of 259 sequenced individuals from 7 species. Results obtained on DiscoSnp-RAD were compared to the prior work of Suchan and colleagues, based on pyRAD analysis  (Suchan et al.
5. To analyse the performance of LFSRs and ECA it is necessary to evaluate how fast their states can reach DO() = 0. 5 in the eventuality of having a suboptimal initial seed in which the number of elements set to 0 differs from the number of those set to 1 (Fig. 3 ). To do so we devise a MATLAB experiment in which we evolve a 64-cells rule-30 ECA and a 64-flip-flops LFSR using 65 different initial seeds having an density that varies from DO(1) = 0 all the way to DO(1) = 1. In the result of the experiment we represent DO() using greyscale elements in which DO() = 0 is coloured black and DO() = 1 is coloured white. In Fig. 3  the top line represents these initial seeds with increasing density, from the leftmost DO(1) = 0 to the rightmost DO(1) = 1. Each step underneath shows the temporal evolution of the PRNG output density evolving from each initial seed configuration.
PSD expresses the distribution of the energy of a waveform among its different frequency components. Any peak in a graphic of PSD() over  at a given frequency   would represent a strong repeating pattern of period 1   ⁄ among the rows of   Given all  ℎ () PSD can be computed as: PSD(𝑓) = 1 𝑁 ∑ |𝜙 ℎ (𝑓)| 2 𝑁 𝑒=1 ( 17 ) where  is the number of elements of the PRNG as well as the number of columns in. The PSD profile of a suitable and efficient PRNG for CS should closely resemble white noise since its energy should equally spread throughout the entire spectrum. Once again we devise a MATLAB experiment in which we evolve a 64-cells rule-30 ECA and a 64-flip-flop LFSR. Fig. 2  shows the PSD profiles of rule-30 ECA and LFSR. These graphics have been produced for  = 1000 time steps starting from an initial random binary seed with DO(1) = 0.
The simulations were carried out in MATLAB. In each set of simulations, 30 trials are performed due to the random nature of generated measurement matrices and the average PSNR is computed. For the BCS sampling, we use the block dimension of size 32 × 32, i. e. = 1024. The value of threshold in the reconstruction algorithm is set to  = 10 -4. We remind the reader that dividing the pixel array into smaller sub-arrays that can be digitized independently is essential for two reasons. It helps the implementation of practical measurement matrices in CS-CISs and it reduces software resources needed during reconstruction. Usually fast and accurate reconstruction algorithms opt for small blocks, typically 8 × 8. On the other hands, blocks that are too small deteriorate the quality of the sensor introducing asymmetries within the pixel array.
This process is repeated several times per image and then the results are averaged. Fig. 7  demonstrates the quality of reconstructed images in terms of PSNR as a function of subrate (S), where S varies from 0. 1 to 0. 5. As shown in Fig. 7 , ternary matrices can be used to compressive sample the images and their performances degrade when the subrate decreases. An interesting observation is that the ternary matrix  ECA T outperforms binary matrices almost reaching the Gaussian random matrix  Gaussian. Fig. 9  represent Lena reconstructed using a ternary measurement matrix generated using ECA with a subrate of 0. 1. We will provide more images and results along with the MATLAB code used to obtain them in an online repository 3. Depending on the subrate, on average, the proposed ternary matrix improves the PSNR of the reconstructed images from 0.
The ARCADIA project aims at using pattern recognition and machine learning to promote a systematic analysis of the large corpus of archaeological pottery fragments excavated in Saran (France). Dating from the High Middle Ages, these sherds have been engraved with repeated patterns using a carved wooden wheel. The study of these engraved patterns allows archaeologists to better understand the diffusion of ceramic productions. In this paper, we present a method that classifies patterns of ceramic sherds by combining deep learning-based features extracted from some pre-trained Convolutional Neural Network (CNN) models. A dataset composed of 888 digital patterns extracted from 3D scans of pottery sherds was used to evaluate our approach. The classification capacity of each CNN model was first assessed individually.
The recognition of the relief patterns stamped by the potters needs to be robust to the variability due to the curvature of the vase, the mode of application, the state of con-servation and so on. In this paper, we propose to mix deep learning-based features to classify the digital patterns extracted from 3D scans of engraved pottery sherds. We test some stateof-the-art Convolutional Neural Networks (CNNs) with a fine tuning on our dataset. Then, we compare different feature pooling strategies to show the relevance of combining CNN-based features using Compact Bilinear Pooling (CBP). The paper is organized as follows: related work is presented in Section 2. Section 3 describes the archaeological materials. Section 4 introduces the proposed pipeline and details strategies for deep learning, feature pooling and classification.
The recognition rate was below 85%  (Debroutelle et al. , 2017). Then, we exploited well-known CNN models : AlexNet  (Krizhevsky, 2014) , VGG11  (Simonyan and Zisserman, 2014)  and ResNet18  (He et al. , 2015) , with a fine tuning on our dataset. By comparing different options for the classification stage (i. e. replacing the fully connected layers of the network with another classifier), the best results were obtained with the combination ResNet18+SVM with a classification rate of about 88 % on the same dataset  (Chetouani et al. , 2018). In continuation of these previous studies, the current work proposes to mix deep learning features by using hybrid network architectures with several pooling strategies. Based on the same set of sherds, greyscale relief maps are used instead of binary images, which leads to additional data.
Archaeological Materials Since 1994, several archaeological excavations have documented a major production site of domestic potteries in Saran (Loiret, France) dating from the High Middle Ages (sixtheleventh Centuries). Many of the wheel-stamped pottery fragments show a repetitive decoration about 1 millimeter deep and 1. 5 to 3 cm wide, depending on the dimensions of the wooden cylinders used by the potters (see Fig. 1 ). The most common patterns notched by potters on wooden cylinders included sticks, squares, chevrons and diamonds in one or several lines. For digital pattern extraction, we used a NextEngine scanner to provide a 3D model of each sherd. Next, we created a 2D shaded view of the surface with the Meshlab software by controlling the orientation of the 3D mesh (with a default illumination model).
Unit Tests of Networks The four selected networks (AlexNet, VGG16, VGG19, ResNet50) were tested with fine tuning on our dataset. We modified the FC layers of each of them and the Softmax layer was adapted to the number of classes (four). The Stochastic Gradient Descent (SGD) was used during the training with a batch size and a learning rate set respectively to 32 and 0. 01. The momentum and the number of epochs were fixed respectively to 0. 9 and 100. At each epoch, we shuffled the training data and we stored the model. We finally kept the model that provided the best performance. To expand the training dataset, we applied flipping and translations (data augmentation). It is obvious that rotation cannot be used because of the confusion it would introduce between square and diamond patterns.
Conclusion In this paper, we proposed a method to classify patterns of ceramic sherds by combining feature vectors provided by CNN models. Four pre-trained CNN models were employed with fine-tuning. The deep learning-based features thus extracted were then combined through different pooling strategies (Concat, EWM, EWS, EWM+EWS, CB and CBP) and the performance of different classifiers (SVM, LDA and FC) was compared. The best performance was obtained with CBP(VGG19,ResNet50)+FC (95. 23%). In future work, we will first increase the dataset by integrating other patterns from several sites. We will consider geometric aspects (lines, shapes, etc. ) to better discriminate between patterns. To go further, we will also try to classify wheels for each pattern class which is still a challenging task.
Functionally relevant network patterns form transiently in brain activity during rest, where a given subset of brain areas exhibits temporally synchronized BOLD signals. To adequately assess the biophysical mechanisms governing intrinsic brain activity, a detailed characterization of the dynamical features of functional networks is needed from the experimental side to constrain theoretical models. In this work, we use an open-source fMRI dataset from 100 healthy participants from the Human Connectome Project and analyze whole-brain activity using Leading Eigenvector Dynamics Analysis (LEiDA), which serves to characterize brain activity at each time point by its wholebrain BOLD phase-locking pattern. Clustering these BOLD phase-locking patterns into a set of k states, we demonstrate that the cluster centroids closely overlap with reference functional subsystems.
, 2008; Cabral et al. , 2011; Deco and Jirsa, 2012; Deco et al. , 2013; Haimovici et al. , 2013; Hansen et al. , 2015). Here, we explore this mechanistic hypothesis using the mathematical formalism from dynamical systems theory and Markov chains in order to characterize the spatio-temporal dynamics of spontaneous brain activity in terms of probabilistic trajectories between recurrent BOLD phase-locking patterns. We validate the functional role of the patterns obtained by comparing them with known RSNs. Furthermore, we evaluate the stability of BOLD phase locking states based on their Fractional Occupancy, Dwell Times and Transition probabilities. While previous works have applied LEiDA to condition-specific datasets, with reduced sample sizes, here we make secondary use of a large open source dataset of healthy participants, demonstrating the reliability of the yielded metrics across subjects and consecutive fMRI recording sessions.