dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sharing in Galaxy Students can upload their own datasets that will live in their Galaxy workspace, called history in Galaxy. The simplest way to do it is via the web front-end, although alternative mechanisms are available for larger datasets  5. For teaching purposes and to reduce processing times, the GTN data is usually small, still yielding meaningful results for its scientific interpretation. Alternatively, all GTN datasets are stored and shared via the Galaxy Data Libraries 6 on the main, public usegalaxy. * servers  7. The power of this feature resides in its scalability, allowing data versioning and preventing data duplication across different user Galaxy histories and long waiting times when uploading datasets into Galaxy. To optimally benefit from this feature, trainers can prepare the Data Libraries in advance.
Galaxy extracts the metadata associated with every dataset, including the file format, a crucial element in life sciences data analysis. Although there are many different formats available, students need to learn, at least, how to identify, convert and visualise the most popular ones. Based on our experience, reinforcing the basic concepts and the different file formats is relevant for the students: first, to understand which information is included in which file format; and second, to acquire the scientific vocabulary for future competent communication with colleagues.
Analysis histories sharing in Galaxy Galaxy histories are the central feature to ensure data analysis transparency, reproducibility, and shareability. Every data analysis step (tool version, selected parameters, etc. ) along with the entire provenance of a dataset are captured in Galaxy histories and constantly available for the students. In an online teaching environment, providing students with real-time feedback can be challenging, due to the lack of visual contact and time-shift restrictions. Consequently, a straightforward shareability method becomes extremely important for the progress assessment and final evaluation of the goals achieved. Galaxy provides a set of features to facilitate shareability, either with only one user (e. g. between peers or with the instructor), with defined groups or publicly.
Workflow sharing in Galaxy Tools can be linked together to perform multi-step and arbitrarily complex analyses. In Galaxy, these multi-step processes can be saved as reusable workflows. Workflows can then be run on different input datasets. Workflows, like histories, can also be shared with individuals, groups, or published to everyone on the server. If a given tool yields an error during the process, the workflow is paused and the successive step would not be executed. In a teaching context, workflows are particularly useful to run a particular analysis that can be exported and shared with the students or the supervisors.
Fourth part: Assessment The GTN tutorials are exemplary work packages that can be distributed to students as homework (Fig. 1 ). To assess the level of understanding during the previous parts of the training, the GTN tutorial can be used with different and more complex datasets. The degree of complexity of the data can vary from real-world datasets or the data from the Personal Genome Project  8. A more advanced exercise to promote critical thinking can be to hand over an open-access article which data analysis will have to be reproduced  [23] , which can be shaped as a group activity to simulate the in-person collective projects. Once the homework has been performed, students can deliver the results, together with the steps in the analysis, and get feedback from the trainer or other students by sharing their Galaxy histories.
Time-stamps and metadata on every dataset are particularly useful to prove authorship. The role of the instructor is again vital at this stage to create a collaborative environment in which students can learn and help each other; in contrast to a model where the teacher is an authority on the subject  [13]. Furthermore, to develop their scientific writing skills, the students can also write a report interpreting the results they obtained from the data analysis in a structured way. After all the assessments have been collected, students may receive direct feedback on their analysis, and optionally, a gold-standard Galaxy history can be distributed as a reference.
To determine the impact of BlpS on the whole regulon controlled by the GSP/ BlpHR module, we quantified by qRT-PCR the transcription levels of 9 different genes of this regulon, one located outside (gallo_rs03700) and eight within the gallocin genomic locus (gallo_rs10335 [encoding a putative bacteriocin], gsp, blpH, blpR, gal-lo_rs10370, gllA2, blpA, and gallo_rs10400) in the WT and DblpS strains expressing blpS under the control of the inducible promoter PtetO. Expression of the 9 tested genes was increased in the DblpS mutant compared to the WT strain in the absence of inducer (Fig. 3D ). Induction of blpS expression reduced the transcription levels of the 9 tested genes in both the WT and DblpS strains (Fig. 3D ). Expression of gsp displayed the highest fold change between the WT and DblpS strains, with more than a 10-fold increase in the DblpS mutant.
Our initial promoter sequence alignment of the gallocin locus genes (i. e. , 250 bp upstream from the initiation codons) using Geneious software identified a conserved 15-bp motif (Fig. 5A ). To improve the robustness of the 15-bp consensus sequence, promoters of the 12 putative operons regulated by BlpHR were analyzed with MEME software (http://meme-suite. org/tools/meme). A larger consensus sequence of 30 bp, including most nucleotides of the previously identified 15-bp motif (12 bp of 15), was identified in all regulated promoters (Fig. 5B ). Mapping of this motif on the whole S. gallolyticus subsp. gallolyticus UCN34 chromosome showed that it is highly specific, as it is present only upstream from the operons in the gallocin locus, as well as the two other bicistronic loci, gallo_rs03700 and gallo_rs05800, identified by the transcriptome analysis (Fig.
S7 ). Our transcriptome sequencing (RNA-seq) data revealed that transcription of five other putative bacteriocin genes was coinduced with gallocin genes. Only one of these has a double glycine motif in the N terminus, similar to the gallocin peptides, while others have very different amino acid sequences (one being very rich in positively charged amino acids). Only two additional operons encoding an ABC transporter and hypothetical proteins, located elsewhere in UCN34 genome, were coinduced with the gallocin locus. We also uncovered the role of a second regulatory protein named BlpS which represses all the genes activated by GSP/BlpRH. This small 108-amino-acid (aa) protein consists almost entirely of a LytTR DNA-binding domain. Most proteins containing a LytTR domain studied previously also contain an additional phospho-acceptor domain typical of TCS regulators (Interpro domain IPR007492)  (21).
Of note, two transcriptional regulators whose architecture is similar to that of BlpS were identified in S. mutans. However, these regulators are in operon with a transmembrane protein which inhibits their activity  (22, 23). Thus, BlpS differs from these so-called LytTR regulatory systems (LRS) as it forms an operon with a classical TCS that it antagonizes. An in silico analysis revealed that 15,409 of the 80,096 LytTR-type regulators (Uniprot database) contained only this functional domain, and of these, 1,565 have a size similar to that of BlpS (between 100 and 120 amino acids). These proteins were found both in Gram-negative and Gram-positive bacteria. Among them, the homologous BlpS protein of the blp locus of Streptococcus pneumoniae was found. This blp locus, which encodes several bacteriocins named pneumocins, displays an organization highly similar to that of gallocin locus  (24).
20020595; Illumina). The directional libraries were controlled on Bioanalyzer DNA1000 chips (Agilent Technologies), and concentrations were measured with the Qubit double-stranded-DNA (dsDNA) HS assay kit (Thermo Fisher). Sequences of 65 bases were generated on the Illumina HiSeq 2500 sequencer. Reads were cleaned of adapter sequences and low-quality sequences using cutadapt version 1. 11  (26). Only sequences at least 25 nucleotides in length were considered for further analysis. Bowtie version 1. 2. 2  (27) , with default parameters, was used for alignment on the reference genome (NC_013798. 1 from NCBI). Genes were counted using featureCounts version 1. 4. 6-p3 (28)  from Subreads package (parameters: -t gene -g locus_tag -s 1). Count data were analyzed using R version 3. 5. 1 and the Bioconductor package DESeq2 version 1.
Finally, some unassigned genes whose expression was very low (50 to 150 reads per gene) but significantly different in the DblpR mutant were also suppressed from this heat map for clarity. For real-time quantitative reverse transcription, cDNAs were obtained from 1 mg of RNA treated with DNase I using the iScript cDNA synthesis kit. Real-time quantitative PCR was carried out on three independent biological replicates in a CFX96 Touch real-time PCR detection system (Bio-Rad) in a 20-ml mixture containing 10 ml EvaGreen universal qPCR supermix (Bio-Rad), 1 ml gene-specific primers (10 mM), and 5 ml of a 100-fold dilution of cDNA. The fold change in expression compared to WT was determined by the 2 2DDCT method. For statistical analysis, qRT-PCR data were analyzed using analysis of variance (ANOVA): for each gene, a model that explains DC T values was fitted, including the replicate effect as random.
The model also includes the strain (Fig. 2C ) or the strain, the condition, and their interactions (Fig. 3D ) as fixed effects. Pairwise comparisons were tested with the emmeans R package version 1. 4. 2, and P values were adjusted for multiple testing using the Tukey method. RACE-PCR. RACE-PCR to determine the transcriptional start site was performed with the 59 RACE system (Thermo Fisher) following the manufacturer's protocol. Briefly, total RNAs were purified from a S. gallolyticus subsp. gallolyticus UCN34 WT culture as indicated above. cDNA of gllA-gip mRNA was obtained by reverse transcription with a gene-specific primer. A homopolymeric tail was added to the 39 end of the cDNA, corresponding to the former 59 end of the mRNA. The cDNA was amplified by PCR with another gene-specific primer located in the cDNA and a primer provided in the kit that anneals to the homopolymeric tails of the cDNA.
ACKNOWLEDGMENTS We particularly thank  Odile Sismeiro  for the construction of the transcriptome library and  Rachel Legendre  for the bioinformatic analysis of the transcriptome and its deposition to the  GEO database. We are also grateful to  Christophe Rusniok  for his help in the search of LytTR-only proteins,  Christine Dang  for the reporter plasmid construction, and  Tarek Msadek  for careful and critical reading of the manuscript. We also thank  Maria Vittoria Mazzuoli  for providing her expertise in EMSAs and  Gaël Millot  for his help with the statistical analysis of qRT-PCR data. This study was funded by the  Institut National contre le Cancer (INCA)   PLBIO 16-025  to  S. Dramsi  and from the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases  (grant no.
We propose a developmental model inspired by the cortico-basal system (CX-BG) for vocal learning in babies and for solving the correspondence mismatch problem they face when they hear unfamiliar voices, with different tones and pitches. This model is based on the neural architecture INFERNO standing for Iterative Free-Energy Optimization of Recurrent Neural Networks. Free-energy minimization is used for rapidly exploring, selecting and learning the optimal choices of actions to perform (eg sound production) in order to reproduce and control as accurately as possible the spike trains representing desired perceptions (eg sound categories). We detail in this paper the CX-BG system responsible for linking causally the sound and motor primitives at the order of a few milliseconds. Two experiments performed with a small and a large audio database show the capabilities of exploration, generalization and robustness to noise of our neural architecture in retrieving audio primitives during vocal learning and during acoustic matching with unheared voices (different genders and tones).
PLOS COMPUTATIONAL BIOLOGY We propose to model a neural architecture inspired by the cortico-basal circuits responsible for processing and organizing the learning between the audio sensory map and the motor control; see  [4, 35]. In the first experiment, we will present results on vocal learning from one speaker only (e. g. , one-to-one correspondence) and how our architecture rapidly constructs its sound repertoire by free-energy minimization of internal signals. In the second experiment, we will show how this architecture solves the correspondence problem from six different speakers (e. g. , one-to-many correspondence), learning a model from the six speakers. We show in that experiment how the predictive coding architecture can help to be robust to noise, for inference. In comparison to  [36] , we use the same neural architecture althought the difference lies on the task applied on vocal learning and on the size of the audio dataset constituted of more than ten thousand audio (MFCC) vectors, which is higher than the 25 vectors only in our previous study.
We prove therefore the scalability of our network to problems of higher dimensions. Although several reward modulated spiking recurrent neural networks exist on vocal learning  [9, 37] , to our knowledge, no one has achieved such performances: (1) for constructing a large audio repertoire and (2) being robust to noise during acoustic matching even from different speakers. The paper is organized as follows. In section, we will describe our model and present some neural justifications supporting it. In section, we will present the neural architecture and its learning mechanisms. In section, we will present the two experimental setups for vocal learning and acoustic matching, respectively from a limited learning database (only one speaker, 3 minutes length) and from a larger database (six speakers of different genders, 27 minutes length).
Experimental setup The aim of our experiments is to study the vocal learning and acoustic matching during selfsupervised learning from the listening of one speaker or from several. The experimental setup for Experiment 1 in section consists of a small audio dataset of 2 minutes length of a native French woman speaker repeating five sentences three times. The audio. wav file is translated into MFCC vectors (dimension 12) sampled at 25ms each and tested either with a stride of 10ms or with no stride. A stride is the temporal shift between two samples. Typically, if we have one sound sample between [0, 25ms] then the next sample will be between [10ms, 35ms]. A stride of 25ms guaranties that there is no overlapping across samples. The whole sequence represents 14. 000 MFCC vectors for the case with strides and 10.
000 MFCC vectors for the case with no strides. The numbers of Striatal and GP units are chosen so that they correspond to the number of MFCC vectors, which means 14000 units (or 10. 000 units without strides) for each layer. We do so in order to test the reliability of our architecture to retrieve input data with an orthogonal representation. The compression rate is, however, low (1:1). We organize the MFCC vectors only depending on the temporal order of appearance in the Wav file. In contrast, Experiment 2 in section will use a bigger audio dataset of 27 minutes length from six native French speakers, the same speaker as in Experiment 1 plus two other women and three men, repeating the same sentences as in the previous experiment. The audio. wav file is translated into MFCC vectors (dimension 12) sampled at 25ms each, which corresponds to 140.
000 MFCC vectors for the case with 10ms stride. The numbers of Striatal and GP units are kept the same as for the first experiment (14. 000 units), which means that the size for the BG layers is now ten times lower than the total number of MFCC to be retrieved in the sequence. The compression rate this time is high (1:10). This second experiment will serve to test the generalization capabilities of our architecture and its robustness to high variabilities with respect to the inputs, replicating the correspondence problem. The sentences used in the audio database were selected because they cover all the syllables in French. Each period takes 10 minutes on a conventional laptop for the supervised method. The stabilization is done depending on the global error and we can decide below a certain threshold or we can choose a maximum number of iteration to stop the learn stage.
The result in this graph shows that for a majority of the STR units (80% of the population), the optimization process permits minimization the prediction error below a value of 0. 3, which means that most of the GP neurons retrieved the optimal input vector that causes the STR to fire. Instead, for a small proportion of them (20% of the population), the error is above 0. 4, which means that the optimization process was not effective. In this case, the INFERNO architecture did not find the relationship between auditory input and the striatal category. In Fig  6B , the reconstruction error is computed as the Euclidean distance between the MFCC vectors presented in the audio database with the nearest GP vectors retrieved through free-energy optimization after normalization. The density probability distribution normalized between [0, 1] shows that the reconstruction process is good with an approximation error centered at 4%.
We can also observe that at the beginning and at the end of the sequence, the relative error is rather small corresponding to background noise when the person did not start speaking and when she ended up in advance. When reconstituting the. wav file in Fig 7C from the retrieved MFCC vectors, we can observe a gradual refining of the audio waveform from the four periods with respect to the ground truth displayed at the bottom chart. The sequence is shown for 11 seconds although the global test was performed over two minutes length of the audio database. After four exposures of the neural architecture to the audio sequence, the retrieved signals gradually converge to the correct waveform. At period #0, the waveform is very discrete with square-like pattern and the amplitude and the wavelength are not respected.
Experiment 2-Correspondence matching with several speakers In this section, we present the experiment carried out on a larger audio database with an architecture of the same size as in the previous section, which means with 14. 000 STR units. The audio database of 27 minutes (140. 000 MFCCs) is more difficult as it consists of sentences pronounced by six different speakers with equal numbers of each gender. As expressed in section, the sentences used here were selected because they cover all the syllables in French. As the ratio between STR units and MFCC to be encoded is now 1:10, we here investigate the generalization and inference capabilities of the network during acoustic matching with unheared voices, known as the correspondence problem. This experiment reproduces some of the conditions faced by babies during acoustic matching when the audio repertoire learned is small and the heared voices are mostly unfamiliar and novel.
As we do not have access to the ground truth classes in the MFCC audio dataset, we cannot compute the basic classification analysis with recall and precision. Instead, we will analyze the performances in term of similarity between the original MFCC sound vector and the generated ones by the Inferno network. This measure is similar to the ABX distance proposed in  [76, 77]  for unlabeled audio database, and we will present it later. The questions we would like to ask are: How well the motor and sound repertoires match novel voices? How robust is the categorization of unheared MFCC vectors in the STR (perceptual) layer and how similar is the reconstructed GP The graph plotted in Fig 10A  corresponds  to the mapping between the STR units that match the closest the MFCC vectors in the audio database.
The Euclidean distance is computed from the GP vectors retrieved in order to measure the correspondence between the MFCC and STR indices. Each MFCC within the audio database is predicted by only one STR unit. Conversely, each STR unit can code for several MFCC vectors. This shows the generalization capabilities of certain STR units within the network. For instance, we found that certain STR units cluster more than 100 MFCC vectors whereas others do not cluster any MFCC of the unheared database. Besides, we plot the Euclidean distance in Fig 10B  to  show the good generalization of the algorithm with a reconstruction error of 5%. Using this mapping, it is possible to construct in Fig  10C  and in 10D a correspondence matrix between the MFCC vectors A and B found in the audio database where A represents the ground truth vector and B the closest MFCC vector to the vector X generated by the STR units.
The Euclidean distance between the A and B vectors computes then the ABX score proposed by  [76]   For instance, the diagonal indicates that the mapping is bijective and that the network has retrieved some perfect matching between MFCC vectors B closest to the STR units X and the MFCC vectors A in the audio database:there is a good mapping between what can perceive and what can "pronounce" the Inferno network. We found 3702 matches between the A and B vectors out of 14. 000 MFCC vectors X; which corresponds to a similarity score and a perfect matching for 26% of the items in the new database with those of the original database. The horizontal stripes indicate the redundancy within the large audio database, as well as some   [76, 77]  computed from the Euclidean distance between the A and B vectors retrieved previously.
In F, an example of a retrieved waveform is provided from an unheared sound sequence after the learning stage. https://doi. org/10. 1371/journal. pcbi. 1008566. g010 classification errors by the network. The zoom in the mapping plotted in Fig  10D  shows that a linear correlation is performed and that the noise due to classification errors is however not so large. The ABX distance histogram in Fig  10D  shows an average error of 4% by the Inferno network with peaks at zero error, which is in average with other predictors' performances but on different unlabeled audio databases  [76, 77]. These results describe how the network performs on a large audio dataset when facing the correspondence problem, the discrepancy indicates that the number of vectors to be retrieved is high in comparison to the number of units within the network.
Despite the dimensionality problem, the BG-CX loop is known to encode conditioning responses and its role is not devolved to the control of the precise serial recall of sequences. Instead, the PFC is known to perform such executive control on the cortico-basal ganglia system to realize a precise control of temporal sequences. This second PFC-BG system is presented in the complementary article in  [46]. In the second experiment in section, we performed the acoustic matching with several speakers constituting an audio sequence ten times longer than the previous ones (27 minutes. wav and 140. 000 MFCC) in order to assess the generalization capabilities of INFERNO to higher dimensions with a limited number of sound primitives. For this purpose, we intentionally kept the number of sound primitives the same as in the first experiment (14.