dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank members of the  Tuthill laboratory  for technical assistance and feedback on the manuscript, particularly  Ellen Lesser , who initially observed that FeCO neurons move within the leg, and  Tony Azevedo , who was involved with the initial identification of the arculum within the X-ray microscopy dataset. We thank  Thedita Pedersen  for reconstructing FeCO neurons and dendrites from the X-ray microscopy dataset. We thank  Katie Stanchak  and  Bing Brunton  for discussions and helpful comments on the manuscript. We thank  Rachel Wong ,  Gwyneth Card  for generous sharing of equipment, and  Jack Waters  and  Kevin Takasaki  for construction of the 3-photon microscope. We thank  Bertil Hille  for suggesting the term goniotopic. We used stocks obtained from the  Bloomington Drosophila Stock Center  ( NIH P40OD018537 ).
Characterizations methods Measurements of the film thickness were performed by spectroscopic ellipsometry using a UVISEL ellipsometer from Horiba-Jobin-Yvon (spectral range from 190 to 830 nm). Obtained data were fitted using Kato-Adachi model with the DeltaPsi 2 software from the UVISEL ellipsometer. The Fourier-transform infrared spectroscopy (FTIR) measurements were performed with a Thermo Scientific Nicolet 8700 system. Films were deposited on thinner silicon wafers (thickness around 250 µm or less). Transmission mode was used to collect the data. Spectra were taken with a resolution of 4 cm -1 and were averaged over 16 scans in the range 4000 -400 cm -1. The X-ray photoelectron spectroscopy (XPS) analysis was performed on a Gammadata Scienta (Uppsala, Sweden) SES 200-2 X-ray photoelectron spectrometer under ultrahigh vacuum conditions (P < 10 -7 Pa).
CASAXPS (Casa Software Ltd, Teignmouth, UK, www. casaxps. com) was used to fit all the peaks and area of each component of XPS data. The microstructured films were characterized using atomic force microscopy (AFM) in the tapping mode, using a FlexAFM model from Nanosurf with uncoated Si ACT-50 cantilever from AppNano with a spring constant between 13 and 77 N. m -1. Scanning was performed at 1 line/s with an image resolution of 256 x 256 pixels. Grazing incidence X-ray diffraction (GIXRD) patterns of the thin films were measured on a SmartLab diffractometer (Rigaku Corp. , Tokyo, Japan) with copper Kα radiation (λ = 1. 5406Å), operated at 50 mA and 40 kV. The angle of incidence (ω) was set between 0. 2 and 0. 8°. Absorption spectra were recorded on a Lambda 950 UV/Vis spectrometer form Perkin Elmer (spectral range 200 -800 nm).
7 nm films is of the same order of magnitude as the nanocrystal size. The analysis of the films after DUV treatment was also conducted by XPS. For comparison, thermal treatments were also carried on (300°C/1 h) in order to compare the effect of DUV treatment with such a thermal annealing. The results are shown in Figure  5. In Figure  5a  the data are analyzed focusing on the proportions of O and C, relative to Zn. The laser treatment does not affect the O/Zn molar ratio. On the other hand, the C/Zn ratio decreases significantly. These values are compared with the values obtained after heat treatment at 300°C. The O/Zn ratio is slightly lower in the case of the heat treatment and of the same order of magnitude for the C/Zn ratio, demonstrating that both processes are effective in removing the organic part.
Acknowledgements This work was carried out as part of the  France-Japan international collaboration and the "DUVNANO "  project  ANR-18-CE08-0022. The authors thank  Loïc Vidal  for XPS and HRTEM analysis. This work of the  Interdisciplinary Institute HiFunMat , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  Inserm , was supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ) and  SFRI  ( STRAT ' US  project,  ANR-20-SFRI-0012 ) under the framework of the  French Investments for the Future Program. The authors are very grateful to  Yann Molard  and  Grégory Taupier  from  ISCR   UMR6226 CNRS-UR1  for the photoluminescence measurements, which were performed using facilities available on  Caphter Platform from ScanMAT, UAR 2025 University of Rennes 1-CNRS. The authors are grateful to  Valérie Demange ,  Serge Paofaï  and  Christophe Derouet  from  ISCR   UMR6226 CNRS-UR1  for their assistance in XRD experiments performed on Osirix platform ScanMAT, UAR 2025 University of Rennes 1-CNRS.
Here, we present MAGNETO, an automated workflow dedicated to MAG reconstruction, which includes a fully-automated coassembly step informed by optimal clustering of metagenomic distances, and implements complementary genome binning strategies, for improving MAG recovery. MAGNETO is implemented as a Snakemake workflow and is available at: https://gitlab. univ-nantes. fr/bird_pipeline_registry/magneto. IMPORTANCE Genome-resolved metagenomics has led to the discovery of previously untapped biodiversity within the microbial world. As the development of computational methods for the recovery of genomes from metagenomes continues, existing strategies need to be evaluated and compared to eventually lead to standardized computational workflows. In this study, we compared commonly used assembly and binning strategies and assessed their performance using both simulated and real metagenomic data sets.
The principal differences between existing binning software usually involve the algorithm used to group contigs into genome bins. Most successful softwares have used density-based clustering  (17) , Gaussian mixture models  (7) , affinity propagation  (18) , or graph clustering  (9). Other methods can also perform binning on genes rather than contigs, relying on the presence of coabundant genes within metagenomes, such as canopy clustering  (19)  and MSPminer  (20). The objects reconstructed by these methods may not be qualified as MAGs, and are commonly referred to as metagenomic species (MGS) and coabundance groups (CAGS). Extracting knowledge from raw metagenomics data requires handling several specific tasks, from assembly to gene calling and annotation, each of them often performed using dedicated software.
As computing coabundance profiles of contigs from multiple metagenomes may increase the precision of the metric  (9, 26) , the impossibility of computing large-scale coabundance may be considered a limitation of these workflows. Here, we present MAGNETO, a fully automated workflow for genome-resolved metagenomics, implementing a coassembly module that integrates a nonsupervised method to define sets of samples to coassemble without a priori knowledge. It also includes complementary strategies to compute abundance metrics from one to n metagenomes, even if they do not participate in the assembly process. In this study, we tested our coassembly module on a set of marine metagenomes, against a coassembly relying on existing knowledge. We also benchmarked four different assembly-binning strategies for MAG reconstruction, on diverse data sets ranging in complexity from a mock data set representing a small bacterial community to human gut microbiome communities.
S8 ). These observed differences demonstrated that cobinning strategies actually produced less contaminated MAGs than single-binning strategies, at all MAGs quality levels. Overall, these distinct results when dereplicating MAGs at species or strain level suggest that no single strategy can fit all needs. Therefore, the choice of an assembly-binning strategy should be informed by a biological question and considering the microbiome complexity under study. Comparing MAGNETO to similar metagenomics workflows. Finally, we compared the performances of MAGNETO to metagenomic workflows dedicated to MAG reconstruction, namely, METAWRAP  (22) , ATLAS  (24)  and nf-core/mag  (25). We chose these three tools as they use similar software to perform assembly and binning, namely, MEGAHIT  (41)  and MetaBAT2  (9).
The comparison of the workflows was performed using the HMP data set. ATLAS is a workflow only permitting single assembly of metagenomes, but integrates a binning refinement module using DAStool  (42) , which constitutes a good opportunity to evaluate whether single assembly could perform better after binning refinement. METAWRAP also contains a binning refinement module, albeit less complex than the DAStool methodology. This refinement module performs pairwise alignment of MAGs to detect redundant genomes, to then only conserve MAGs showing the best quality among detected duplicated MAGs. nf-core/mag uses the exact same tools as our workflow to perform assembly and binning. As compared to ATLAS, we observed that MAGNETO systematically reconstructed more MAGs using any of the four assembly-binning strategies (Table  1 ).
However, it reconstructed less MAGs than METAWRAP. The higher number of MAGs produced by METAWRAP may be explained by its refinement module coupling several binners, as these binners may reconstruct more nonredundant MAGs, thus increasing their numbers. However, MAGNETO and nf/core-mag reconstructed the same number of MAGs for both CASB or CACB strategies. These similar results are most likely explained by the absence of a bin refinement module and by the fact that in both workflows, the binning step used the exact same parameters. Design and implementation. MAGNETO is a Snakemake  (43)  workflow connecting open-source bioinformatics software, all available from Bioconda and conda-forge. Snakemake was chosen for its flexibility, its capacity to run both locally and on clusters, and its Conda management automating software installation.
MAGNETO includes several tools designed for metagenomic studies. First, reads trimming is performed using fastp  (44)  and FastQ Screen  (45). The coassembly module relies on Simka  (35) , which estimates metagenomic distances between samples based on their k-mers composition. MEGAHIT (41) then performs reads assembly/coassembly. We use MetaBAT2 (9) to bin contigs, and we assess the quality of bins using CheckM  (46). The dereplication of bins into MAGs (bins of at least high of medium quality) is performed using dRep  (37). Notably, MAGNETO can also be used to establish gene catalogs, to better capture metagenomic gene diversity by producing a nonredundant set of genes through sequence clustering at a user-defined sequence identity cutoff (e. g. , 95%) using Linclust  (47). GTDB-tk (  48 ) is used to perform taxonomic annotation of dereplicated MAGs, and eggNOG-mapper (  49 ) is used to perform the functional annotation of MAGs as well as the gene catalog (see Fig.
5 ). A more complete description of each module implemented in MAGNETO is available in the Material and Methods section. The four binning strategies are directly configurable by the user, and a quick configuration allows performing from a single to all strategies for reconstructing MAGs. Notably, MAGNETO is currently the unique workflow providing an automated approach to define clusters of metagenomes for coassembly. Importantly, MAGNETO and nf-core/mag are also the only workflows allowing users to perform a cobinning strategy. A synthetic comparison of functionalities provided by the workflows tested in this study is available in Table  2.
The rationale behind our metagenomic distance-based approach to perform coassembly was to infer which metagenomes should be grouped together in an unsupervised fashion without a priori knowledge. The aim was to develop an approach that could guarantee the actual closeness of the metagenomes to coassemble, thus emphasizing the increase in species-specific reads abundance for the assembler. Although the coassembly of closely related metagenomes has been shown to erode contigs quality  (12, 15) , we could show that our approach did not increase fragmentation or misassemblies within contigs (Fig. S4 ). In fact, our MD approach reconstructed MAGs that are more complete and less contaminated than the OR approach (Fig. S4 ). Although both metrics we used to estimate MAG contamination reported contradictory results, we argue that GUNC (38) likely provides  FIG 5  Overview of the MAGNETO workflow summary view of modules implemented in the MAGNETO workflow, with the name of the software or script associated with each task.
As a MAG may be reconstructed independently in either two (or more) samples or two (or more) cosamples, MAGs are also dereplicated using dRep  (37). Two MAGs were considered to be duplicated if their pairwise ANI (average nucleotide identity) score was above a given identity threshold t (t being a percentage of sequence identity) on more than 60% of their bases  (67). We considered two different values for t: t = 0. 95, which corresponds to a dereplication at species level, and t = 0. 99, which corresponds to a dereplication at strain level  (68). Genome annotation module. Functional and taxonomic annotations were performed for the strain-level MAGs collection, which encompasses the species-level collection. To perform functional annotation of MAGs, we used eggNOG-mapper  (49) , and we used GTDB-tk  (48)  to perform taxonomic annotation.
Finally, reads of each sample were mapped back onto both species-and strain-level MAG collections using Bowtie2, and an abundance table was produced using an in-house Python script. Gene annotation module. Coding DNA sequences (CDSs) were detected on assembled contigs per sample (single assembly) using Prodigal  (69). Genes from all samples were clustered at 95% identity using Linclust  (47)  in order to produce a nonredundant set of genes (gene95 collection). EggNOG  (49)  and MMSEQ2  (70)   Downloaded from https://journals. asm. org/journal/msystems on 16 November 2022 by 2a01:e34:ec33:4200:e460:a45d:3c2f:d627. respectively. Finally, reads from each sample were mapped back onto the gene95 collection using Bowtie2, and an abundance table was produced. Data sets. The marine metagenomes data set corresponds to the same 93 oceanic metagenomes as processed in Delmont et al.
(13) , which are available at the European Bioinformatics Institute (EBI) repository under project ID ERP001736. In order to benchmark the assembly-binning strategies, we simulated two different mock metagenome data sets. First, we used the CAMI (15) high-complexity data set, which is a 75-Gbp time series data set sequenced into short reads, composed of five samples from a high-complexity community with correlated log normal abundance distributions (596 genomes and 478 circular elements). However, the data set above did not allow us to assess our clustering method, which was not able to determine an optimal clustering to perform CASB and CACB. Thus, a simple coassembly strategy, gathering all 5 samples, was performed on this data set and was referred as a CASB strategy. We also simulated two more data sets (Text S1; available at https://doi.
org/10. 5281/zenodo. 6613455), composed of more samples than the CAMI data set, in order to better evaluate our coassembly module. The first data set we simulated was composed of 10 metagenomes containing different abundances of 20 genomes, with each metagenome of size 1 Gbp. Our second data set was composed of 20 metagenomes containing different abundances of 100 genomes, each sample of size1 Gbp. Both these simulated data sets were generated using CAMISIM  (39)  with each metagenome's size fixed to 1 Gbp. Each metagenome contained the 100 source genomes, with specific abundance distribution created by sampling from a log-normal distribution with m set to 1 and s to 2 (default values). To simulate the first mock data set, we used the 20 test genomes used by default in CAMISIM. The 100 genomes used for the simulation of the second mock data set were randomly sampled from CAMI high-complexity source genomes.
Other CAMISIM parameters were set to their default values. For the human gut microbiome data set, we collected raw data from the Integrative Human Microbiome Project (iHMP)  (40). Based on the availability of both metagenomics and metatranscriptomics data, we selected a subset of 150 metagenomes (Text S1; available at https://doi. org/10. 5281/zenodo. 6613455), representing 80 individuals and patients followed during 1 year. Metagenomes were extracted from stool samples and were sequenced using Illumina technology. The samples subset we analyzed here was composed of three sample groups of strictly equal size characterized by diagnosis, with individuals with no inflammatory bowel disease (non-IBD) and patients either diagnosed with Crohn's disease (CD) or Ulcerative Colitis (UC).
ACKNOWLEDGMENTS We thank the  CNRS MITI  through the  interdisciplinary program Modélisation du Vivant  ( GOBITMAP grant  to S. C. ), and the  H2020 European Commission project AtlantECO  (award number  862923 ). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. We thank the  Bioinformatics Core Facility of Nantes BiRD , member of  Biogenouest ,  Institut Français de Bioinformatique (IFB)  ( ANR-11-INBS-0013 ) for the use of its resources and for its technical support. B. C. and S. C. designed the study. B. C. and M. M. performed the experiments. B. C. , M. M. , A. B. , G. F. , and S. C. analyzed the data and interpreted the results. B. C. , G. F. , and S. C. wrote the paper, with input from M. M. and A. B. We declare that we have no competing interests.
Experimental design We analyze 99 subjects from the Mother Of Unification Studies (MOUS) dataset  [71]  who performed a one-hour reading task while being recorded with a 273-channel CTF MEG scanner. The task consisted of reading approximately 2,700 words flashed on a screen in rapid series. Words were presented sequentially in groups of 10 to 15, with a mean inter-stimulus interval of 0. 78 seconds (min: 300ms, max: 1,400ms). Sequences were either random word lists or actual sentences (50% each). For this study, both conditions were used. However, this study does not investigate the differences obtained across these two conditions. Out of the original 102 subjects, 3 were discarded from the study because we could not reliably parse their stimulus channels.
Stimulus preprocessing We focus on four well-known features associated with reading, namely word length (i. e. , the number of letters in a word), word frequency in natural language (as derived by the wordfreq Python package  [72] , and measured on a logarithmic scale), and a binary indicator for the first and last words of the sequence. At a given time t, each stimulus u t ∈ 4 is therefore encoded with four values, fed to the models as a square function that is non-zero for the duration of the stimulus. Each feature is standardized to have zero mean and unit variance. Word length is expected to elicit an early (from t=100 ms) visual response in posterior MEG sensors, whereas word frequency is expected to elicit a late (from t=400 ms) left-lateralized response in anterior sensors. In the present task, word length and word frequency are correlated R=-0.
MEG Preprocessing As we are primarily interested in evoked responses  [73] , we band-pass filtered between 1 and 30 Hz and downsampled the data to 120 Hz using the MNE software  [74]  with default settings: i. e. a FIR filter with a Hamming window, a lower transition bandwidth of 1 Hz with -6 dB attenuation at 0. 50 Hz and a 7. 50 Hz upper transition bandwidth with an attenuation of -6 dB at 33. 75 Hz. To limit the interference of large artefacts on model training, we use Scikit-learn's RobustScaler with default settings  [75]  to normalize each sensor using the 25 th and 75 th quantiles. Following this step, most of the MEG signals will have a scale around 1. Since we observed a few large scale outliers, we chose to reject any segment of 3 seconds that contains amplitudes higher than 16 in absolute value (fewer than 0.
| Code availability The code developed for the present study is available at https://github. com/facebookresearch/deepmeg-recurrentencoder. We first evaluate the DRE's ability to predict brain responses to written words presented in rapid serial visual presentation and measured with MEG, and compare these brain predictions to those of linear encoding models (TRF, RTRF). Then, we show with ablation experiments which elements of the DRE help address the challenges of rich dynamics, inter-subject, and inter-trial variability. Finally, we show how feature importance helps address the third challenge introduced above, namely: identifying the relationship between brain responses and stimulus features. Modeling rich MEG dynamics: model comparison. DRE outperforms the baseline TRF and RTRF models with up to a three-fold improvement (Figure  2 ).
DRE's recurrence specifically captures alpha-dependent evoked responses. To further explore how DRE learns inter-trial variability, we investigate a well-known interaction between evoked responses and pre-stimulus activity. Specifically, brain responses to sensory input are known to be modulated by pre-stimulus oscillatory activity in the "alpha" frequency range (8 -13 Hz)  [29]. To test whether this phenomenon can be detected in the present dataset, we compared the average evoked responses to words for "high pre-stimulus alpha" versus "low alpha" trials, using a median split for each subject separately. The results (Figure  3 ) show an effect of up to 50 × 10 -12 T in fronto-temporal channels, peaking around 400 ms after word onset. Critically, while the singletrial predictions of DRE capture this phenomenon, neither TRF nor RTRF learn to modulate their evoked responses depending on the alpha power (Figure  3B.
Deep neural networks have not yet emerged as the go-to method for neuroimaging  [82, 83]. Nevertheless, several studies have successfully used deep learning architectures to model brain signals  [84, 85, 86, 87, 45]. In particular, deep nets trained on natural images  [88, 89, 90] , sounds  [5] , or text  [91]  are being increasingly used as an encoding model for predicting neural responses to sensory stimulation  [6, 92, 7, 8]. Conversely, deep nets have also been trained to decode sensory-motor signals from neural activity, successfully reconstructing text  [93]  from Electrocorticography (ECoG) recordings, or images  [94]  from functional magnetic resonance imaging (fMRI). Despite these successes, we would like to argue that what possibly limits a wider impact of deep learning in human cognitive neuroscience is a combination of factors including: (i) low signal-to-noise ratio, (ii) small datasets, and (iii) a lack of high temporal resolution, where nonlinear dynamics may be the most prominent.
The present experimental results make a step in this direction, and could thus open an avenue towards leveraging the many existing shorter naturalistic stimulus datasets collected on many subjects. This could be an alternative to making new long recordings of many hours of data from a handful of subjects  [95]. While the DRE's architecture may be efficient at handling the dynamical structure of brain data, the dynamics assessed in this study are driven by specific linguistic features (i. e. word-length and word frequency). By contrast, recent ECoG and MEG studies have used more complex word features, represented as activations of a deep network pretrained on visual or language tasks  [96, 97, 98] , and then predict the brain response in a way that is unaware of the dynamics (using a linear classifier for each time sample independently).
Given the successes independently observed with these two approaches, a natural extension of this work would be to combine the two and learn to map complex stimuli to brain responses using (1) rich representations for the stimuli (such as the activations of a pretrained deep network), followed by (2) a rich dynamical model such as the DRE. It is however worth pointing out that this approach would naturally lead to more high-dimensional parameter spaces, which would require larger datasets to limit potential overfitting. The present work is based on a deterministic and predictive framework using deep learning. Other complementary approaches such as Hidden Markov Models (HMMs) and Gaussian Processes (GPs) have also been proposed to model brain data in a probabilistic framework. Such approaches have been exploited to explore the spatio-temporal statistics of fMRI or MEG data  [99, 100] , but also in an encoding context  [101, 102].