dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of neuronal responses Several studies aim to relate neuronal activity to experimental events (e. g. , sensory stimulation, behavioral response, etc. ). Indeed, imaging is particularly suited for revealing the spatial distribution of neuronal responses (i. e. , topographic sensory/behavioral functional maps), which may be crucial to understand brain coding strategies. The toolbox allows associating ROI fluorescence responses with a given experimental variable (provided by the user), and visually displays them on the imaged optical plane to assess their topographical organization. To illustrate the capabilities of the response-analysis module, we present the results obtained with two different datasets. The first one is the case study included in the toolbox tutorial (Fig 5A  -5E ).
For this case study, GCaMP3 was imaged to monitor the responses of the zebrafish optic tectum to visual stimulation consisting of 3 trials of 4˚light spots presented at different azimuths of the visual field (-45˚to 45˚in 5˚steps, 0˚represents  [37]. Segmentation was performed with the "labeled nuclei" and "bigger ROI" options, and we set parameters "local contrast" to ~20 for spatial normalization of the image, thr neuropil and thr soma to ~0. 45 and ~0. 095, respectively, and "minimal ROI area" to 18 pixels. (B) Mouse somatosensory cortex, where nuclei of excitatory neurons are transgenically labeled with mCherry (two-photon imaging, 256x256 pixels, 7 Hz sampling rate). Calcium dynamics monitored with GCaMP6s. Data from Peron et al  [11, 38]. Parameters used: "labeled nuclei" and "bigger ROI" options, "local contrast" ~43, thr neuropil ~0.
pcbi. 1005526. g002 directly in front of the larva). Event-locked single-trial and trial-averaged ROI ΔF/F0 responses are automatically displayed (see Fig  5D  and 5E ), allowing the interactive exploration of any ROI of interest. Moreover, the information in these trial responses is also summarized by calculating the "tuning curves" of each ROI. For this purpose, the module plots the trial-averaged ROI ΔF/F0 responses as a function of the experimental variable (see bottom right panels in Fig 5D  and 5E ). Finally, these ROI tuning curves are color-coded and superimposed on the imaged optical plane, using the hue-saturation-value (HSV) colormap described in Design and Implementation (see Fig  5C ). We observe that neighboring visual-field positions are mapped on neighboring positions in the optic tectum, reflecting the tectum's functional retinotopic map  [41, 42]   The second dataset used was obtained through multi-plane light-sheet imaging, where the activity of >40,000 simultaneously monitored ROIs were sampled at 2.
1 Hz in a GCaMP5-expressing transgenic zebrafish larva. Since fast volumetric light-sheet imaging does not guarantee single-neuron resolution, we used arrays of hexagonal ROIs covering the totality of each of the 40 optical planes imaged (separated by steps of 5 μm). Every 20 min, the larva was visually stimulated using whole-field gratings capable of inducing optomotor response (OMR; in from different neurons in the cai-1 ground-truth dataset  [28, 39]. For each example, we show: top, GCaMP6f ΔF/F0 traces (black) and the significant fluorescent transients detected by the module (red); bottom, ticks representing the simultaneously recorded spikes (those associated with a significant calcium event are highlighted in red; asterisks mark single spikes); middle, spiking rate of the neuron calculated by temporally convolving spikes with a Gaussian filter of σ = 20 ms.
Examples of the spatial topographies of the detected assemblies are shown in Fig  6B , which reveal a majority of spatially compact neuronal clusters. Remarkably, displaying these assemblies color-coded according to their spatial centroid position (Fig 6C  and 6D ), clearly recapitulates the functional retinotopic map of the optic tectum (Fig  5C ). As explained before, this dataset consists of tectal light-spot-induced neuronal responses. Thus, this demonstrates that the clustering procedure is capable of exposing the functionally relevant spatial organization of tectal responses, without any information on the timing of the stimulation. We applied the same PCA-promax procedure on the mouse somatosensory cortex GCaMP6s-imaging dataset (the same of Fig  2B ). In this experiment, the superficial barrel cortex was imaged while the mouse performed a single-whisker object localization task.
Activity fluctuations along the experiment were limited to <5% of total neuronal population imaged (1025 neurons), however, clustering neurons according to their activities revealed the coordinated activation of several sets of neurons  Fig 7). Interestingly, one of these neuronal assemblies (assembly #11) consisted of a group of correlated neurons that was responsive to the mouse contacting the whisker with the object. Apart from detecting this group of functionally related neurons, the clustering algorithm also exposed the ongoing concerted activity of other assemblies along the task, that may convey additional contextual or task-related signals. This kind of analysis can also be performed in large-scale activity data. In Fig  8  and S2 and S3 Videos, we illustrate the result of using the PCA-promax method to detect assemblies in a brain-wide light-sheet imaging dataset, and compare it to the k-means clustering.
The algorithm revealed several bilaterally symmetric assemblies and unilateral assemblies with their corresponding contralateral counterparts. In particular, we observed a symmetric pair of assemblies distributed over the pre-tectum and the anterior and caudal hindbrain (Fig 8D and S2 Video), whose activities were tightly related to experimental events (OMR-inducing visual stimulation; Fig  8I ), stressing their relevance. In agreement with recent findings  [43, 44] , this particular assembly was also apparent when averaging ROI responses during OMR stimulation (Fig 5F and S1 Video), but the average-response map was much noisier and less anatomically defined than the assemblies found through activity clustering. Moreover, we also found topographically precise clusters that were missed by averaging ROI responses to visual stimulation, These examples illustrate the capability of PCA-promax clustering to expose the biologically relevant spatio-temporal organization of high-dimensional datasets, going beyond the analysis of trial-averaged responses by allowing the study of single-trial population dynamics.
Finally, when validating results obtained from large multidimensional datasets, the use of appropriate control surrogate datasets is critical. As an example, since neighboring neurons tend to be more highly correlated, we asked if the spatially compact assemblies of the optic tectum case study were the result of simply grouping nearby neurons. We demonstrate that this is not the case, since we found that compared to Topographical surrogate assemblies, the assemblies of the optic tectum do not show any particular trend in the average activity level of their component ROIs (Fig  9B ), but they do significantly group correlated ROIs (Fig 9C ). This example allows the user to further validate the clustering results, which effectively groups correlated ROIs, without showing a bias for the most active ones (a common concern in clustering procedures).
optical sections see S2 Video). (A-D) Pairs of symmetric unilateral assemblies. (E-F) Single bilaterally symmetric assemblies. (G) All ROI assemblies found with the PCA-promax approach, displayed over the maximal intensity projection of all imaged optical sections (for individual optical sections see S3 Video). Assemblies are colored according to the similarity of their activity dynamics (the more temporally correlated, the more similar the color of the assemblies). This color code was also used in the previous panels (except for the assembly pair shown in c, which was colored differently to facilitate visualization). (H) Same as G, but for assemblies found using k-means, colorcoded according to the similarity of their activity dynamics. To allow for a comparison between G and H, the dimensionality of the dataset was reduced through PCA before clustering (otherwise, clustering did not converge).
Availability and future directions The toolbox is an open-source project freely available to the community. It was developed under Matlab and verified to run properly through a variety of versions, from R2010a up to 2015b. It requires the Matlab's Curve Fitting, Image Processing, Statistics and Machine Learning, and Parallel Computing toolboxes. The source code can be downloaded, together with a detailed tutorial  [27]  and a case study dataset, from https://github. com/zebrain-lab/Toolbox-Romano-et-al. Data analyzed here includes the case study included in the toolbox, and datasets downloaded from https://crcns. org/data-sets/ssc/ssc-1, https://crcns. org/data-sets/vc/pvc-10 and https://crcns. org/data-sets/methods/cai-1. The pipeline can be run on any desktop computer, but we recommend multicore computers for shorter computing times, as several algorithms allow for parallelization.
For analysis of large light-sheet imaging dataset, we used a computer cluster of 328 CPUs based on an HTCondor parallelization system. Imaging videos of long experiments can result in file sizes of several gigabytes (GB), and therefore we recommend 64-bit architectures with at least 12 GB of RAM. We also recommend screen resolutions of at least 1280x800 pixels for a better experience with the GUI. While calcium indicators have become the standard for imaging neuronal activity, genetically encoded voltage indicators (GEVIs) are a promising technology  [45, 46] , despite important constraints on the technique  [12]. In principle, the toolbox could be extended without difficulty to allow the analysis of imaging data using GEVIs. Furthermore, clustering of large multivariate datasets is a complex mathematical problem.
(Fig 2A; dataset pvc-10 from crcns. org); ii) for mCherry-labeled nuclei in a GCaMP6s imaging experiment obtained by viral injections in the mouse somatosensory cortex[11,38] (Fig 2B; dataset ssc-1 from crcns. org); iii) for large-field imaging of nuclei-excluded GCaMP3 in transgenic zebrafish larvae (Fig 2C); iv) for a preparation similar to iii but imaging a smaller field (the provided case study); and v) an example of single-neuron ROI detection in single-photon high-acquisition rate (100 Hz) light-sheet imaging of a transgenic zebrafish expressing GCaMP5 (see Fig 2D). These examples illustrate the algorithm's performance in settings that differ markedly in the density of neuronal labeling (examples iv and i being the most and less dense, respectively), and with imaging techniques of different spatial resolution and acquisition rates (two-photon laser scanning and single-photon light-sheet microscopies).
For both mouse cortical datasets (Fig 2A and 2B) it took less than 5 mins to obtain 149 and 505 ROIs, respectively. For the large-field dataset in zebrafish (2178 ROIs), it took ~10 mins (Fig 2C). Finally, for the light-sheet imaging dataset we obtained 438 ROIs in less than 10 mins (Fig 2D). Typically, we obtained a >85-90% success rate in automatically detecting ROIs corresponding to single neurons, depending on imaging quality. For the present examples, after setting the parameters for ROI segmentation and filtering, we had to curate 8% of the ROIs for the case study, 9% for both mouse cortical datasets (Fig 2A and 2B), while for the large-field zebrafish dataset, 8% of the ROIs were curated (Fig 2C). For the single-plane single-photon light-sheet imaging dataset shown in Fig 2D, we had to curate 15% of the ROIs, due to the lower spatial resolution of this technique.
Assemblies obtained with the PCA-promax algorithm. For each panel, the corresponding axial projection of the example assemblies is initially displayed, followed by their layout over each one of the 40 imaged optical planes (plane depths are indicated in lower right corners). (MP4) S3 Video. Volumetric distribution of all the assemblies shown in Fig 8G. Assemblies obtained with the PCA-promax algorithm. The axial projection of all the assemblies found in the dataset is initially displayed, followed by each one of the 40 imaged optical planes (plane depths are indicated in lower right corners). (MP4).
Acknowledgments We thank  Simon Peron ,  Jasper Akerboom ,  Karl Svoboda  and collaborators, and  Nicholas Priebe ,  Boris Zemelman  and collaborators for sharing with us their datasets through the crcns. org data sharing portal (datasets ssc-1, cai-1 and pvc-10). We thank the members of the  Sumbre lab (Meena Murmu and Anirudh Kulkarni)  for helpful discussions and continuing testing of the toolbox, and  Patricia Gongal  for editorial assistance. We thank the  Marin Burgin lab at IBioBA  for their support.
Funding: GS was supported by the  European Research Council   Starting Grant  (# 243106 ; https://erc. europa. eu/), the  European Research Area for Systems Biology  (Zebrain; https://www. erasysbio. net/),the  Agence Nationale de Recherche  ( ANR-10-LABX-54   MEMO LIFE  and  ANR-11-IDEX-0001-02   PSL Ã Research University ; http://www. agence-nationalerecherche. fr/) and the  Institut national de la sante ét de la recherche medicale  (Avenir; http://www. inserm. fr/). SAR was supported by the  Argentine Agency from the Promotion of Science and Technology  ( PICT 2013-0182 ; http://www. agencia. mincyt. gob. ar/) and the  Structural Convergence Fund FOCEM-Mercosur  ( COF 03/ 11 ; http://focem. mercosur. int/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
(Fig 4. 4 Fig 4. Toolbox performance in inferring neuronal activity from calcium imaging data. (A) Two examples with different signal-to-noise ratios (SNRs)from different neurons in the cai-1 ground-truth dataset [28, 39]. For each example, we show: top, GCaMP6f ΔF/F0 traces (black) and the significant fluorescent transients detected by the module (red); bottom, ticks representing the simultaneously recorded spikes (those associated with a significant calcium event are highlighted in red; asterisks mark single spikes); middle, spiking rate of the neuron calculated by temporally convolving spikes with a Gaussian filter of σ = 20 ms. Imaging was performed at 60 Hz. A spike was considered as associated with a significant calcium event if it was followed by an event of significant fluorescence within 40 ms (GCaMP6f rise time τ peak = 45± 4 ms, for 1 spike [28] ).
To address these problems, we propose Adaptive Validation for Prediction, (AV Pr ), a novel variable selection scheme. A pseudo-code description of the algorithm is given as Algorithm 1. Our proposal is closely related to the recently introduced ∞ -Adaptive Validation (AV ∞ ) scheme  [12] , which is based on tests inspired by isotropic versions of Lepski's method  [11, 24, 25]. This approach has been shown to provide fast and optimal calibration of the lasso for (one-step) estimation with respect to ∞ -loss. For the two-step prediction considered in this paper, however, a considerably different and more technical approach inspired by non-isotropic tests is required. As a practical example, Figure  1  compares AV Pr and standard methods on the Leukemia dataset discussed above. The methods under consideration to select the lasso tuning parameter are AV Pr , 10-fold cross-validation, Bayesian Information Criterion (lassoBIC), and an estimator obtained by selecting with BIC a least-square estimator over the supports geneterated by a the lasso path (lslassoBIC) following  [2]  (see the Appendix for further information about the implementation of the latter approach).
The AV Pr Algorithm The AV Pr scheme is summarized in Algorithm 1. As inputs, it takes the data (Y, X), a set of tuning parameters Λ, and a constant a > 0 specified in the following section. It then conducts simple tests along the tuning parameter path of the lasso until a stopping criterion is met. It returns the index of the current tuning parameter i as well as the corresponding two-stage estimator β i. The algorithm requires the computation of a single lasso path and leastsquares estimators along this path. The computation of the paths can be conducted with readily available, easy-to-use, and highly efficient software such as glmnet (in R) or scikit-learn(in Python)  [14, 29]. For the computation of the least-squares estimators, off-the-shelf solvers can be used since the number of active variables of the second step is typically small.
A. 3. Proofs of Results From Section 2 Recall that the path of active sets is | Ŝ1 | ≤ | Ŝ2 | ≤ • • • ≤ | Ŝr |. The cardinality r is typically random, but we can always bound it almost surely by some constant R n , so that 1 ≤ R n ≤ r almost surely. This constant should be independent of the data but can be chosen to vary with n and p. For example, we might have agreed a priori with considering 50 sets, or our variable selection method might always yield at least min(n, p) different sets by construction. Corollary A. 3 (Oracle benchmark). Say the oracle set exists, that the design satisfies Assumption 2. 1. Then, for any t ≥ 6, there exists a deterministic integer N such that with probability at least 1 -t -1 -R -1 n √ 1 + t log R n , ī,i * -Xβ 2 2 ≤ σ 2 (1 + t log r)| S ∪ S * | + 0 ≤ 2σ 2 (1 + t log r)|S * |.
Sequence processing A total of ~11. 7 million reads were obtained from MiSeq sequencing. Prokaryotes, including Bacteria and Archaea, contributed ~6. 6 millions reads in DNA and ~1. 5 million reads in RNA (abbreviated into 16SDNA and 16SRNA, respectively), and eukaryotes ~2. 5 millions reads in DNA and ~1. 1 million reads in RNA (18SDNA and 18SRNA, respectively). These were assembled with the vsearch tool (https://github. com/torognes/vsearch) and cleaning procedures consisted in the elimination of sequences < 200 bp, presenting a mismatch in the forward or reverse primer, having ambiguous bases "N", PHRED quality score < 25. The putative chimaeras were detected by vsearch. The remaining rRNA 16S (prokaryotes) and 18S (eukaryotes) sequences were clustered into "molecular species" (Operational Taxonomy Units, OTUs) at a 97% and 95% similarity threshold (OTU 0.
03 and OTU 0. 05 , respectively), according to  [45]  and  [46]  with vsearch (option cluster_small sorted by length). The representative sequence for each OTU was inserted into phylogenetic trees for taxonomic annotation. The seed OTUs were finally affiliated by similarity and phylogeny from reference sequences extracted from the SSURef SILVA database  [47] , according to the following criteria: length > 1 200 bp, quality score >75% and a pintail value > 50. After comparing the OTUs with the reference sequences using a similarity approach (vsearch tool), trees including OTUs with their closest references were built with FastTree  [48]. The different taxonomic affiliations obtained were checked for inconsistency. This process was implemented using the pipeline PANAM (Phylogenetic Analysis of Next-generation AMplicons https://github.
com/panammeb/) and is described in more detail in  [49, 50]. The resulting OTUs were subjected to additional conservative filtering intended to remove potential sequencing artefacts (OTUs represented by less than 3 reads), contaminants (OTUs detected in the control samples) and phantom OTUs (OTUs detected in RNA and not in the DNA fraction of the corresponding sample), totals of 761 729 and 140 645 reads and 48 202 and 37 504 reads remained in the DNA and RNA fractions for prokaryotes and eukaryotes, respectively. The corresponding sequence files were deposited to NCBI's Sequence Read Archive (SRA BioProject ID PRJNA380262). Data were and normalized (proportions) rather than rarefied to prevent loss of information and possible resulting biases  [51]. Results obtained on datasets rarefied at different depth are summarized in S8 Fig for allowing comparison with other studies.
Data analyses Data analyses were performed using the R environment version 3. 2. 2 [52] , implemented with the Phyloseq package (version 1. 18. 1; [53] ) for calculating Shannon-Wiener indexes and Abundance-based Coverage Estimators (ACE); Phyloseq was also employed for rarefying the datasets to depths similar as data found in the literature for richness comparison. Gini's coefficient was calculated using the ineq package (version 0. 2. 13;  [54] ). Rarefaction curves were plotted using the ggplot2 package (version 2. 2. 0;  [55] ) from community analyses made with the vegan package (version 2. 4. 1; [56] ); Venn diagrams were made using Venny 2. 1. 0 [57].
Prokaryote community. In total, 30 different phyla were detected (of which 1 in Archaea), distributed over 60 classes, 113 orders, 190 families and 286 genera. A complete list of the abundance and taxonomic affiliation of prokaryotic OTUs is presented in S1 File. In all samples, the community was largely dominated by 4 bacterial phyla, which represented 75% to 79% of the reads in DNA datasets and 85 to 88% in RNA (Fig (5-30%), Firmicutes (5-18%) and Actinobacteria (6-13%). These phyla are almost systematically reported dominant in outdoor airborne bacterial communities studies (e. g. [3, 6, 8, [59] [60] [61] [62] ). A total of 1,593 OTUs distributed over 103 genera were common to all samples (S4A Fig). These represented 64% to 96% of the reads identified down to this taxonomic level, and 15%-  The samples had clear distinct patterns: ~70% of the total OTU richness observed in prokaryotes was contributed by the "Polluted" type sample, with most of them being characteristic, i.
A the order level, dominant Fungi included Magnaporthales and Pleosporales in Ascomycota, Polyporales and Sporidiobolales in Basidiomycota), SAR (Bicosoecida) and others such as Syndiniales, a group of dinoflagellates. Figs 3 and 4 compile overall most represented bacterial genera and eukaryotic orders, respectively, in corresponding DNA and RNA datasets. The relative abundance of RNA, respect to DNA, in an OTU (abbreviated into RNA:DNA ratio for clarity) is often used for estimating its relative level of metabolic activity, with higher ratios linked with potentially higher metabolic activity  [63, 64]. RNA:DNA ratio ranged between 0 and 210 in eukaryotes, and from 0 to, exceptionally, 11,760 in prokaryotes in an OTU affiliated to Spirosoma (Bacteroidetes). Low abundance groups tended to exhibit high ratios, in prokaryotes and in a lesser extent also in eukaryotes (see S7 Fig) ,.
qRT-PCR data were analyzed using the CFX Manager software 3. 1 (Bio-Rad, USA). Amplicon size of qRT-PCR products was confirmed in a 2% agarose gel. Gene expression for each target gene was normalized to gene expression levels of chicken hypoxanthine-guanine phosphoribosyltransferase and β-2-microglobulin (β-2-M). Relative normalized expression was calculated using the 2 -ΔΔCt method, and data are represented as fold increase as compared to control group.
Protein homology analysis Protein multiple sequence alignment for PAFR and LPCAT 2 (LPCAT2) from chicken, mouse, and human were performed using ClustalW2 through Geneious R10 software (Biomatters Ltd. , New Zealand). Homology analyses of transmembrane and cytoplasmic domains for PAFR or LPCAT2, including exclusive LPCAT2 enzymatic-related domains, were performed using Uniprot Blast. 2 Predicted phosphorylation sites were identified using NetPhos 3. 1 server. 3 Only intracellular predicted sites with a score above 0. 650 and conserved in the three analyzed sequences 2 http://www. uniprot. org/blast/. 3 http://www. cbs. dtu. dk/services/NetPhos/. were annotated. Predicted N-glycosylation sites conserved in the three analyzed sequences were identified using NetNGlyc 1. 0 server. 4 NCBI Reference Sequence for these proteins were as follows: PAFR (Homo sapiens: NP_001158195, Mus musculus: NP_001074680, and Gallus gallus: XP_004947758) and LPCAT2 (H.