dataesr/scientific-paragraphs-categorization huggingface dataset:
An in situ microscope based on pulsed transmitted light illumination via optical fiber was combined to artificial-intelligence to enable for the first time an online cell classification according to well-known cellular morphological features. A 848 192-image database generated during a lab-scale production process of antibodies was processed using a convolutional neural network approach chosen for its accurate real-time object detection capabilities. In order to induce different cell death routes, hybridomas were grown in normal or suboptimal conditions in a stirred tank reactor, in the presence of substrate limitation, medium addition, pH regulation problem or oxygen depletion. Using such an optical system made it possible to monitor real-time the evolution of different classes of animal cells, among which viable, necrotic and apoptotic cells.
Labelling of cells extracted from ISM micro-photographs The starting point of this work consisted in the thorough examination of a 4000 images database. Collected images (bitmap, 8 bit) have a size of 1392 × 1040 pixels, corresponding to 6. 6 × 8. 8 mm 2 (available in the CNRS Research Data repository). A part of the cellular objects contained in the ISM-image database were far from the focus plane and appeared as blurred objects, and therefore not considered in the sequel of this work. For sharp cells that belonged to the focal plane, slight variations of the exposure of the ISM-cell images could be observed. Cells located between the illumination diode and the focus plane could act as lenses leading to brighter spots while the other ones, which were located behind the focal plane, had a darker appearance.
These cells were attached to class 5 and were labelled as late-stage necrotic cells. Hybridoma cells studied in this work could thus be classified in at least 5 different classes. Similar cell morphological features were already shown on ISM-images for other    proprietary hybridoma and Jurkat (DSMZ ACC 282) cell lines  29. As this classification was obtained according to well-known morphological features of mammalian cells  41  , it should be possible to apply to different types of cells. In the 4000 images database, it was also possible to observe a particular cell pattern with a slightly less homogeneous texture, as well as a less circular contour, compared to viable cells belonging to classes 1 and 6. This visual image inspection was confirmed by the surface representation of the cells.
At this stage of the work, it was hard to establish a clear relationship between this distinctive cell pattern and previous bibliography. As these cells showed typical bulges at their surface, they were simply labelled as "viable cells displaying bulges" in the learning database (class 4). To summarize, the ISM-cell images belonging to class 1 and 6 corresponded to viable cells (single and pair), class 2 to apoptotic cells, class 3 to necrotic cells, class 4 to viable cells displaying bulges and class 5 to necrotic late-stage cells.
Implementation of the AI-ISM cell classifier and application to a bioprocess As shown in Fig. 1A , the labelling of a database made up of 4000 images was done according to the identification rules of cellular objects presented in Table  1. Objects localized at the periphery of the ISM-image were not labelled when less than half of the cell body was visible. In the case of overlapping cells, a seldom event, it was chosen not to label the cells. In these conditions, 907 cells belonging to the six different classes of cells observed in the 4000 images were labelled: A set of 3400 images taken from the annotated database was used for the training phase of the object detection algorithm. A set of 600 images was then devoted to the test phase of the model allowing to measure its performance for each class of cells, measured through the determination of a mean average precision (mAP) (see Fig.
CNN YOLO was chosen because of its ability to detect objects with inference speeds compatible with real time applications, allowing to get an output in a few milliseconds  53. The performance of YOLOv4 applied to ISM-images resized by default (608 × 608 pixels) allowed an inference speed superior to 30 images per second with using a conventional Nvidia GeForceGTX 1070 GPU for laptop. As ISM-sensor data were made available with a frequency of 5 images per second, real-time monitoring of cell classes became feasible, without upgrading YOLO toward its latest version (v10). The labelling of a learning database was first done. The annotation of 4000 images by two interchanging experimenters was done according to the identification rule. The database was made up of images taken by ISM in suspended cultures showing viability values ranging from 90 to 20%.
For each scenario, the posterior distribution of R 0i was determined by randomly sampling 500 values in the posterior distributions of the parameters, as established in  [3]  and summarised in Table  1. The spatial distribution of R 0 values was smoothed using ordinary kriging method from gstat package  [19]  to interpolate the R 0 from the point data to the rest of the area and displayed using R software version 4. 0. 2 [20]. R0 j = i 1 -exp -δ * j→i (4) j→i (t) = ψ j. ϕ i. β N j 15km Ind d ij < 15km.
Orthologs clustering All proteomes from the SPAdes assemblies and from five reference genomes (Alcoy, NC_014125; Corby, NC_009494. 2; Philadelphia, NC_002942. 5; Lens, NC_006369; Lorraine, NC_018139. 1) were clustered into ortholog protein families using OrthoMCL  [41]. Variants were then matched to their respective proteome to identify the consequences of the mutations, and to which protein family they belonged. Annotation of the protein families in which multiple variants were found between independent samples was done by searching for the protein accession numbers in the first reference genome and using each region as a query for a blast search.
Functional annotation An amino acid sequence search in the RCSB Protein Data Bank  [50]  was used to identify homologous structures, and protein sequences were then aligned using MAFFT-LINSI. The structural effects of the mutations were predicted via NetSurfP-2. 0  [51]. Mutations were visualized on the predicted structures of Pseudomonas aeruginosa homologues (PDB accessions: 3DWO for the OmpP1/FadL homologue and 4RNH for the EAL-containing protein) via YASARA View  [52].
Identification of variants in databases To assess whether the mutations identified in the two genes lpg0707 and lpg0891 were present in other sequenced strains, we assembled an L. pneumophila-specific protein database, using SDBW  [53]. The database was queried with blastp  [54] , using the one representative of each of the two three-times mutated genes as query, and an E-value threshold of 10 -6. The results were then filtered and only proteins 80 % or more similar to the query were kept. All resulting hits were retrieved from the database, and aligned with MAFFT-LINSI  [48]. The variations in amino acid residues and in length were examined in Seaview  [55].
The outer membrane protein belongs to the OmpP1/FadL family. Two of the mutations led to proteins being respectively 97 and 60% shorter than the wild-type protein by introducing a stop codon, while the third mutation did not lead to a change in secondary structure. The EAL-containing domain protein was also mutated three times, but none of the three mutations resulted in a shortened protein. These two genes are referred to as potentially adaptive in the human host (PAHH). The sequence of these variants is available in Supplementary Data 1. We searched all published L. pneumophila proteomes for the specific mutations in the EAL-containing protein and in OmpP1/ FadL. The mutations in the EAL-domain-containing protein were specific to our strains and could not be found in any other published L.
Acknowledgements The authors would like to thank  Matilda Morin ,  Elisabeth Hallin  and  Daniela Klingenberg  ( Public Health Agency of Sweden) , as well as  Carmen Pelaz  ( Legionella Reference Laboratory in Spain ) for providing strains from their collection;  Ulrika Lustig  for her patient technical assistance; and  Elisabeth Kay  for providing the pXDC101 plasmid. The authors would also like to acknowledge support from  Science for Life Laboratory , the  National Genomics Infrastructure, NGI, and Uppmax  for providing assistance in DNA sequencing and computational infrastructure.
Mechanisms of γ-oscillations at the SDs' onset and in the sub-SD zone likely involve mutual excitatory/inhibitory synaptic interactions, gap junctions and intrinsic subthreshold membrane potential oscillations  29, 30, 59. Of note, because of the limited extent of γ-oscillations, their volume-conducted signals poorly propagate at a distance and are therefore barely visible at the cortical surface  25, 60. The question arises, why SD phenotypes with unchanged or increased activity at the cortical surface have not been reported before? Because the changes in AC-ECoG activity critically depend on the depth of SD penetration, we suggest that previous SD descriptions mainly dealt with full SDs and the depression phenotype, whereas the dataset used in the present study involved SDs organized in clusters.
A subdermal platinum/iridium needle electrode was used as a reference for monopolar recordings with a DC-coupled amplifier (BrainAmp MR plus, Brain Products, Munich, Germany) and BrainVision Recorder software (Brain Products). From this dataset of 31 aSAH patients (21 females, 10 males, mean age: 57 ± 11 years), all non-isoelectric SDs with good recording quality were selected in 26 patients. Around each SD, a total of 1360 fragments of 9 minutes in length were collected. The patients' ECoG were originally recorded and SDs manually detected with a Powerlab 16/SP and LabChart-8 software (ADInstruments, New South Wales, Australia) with a sampling frequency of 200 Hz. The selections of SDs from the LabChart files were exported in. txt files to later be analyzed with Python and Matlab.
Patch electrodes were pulled from borosilicate glass capillaries (BF150-86-10, Sutter Instrument, Novato, CA, USA) and had a resistance of 5-7MΩ. Patch pipettes were filled with a solution of the following composition (in mM): 131 potassium gluconate, 4 KCl, 10 HEPES, 10 phosphocreatine, 4 MgATP, and 0. 3 Na2GTP (adjusted to pH 7. 3 with KOH). The patch pipette was inserted into the cortex at ~0. 5 mm distance from the insertion point of the silicon probe and at a 50-65°angle towards the silicon probe. Membrane potential values were corrected for liquid junction potential of +15 mV. Series resistance was 25-75MOhm. Patchclamp recordings were digitized at 40 kHz with a Digidata 1440 A interface card using Clampex 10. 3 (Molecular Devices, USA). Data analysis. Raw data were preprocessed using a custom-developed suite of programs in the Matlab and Python environments.
SD stop depth was determined from the depth of the deepest electrode which displayed SD' peak >1 mV/s. Spectral power was estimated using direct multi-taper estimators (0. 5 Hz bandwidth, 3 tapers, 5 s spectral window with 1 s overlap, Chronux toolbox in Matlab) for the signal in the range of 0. 5-45 Hz (AC-band) during intervals [-40 -10]s before the SD onset (pre-SD) and [10 40]s after the SD onset (SD). The SD/pre-SD ratio and the corresponding p-value were calculated as described above for the human data analysis. The UP states of δ-oscillations were detected as negative events exceeding 1 STD (standard deviation) of the 0. 5-4 Hz bandpass filtered FP deflections from L6. γ-troughs were detected as negative events exceeding 1 STD level from the FP signal filtered within the bandpass 30-150 Hz.
Сurrent source density (CSD) was computed according to a differential scheme for the second spatial derivative of FP along the probe axis and smoothed with a triangular kernel of length 3  74. Spatial discrimination of FP generators of δ-oscillations was performed using the kernel density Independent Component Analysis (ICA) algorithm (KDICA) that was customarily implemented in Matlab  75, 76. Recorded FP signals u m (t) were considered as the weighted sum of the activities of N neuronal sources or FP-generators: u m ðtÞ = P N n = 1 V mn s n ðtÞ, m = 1,2,:::,M ð1Þ where (V mn ) is the mixing matrix composed of the spatial weights of N FP-generators on M electrodes and s n (t) is the time course of the nth FPgenerator. Using u m (t) the ICA found the (V mn ) and s n (t) and the joint group of spatial weights (V mn ) was ordered into instant depth profiles of the voltage according to electrode position.
Action potentials (APs) were detected in the 50 Hz highpass filtered signal as events exceeding the 5 mV threshold. The maximal depolarization level of neurons during SD and the threshold of AP depolarization block were calculated from a lowpass filtered ( < 0. 2 Hz median filter) membrane potential signal to eliminate APs. The AP burst duration during SD was calculated as the time interval between the first and AP in the burst with firing rate threshold of 5 APs/second. Statistical analysis. Statistical analysis was using the Matlab Statistics toolbox. The normality assumption of the data distributions was assessed using the Shapiro-Wilk test. Since the test the corresponding null hypothesis, Wilcoxon rank sum and signed-rank two-sided tests were used. For statistical comparisons between the three groups of data, the Kruskall-Wallis test was used.
Acknowledgements We thank  Drs. G. Buzsaki ,  I. Timofeev ,  A. Sirota ,  R. Cossart ,  K. Staley  and  C. Bernard  for their valuable comments on the manuscript, and  Suchkov D. ,  Mamleyev A. and  Minlebayev M. for customizing the platform for fixation of the animal's head. This research was supported by  Russian Science Foundation  grant  22-15-00236  (AN, DV, RK, GBZ, KC) (electrophysiological experiments and data analysis in rodents);  Deutsche Forschungsgemeinschaft  grants  DFG DR 323/5-1  and  DFG DR 323/10-2 , and  Bundesministerium fuer Bildung und Forschung  grant  Era-Net Neuron EBio2  (JPD) (electrophysiological recordings and data analysis in patients);  Ministerio de Ciencia e Innovación of Spain (MICINN)  grant  PID2019-111587RB-I00  (OH) (decomposition of cortical generators), and performed in the framework of the  PRIORITY-2030 program  of the  Kazan Federal University.
Total RNA-Seq data processing and analysis Briefly, reads were trimmed using Trim Galore (https://github. com/FelixKrueger/TrimGalore). They were then mapped on the S288C reference genome (R64-2-1, including the 2-micron plasmid), with addition of ERCC RNA spike-in sequences (Ambion) using version 2. 2. 0 of Hisat  (Kim et al. , 2019) , with default parameters and a maximum size for introns of 5000 nt. Uniquely mapped reads were used for all subsequent analyses. Gene counts were determined using featureCounts v2. 0. 0  (Liao et al. , 2014) , and then normalized using the estimate Size factorsForMatrix function of DESeq2  (Love et al. , 2014). Tag densities were obtained as: normalized gene count/gene length. Given the effect of Dbp2 inactivation and Mtr4 depletion on snoRNAs (Supplementary Figures  S1A, S1B ), the corresponding RNA-Seq data were normalized on the ERCC RNA spike-in signal.
Acknowledgments We would like to thank  Sara Cloutier  and  Elizabeth Tran  for the dbp2Δ and dbp2-AA strains, as well as  Alice Lebreton  and  Bertrand Séraphin  for sharing with us their unpublished tetOFF::MTR4 (BSY1756) strain. We are also grateful to  Camille Gautier  for preliminary bioinformatics analyses. Data management, quality control and primary analysis were performed by the Bioinformatics platform of the  Institut Curie.
Funding This work has benefited from the  ANR "DNA-life"  ( ANR-15-CE12-0007 ) grant and the  ERC "DARK " consolidator grant allocated to AM. High-throughput sequencing was performed by the  ICGex NGS  platform of the  Institut Curie  supported by the grants  ANR-10-EQPX-03  ( Equipex ) and  ANR-10-INBS-09-08  ( France Génomique Consortium ) from the  Agence Nationale de la Recherche  (" Investissements d'Avenir" program ), by the  ITMO-Cancer Aviesan (Plan   Cancer III ) and by the  SiRIC-Curie program  ( SiRIC Grant   INCa-DGOS-465  and  INCa-DGOSInserm_12554 ). SA has been supported by a  PhD fellowship  from  PSL University  and by the  Fondation pour la Recherche Médicale (FRM).
Only P. clavata was collected in the Portofino MPA. Considering the relatively small size of the latter (346 ha surface area), coral sampling was conducted at only one site (Fig. 1 ) with the consent of the Portofino MPA. The collection depth ranged from 15 to 35 m depending on the collection site (Fig. 1 ). In 2022, the Mediterranean Sea experienced record sea surface temperatures from May to September, although temperature regimes varied by location during the MHWs. Hourly measurements of seawater temperature at Riou Sud (Calanques National Park, France) over the past two decades, and at Portofino (MPA, Italy) from 2019 to 2022 have been extracted from the T-MEDNet database (www. t-mednet. org) (Fig. 2  and Fig. S1 ). In the Northwestern Mediterranean Sea, thermal anomalies are either characterized by short periods (a few days) at temperatures reaching 27 °C or by long periods (several days and weeks) at warm and stable temperatures of 23-24 °C in shallow waters  [15, 23].
MiSeq amplicon sequencing of the 16S rRNA gene Library preparation and sequencing Extracted DNA samples were sent to STAB VIDA (Portugal) for a paired-end sequencing (2 × 300 bp, 600 cycles) with the V3 chemistry on Illumina MiSeq platform. Amplicon library preparation was conducted using Illumina's standard "16S Metagenomic Sequencing Library Preparation" protocol  [37]. The V3-V4 region (~ 550 bp) of the 16S rRNA gene was amplified using the forward primer 341F 5'-CCT ACG GGNGGC WGC AG-3' and the reverse primer 785R 5'-GAC TAC HVGGG TAT CTA ATC C-3'. The fastq files containing the raw sequencing data have been deposited in the NCBI's Short Read Archive (SRA) under the BioProject accession number PRJNA991959.
Bioinformatics data processing The 16S rRNA gene amplicon data were processed using the DADA2 pipeline (version 1. 16; https:// benjj neb. github. io/ dada2/ index. html;  [8]. The sequencing of ten P. clavata samples and one C. rubrum sample was cancelled as no amplification was detected during the library preparation (amplicon PCR). On the 85 remaining samples,   S2 ). Before any further analysis, read quality profiles were inspected and reads were filtered and trimmed with the following settings: truncLen = c(290, 250), maxN = 0, maxEE = c(2, 2). Primer sequences were also trimmed at the 5′-end of each read (17 bp and 21 bp on the reads R1 and R2, respectively). Error rates were computed and used for sequence inference. An amplicon sequence variant (ASV) table was constructed based on de-noised and merged R1 and R2 reads.
Sequences smaller than 390 bp and longer than 450 bp were removed. Chimeras were detected and removed from the table using the removeBimeraDenovo function. The number of reads and sequences passing the different steps of the pipeline per sample is presented in Table  S3. Taxonomy was assigned to the 12 509 ASVs using the assignTaxonomy function and the SILVA SSU reference database (version 138. 1). The ASV table, the.
Analysis of 16S rRNA gene sequencing data All statistical analyses were conducted in the R environment (version 4. 2. 1) using the R-package phyloseq  [50]. Observed species richness and evenness (Shannon index) were calculated using the R-package vegan  [22]. Given the fact that rarefaction curves mostly reached the plateau (Fig. S2 ) and the current debate concerning the applicability of unrarefied versus rarefied data  [51, 101] , we conducted our alpha diversity analyses on the unrarefied ASV count table. Potential differences in observed richness were explained by constructing a generalized linear model (GLM) using a negative binomial distribution and considering site and tissue loss condition as fixed factors without taking into account the interaction as its effect was not significant (formula = Observed richness ~ Condition + Site; Table  S7  ).
Post-hoc tests were performed to identify potential pairwise differences (R-package emmeans  [45] ). Compositional data analysis (CoDA)  [31, 74]  was used to investigate the changes in the composition of the microbiota of C. rubrum and P. clavata. The raw ASV abundances were centered logratios (clr) transformed using the R-package compositions  [4] , after imputing zero counts based on Bayesian multiplicative replacement (Bayes-LaPlace BM method of the cmultRepl function in the R-package zCompositions;  [63] ). An Aitchison distance matrix was generated by computing Euclidean distance between samples using the clr-transformed data table. Principal component analyses (PCA), permutational multivariate analysis of variance (using functions adonis(); R-package vegan) were conducted to evaluate differences in the bacterial community structure between sites and conditions based on the percentage of tissue loss for each octocoral species (formula = Aitchison matrix ~ Condition + Site; Table  S8 ).
Potential pairwise differences were then considered (using the function permanova_pairwise(); R-package ecole  [86] ) adjusting the p-values with the false discovery rate correction (p. adjust( function; R-package stats; Table  S8. Differential abundance analyses were performed to identify ASVs that were differentially abundant between health conditions (0% versus 90% of tissue loss and collection sites when the number of samples was sufficient using the R-package ANCOM-BC (  [46] ; ancombc2() function using prv_cut = 0. 30 and alpha = 0. 05; version 02-2023; Table  S9 ).
Quantitative PCR (qPCR) Comparative qPCR was used to track changes in the abundance of total bacteria as well as the abundance of bacteria belonging to the Spirochaetaceae family, Vibrio genus and Endozoicomonas genus in octocoral cells. Four primer pairs targeting the 16S rRNA gene were used including universal primers (total bacteria;  [26] ) and primers specific to the Spirochaetaceae group  [36] , Vibrio genus  [89]  and Endozoicomonas genus (modified from  [83] ) (Table  S10 ). To test the specificity of the taxon-specific primers on our octocoral samples, the amplification products obtained from four samples (two per octocoral species) were sequenced using the Sanger technology (Eurofins genomics, Germany) and aligned on the nr database using the blastn algorithm. For primer pairs targeting a sequence in the V3-V4 region of the 16S rRNA gene, primer sequences were aligned on all the ASV sequences annotated as the targeted taxon (58 ASV for Endozoicomonas and 190 ASV for Vibrio using Clustal Omega (default parameters;  [84] ) and sequence complementarity was verified (Tables  S11  and S12 ).
Acknowledgements We thank  CHANEL  and the  Government of the Principality of Monaco  for financial support. We thank  Solène BASTHARD-BOGAIN ,  Tiffany MONFORT et Margaux FARGETTON  ( Septentrion Environnement ),  Nathaniel BENSOUSSAN  ( AMU ),  Pauline VOURIOT et Patrick BONHOMME  ( Parc national des Calanques ) and  Joaquim GARRABOU  ( ICM-CSIC ) for their participation in the collection of octocorals. We thank the members of the  Coralligène Consortium ,  Marc BALLY et Didier AURELLE  ( MIO-AMU ),  Stéphane SARTORETTO  ( IFREMER ),  Jean-Baptiste LEDOUX  ( CIIMAR ) for their fruitful discussions. We also thank  Dorian GUILLEMAIN  ( Institut Pythéas, CNRS ),  Valentina CAPPANERA  ( Portofino Marine Protected Area ) and  T-MEDNet  for providing seawater temperature data in the  Calanques National Park (France)  and the  Marine Protected Area in Portofino (Italy).