dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2021). Most current analyses on the variation and structure of microbial symbionts within Alviniconcha and Ifremeria stem from studies in the Lau Back Arc Basin, while little is known about these patterns in populations from other spreading systems within the distributional range of these genera. Here, we compiled an extensive dataset of 761 snail samples from 10 geographic regions of the Indo-Pacific Ocean (Fig. 1 ), some of which were previously unexplored, to assess the global diversity of chemosynthetic gill endosymbionts within Alviniconcha and Ifremeria through identification of 16S rRNA amplicon sequence variants (ASVs). Using ordination analyses and correlative statistics, we determined the influence of host species, host size, depth and geography on symbiont composition and distribution.
Symbiont richness is not saturated Although we analyzed symbiont 16S rRNA composition in over 700 snail individuals, symbiont discovery did not reach saturation in our dataset (Fig. 5 ). The number of ASVs within A. hessleri and I. nautilei, which both host symbionts of the genus Thiolapillus, was closest to reaching a plateau, while ASV accumulation curves for all other species showed a steady increase (Fig. 5 ). This is an interesting finding given that A. hessleri and I. nautilei were sampled across a relatively restricted area compared to some of the other species (Appendix 1: Table  S1 ). For other taxa that were represented by few individuals and geographic locations (e. g. , A. adamantis, A. marisindica), but also those with widespread distributions (e. g. , A. kojimai, A. boucheti), increased sampling efforts will probably reveal a currently hidden diversity of symbiont ASVs in the future.
Consequently, while our dataset does not allow comparisons of diversification between symbiont genera or species at this time, more ASVs especially for some of the gammaproteobacterial taxa (e. g. , unclassified Thiomicrospiraceae, Ca. Thiobios) will likely be recovered given the prevalence of gammaproteobacterial symbioses in provannid snails and other vent invertebrates  (Dubilier et al. , 2008).
(Beijing, China), respectively (Appendix 2). Host species were identified through shell morphology  (Laming et al. , 2020)  and subsequent sequencing of the mitochondrial COI gene with universal primers  (Folmer et al. , 1994; Geller et al. , 2013). explanatory factor. Relationships between number of ASVs and host size were determined based on Spearman rank correlations with the GGPUBR package  (Kassambara, 2020). Three different models were run to assess the effects of DNA preservation, extraction and sequencing method on patterns of symbiont diversity: 1) Model including the complete dataset and controlling for effects of methodology, 2) Model restricted to A. boucheti, A. kojimai and A. strummeri from the ELSC and controlling for effects of methodology, 3) Model restricted to A. boucheti, A.
Acknowledgements We thank the captains, crews and pilots of the  R/V Falkor (ROV Ropos and SuBastian) ,  R/V L'Atalante  (ROV Victor) and R/V Xiangyanghong 9 ( HOV Jiaolong ) for supporting the sample collections that made this study possible. Verena Tunnicliffe  and  Amanda Bates  are gratefully acknowledged for providing snail specimen from the  Mariana Arc and Back-Arc. We further thank the technical staff at the  Argonne National Laboratory and Novogene Co. for preparing and sequencing the Illumina 16S rRNA amplicon libraries. This study was supported by the  Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)  ( GML2019ZD0409 ) (to P. Y. Q. ), the  General Research Fund of Hong Kong SAR  ( 16101219 ) (to P. Y. Q. ), the  Schmidt Ocean Institute , a  University of Rhode Island Proposal Development Grant award  (to R.
A. B. ), the  U. S. National Science Foundation  (grant numbers  OCE-1536331 ,  1819530  and  1736932  to R. A. B. and  EPSCoR Cooperative Agreement OIA -# 1655221 ), and the  Agence Nationale de la Recherche   Cerberus  project ( ANR-17-CE02-0003-01 , coord. S. Hourdez ).
Data availability All bioinformatic scripts and final files for analysis are available on GitHub under https://github. com/cbreusing/Provannid_16S_SSU_meta-analysis. Raw 16S rRNA amplicon reads have been deposited in the Sequence Read Archive under BioProjects PRJNA473256, PRJNA473257, PRJNA610289, PRJNA610290, PRJNA763784 and PRJNA767887, while host COI sequences are available in GenBank under accession numbers listed in Appendix 2.
Ucn-2 recovers heart contractility and prevents I/R-induced fibrosis We investigated the cardioprotective effect of Ucn-2 (150 mg/Kg) infused 5 min before reperfusion, using different approaches. Data in Figure  2A  and Table  1  indicate that 1 week after surgery the left ventricle ejection fraction (LVEF) and the left ventricle fractional shortening (LVFS), as well as the left ventricular end-diastolic volume (LVEdV) and the left ventricular end-systolic volume (LVEsV), recovered significantly in Ucn-2-treated rats as compared with I/R non-treated rats. Meanwhile, the I/R-induced increase in the left ventricle diastolic diameter was not affected by Ucn-2. Next, we examined the effect of Ucn-2 on cardiac fibrosis. As shown in Figures  2B-2D , the administration of Ucn-2 decreased the fibrotic areas of the infarcted hearts assessed in vivo by cardiac magnetic resonance and by Masson's trichrome staining.
Wheat germ agglutinin conjugated with Alexa Fluor 488 was used to stain cell membrane, while DAPI was used to stain nuclei. Five snapshots per condition were acquired using a fluorescence microscope Olympus BX61 (Tokyo, Japan) with a 40Â objective, and images were analyzed to count the different proportion of red stained cells with Im-ageJ 1. 45 software (Wayne Rasband, National Institute of Health, Bethesda, MD).
Annexin V-fluorescein isothiocyanate staining Annexin V-fluorescein isothiocyanate (FITC) staining was used to detect apoptosis in adult rat cardiac myocytes seeded in 6-channel m-slides VI 0. 4 (Ibidi, Gräfelfing, Germany), incubated with 100 mL of the binding buffer supplied with the kit (Trevigen, Minneapolis, MN), and 1 mL of annexin-FITC reagent, during 35 min at 25 C. Images were taken with confocal microscope Leica TCS SP2 (Leica). Five snapshots per condition were acquired using an HCX PI Apo CS dry 20Â objective with 2Â zoom in z-stack intervals, and the maximum projection was recorded and analyzed with ImageJ software ImageJ 1. 45 software (Wayne Rasband, National Institute of Health), to count the proportion of labeled cells. All assays were performed per triplicate, and counts were independently conducted by two people in a blind manner.
Prior to quantitative real-time PCR reactions, cDNA was diluted 1 in 5 for PCR assays. PCR assays of miRNAs were performed using 10Â universal primer (miScript SYBR Green PCR Kit, Qiagen), Sybr Green reactive (iTaq Universal SYBR Green Supermix, Bio-Rad), and specific oligos of each miRNA: miR-103, miR-125a, miR-133, miR-139, miR-29a, miR-320, miR-324-3p, miR-324-5p, miR-339-5p, miR-423_1, miR-451_1*, and miR-499_1 (Qiagen) according to the manufacturer instructions, using a ViiA 7 Real-Time PCR System (Applied Biosystems). The average expression levels of miRNAs in cells were normalized to miRTC1. Reactions for genes PCR assays were performed in a 10-mL reaction mixture volume with 100 nM forward primer and 100 nM reverse primer for mRNA. Primer sequences are described in Table  S2. The average expression levels of genes were normalized to b-actin.
ViiA 7 Software version 1. 2 (Life Technologies, Carlsbad, CA) was used to calculate the quantification cycle (Ct) value, which is defined as the number of cycles at which the fluorescence signal is significantly above the threshold; expression of each mRNA and miRNA was defined from the threshold cycle (Ct), and relative expression levels were calculated using the 2 ÀDDCt method after normalization with the internal control, miRTC1 and b-actin for miRNA and mRNA, respectively. Data are expressed as relative expression of log fold change of means ± standard error of the mean of at least four replicates of each experiment.
In silico analysis of targeted genes by miRNAs The analysis of targeted genes predicted to be regulated by miRNAs was done using different bioinformatics resources: miRBD (miRDB v7. 2, http://mirdb. org, Washington University St. Louis, MO), Tar-getScan (Release 7. 1, www. targetscan. org, Cambridge, MA) databases, and Exiqon tool application (http://www. exiqon. com/ microrna-target-prediction). To identify miRNA target gene pathways, we also used the online platform Gene Ontology browser PANTHER (Protein Analysis THrough Evolutionary Relationships) v14. 1 (http://pantherdb. org/genelistanalysis. do, University of Southern California, Los Angeles, CA).
Statistical analysis Analyzes were performed with GraphPad (GraphPad Software, Inc. , La Jolla, CA), using Shapiro-Wilk as normality test. For normally distributed variables we used the ordinary one-way ANOVA, and we performed the multiples comparisons using t test without correction (Fisher's least significant differences test). We also used the nonparametric test Kruskal-Wallis with multiple comparisons corrected by Dunn's Test for non-normally distributed variables. Values were subjected to log-transformation to represent numerical features in the dataset to have a mean of 0 and a variance of 1 and to express data as relative gene/miR expression (log fold change). The outliers were removed based on results of QuickCalcs, an online tool of Graphpad. Results are presented as the mean ± standard error of the mean.
ACKNOWLEDGMENTS The authors thank  Eva Sanchez de Rojas de Pedro  for her technical help. The graphical abstract was created with Biorender. com(http://biorender. io). This study was co-financed by  FEDER Funds  [ US-1381135 ],  Agencia Estatal de Investigación  [ PID2019-104084GB-C22/AEI/10. 13039 /.
, 2016; Yger et al. , 2018)  are assessing the performance at the single cell level, to obtain an exhaustive assessment of the spike sorting performance at the population level, one must turn to use fully artificial or hybrid dataset  (Buccino and Einevoll, 2020; Magland et al. , 2020)  to properly compare and quantify the performances of the algorithms. But even with such dataset, in most of the studies, errors are only measured as false positive (FP)/false negative (FN) rates, and the reasons behind failures of the algorithms are often overlooked. In this study, we focused on a key property of the spike trains, at the core of most of these failures, i. e. , their fine temporal correlations. Indeed, temporal correlations are ubiquitous in the brain, and the higher the number of recorded cells because of the increased density of the probes, the more prominent they are.
Simulated datasets We used the MEArec simulator  (Buccino and Einevoll, 2020)  to generate 30-min-long synthetic ground truth recordings. In brief, MEArec uses biophysically detailed multicompartment models to simulate the extracellular action potentials, or so called "templates. " For this study, we used 13 cell models from layer 5 of a juvenile rat somatosensory cortex  (Markram et al. , 2015; Ramaswamy et al. , 2015)  to get a dictionary of biologically plausible templates. Given this database, we took the layout of a NeuroNexus probe (A1x32-Poly3-5 mm-25s-177-CM32 with 32 electrodes in three columns and hexagonal arrangement, a x-pitch and y-pitch of 18 and 22 mm, respectively, and an electrode radius of 7. 5 mm), and randomly positioned 20 cells in the vicinity of the probe, so that every simulated neuron has a unique template (i.
Spike sorting comparison All the quantitative metrics between the results of the spike sorting software and the ground-truth recording were made via the SpikeInterface toolbox. When comparing a spike sorting output to the groundtruth spiking activity, first an agreement score between each pair of ground-truth and sorted spike trains is computed as: score ij ¼ #n matches #n i gt 1 #n j sorted À #n matches ; where #n i gt and #n j sorted are the numbers of spikes in the ith ground-truth spike train and the j-th sorted spike trains, respectively. #n matches is the number of spikes within 0. 4 ms between the two spike trains. Once scores for all pairs are computed, a Hungarian assignment is used to match ground-truth units to sorted units  (Buccino et al. , 2020). All spikes from matched spike trains are then labeled as: true positive (TP), if the spike is found both in the ground-truth and the sorted spike train; FP, if the spike is found in the sorted spike train, but not in the ground-truth one; and FN, if the spike is only found in the ground-truth spike train.
In order to generate the spike lag versus recall figures (e. g. , Figs. 3 4 5 6 ) we expanded the SpikeInterface software with several novel comparison methods and visualization widgets. In particular, we extended the groundtruth comparison class to the CollisionGTComparison, which computes performance metrics by spike lag. In addition to the agreement score computation and the matching described in the previous paragraphs, this method first detects and flags all "synchronous spike events" in the ground-truth spike trains. Two spikes from two separate units are considered to be a "synchronous spike event" if their spike times occur within a time lag of 2 ms. The synchronous events are then binned in 11 bins spanning the [-2, 2] ms interval, and the collision recall is computed for each bin.
Generation of the ground-truth recordings To test how robust the recently developed spike sorting pipelines are against spike collisions  (Pachitariu et al. , 2016; Chung et al. , 2017; Hilgen et al. , 2017; Yger et al. , 2018; Lee et al. , 2020) , we generated synthetic datasets using the MEArec simulator  (Buccino and Einevoll, 2020 ; see Materials and Methods). More precisely, we took the layout of a NeuroNexus probe with 32 electrodes in three columns and hexagonal arrangement, and randomly positioned 20 cells in the vicinity of the probe (see Fig. 1A ), so that every simulated neuron has a unique template (i. e. , average extracellular action potential). Figure  1B  shows three sample templates with, respectively, low, almost null, and high similarity. The similarity between templates is computed as the cosine similarity of the flattened signals (see Materials and Methods) and the random generation of the positions and cell types of the simulated neurons (and thus of the templates) gives rise to the similarity matrix displayed in see Figure  1C.
Global performance of the spike sorters In order to assess the global performances of the sorting procedure on our synthetic datasets, we generated five recordings with various random seeds and averaged the results. Figure  2  summarizes the main findings. First, we noticed that, as seen in Figure  2A , the run time was roughly constant across sorters, except for HDSort, with its higher run time. The number of well detected units is similar among sorters, as shown in Figure  2B , but it is worthwhile noticing that Kilosort 3 is the only sorter producing many FP and redundant units (see Materials and Methods for classification of units). Kilosort 2 and HDSort also identify more FP than well detected units. Importantly, we did not perform any curation of the spike sorting output, but we consider the raw output of each sorter as is.
It is worthwhile noting that the decrease in performance for Kilosort 3 is surprising, since the authors confirmed the software is using the exact same template-matching procedure than in previous versions. This means that errors are likely originating either from subtle variations in the preprocessing steps, and/or in the clustering that has been changed and thus might lead to slight differences in the templates. However, while performances are still good for Kilosort 1 and 2 even when the average cosine similarity between pairs is increased, they slightly degrade for Spyking-circus. Density-based sorters (HerdingSpikes and IronClust), on the other hand, do not have a spike collision resolution strategy and this is reflected by their overall poorer performance. It is interesting to notice that Tridesclous, HDSort, YASS, and Kilsort 3, also using a template-matching-based procedure to resolve the spikes, are not properly resolving the temporal correlations even for dissimilar templates.
Discussion In this study, we showed in a systematic and quantitative manner how spatiotemporal correlations can be underestimated during spike sorting. Using synthetic datasets, we compared a large diversity of modern spike sorters and showed how they behaved as function of the similarity between the templates and the temporal lags between spikes. As expected, the closer the spikes are in time, the harder is it, for all sorter, to properly resolve the overlaps. However, more interestingly, the more similar the templates are, the higher the failures are. These failures are striking especially for spike sorters that are not relying on template-matching-based approaches (HerdingSpikes, IronClust). For the ones using a template-matching-based approach (Kilosort, Spyking-circus, Tridesclous, HDSort), the problem is less pronounced (with the exception of HDSort) but still present, and therefore this phenomenon should be taken into account when making claims about the synchrony.
Only a 90% reduction could be considered as the classification accuracy drop is moderated. Experiment 3: an online T9 experiment with improved comfort. The latter experiment involved an online setting of PIN code typing trough a T9 keyboard. Based on results of Experiment 1 and 2, we have considered a first condition using low frequencies stimuli (8-12 Hz) with 60% of the maximal amplitude depth, then high frequencies (28-32 Hz) stimuli with 90% of the maximal amplitude depth to finally be compared to the reference condition of low frequency (8-12 Hz) with full amplitude ( 100%). User experience. The participants were surveyed about how visually comfortable the stimuli were, how tired they felt after completing calibration plus testing phase and how frustrating the testing phase was for each condition.
Capitalizing on this technical advance, we were able to readily manipulate features of visual stimuli presented at high frequency on a computer monitor. The stimulus presentation program was written in C for the two first experiments and in Python using Psychopy2  35  for the online T9 experiment. A lower-level programming language, C was preferred for the first experiment as it allowed a faster computation for the presentation of stimuli at high frequencies (above 50 Hz). This was not required for the online experiment as the frequencies selected only went up to 35 Hz. The code used for the online classification is written in Python. The Python code of the T9 graphical interface and the classification is available online (https:// github. com/ ludov icdmt/ t9_ ssvep). Classification was performed on EEG data and marker streams received trough LSL  36  from the stimulus presentation program.
This study also aimed to investigate the limitations of high frequency stimuli in an online BCI context. The classification was therefore performed online and direct feedback was provided to the user. The BCI consists of a T9 keyboard, smartphone keyboard, with numbers from 0 to 9, and Back (11 classes problem). We have asked the participants to type 10 codes of 4 numbers, PIN codes. The specified PIN code was displayed on the left of the T9 and the output of the BCI on the left. You can find a screen-shot of the interface in the Supplementary Information. In addition to the visual feedback on the right of the screen, an auditory feedback was provided with a low-pitch tone for an error of classification and a high-pitch tone for a correct classification. Background color was set to black to further improve contrast with the RVS.
Finally, a pause of 0. 7 s as inter-trial was completed. During the online test phase, the inter-trial was set to 2. 5 s to allow the user to check between each trial the specified PIN code on the left and output from previous commands on the right. We could have reduced the inter-trial time to increase the ITR but our aim was to have a user-friendly interface that allows realistic interactions with the BCI. Stimulation was also set to 2s during the test phase. The pause duration between calibration blocks and the input of 4 consecutive digits PIN code was left to participants' appreciation (space bar had to be pressed to continue the experiment). Six healthy individuals (1 female, 7 males, mean age = 28, SD = 4) took part in the single session of this experiment. EEG data acquisition. EEG data was recorded from 32 electrodes fitted in an elastic cap according to the 10-20 international system and connected to a LiveAmp amplifier (Brain Products, Munich, Germany).
The ground electrode was placed at the Fpz electrode location and all electrodes were referenced to FCz electrode. The electrode impedance were brought below 20k prior recording. The signal was acquired at a rate of 500 Hz with a digital band-pass filter ranging from 0. 1 to 250 Hz. At the onset of every stimulus presentation, an event trigger was generated by the stimulus presentation program and synchronized to the EEG data stream via Lab Streaming Layer (LSL)  36. Eventually, only height electrodes (O1, O2, Oz, P3, P4, Pz, P7, P8) are manually selected to perform classification while all electrodes are used for Fourier frequency analyses. The manual selection of electrodes was centered around the occipital region. The raw data from the three experiments, with all electrodes included, are available online https:// zenodo.
The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface-to-bottom ocean biogeochemical bottle data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of seawater samples. GLODAPv2. 2022 is an update of the previous version, GLODAPv2. 2021  (Lauvset et al. , 2021). The major changes are as follows: data from 96 new cruises were added, data coverage was extended until 2021, and for the first time we performed secondary quality control on all sulfur hexafluoride (SF 6 ) data. In addition, a number of changes were made to data included in GLODAPv2. 2021. These changes affect specifically the SF 6 data, which are now subjected to secondary quality control, and carbon data measured on board the RV Knorr in the Indian Ocean in 1994-1995 which are now adjusted using certified reference material (CRM) measurements made at the time.
GLODAPv2. 2022 includes measurements from almost 1. 4 million water samples from the global oceans collected on 1085 cruises. The data for the now 13 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, chlorofluorocarbon-11 (CFC-11), CFC-12, CFC-113, CCl 4 , and SF 6 ) have undergone extensive quality control with a focus on systematic evaluation of bias. The data are available in two formats: (i) as submitted by the data originator but converted to World Ocean Circulation Experiment (WOCE) exchange format and (ii) as a merged data product with adjustments applied to minimize bias. For the present annual update, adjustments for the 96 new cruises were derived by comparing those data with the data from the 989 quality-controlled cruises in the GLODAPv2.