dataesr/scientific-paragraphs-categorization huggingface dataset:
5 ) or 10 Hz (Fig. 8 ) with a field of view (FOV) of 340 × 340 µm (pixel size = 0. 66 µm). We chose to record planes representative of the different anatomical regions we imaged from, corresponding to planes at depths of 182, 225, 257 and 288 µm in the mapzebrain brain atlas. After imaging calcium activity in each plane, we systematically recorded an anatomical Z-stack of the entire hindbrain to localize the site of stimulation. https://doi. org/10. 1038/s41593-023-01418-0 Single-plane calcium imaging data analysis. Preprocessing of time series. For all analyses of the fluorescence movies, we used the suite2p 87 (v. 0. 8. 0, https://github. com/MouseLand/suite2p). The pipeline first corrected the movies for motion artifacts in 2D using rigid and nonrigid registration. Most ROIs were automatically identified by the pipeline, and we manually added ROIs on cells that were not detected, including the soma of neurons that did and did not show activity during recording.
Then, using a custom-built Python script, for each ROI: (1)  we corrected the raw fluorescence trace by subtracting the neuropil signal (that is, the sum of surrounding pixels' fluorescence, with a factor n = 0. 7); (2) we calculated the ΔF/F(t) = (F(t) - F0) / F0, where F(t) is the fluorescence at time t and F0 is the cell baseline fluorescence (median fluorescence value selected on a 3-s period of inactivity); (3) we estimated the noise as the standard deviation of the signal during the same 3-s period of inactivity, which was further used to set the recruitment threshold of the cell; (4) the ΔF/F was filtered using an average running filter with three time steps. For visualization purposes and regression analysis, a low-pass filter was applied instead. In the tail-free experiments, when the motion artifact was larger and the 2D registration was not satisfactory, we removed the artifactual values by adding nonexistent values for frames in which the correlation to the mean image was low (we used the three lowest percentiles of correlation in the suite2p ops['corrXY'] output).
Analysis of calcium signals for single-plane imaging. Linear regression between maximum ΔF/F and the number of oscillations. We used the package linregress from scipy. stats to model the maximum ΔF/F using the natural log of the number of oscillations. We used a logarithmic transformation to diminish the effects of the outliers in the distribution of the number of oscillations. Motor regressors. To compare neuron's activity to motor activity, we built multiple motor regressors defined as fictive calcium traces modeling distinct kinematic parameters. Specifically, we first built four time series describing TBA and TBF, and for each of them, the binary value (0,1) based on their positive differential reflecting their increase over time. These were built as follow: instantaneous tail beat amplitude: we resampled the tail angle trace (acquired at 300 Hz) to the corresponding sampling rate of calcium imaging (10 Hz), taking for each calcium imaging frame the maximal (in absolute) tail angle value; iTBF: we calculated the mean TBF of all the cycles happening during each calcium imaging frame; binary value of the positive derivative of the kinematic variables: for each parameter computed above, we computed a binary variable equal to 1 when the parameter was increasing over time and equal to 0 the rest of the time.
We also added components below the sample holder to record tail movement that are further described in the behavior recording and analysis section. The camera was controlled by the Andor Solis software, which was interfaced with a MATLAB program written by the Hillman lab to control the acquisition. System calibration. To compute the pixel size in each axis, we followed the process indicated by the Hillman lab. For each given axis (x, y, z), we proceeded as follow: we imaged a volume of a sample of fluorescent beads (FluoSpheres 505/515, 1 µm, catalog no. F8888, Invitrogen). Then, we moved the sample of 0. 1 mm along the given axis. We imaged the sample again after translation. We repeated this process to get the same sample of beads at ten given positions along the desired axis. Then, we measured the average displacement of each bead across these iterations.
One solution we found was to add an extension to the support breadboard, but we did not manage to further improve the size of the FOV resolved. Development of an open-source pipeline for deconvolution, motion correction and reshaping of the volumetric images. Once we had a functioning and aligned system, we aimed to build an analysis pipeline to extract from the recordings the positions and fluorescence signals of neurons monitored during the experiment. Briefly, the output images of an experiment, as acquired with the software from the Hillman lab, are N(t) 3D images of dimension (L z , L x , L y ), where N(t) is the number of time steps. We combined the functions given by the Hillman lab, the open-source algorithms and homemade transformation scripts to transform these images into meaningful fluorescence traces.
Then, each uncorrected functional volume of dimension (L z , L x , L y ) was deconvolved with the Richardson-Lucy algorithm (RL) as implemented in the IOCBIO/deconvolve library (https:// gitlab. com/iocbio/deconvolve). The RL deconvolution algorithm is well known and widely applied in biological imaging  88. It follows an iterative maximum-likelihood approach and assumes that the noise is Poisson-distributed. Each pixel in an image deconvolved with RL is https://doi. org/10. 1038/s41593-023-01418-0 granted to be nonnegative. The RL algorithm needs the specification of only one parameter: the number of iterations, which needs to be set heuristically to enhance image quality yet avoid noise amplification artifacts  89. The images presented in the manuscript were treated to obtain a Poisson noise fully compliant image and deconvolved with 15 iterations of the RL algorithm.
On one central processing unit (CPU) (Intel Xeon(R) Silver 4214R CPU at 2. 40 GHz), the deconvolution of a file of dimension (L x : 130, L y : 510, L z : 279 pixels, or approximately 34 MB) took approximately 91 s to complete. The deconvolution significantly improved the contrast in the images and sharpened the borders between neurons, which became much easier to distinguish visually and were better detected by our segmentation algorithm (see four-plane wise segmentation, signal extraction and spike rate deconvolution using suite2p). We asked whether the deconvolution was also effective at reducing the levels of correlation between neurons' fluorescence signals. Using the same pixel masks, we extracted the fluorescent traces of neurons with and without the deconvolution step with the PSF. We found that the correlation values were overall lower and more centered around zero when deconvolution was applied (Extended data Fig.
One can compensate for such motion artifacts by transforming the image at which the motion happened until it looks the most similar to a reference image, a process called registration. To perform registration, we used a 3D registration library in ANTsPy (https://github. com/ANTsX/ANTsPy). The major source of motion artifacts in our recordings comes from brain movement when the fish contracts its musculature in the head or tail, during which the brain can change volume and the resulting brain image gets warped. Therefore, we chose to apply a nonrigid, deformable transformation, which gave better results than rigid or affine transformations. We registered the images in the imaging referential (before skew correction) for the same optimization reasons explained in the deconvolution section. We took the mean of the images in a whole video as a template and we registered each image against it.
The mean image was chosen for its cost-effective calculation, low signal to noise ratio and robustness to the fluorescence fluctuation. Then, we applied affine and deformable registration (referred to as SyN) with default parameters. The typical processing time on one CPU for a file of dimension (L x : 130, L y : 510, L z : 279 pixels) was around 2 min. Skew correction. Until this step, the images were processed in the SCAPE referential with dimension (L z , L x , L y ). These images needed to be corrected for the angle at which the sample was scanned due to the tilted light sheet geometry to reconstruct the volume into a regular referential with dimension (L z , L x , L y ). This step was performed using a MATLAB script adapted from the Hillman lab with an angle of correction of -47°. This angle was computed theoretically (from the distance of the center of the objective's back aperture) and measured experimentally during SCAPE calibration.
We reshaped the N(t) acquired volumetric images to obtain Nz stacks of images. Each stack represents a movie of a given sample plane over time. We then used the open-source software suite2p to segment the image into ROIs, extract their pixel masks and their fluorescence signals. We used anatomical detection of the ROIs adapted from cellpose  90  , with the parameter diameter of ROIs as 6 or 7 pixels. After segmentation and mask creation, ROI fluorescence was extracted as the sum of the fluorescence of all pixels within the mask. For each ROI, we then corrected the fluorescence trace by subtracting the neuropil signal (with a factor n = 0. 7). We calculated the ΔF/F of each cell as (F-F0) / F0, where F0 is the baseline signal calculated as the median fluorescence value on the lowest 10th percentile value.
Behavioral analysis and classification of swim bouts during high-speed volumetric imaging Behavioral recording. Head-embedded larval zebrafish were illuminated from the side with a 45° angle using an 890 nm LED and the reflected image was recorded at 300 Hz from below through a ×5/0. 25 12. 5 mm microscope objective and imaged on a high-speed camera (UI-3060CP-M-GL R2, iDS) with a 50-mm lens. The 488-nm imaging laser was blocked with a 600-nm high-pass filter (Thorlabs) located in front of the camera. The high-speed camera was controlled using the Hiris software (RD Vision, https://www. rd-vision. com/r-d-vision-eng).
Tail tracking and bout extraction. We used the open-source software ZebraZoom (https://zebrazoom. org/) to track tail movement and extract bouts as discrete periods of time where motion was detected. We segmented the tail into 20 segments from the swim bladder to the tip of the tail and extracted the corresponding angles from each of those. For the tail curvature plots shown in Fig. 7 , only the 17 most caudal segments were depicted. Proper tracking was assessed by visual inspection. The few swim bouts with clear tracking errors were discarded from the analysis. Analysis of behavioral kinematics. Using ZebraZoom, we extracted different kinematic parameters to characterize swim bouts (https:// zebrazoom. org/): absolute maximal bend amplitude, median bend amplitude, median TBF, bout duration and the number of oscillations.
In vivo recording of calcium imaging activity using the SCAPE microscope. For the in vivo recording, 6-dpf enucleated Tg(vsx2: GAL4;UAS:GCaMP6s) larvae were placed under the ×20 objective and placed at optimal x, y and z positions. The FOV was set to capture the fluorescence from the entire hindbrain and, when possible, the first segments of the spinal cord. For whole hindbrain recordings, the volume rate of acquisition was always set between 4 and 5. 55 volumes per second, and the scanning step (x' step) was set to 1. 5 µm. The recordings lasted 10 min. Acquisition was synchronized to behavior monitoring by sending, using the MATLAB graphic user interface (MathWorks), a short TTL pulse to a waveform generator feeding frame signals to the high-speed camera. After the functional recording, we imaged the larval brain using a high-resolution stack with smaller steps of sampling to get an anatomical image for future registration to a zebrafish brain atlas.
Calcium imaging data analysis and statistical tests. Detection of active neurons during swim bouts. To detect if a given neuron was active during a type of swim bouts (forward, left turns, right turns), we compared the distribution of spikes during all swim bouts of a given type to the distribution of inferred spike rates when the fish was not swimming. To build this distribution for a given neuron and a given type of swim bout, we processed as follows: (1) for a given swim bout, we extracted the spiking rates between 200 ms before the start of the bout and the end of the bout; (2) we compiled these spiking rates for all swim bouts of the same type; (3) we sampled the spiking rate when the fish was not moving as periods of time where the tail angle was zero; (4) these distributions did not follow a Gaussian distribution and did not have the same sample size.
All data met the assumptions of the statistical tests used. Data collection and analysis were not performed blind to the conditions of the experiments except for the manual segmentation and categorization of behavior in Figs. 2  and 3. Statistical tests were performed in R, except for the statistical analysis in Fig. 7  (details in the previous section), and the multiple linear models used in Fig. 8 , for which we used the OLS function from the library stats. models developed for Python (https://github. com/statsmodels/statsmodels).
Acknowledgements We thank  A. Hocquemiller  and  L. Tocquer  for the injections, and  S. Nunes Figueiredo ,  M. Dicu  and  A. Arneau  for fish care. We thank  H. Burgess  for providing inputs to the original version of the paper, and  D. Ryczko  and  E. Lunsford  for providing input to the final version of the paper. We thank  C. Perez Campos ,  W. Li  and  V. Voleti  for the documentation to build the SCAPE microscope. We thank  F. Ginoux  for the morphological registration pipeline and  G. Sridhar  for his input on the modeling approach. We thank  D. Veilleux  for making the monopolar microelectrodes and for her help with histology. We thank  S. -I. Higashijima ,  M. Ahrens  and  I. Bianco  for sharing published transgenic lines. M. C. -T. was supported by a  Campus France   PRESTIGE postdoctoral research fellowship  ( 2017-2-0035 ).
Statistical Analysis Statistical analysis of the data was performed using SPSS (Statistical Package for the Social Sciences) software version 16. 0. The corrected mortality percentages were submitted to an analysis of variance (ANOVA) to obtain the average of the mortality percentages and the standard error between these different means. The separation of means was performed using Tukey's comparison test at 5% (P = 0. 05). Probit analysis was performed to determine the lethal concentrations that caused 50% (LC 50 ) and 95% (LC 95 ) mortality of mosquito larvae.
Data Normalization and Statistical Analysis Metabolic activities are normalized from the quantity of metabolite produced or consumed/hour/million cells (quantify by DNA extraction as described in the Supplementary Materials). All results were obtained from at least four independent experiments and expressed as mean ± standard deviations. Statistical analysis was performed by one-way ANOVA with the Tukey-Kramer test for multiple comparisons. Values were considered to be significant at p-values of <0. 0001 (****), <0. 001 (***), <0. 01 (**) and <0. 05 (*).
Contributions: Conceptualization, A. D. -K. , C. L. and A. M. ; methodology, M. H. , M. P. , U. P. , N. B. and A. M. ; investigation, M. H. , M. P. , N. B. and A. M. ; resources, A. D. -K. and C. L. ; writing-original draft preparation, M. H. ; writing-review and editing, M. H. , U. P. , A. D. -K. , C. L. and A. M. ; visualization, M. H. ; supervision, A. D. -K. , C. L. and A. M. ; project administration, J. -C. D. -V. , A. D. -K. , C. L. and A. M. ; funding acquisition, J. -C. D. -V. , A. D. -K. and C. L. All authors have read and agreed to the published version of the manuscript. Funding: This work was funded by the RHU program "iLite" on "Innovations for Liver Tissue Engineering" granted by PIA2 through ANR-16-RHUS-0005. M. H. was supported by fellowships from iLite, FRM (Fondation pour la Recherche Médicale) project n • REP202110014151, and IFBF (Institut Français de BioFabrication).
Author Contributions: Conceptualization, A. D. -K. , C. L. and A. M. ; methodology, M. H. , M. P. , U. P. , N. B. and A. M. ; investigation, M. H. , M. P. , N. B. and A. M. ; resources, A. D. -K. and C. L. ; writing-original draft preparation, M. H. ; writing-review and editing, M. H. , U. P. , A. D. -K. , C. L. and A. M. ; visualization, M. H. ; supervision, A. D. -K. , C. L. and A. M. ; project administration, J. -C. D. -V. , A. D. -K. , C. L. and A. M. ; funding acquisition, J. -C. D. -V. , A. D. -K. and C. L. All authors have read and agreed to the published version of the manuscript. Funding: This work was funded by the RHU program "iLite" on "Innovations for Liver Tissue Engineering" granted by PIA2 through ANR-16-RHUS-0005. M. H. was supported by fellowships from iLite, FRM (Fondation pour la Recherche Médicale) project n • REP202110014151, and IFBF (Institut Français de BioFabrication).
Confocal analysis of dendritic spines and mitochondrial content GFP+ neurons expressing MitoDsRed in the dorsal hippocampus were imaged with a confocal microscope. Images were deconvoluted (Huygens Essential deconvolution) (Extended Fig. 1A ) and analyzed using the 3D Imaris XT software (Bitplane AG) (Extended Fig. 1B ). Dendritic spine density and morphology were analyzed using GFP staining. Mitochondrial parameters were determined for 100µm 3 of GFP volume.
Analysis of dendritic spine density and shape Spines were analyzed within the distal dendritic part of adult hippocampal new neurons in order to describe the morphological synaptic integration of these neurons. The distal dendritic compartment was defined as the dendritic shaft located between the beginning of the middle molecular layer and the end of the outer molecular layer, comprising mainly segments located after the second dendritic branching point. Spine analysis included spine density (number of spines/10µm dendritic length) and morphological classification. The 3D reconstruction of the dendrites was performed with the Imaris software. Spines volume and number were determined with the Imaris surface tool module, while dendritic length was measured with the Imaris Filament tracer module.
Mitochondrial content of adult-born hippocampal neurons MitoDsRed-positive mitochondria inside GFP-positive neurons were analyzed in the somatic and distal (as above defined) dendritic compartments. The total volume of mitochondria, their mean volume and their number were automatically determined using the Imaris software (Bitplane). For both the somatic and distal dendritic compartments, mitochondrial total volume was normalized for 100μm3 of GFP volume of the corresponding compartment, mitochondrial number and mitochondrial mean volumes were calculated within the same portions. Briefly, GFP volume, reflecting the total volume of the soma or dendritic shaft, was determined using the Imaris surface tool with the following parameters: smoothing enabled (surface detail 0. 0687), absolute intensity thresholding, filtering by number of voxel (>10).
Behavioral experiments Behavioral tests were performed by experimenters blind to genotypes, using published protocols. Opa1+/+ and Opa1+/-mice were tested for anxiety-related and exploratory behaviors. The non-spatial (cue) version of the Barnes maze (vision-guided) and the object recognition task allowed to evaluate hippocampal-independent memory. The spatial version of the Barnes maze, the pattern separation and object location tasks were used to evaluate hippocampal-dependent memory. Consequences of voluntary exercise were assessed using a standard running procedure. All parameters were measured using Ethovision software (Noldus, The Netherlands).
Detailed protocols. A comprehensive battery of tests was deployed to evaluate several aspects of mice behavior. Before each experimental session, mice received 3 to 4 days of handling to get habituated to the experimenter. Mice were pseudo-randomly distributed into batches of 8-14 mice with counterbalanced numbers of Opa1+/+ and Opa1+/-genotypes, experimenters being blind to genotypes. All experiments were video-recorded and locomotor activity was automatically analyzed using Ethovision XT software (Noldus, The Netherlands) except tasks involving objects, for which the exploration time was recorded manually. The setups were cleaned thoroughly with 70% ethanol followed by water between each mouse session, to ensure the absence of olfactory cues. General behavior: Anxiety-related behavior was assessed using an elevated plus maze.
In order to prevent memory extinction, animals were given three learning trials after the first probe test (data not shown). The target hole was disconnected from the escape cage and the mouse was given 5 min to explore the maze during which numbers of visits to each hole were counted. Object location: This task evaluates spatial hippocampal-dependent memory and relies on the ability of rodents to discriminate between a novel and a familiar spatial location. Performances in this task are sensitive to the depletion of hippocampal adult neurogenesis. This test takes place in a circular arena (40 cm diameter) containing a visual cue (rectangular striped pattern), surrounded by a white curtain, and is conducted in three phases: familiarization, exploration and test. One day before acquisition, each mouse was familiarized to the empty arena during 10 min.
Statistics Data analysis was performed with Prism software (GraphPad. 9). Morphological data analyses were evaluated with unpaired t-test, or Mann-Whitney test if the distribution data was not normal, to compare parameters between groups. Behavioral analyses were evaluated with one sample t-test to compare preference index with chance level 50% or with 0. 5 ratio and unpaired t-test to compare between genotypes. Repeated measurement two-way ANOVA with Bonferroni post hoc analyses were used when allowed. Mean ± SEM, appropriate.
Opa1+/-mice show spatial memory deficits Next, we evaluated spatial (hippocampal-dependent) and non-spatial (hippocampalindependent) memory of OPA1 deficient mice. In the spatial version of the Barnes maze (Fig. 2A ), both Opa1+/+ and Opa1+/-mice showed spatial learning (Fig. 2B , session: F (5, 95) = 25. 92, *** p<0. 0001, two-way ANOVA with repeated measures) and memory capacities Fig. 2C , target quadrant: F (3, 76) =48. 8, *** p<0. 0001; two-way ANOVA). However, compared to control mice, Opa1+/-animals displayed a less precise memory (Fig 2D Thus, our data indicate that Opa1+/-mice are capable of spatial learning and have long-term memory, but that their memory persistence or robustness is lower than that of control mice. Then, these mice were submitted to the object location task (Fig.
In this article, we provide an in-depth analysis on the drug-resistance phenotypic characteristics of a cohort of 325 tuberculosis and characterize by Whole Genome Sequencing 24 isolates from Nigeria belonging to L4, L5 and L6. Our results suggest an alarming rate of drug-resistance of the L4. 6. 2. 2 Mycobacterium tuberculosis complex (MTBC) lineage and a high diversity of L5. We compiled these new Sequence Read Archives (SRAs) to previously published ones from available Bioprojects run in Nigeria. We performed RAxML phylogenetic reconstructions of larger samples that include public NCBI SRAs from some neighboring countries (Cameroon, Ghana). To confront phylogenetic reconstruction to metadata, we used a new proprietary database named TB-Annotator. We show that L5 genomes in Northern Nigeria belong to new clades as the ones described until now and allow an update of the taxonomy of L5.