dataesr/scientific-paragraphs-categorization huggingface dataset:
High-throughput phenotyping of genome-reduced strains Growth was determined by measuring light scattering of microtiter cultures using the BioLector (m2p labs, Baesweiler, Germany). This device controlled the temperature and humidity of the chamber and was able to measure light scattering without a pause in shaking, enhancing aeration. Clear-bottom microtiter plates with a well volume of 300 l and a culture volume of 150 l were used. Frozen glycerol stocks were thawed and plated on LB medium with appropriate antibiotic for one day, and 1 ml NMS precultures in 96-well deep well (2 ml) plates were inoculated from these colonies. After 24 h these were diluted 1/20 into fresh prewarmed NMS and immediately placed in the BioLector. All growth was done at 37˚C. Because the initial phase typically involved a decline in light scatter for unknown reasons, light scattering data were normalized to the lowest recorded value for each well.
Labelling of the sample and hybridization were performed in strand specific conditions by NimbleGen, as previously described  (28). Data processing is described in Supplementary Materials and Methods. Raw data and genelevel expression values after between-sample normalization have been deposited in GEO database (GSE207089). Detailed results of the differential expression analysis are provided in Supplementary Table  S3. High-resolution expression profiles along the genome can be explored interactively using the Genoscapist data browser  (33).
ACKNOWLEDGEMENTS We acknowledge the  Migale platform (INRAE, Jouy-en-Josas, France)  for their support for providing useful software for genome sequence analysis. Author contributions: M. J. , P. N. , K-I. Y. , U. S. and  P. Ni. conceived the project and designed the experimental plan, E. D. , A-G. P. , K. T. , V. C. and F. L. performed the experiments, M. J. , C. G. and  P. Ni. performed statistical treatments, M. J. , E. D. and A-G. P. analyzed the data, C. G. and S. D. extended the genoscapist genome browser, M. J. , P. N. , E. D. and A-G. P. wrote the manuscript.
FUNDING French National Research Agency  [ ANR-18-CE44-0003 ,  ANR-18-CE43-0002 ,  ANR-15-CE21-0008 ];  European Commission  [ LSHG-CT-2006-037469 ,  FP7-244093 ]. Funding for open access charge:  French National Research Agency  [ ANR-18-CE43-0002 ,  ANR-18-CE44-0003 ].
DATA AVAILABILITY Gene expression data have been deposited with the Gene Expression Omnibus (GEO, https://www. ncbi. nlm. nih. gov/ geo/) under accession number GSE207089 and can be visualized with the Genoscapist B. subtilis Expression Data Browser (http://genoscapistdev. migale. inrae. fr/smgeb/). Genoscapist is an open-source initiative available in the GitHub repository (https://github. com/ sderozier/genoscapist) and in the Zenodo repository (https://zenodo. org/record/7645915). Genome sequences of 43 genome-reduced strains are available in the NCBI Database at https://www. ncbi. nlm. nih. gov/nuccore/ and can be accessed with GenBank accession numbers from CP116828 to CP116870.
ABSTRACT 350 Background. People living with HIV account for 38-50% of those affected in the 2022 multicountry mpox outbreak. Most reported cases had high CD4 counts and similar outcomes to those without HIV. Emerging data suggest worse clinical outcomes and higher mortality in more advanced HIV. We describe the clinical characteristics and outcomes of mpox in a cohort of persons with HIV and low CD4 cell counts (CD4 <350 cells/mm 3 ). Methods. A network of clinicians from 19 countries provided data from confirmed mpox cases between May 11 and Jan 18 , 2023, in persons with HIV infection and CD4 counts <350 cells/ mm 3. We describe their clinical presentation, complications, and causes of death. Analyses were descriptive. Findings. We include data of 382 cases: 367 cisgender men, 4 cisgender and 10 transgender women with a median age of 35 years.
Data collection Each contributing centre completed a de-identified structured case-report sheet (CRS) adapted from one used in our prior case series to include variables of interest relevant to persons living with HIV and to capture more severe outcomes (Supplementary Figure  1 ). The CRS used drop down-menus and free-text fields to capture routinely collected data from electronic or paper medical records. The CRS focus on HIV status included CD4 cell count, HIV viral load, concurrent opportunistic infections, and adherence to ART. These data were included with information on mpox presentation, diagnosis, clinical features, complications, and outcome. We also considered four outcomes for management: outpatient, hospitalization, ICU-level care, and death. Duration of hospitalization was the number of days until discharge or until data collection if ongoing by the end of data collection.
This form of mpox affected those with CD4 counts <200 cells/mm 3 -the precise CD4 threshold considered to be AIDS-defining in international guidelines (Supplement table  4 ). Given the 15% mortality in this group, strong consideration should be given to designating this disseminated form of severe mpox as a new AIDS-defining condition in definitions and guidelines. Several limitations of our research need to be highlighted. Our data are derived from an observational retrospective convenience case series from countries with high numbers of mpox infections. We were, therefore, unable to assess how well our cohort represents the entire population of people living with HIV and who developed mpox infection. Study sites may have been more likely to include individuals with more severe outcomes which may have biased the relationships we saw between both CD4 counts and HIV viral load and clinical outcomes.
Individuals included in this case series had symptoms that led them to seek medical care; therefore, persons who were asymptomatic, had milder symptoms, or lacked access to medical care could have been missed and thus we may have overestimated illness severity. 29 Also outcomes may have been missed if people reattended with severe disease at different sites that were not part of the case series. Due to data collection from multiple sites some characteristics may have been collected in a heterogeneous manner, and laboratory techniques also differed from site to site. Many of the cases had a concomitant opportunistic infection and it may be difficult to ascertain many of the outcomes to mpox relative to the other pathogens. For example, some healthcare settings included did not have access to certain radiological and microbiological investigations so we cannot be certain of the role of mpox as opposed to other opportunistic infections.
ACKNOWLEDGMENTS We thank  Roser Escrig  for her assistance in administering data sharing agreements, and  Gerard Carot-Sans  for providing comments to the manuscript. We would like to thank the  Swiss HIV Cohort Study network , who made possible and facilitated data collection. We wish to acknowledge  Dr Maria Beiras-Garcia ,  Department of imaging and Radiology, Complejo Hospitalario de Pontevedra, Pontevedra, Spain  for reviewing and commenting on the radiological images. Thank you to the individuals with mpox and families of deceased individuals who graciously consented to contribute data to further knowledge of this disease.
Funding. This work was funded by a predoctoral fellowship from the  Doctoral School Hématologie -Oncogenèse -Biothérapies ED 561  (to CM), by the  European Research Council  grant  759880  (to YB) and by  French National Research Agency (ANR)   ANR-19-CE14-0028-03  (to PB). The purchase of the  Ultrasound Doppler (Acuson Sequoia -Siemens)  was funded by the grant  ANR-18-RHUS-0001  ( RHU Booster  (to MM).
A supervised multivariate modeling approach using partial least squares discriminant analysis (PLS-DA) was applied to highlight differences between the MPTP-treated and control brain sections (Supplementary Fig. 4 ). Following optimization of the PLS-DA models, m/z values with high VIP values were selected for identification (Supplementary Table  1 ). The detected m/z values with high VIP values were initially identified by accurate mass matching against databases, with the identification confirmed by on-tissue MS/MS. Several m/z values heavily contributed to the separation of the control and MPTP groups; interestingly, majority of these values were particularly identified as long-chain (≥40) hydroxylated or non-hydroxylated sulfatides. Therefore, we    focused our subsequent statistical investigation on region-specific changes of hydroxylated and non-hydroxylated sulfatides.
This suggests that the mechanisms of sulfatide hydroxylation in oligodendrocytes differ between grey and white matter regions  35. In line with this, studies using neonatal rodent brains revealed that the ratio of hydroxylated/non-hydroxylated sulfatides increases during myelination  65, 66. Oligodendrocytes lie in longitudinal arrays in the white matter, whereas grey matter oligodendrocytes frequently demonstrate a satellite formation around neurons, wrap around nerve axons, and possibly help regulate the neuronal microenvironment  53, 67. Our data show that long-chain hydroxylated sulfatides must be associated with myelination in the grey matter, particularly in the motor-related brain regions. To summarize, we imaged several intact glycerophospholipids and sphingolipids using dual polarity ionization mode MALDI-MSI and reported the associated distributions within several brain regions in coronal brain tissue sections from non-human primate brains (control and MPTP-lesioned).
Tissue staining A Luxol fast blue staining kit (Abcam, ab150675; Abcam, Cambridge, UK) was used with a similar protocol as previously described for myelin staining  73. Briefly, after MALDI-FTICR-MSI, the slides were immersed in methanol for about 1 min to remove remnants of the norharmane matrix from the tissue sections. Then, the slides were immersed in the Luxol fast blue solution for 2 h at 60 °C, rinsed and differentiated according to the staining protocol, and incubated in Cresyl Echt violet for 5 min to visualize neurons. The slides were then rapidly dehydrated in absolute alcohol, cleared, and mounted with DPX (Merck, Darmstadt, Germany). The sections were scanned using a high-resolution slide scanner (Epson Perfection V750 PRO). Data processing and analysis MALDI-MSI data from five pairs of control and MPTP-lesioned samples were visualized in FlexImaging (v.
Identification of lipids An untargeted mass list including 5225 m/z values was used as an input to the PLS-DA analysis. The m/z values with high VIP values (Supplementary Table  1 ) were selected for identification and searched against the LIPID MAPS database with a 0. 01 m/z tolerance for both negative and positive polarities, including all ion types. Through mass accuracy and MS/MS analysis, several m/z values with high VIP scores (Supplementary Table  1 ) were identified as SHexCers. Hence, our research focused on SHexCers. However, it should be noted that not all SHexCer species are included in the LIPID MAPS database. Therefore, we also included additional hydroxylated and non-hydroxylated SHexCer species from published literature, which are not present in LIPID MAPS  35, 75. In a separate analysis, we included several identified glycerophospholipids and sphingolipids to examine their distributions in the brain tissue sections.
Consequently, they have been included in our lipid assignment list (see Supplementary Table  3 ). No deisotoping of the spectra was applied prior to lipid assignments. MALDI-tandem MS (MS/MS) was performed on tissues by collecting spectra from brain regions where the target ion was abundant. The resulting product ions were then compared to product ion spectra of standards in the LIPID MAPS database (Nature Lipidomics Gateway, www. lipidmaps. org) and/or previously published data  76, 77. In cases where sodium and/or potassium adduct ions of the same lipid species were identified, the brain tissue distribution of the adducts and [M + H] + ions were compared (Supplementary Figs. 6-16). Lipids were assigned based on mass accuracy in instances where the peak intensity was insufficient for MS/MS analysis of the tissue.
ACKNOWLEDGEMENTS This work was supported by the  Swedish Brain Foundation  (grant  FO2021-0318 ), the  Swedish Foundation for Strategic Research  (grant  RIF14-0078 ), the  European   FP7 ITN Community 's  Seventh Framework Program ARIADME  (grant  607517 ), the  Swedish Research Council  ( VR-MH  grant  2018-03320 ,  VR-NT  grant  2022-04198 ,  VR-TVAR 2021-03293 ), and the  Science for Life Laboratory  (all to  P. E. Andrén ),  Agence Nationale de la Recherche  grants  ANR-07-MNP   TRAFINLID ,  ANR-08-MNP-018 MCHPRIMAPARK ,  Federation pour la Recherche sur le Cerveau  grant,  Biothèque Primate -CNRS Life Sciences Department  (all to E. Bezard). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgements The authors thank  Violaine Moreau  and  Jean-Luc Pons  for hardware survey and continuous support. We also thank  Gilles Labesse  and all beta-testers for their helpful remarks. Finally, we thank  Po-Hsien Lee  and associated authors of the original TSA-CRAFT (4) software for letting us the opportunity to embed their tool under a free and accessible cross-platform web service.
Funding Victor Reys  and  Julien Kowalewski  were supported by  la Ligue Nationale contre le Cancer , the  Agence Nationale de la Recherche  [ ANR-17-CE18-0011 ], the  Association Vaincre la  mucoviscidose and the  Association Grégory Lemarchal. The CBS is part of the ChemBioFrance research infrastructure and is a member of the  France-BioImaging (FBI)  and the  French Infrastructure for Integrated Structural Biology (FRISBI) , 2 national infrastructures supported by the  French National Research Agency  ( ANR-10-INBS-0004  and  ANR-10-INBS-0005 , respectively).
| Mass spectrometry proteomic analysis Raw data collected during nanoLC-MS/MS analyses were processed and converted into a *. mgf peak list format with Proteome Discoverer 1. 4 (Thermo Fisher Scientific). MS/ MS data were analyzed using search engine Mascot (version 2. 4. 0, Matrix Science) installed on a local server. Searches were performed with a tolerance on mass measurement of 10 ppm for precursor and 0. 02 Da for fragment ions, against a composite target-decoy database built with a M. smegmatis mc 2 155 UniProt database (strain ATCC 700084/mc(2)155, taxo 246196, July 20172021, 12666 entries) fused with recombinant trypsin and a list of classical contaminants (118 entries). Cysteine carbamidomethylation, methionine oxidation, protein N-terminal acetylation, and cysteine propionamidation were searched as variable modifications.
Up to one missed trypsin cleavage was allowed. The identification results were imported into Proline 2. 0 software installed on a local server (http://proli ne. profi prote omics. fr) for validation. 26 Peptide spectrum matches taller than nine residues and ion scores >10 were retained. The false discovery rate was then optimized to be below 2% at the protein level using the Mascot Modified Mudpit score. Spectral counting analyses were performed with Proline. For BioID analyses, we introduced a corrective factor in order to compare the results, since the total number of spectra may vary between independent biological replicates. For each condition, the number of spectra for one protein was normalized against Replicate 1 and was calculated as follows: Normalized number of spectra (Protein X) = number of spectra (Protein X) in replicate 1 + [number of spectra (Protein X) in replicate 2 × total number of spectra in replicate 1/total number of spectra in replicate 2] + [number of spectra (Protein X) in replicate 3 × total number of spectra in replicate 1/total number of spectra in replicate 3].
Distinct maps of V2a medullary neurons for forward locomotion and steering To understand how MLR neurons recruit V2a RSNs to produce forward locomotion, we first investigated how hindbrain V2a neurons were recruited during spontaneous voluntary locomotion (Fig. 7 ). To achieve the high-speed volumetric imaging required to do so, we built a swept confocally aligned planar excitation (SCAPE) microscope based on a previous design  45, 46. This microscope enabled us to record calcium activity of the entire V2a hindbrain population at 5. 55 volumes per second while recording tail movement of 6-dpf head-fixed, tail-free Tg(vsx2:GAL4;UAS:GCaMP6s) transgenic larvae (Fig. 7a, b ). We optimized the resolution and cell detection accuracy within the dense V2a population by developing an open-source pipeline to preprocess the large imaging dataset that relies on deconvolution and motion correction of volumetric images (Figure  7b1  and Methods).
In addition, we showed evidence that MLR neurons receive dopaminergic modulation via the D 1 receptor. As shown in other species, the MLR locus in zebrafish is defined by its ability to initiate graded forward locomotion, and this function is carried by glutamatergic neurons projecting onto RSNs. Furthermore, we collected a large whole-brain imaging dataset of larval zebrafish performing forward locomotion either spontaneously or when visually stimulated by moving gradings. As shown in rodents, where the activity of a fraction of MLR neurons encoded locomotor speed  48, 49  , the activity of a fraction of MLR neurons in zebrafish is. e, Location of neurons in the medulla in dorsal view, color-coded according to the functional cluster. The empty circles represent neurons not active. f, Medullary V2a RSNs in the forward clusters were recruited more during forward swimming than during struggle behavior.
Online content Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41593-023-01418-0. To avoid pigmentation, all experiments were performed on Danio rerio larvae of AB background with the mitfa -/-mutation. Adult zebrafish were reared at a maximum density of eight animals per liter in a 14/10 h light-dark cycle environment at 28. 5 °C. Larval zebrafish were typically raised in Petri dishes filled with system water under the same conditions in terms of temperature and lighting as for adults. Transgenic larvae expressing Kaede and used for the photoconversion experiments were exceptionally raised in complete darkness to minimize global photoconversion at the early stages.
Stimulation was delivered using an isolated stimulation unit (2100, A-M Systems) triggered by a Digidata series 1440A Digitizer (Axon Instruments, Molecular Devices) that synchronized electrical stimulations and behavioral recordings controlled by the Clampex v. 10. 3 software (Axon Instruments, Molecular Devices). For the initial search of the MLR, the behavioral response to stimulations in each locus was monitored by applying continuous stimulation at 10 Hz and 1 µA for less than 30 s; a rest period of at least 3 min was allowed between successive loci. After five tested loci without obvious observed behavioral response, the larva was discarded. Once the MLR locus was found, we implemented experimental trials lasting 1 min during which the stimulation protocol consisted of trains with 2-ms negative pulses, occurring at 5-20 Hz with 0.
Behavioral recording and analysis Behavioral recording. Head-embedded larval zebrafish were illuminated from the side with a 45° angle using an 890 nm light-emitting diode (LED) (part no. ILH-IW01-85SL-SC211-WIR200, Intelligent LED Solutions) and behavior was recorded at 300 Hz from below through a ×5 ×20/0. 25 12. 5-mm microscope objective (part no: 440125-0000-000, ZEISS) and imaged on a high-speed camera (acA640-750um, Basler) with a 50-mm lens (catalog no. MVL50M23, Navitar). When the behavioral recording was coupled with functional calcium imaging, the 488-nm imaging laser was blocked with a 600-nm high-pass filter 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 automated segmentation of bouts. We used the open-source software ZebraZoom (https://github. com/oliviermirat/ ZebraZoom) to track the position of the tail for each frame and extract bouts, defined as discrete events when the tail was continuously moving. The tail angle was defined as the angle between the body axis of the fish and the tip of the tail. The quality of tail tracking was carefully assessed by visual inspection. Electrically induced bouts were defined as all bouts starting during the electrical train stimulation. Swim bouts starting just before the stimulation were excluded from the analysis. 'Spontaneous' bouts were defined as bouts starting and ending before the first electrical stimulation of the trial. Note that bouts occurring in the 1-min time window after a stimulation train were excluded from the analysis because their properties could have been impacted by the previous electrical stimulation.
Analysis of tail kinematics. From the tail angle, the ZebraZoom algorithm extracted the timing and amplitude of each tail bend that we then used to calculate iTBF and iTBA. For each episode, we estimated the corresponding duration, number of oscillations and maximum tail bending amplitude as previously done for bouts in freely swimming animals, together with the median iTBA and iTBF (https://github. com/ wyartlab/Carbo-Tano_Lapoix_2023).
github. io/bouter/index. html). Briefly, vigor was estimated as the sliding standard deviation of the tail trace with a time window of 50 ms. Swim bouts were then automatically detected by thresholding the vigor trace. Three types of regressors were constructed for the correlation analysis: a stimulus regressor, a motor regressor and a vigor regressor. These were obtained by, respectively, convolving the on/off stimulus trace, on/off bout trace and the vigor trace with an exponential kernel using the decay time constant of GCaMP6s. These convolved traces were then downsampled to the functional imaging frequency to get the actual regressors. Finally, we calculated the Pearson correlation between each regressor and the fluorescence traces from the MLR. We then defined MLR neurons as correlated with motor activity (Fig.
Anatomical investigations Registration of electrical stimulation sites in the brain atlas. To assess the position of the stimulation electrode, Z projection confocal stacks were acquired combining the GFP signal and the DIC channel using a ×20/1. 0 DIC D = 0. 17 M27 75 mm objective. The Z-stacks were acquired with a step of 1-µm depth using the Slidebook software v. 6. 0 (3i Intelligent Imaging Innovations). From each image volume, we annotated manually in Fiji (https://fiji. sc/Fiji) the X, Y and Z coordinates of the electrode position from the DIC channel and three anatomical reference coordinates from the GFP pattern of expression of the Tg(vmat2:GFP) transgenic line (medial boundary of the LC on each side, anterior limit of the superior raphe and anterior limit of the inferior raphe). Using a custom-written Python script, the coordinates of the anatomical landmarks were transformed to the mapzebrain fish space (https://mapzebrain.
The positive backfilled neurons selected for this analysis were taken from the MLR area defined by electrical stimulations (with a diameter of approximately 40 µm). For the experiment in Extended Data Fig. 1a , the injections were done in wild-type fish. As the dye injection forms a background labeling in the entire nervous system, we chose four anatomical landmarks also easily distinguishable in the mapzebrain 4,6-diamidino-2-phenylindole fish space. For the experiments in Extended Data Fig. 1c , the four anatomical landmarks were selected on the basis of the DsRed fluorescence pattern in each transgenic Tg(vglut2:DsRed) larva and from the mapzebrain Tg(vglut2a:DsRed) fish space. The script used and the registration steps applied were the same as described in the previous section.