dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions and Outlook The MVA/cholesterol pathway is crucial for CNS development and function and consequently, any dysfunction of this fundamental metabolic pathway is likely to provoke pathologic changes in the brain. Mutations in genes directly involved in MVA/cholesterol metabolism cause a range of diseases, many of which present neurologic and psychiatric symptoms (e. g. SLOS, NPC, desmosterolosis). This raises the question whether other diseases presenting similar symptoms are related albeit indirectly to the MVA/cholesterol pathway. Up to now, there is no direct proof that ASD and RTT are caused by defects in MVA/cholesterol pathway. However, the available data clearly demand new studies to investigate the state of MVA/cholesterol homeostasis in RTT and ASD. This includes more efforts to clarify the reliability of experimental models, and to delve deeper into the role of cholesterol metabolism in this neurological disorders.
FRET data analysis The FRET efficiency was calculated using I A /(I D ϩ I A ) where I D and I A represent the donor and acceptor intensities, respectively. Basic data analysis was carried out by scripts written in MATLAB, and all data fitting was performed by Origin 8. 0. All fluorescent spots in each movie were selected unless the trace showed a poor signal:noise ratio or the intensity changes of the donor and acceptor did not match well. Each FRET histogram was constructed from more than 300 traces.
Measurement of Young's modulus of PDMS material Measurement of the Young's modulus relied on the compressive force technique, using the ElectroForce 3100 Bose instrument and the WinTest software. The measurement was done on dimensionally calibrated PDMS macroscopic posts (height = 10 mm, and diameter = 9 mm) made in the same polymerization conditions as the micropillars. The instrument has a maximum force capacity of ±22 N. The post was compressed by 10% of its height at a compression speed of 1mm/sec. This technique gives the compression force and the dimensional changes of the PDMS post, allowing the Young's modulus measurement. In this compression experiment, the linear elasticity theory gives a straightforward relation between the applied force and the deformation, supporting the hypothesis of uniaxial stress: F S ¼ E � DL H where F is the compression force, ΔL is the compressed deformation, S is the post section (= P r 2 ), r is the post radius, and H is the post height.
COMSOL simulation The commercial finite element software COMSOL 4. 3 was used for the 3D simulation of micropillar mechanical deformation. This software solves equations of linear elasticity. A PDMS pillar was modelled as a 3D elastic cylinder with a Young's modulus E = 2. 6 MPa, a Poisson's ratio of 0. 499, and a density ρ of 927 Kg/m 3. The pillar height was always 300μm and the diameter changed according to the diameter of pillars of the experimental data (28 and 36 μm). The modelled pillar was discretized into tetrahedral mesh elements with a predefined element size: Fine (minimum and maximum element size: 3 and 24 μm, maximum element growth rate: 1. 45, resolution of curvature: 0. 5, and resolution of narrow regions: 0. 6). The applied boundary force was horizontal, unidirectional and distributed uniformly on the upper 175μm of the micropillar, as determined experimentally.
To determine the pillar deflection from the confocal images and to measure the 3D deformations, we developed a specific pillar reconstruction routine in MATLAB. This segmentation tool takes into account technical issues, such as optical degradation or altered pillar shape on images, and produces the skeleton of each pillar as well as its radius at different heights (Fig  2D ). We could apply this segmentation technique also for complex pillar deformations caused by spheroids that did not induce a radial pillar displacement because their cells aggregated around some of the pillars (see the example of GM637-derived spheroids in Fig C in S1 File). The algorithm is described in detail in Supporting Information. From the 3D analysis, we calculated the deflection curves for each pillar based on the x,y coordinates of the pillar centre along the 300 μm of its length (Fig 2D  and 2E ).
Measurement of the forces exerted by growing spheroids Then, we modelled the force/deflection relationship using FEM analysis (COMSOL). We considered each PDMS pillar as a cylinder, and used scanning electron microscopy and confocal microscopy to precisely measure the pillar geometrical dimensions. Microfabrication techniques allow controlling the pillar diameter, thus making possible to tune their spring constant (stiffness). For this study, we cultured spheroids in microdevices made of pillars of a constant height (300 μm), but with two different diameters (28 and 36 μm). We measured the PDMS Young's modulus using the mechanical compression technique (E = 2. 6 MPa). Then, we introduced these data in the model and used it for the mechanical simulation of deflection. To define where to load the force on the cylinder, we experimentally determined the contact zone between spheroid and pillars by measuring the height of the imprints left by the pillars on each MCF-7 spheroid after removal from the microdevice (after 4 days of culture) (Fig  3 ).
Moreover, we used a parametric sweep technique with applied forces ranging from 0 to 1000 nanoNewtons (nN) and a step of 10nN, and extracted the resulting range of simulated deflection curves (blue curves in Fig 4E  and 4F ). Finally, to determine forces generated by growing MCF-7 spheroids and responsible for pillar deflection, we compared the whole range of simulated deflection curves with the experimental deflection profile of each pillar in the microdevice, determined with the 3D reconstruction algorithm (red curve in Fig 4E  and 4F ). A MATLAB program allowed combining the results from the COMSOL simulations and the 3D reconstruction algorithm. We used spline interpolation for the COMSOL data to obtain the same discretization. We computed three parameters (the sum of squares of residuals SS res , the coefficient of determination R 2 , and the largest shift M) to identify the simulated curve that best fitted the experimental curve for each 3D reconstructed pillar, using the equations described in the Methods section.
Force changes over time To better characterize the forces exhibited by growing spheroids, we investigated force changes over time. To that end, we monitored pillar deformation in a large number of microdevices during spheroid growth for four days. By comparing, as before, the experimental data obtained by volume imaging and 3D reconstruction with the simulation results, we could follow the changes in the forces generated by growing spheroids over time. For spheroids growing in the microdevices with soft pillars (K = 8 nN/μm), the mean values of the exerted forces progressively and significantly increased every day, from day 1 (121 nN) to day 4 (230 nN) (Fig 5A ). Conversely, for spheroids growing in devices with stiffer pillars (K = 23 nN/μm), the force increase (Fig 5B ) was less important.
Acknowledgments We are grateful to the members of our groups for discussions and support of this project. LA was a recipient of a doctoral fellowship from the foundation 2RITC (Toulouse). The authors thank  Elisabetta Andermarcher  for expert manuscript editing. The authors wish to acknowledge the clean room facility of  LAAS-CNRS  and the staff of engineers belonging to the  Renatech technological French network in nanotechnology. TRI-Genotoul  and ITAV imaging facilities are also greatly acknowledged. This work was supported by the  CNRS, Universite Ṕaul Sabatier and Region Midi-Pyrenees.
The trait data were also analyzed by analysis of variance (ANOVA) using the mixed linear model (MIXED) procedure with genotype as fixed effect and environment as random effect in SAS University Edition  [33]. The statistical model is Y ij = β 0 + β 1 x i + u 1 z j + ε ij , where Y ij is the response variable, β 0 is the overall mean, β 1 is the genotype effect, u 1 is environmental effect, and ε ij is the effect from errors terms. The broad-sense heritability (H 2 ) was calculated for each trait as H 2 = σ G 2 /[σ G 2 + (σ GE 2 /e) + σ e 2 /re], where σ G 2 = genotypic variance, σ GE 2 = variance due to interaction between genotype and environment, σ e 2 = error variance, e = number of environments, r = number of replicates. The variance components were calculated by SAS PROC MIXED with all the effects considered as random effects.
PROC UNIVARIATE option of SAS was used to generate summary statistics including skewness and kurtosis  [33]. Pearson correlation coefficients among the traits were calculated using SAS PROC CORR. QTL analysis. The BP-Duarte linkage map  [26]  was comprised of 914 markers covering 1073 cM on seven linkage groups (LG) and used genotyping data obtained from SSRs  [25]  and cDNA derived SNPs  [34]  markers on 'Baccara' x 'PI180693' 178 RIL population. The Baccara x PI180693 map was from one of the four RIL populations used to build the composite genetic map described in Duarte et al. [34]. The linkage map included 701 SNPs identified from transcriptome sequencing and derived from a Goldengate assay  [34] , as well as 179 SSRs and 35 other markers (RAPDs, specific PCRs, morphological genes) used in the initial version of the genetic map established from this population  [25].
Composite interval mapping (CIM) implemented in QTLCartographer 2. 5 software was used to identify significant QTL using mean scores for root disease severity, disease plant height (control vs inoculated), and disease plant weight (control vs inoculated)  [35]. Significant LOD threshold was set to 2. 8 using Doerge and Churchill's  [36]  test of 1000 permutations before CIM. Settings for CIM included QTLCartographer Model 6 (default) except for selection of a walk speed of 2 cM and the forward and backward regression method (P < 0. 1). QTL were declared exceeding the LOD threshold and defined with the range of ±1 LOD (95% confidence interval). MapChart was used to draw the linkage map and QTL locations  [37].
Data availability The authors declare that all data supporting the findings of this study are available within the article and Supplementary Information files, and also from the corresponding author upon reasonable request. The X-ray crystallographic coordinates for the structures reported in this article have been deposited at the Cambridge Crystallographic Data Centre (CCDC), under deposition numbers CCDC 1872042 and 1872043. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre at www. ccdc. cam. ac. uk/data_request/cif.
These data further link RASSF1A to the MTs network. Finally, RASSF1A has not been demonstrated to colocalize to actin or intermediate filaments. However, given the coordination of the organization of MTs and actin filaments in cells and the alteration of the actin cytoskeleton induced by RASSF1A depletion  46  , the impact of RASSF1A on actin seems to require intermediates. For instance, the LATS kinases regulate the activity of proteins involved in actin filament nucleation and elongation, such as LIMK or Zyxin 47 (Fig. 2 ). In addition, RASSF1A interacts with MAP proteins, identified as key players that directly cross-link the two cytoskeletons  48. As a functional consequence, RASSF1A probably coordinates polarized cell migration and cell trafficking, which are the prime instances, in which actin and microtubules become physically linked.
Data extraction and analysis In order to compute the horizontal movement of the ants' main body parts (gaster, thorax, head or head and load if one was carried, see SI for details) and of its overall center of mass (CoM) we used the software Kinovea (version 0. 8. 15, https://www. kinovea. org) to semiautomatically track points of interest on the videos of the top camera (Figure  2 ). When several video shootings of the same ant had been made, we selected the video in which the ant had the straighter path. As a criteria for path straightness we calculated the ratio of the distance actually traveled by the ant (based on the horizontal trajectory of its center of mass) on the straight line distance between the first and last point of the trajectory and considered that the path was sufficiently straight when this ratio was lower than 1.
2. Using the software Kinovea and ImageJ (version 2. 0. 0, FIJI distribution, https://fiji. sc/) we then accessed the information corresponding to the stepping pattern of the ant. For each video frame we visually determined the state of each leg during locomotion (i. e. in stance phase, swung or dragged) on the lateral view of the ant and recorded the spatial position of the leg tarsus during the stance phases on the dorsal view. These positions were expressed in a coordinate system centered on the neck of the ant, with the X axis corresponding to the longitudinal axis of its body and the Y axis to the transverse axis (Figure  2 ). In order to compare ants of different sizes, all distances were normalized to the body length of the ant (calculated from the tip of the gaster to the tip of the mandibles).
, 2008). This would lead to a reduction in relative load capacity in big ants compared to small ants. However, this reasoning would hold only if big ants were a simple enlargement of small ones, i. e. if their body parts grew isometrically. As mentioned before, this is not exactly the case in M. barbarus: compared to small ants, big ants not only have larger heads  (Bernadou et al. , 2016 , Fig. S5 ) but they have also relatively shorter legs (Fig. S6 ). Nonetheless, the scale effect could still apply to some extent. To assess its importance we compared our data of load carrying capacity in ants of different sizes to those that would be expected if the predictions of the scale effect were computed on ants of different sizes but with same morphology. We considered as the basis for the computation of our predictions the carrying capacity we observed for an ant weighing 1.
Acknowledgements We thank  Elisa Thébault  and two anonymous reviewer for their constructive comments on the manuscript. This work is funded by the  FRAGCLIM Consolidator Grant  (number  726176 ) to  Jose M. Montoya  from the  European Research Council  under the European Union's  Horizon 2020 Research and Innovation Program. Version 7 of this preprint has been peer-reviewed and recommended by  Peer Community In Ecology  (https://doi. org/10. 24072/pci. ecology. 100039).
The nucleotide sequences generated in this study were deposited in ENA and are available through the INSDC databases under accession numbers MN091365 (SB6411 T = SPARK775C1 T ), MN091366 (SB6412 T = SPARK836C1 T ) and MN104661 to MN104677 (16S rRNA), MN076606 to MN076643 (gyrA and rpoB), and MN030558 to MN030567 (blaOXY). Complete genomic sequences were submitted to European Nucleotide Archive under the BioProject number PRJEB15325.
DNA was stored at -20°C until sequencing on an Illumina HiSeq X Ten platform with a 2 x 150 nt paired-end protocol. Reads were assembled using SPAdes v3. 11 and the assemblies were annotated using Prokka v1. 12  (16). JSpeciesWS  (17)  was used to calculate the average nucleotide identity (ANI) using the BLAST algorithm (ANIb), whereas in silico DNA-DNA hybridization (isDDH) was performed through GGDC tool (http://ggdc. dsmz. de; formula 2)  (18). Sequences of gyrA and rpoB genes were obtained from genome assemblies using BLASTN, while 16S rRNA gene sequences were obtained using Barrnap (https://github. com/tseemann/barrnap). The chromosomal blaOXY sequences were also extracted, and the new amino-acid sequence variants were submitted to the Institut Pasteur MLST nomenclature database (https://bigsdb.
pasteur. fr/klebsiella) for variant number attribution, and to NCBI for accession number attribution. 16S rRNA, gyrA, rpoB and blaOXY beta-lactamase gene sequences were aligned using Muscle  (19) , concatenated (in the case of rpoB and gyrB) and phylogenetic relationships were assessed using MEGA v7. 0  (20). Genetic distances were inferred using the neighbor-joining method with the Jukes-Cantor correction  (21)  in the case of nucleotide sequences or maximum-likelihood with Jones-Taylor-Thornton (JTT)  (22)  model in the case of the beta-lactamase protein sequences. The genome-based phylogenetic analysis was performed on the concatenation of 3,814 core genes defined using Roary v3. 12  (23)  with a blastP identity cut-off of 80% and presence in more than 90% of the isolates. K. pneumoniae ATCC 13883 T (GCA_000742135.
The inability to metabolize L-proline and tricarballylic acid differentiated Ko3 strains from other phylogroups except Ko8, which can be differentiated based on its unique ability to utilize 3-O-methylglucose. Ko4 had a weak but unique capacity to utilize glyoxylic acid, and differed from Ko6 (K. grimontii) by its inability to metabolize D-melezitose; Ko4 was otherwise similar to Ko6 for many features, consistent with their phylogenetic association. We also analysed the MALDI-ToF MS peak patterns of the different members of the K. oxytoca complex. Based on the MALDI Biotyper Compass database version 4. 1. 80 (Bruker Daltonics, Bremen, Germany), the thirty strains were identified either as K. oxytoca  (23 strains, all  belonging to Ko1, Ko2, Ko4, and Ko6) or as Raoultella ornithinolytica (7 strains, all strains of Ko3 and Ko8).
These misidentifications can be explained by the lack of reference spectra of most phylogroups in the reference database. Figure  S6  summarizes the peak positions found in each strain. A total of 31 biomarkers (2383-10152 m/z) associated with specific members of the K. oxytoca complex were identified (Table  S1 , Figure  S6 ). Consistent with genetic and biochemical findings, we also observed that Ko4 shared most of its spectral peaks with Ko1 and Ko6, presenting only one specific peak (which was variably present) at 3681 m/z, whereas Ko3 shared six peaks with only Ko8 and presented two unique peaks at 5178 and 6795 m/z. For the remaining phylogroups, specific peaks were observed for Ko2 and Ko8, whereas Ko1 and Ko6 could be identified by specific peak combinations. Based on the current dataset, the specificity and sensitivity of their distribution among phylogroups ranged between 60-100% and 80-100%, respectively (Table  S1 ).
This finding paves the way to identify isolates of the K. oxytoca complex at the species (or phylogroup) level based on MALDI-ToF MS analysis, pending incorporation of reference spectra of the various taxa into reference spectra databases. Based on the above genomic, phenotypic and proteomic characteristics, we propose Ko3 and Ko4 to be considered as two novel species, which we propose to name K. spallanzanii and K. pasteurii, respectively. The description is based on 3 strains. Cells are Gram-negative, non-motile, non-spore-forming, straight, rod-shaped and capsulated. Colonies are smooth, circular, white, dome-shaped and glistening. The general characteristics are as described for the genus Klebsiella. Indole-positive, ONPG-positive, lysine decarboxylase positive and ornithine decarboxylase negative.
The type strain is strain SPARK_775_C1 T (=SB6411, CIP 111695T, DSM 109531), isolated in 2017 from the urine of a patient in Pavia, Italy. The INSDC (GenBank/ENA/DDBJ) accession numbers of the gyrA, rpoB and rrs (coding for 16S rRNA) genes are MN076620, MN076626 and MN091365, respectively. The genome sequence accession number is: in process. The DNA G+C content of the type strain is 53. 3 %. Description of Klebsiella pasteurii sp. nov. Klebsiella pasteurii (pas. teu 'ri. i N. L. gen. n. referring to Louis Pasteur, a French microbiologist, who made seminal contributions to microbiology and infectious diseases, vaccination and pasteurization. He contributed decisively to disprove the theory of the spontaneous generation of microbes).
K. grimontii by the ability to ferment D-melezitose and inability to ferment alpha-keto-glutaric acid, and from the remaining K. oxytoca groups by the unique weak ability to ferment glyoxylic acid. K. pasteurii isolates were recovered from faeces of cows, turtles and humans. The type strain is strain  SPARK_836_C1 T (=SB6412 ,  CIP 111696T ,  DSM 109530 ), isolated in 2017 from the faeces of a patient in Pavia, Italy. The  INSDC (GenBank/ENA/DDBJ ) accession numbers of the gyrA, rpoB and rrs (coding for 16S rRNA) genes are MN076619,  MN076625  and  MN091366 , respectively. The genome sequence accession number is: in process. The DNA G+C content of the type strain is 55. 4 %.
Funding This work received financial support from the  SpARK  project "The rates and routes of transmission of multidrug resistant Klebsiella clones and genes into the clinic from environmental sources", which has received funding under the 2016  JPI-AMR call "Transmission Dynamics " (MRC reference  MR/R00241X/1 ); and by the  French government 's  Investissement d'Avenir program Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  ( ANR-10-LABX-62-IBEID ). CR was supported financially by the  MedVetKlebs  project, a component of European Joint  Programme One Health EJP , which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No 773830. JC was funded by the  ERC  grant no. 742158  and by the  Norwegian Research Council JPIAMR  grant no.
The analysis of bacteria at the single cell level is essential to characterize processes in which cellular heterogeneity plays an important role. BACMMAN (BACteria Mother Machine ANalysis) is a software allowing fast and reliable automated image analysis of high-throughput 2D or 3D time-series images from experiments using the "mother machine", a very popular microfluidic device allowing biological processes in bacteria to be investigated at the singlecell level. Here we describe how to use some of the BACMMAN features including i) segmentation and tracking of bacteria and intracellular fluorescent spots, ii) visualization and editing of the results iii) configuration of the image processing pipeline for different datasets, and iv) BACMMAN coupling to data analysis software for visualization and analysis of data subsets with specific properties.
By using the mother machine and a fluorescent marker for nascent mutations created by DNA replication errors, we recently developed a new single-cell approach to study mutagenesis and evolution in bacteria  [12]. Long-term imaging of cells growing in the mother machine produces a large amount of data, typically several hundreds of Gb of images per experiment. Therefore data managing, image processing and analysis very rapidly becomes a rate-limiting step and requires the implementation of a fast and reliable solution for automated image analysis. To address this challenge, we recently developed a software dedicated to mother machine data analysis  [12]. This software, that we call BACMMAN (BACteria Mother Machine ANalysis), allows the processing of large datasets rapidly and with high-accuracy.
Development of BACMANN BACMMAN was initially developed to process data from our experiments in which we follow mutagenesis and evolution in Escherichia coli growing in mother machine chips (see examples of these datasets at github. com/jeanollion/bacmman/wiki/Example-Datasets [13] ). Briefly, to follow the mutation occurrences at the single cell level we developed MV approach (for Mutation Visualisation), that is based on time-lapse imaging, mother machine and expression of fluorescently labelled Mismatch Repair protein MutL  [14]. The latter makes foci on such DNA sites and thus allows visualizing mutations as bright fluorescent spots inside the cells. µMA (for microfluidic Mutation Accumulation) also based on on time-lapse imaging and mother machine allows following fitness evolution during mutation accumulation and characterizing the distribution of fitness effects of spontaneous mutations.