dataesr/scientific-paragraphs-categorization huggingface dataset:
Data replicates. All measurements reported in this study are based on at least 2 replicates. Quantification of growth rate. The growth rate of the cells was computed using the evolution of the area of segmented cells (and buds) over time. Identification of growth phases. To determine the limit between successive growth phases, we used a piecewise linear fit to the evolution of the total number of cells with time (D'Errico 2017). The time point of the intersection between two pieces of the piecewise linear fit was defined as the time of transition between the two corresponding phases (Figure  1  -Figure  supplement 1G ) Globularization score. The Ilv3-mCherry globularization score was measured in each cell by calculating the mean intensity of the 5 brightest pixels of the cell minus that of the other pixels, as previously described  (Cai, Dalal, and Elowitz 2008).
C) Fold increase in cell number over an entire life cycle for the microcolony displayed in B; each shaded area represents a distinct proliferation phase, which was determined using piecewise linear fitting to cell proliferation data (see Methods and  Fluorescence profile of the first loop (gray) and 5th loop (green) along the channel's cross-section. The value is normalized to the maximum of each condition. In the upper part is displayed the cumulative signal on each side of the channel of the 5th loop. F) Schematics of the whole closed-loop fluidic platform; The flask is connected to a peristaltic pump, which drives media flows to the dust filter. The dust-free media then flows into the inlet of the spiral. The cell outlet of the spiral is redirected into the flask while the cell-free outlet irrigates the observation device.
Acknowledgments We thank  Sara Mouton  and  Liesbeth Veenhoff  for discussions and for sharing the pHluorin strain ahead of their publication,  Audrey Matifas  for constant technical support throughout this work,  Titus Franzmann  for fruitful discussions,  Sophie Quintin  and  Sandrine Morlot  for careful reading of the manuscript. We thank  Denis Fumagalli  at the  Mediaprep facility. This work was supported by the  Fondation pour la Recherche Médicale  (FRM, B. J, and G. C. ), the  Agence Nationale pour la Recherche  (T. A. and G. C. ), the grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02.
The source code for these models are freely available and there is even The Virtual Brain (https://www. thevirtualbrain. org/). As can be seen from this code base, for estimating the anatomical structural connectivity, excellent results can be obtained from diffusion MRI  21. For modelling local dynamics, initially complex models of neural dynamics going from spiking neuronal circuits  22  to dynamical mean field  23  were used. More recently a more efficient and yet more simple, yet very effective model has been shown to be a mesoscopic system using the so-called Stuart-Landau oscillators  24. As an example of the power of the whole-brain models, the Hopf models is able to describe up to 80% of the resting state functional connectivity. Still, there are some important features of brain dynamics such as non-stationarity and nonequilibrium which the current models are not yet able to capture.
In neuroscience, the whole-brain model using dMRI and Stuart-Landau oscillators is called the Hopf whole-brain model in honour of the German mathematician Eberhard Hopf who described the normal form of the Hopf bifurcation, which describes the behaviour of a Stuart-Landau non-linear oscillating system  27. In physics, similar large-scale system of spatially coupled oscillators has been shown to give rise to a wealth of spatiotemporal patterns, ranging from regular laminar waveforms to highly turbulent dynamics. Interestingly, similar to these findings in physics, the Hopf wholebrain model has recently been used to capture the regularities of empirical neuroimaging data exhibiting turbulent dynamics  28. Using a large, high-quality state-of-art dataset of 1003 HCP participants it was shown that human brain dynamics exhibit turbulence a power law similar to that shown by Kolmogorov in fluid dynamics.
Here we investigated the functional consequences of having EDR or EDR+LR anatomy in the Hopf whole-brain model fitted to very large-scale empirical neuroimaging data from 1003 participants in the human connectome project (HCP). First, we used diffusion (dMRI) tractographic data to identify the relationship between connection weight (in tractography referred to as streamline density) and distance. As expected, we found that the empirical tractographic connectivity data in the human cortex fits an EDR. Second, we identified rare long-range exceptions corresponding to those long-range connections with stronger weights than expected from the EDR. Third, whole-brain modelling was used to investigate the functional consequences of the EDR and EDR+LR models. We found that the addition of the long-range exceptions leads to a significant improvement to fitting the empirical neuroimaging data and, more importantly, a very significant enhancement of information processing, which we measured in four complementary ways.
Results We hypothesised that adding the rare long-range exceptions to EDR crucially enhances information processing (as measured by our four measures of long-range connectivity, information cascade, susceptibility and predictability) over and above having only EDR as a wiring principle. While it is not possible to empirically remove them to test their functional consequence, we were able to use whole-brain modelling fitted to large-scale human neuroimaging data to explore their functional relevance. The overall strategy is described in Figure  1. First, we fitted an EDR to human diffusion MRI and extracted rare long-range exceptions on top of these (EDR+LR). Figure  1A  shows cartoons of the two different anatomical models: EDR model (blue) and EDR+LR model (red). Figure  1B  shows how we built two different whole-brain models based on the two different anatomical models to test the differential functional role of rare long-range exceptions.
Rare long-range EDR outliers in human brain architecture improves information processing We aimed to investigate the hypothesis the effect of rare long-range EDR outliers in information processing as measured by three measures of information transmission in brain vortex space and one measure of long-range correlation in MNI space. We used whole-brain models since they offer the advantage that they can be systematically altered to fit the empirical data, making it possible to test different hypotheses  21. As shown in the previous section, from the empirical anatomical data we extracted two connectivity matrices, C ୬୮ ாோ , with the EDR and with long-range connections (larger than 40mm) on top (EDR+LR), C ୬୮ ாோାோ (see Methods). We used these for the anatomical connectivity matrices in two separate Hopf whole-brain models fitting empirical functional neuroimaging from 1003 human subjects (see Methods).
001, Wilcoxon rank sum). This emphasises the important role of the long-range connections for brain function. Importantly, as shown in the boxplots of Figure  3C , the two models are able to fit the level of turbulence in the empirical data (compare empirical, EDR+LR and EDR models). Turbulence is measured as the variability across time and space of the local synchronisation level (see Methods). In order to show the significance of turbulence in both empirical data and models, we constructed and computed turbulence for surrogate data shuffled while maintaining the spatiotemporal characteristics of the empirical data  35. The levels of turbulence of empirical data and models are significantly different (p<0. 001, Wilcoxon rank sum) from surrogate data. This shows that EDR alone is enough to fit turbulence, which is not surprising given that other diffusive systems (i.
Specifically, we used three different complementary measures measuring the information cascade, susceptibility and predictability. These measures allowed us to confirm our hypothesis that the long-range exceptions confer a significant improvement in information processing using a large empirical neuroimaging dataset of 1003 human subjects. Specifically, we contrasted two whole-brain models, EDR and EDR+LR. In terms of function, the latter better fitted the functional empirical data and in addition showed a significant increase in information processing as measured by the specific measures of long-range FC and information cascade. Importantly, the EDR+LR model was also better able to fit the emergence of the classical resting-state networks, providing potentially a link to cognition. Our quantification of information processing were inspired by the pioneering work of Yoshiki Kuramoto, who demonstrated turbulence using coupled oscillators to describe the rich variability of local synchronisation  30.
This will need to be further explored in cross-species investigations. Research has investigated the hierarchical organisation of brain structure and function  7, [46] [47] [48]  , which has demonstrated a common network available for the orchestration of task and rest  28. Previous research has demonstrated that the turbulent core of brain activity is largely overlapping with brain regions known to be involved in lower level sensory processing  28. We therefore hypothesised that the anatomical basis of this orchestration could be well served by the long-range outliers, which effectively are able to control brain activity in the turbulent core sustained by the underlying EDR. An obvious next step would therefore be to study a task in a large empirical dataset, a challenging prospect given the difficulty at the required granularity for whole-brain models to capture the dynamical complexity specific to a given task.
METHOD DETAILS Neuroimaging acquisition for fMRI HCP The 1003 HCP participants were scanned on a 3-T connectome-Skyra scanner (Siemens). We used one resting state fMRI acquisition of approximately 15 minutes acquired on the same day, with eyes open with relaxed fixation on a projected bright cross-hair on a dark background. The HCP website (http://www. humanconnectome. org/) provides the full details of participants, the acquisition protocol and preprocessing of the data for resting state.
Preprocessing and extraction of functional timeseries in fMRI resting data The preprocessing of the HCP resting state and task datasets is described in details on the HCP website. Briefly, the data is preprocessed using the HCP pipeline which is using standardized methods using FSL (FMRIB Software Library), FreeSurfer, and the Connectome Workbench software  66, 67. This preprocessing included correction for spatial and gradient distortions and head motion, intensity normalization and bias field removal, registration to the T1 weighted structural image, transformation to the 2mm Montreal Neurological Institute (MNI) space, and using the FIX artefact removal procedure  67, 68. The head motion parameters were regressed out and structured artefacts were removed by ICA+FIX processing (Independent Component Analysis followed by FMRIB's ICA-based X-noiseifier  69, 70  ).
Preprocessed timeseries of all grayordinates are in HCP CIFTI grayordinates standard space and available in the surface-based CIFTI file for each participants for resting state. We used a custom-made Matlab script using the ft_read_cifti function (Fieldtrip toolbox  71  ) to extract the average timeseries of all the grayordinates in each region of the Schaefer parcellation, which are defined in the HCP CIFTI grayordinates standard space. Furthermore, the BOLD time series were transformed to phase space by filtering the signals in the range between 0. 008-0. 08 Hz, where we chose the typical highpass cutoff to filter low-frequency signal drifts  72  , and the lowpass cutoff to filter the physiological noise, which tends to dominate the higher frequencies  72, 73. We then applied the Hilbert transforms in order to obtain the phases of the signal for each brain node as a function of the time.
Structural connectivity using dMRI The Human Connectome Project (HCP) database contains diffusion spectrum and T2-weighted imaging data from 32 participants with the acquisition parameters described in details on the HCP website  74. The freely available Lead-DBS software package (http://www. lead-dbs. org/) provides the preprocessing which is described in details in Horn and colleagues  75  but briefly, the data was processed using a generalized q-sampling imaging algorithm implemented in DSI studio (http://dsistudio. labsolver. org). Segmentation of the T2-weighted anatomical images produced a white-matter mask and co-registering the images to the b0 image of the diffusion data using SPM12. In each HCP participant, 200,000 fibres were sampled within the white-matter mask. Fibres were transformed into MNI space using Lead-DBS  76.
Schaefer parcellation Schaefer and colleagues created a publicly available population atlas of cerebral cortical parcellation based on estimation from a large data set (N = 1489)  77. They provide parcellations of 400, 600, 800, and 1000 areas available in surface spaces, as well as MNI152 volumetric space. We used here the Schaefer parcellation with 1000 areas and estimated the Euclidean distances from the MNI152 volumetric space and extracted the timeseries from HCP using the HCP surface space version.
Long-range functional connectivity Rather than working in brain vortex space, it is also desirable to characterize information processing by using conventional functional connectivity as a function of a given distance, given by FC(r) in MNI space. In fact, this can be derived from Kolmogorov's concept of structure function for a given distance, S(r), of a variable u  40, 41  in spatiotemporal data in the following manner: ܵሺrሻ ൌ ‫ۃ‬ሺ‫ݑ‬ሺ‫ݔ‬ҧ ‫ݎ‬ሻ െ ‫ݑ‬ሺ‫ݔ‬ҧ ሻሻ ଶ ‫ۄ‬ ௫,௧ ൌ 2ሾ‫ܥܨ‬ሺ0ሻ െ ‫ܥܨ‬ሺrሻሿ (6) In this equation the basic functional connectivity, FC, between two points separated by the Euclidean distance r, is given by: ‫‪ሻ‬ݎ‪ሺ‬ܥܨ‬ ൌ ‫‪ҧ‬ݔ‪ሺ‬ݑۃ‬ ‫ݎ‬ሻ‫ݑ‬ሺ‫ݔ‬ҧ ሻ‫ۄ‬ ௫,௧ (7) where the symbol ‫ۃ‬ ‫ۄ‬ ௫,௧ refers to the average across the spatial location ‫ݔ‬ҧ of the nodes and time. Using these relations, we are able to study the impact of different architectures on the functional connectivity for long-range distances (FC long-range), here defined as distances over 40mm.
Acknowledgements We would like to acknowledge the important theoretical contributions made by  Profs Yoshiki Kuramoto ,  Hiroya Nakao ,  Ryan Raut  and  Marcus Raichle. G. D. is supported Spanish national research project (ref. PID2019-105772GB-I00 MCIU AEI ) funded by the  Spanish Ministry of Science, Innovation and Universities (MCIU) ,  State Research Agency (AEI) ;  HBP SGA3 Human Brain Project Specific Grant Agreement 3  (grant agreement no. 945539 ), funded by the  EU   H2020 FET Flagship programme ;  SGR Research Support Group  support (ref. 2017 SGR 1545 ), funded by the  Catalan Agency for Management of University and Research Grants (AGAUR) ;  Neurotwin Digital twins for model-driven non-invasive electrical brain stimulation  (grant agreement ID:  101017716 ) funded by the  EU   H2020 FET Proactive programme ;  euSNN European School of Network Neuroscience  (grant agreement ID:  860563 ) funded by the  EU   H2020 MSCA-ITN Innovative Training Networks ;  CECH The Emerging Human Brain Cluster  (Id.
In vitro binding assay for small GTPases Glutathione sepharose beads with 60 μg small GTPases were loaded with GTPγS or GDP in GTPase loading buffer [20 mM HEPES ( pH 7. 5), 25 mM NaCl, 10 mM EDTA, 2 mM GTP/ GTPγS] for 20 min at 37°C. NL100 buffer [20 mM HEPES ( pH 7. 5), 100 mM NaCl, 5 mM MgCl2, 0. 1% (v/v) Triton X-100] containing 0. 1 mM GTPγS or GDP was added immediately afterwards to stop nucleotide exchange. S2 cell lysate was added to the beads in NL100 buffer. The binding was performed at 4°C for 1 h. Western blotting and co-IP assays S2-DGRC cells (Cellosaurus CVCL_TZ72) were obtained from the Drosophila Genomics Resource Center and transfected with Effectene (Qiagen). For co-IP assays, cells were lysed in HEPES lysis buffer [50 mM HEPES ( pH 7. 4), 150 mM NaCl, 0. 5% (v/v) Triton X-100] supplemented with phosphatase inhibitor cocktails 1 and 2 (Sigma) and Protease Inhibitor Cocktail (Roche) on ice for 15 min.
Quantification of Ed and Rab11 colocalisation Quantification of Rab11 expression or colocalisation of Ed with Rab11 was achieved using a FIJI Plugin called Particle Mapper: https://github. com/ djpbarry/CALM/wiki/Particle-Mapper (see  Wanaguru et al. , 2018 for details). However, because Particle Mapper requires a nuclear marker as one of its inputs, additional processing was required in order to generate a pseudo-nuclear channel for the purposes of this study. Pseudo-nuclear markers were generated as follows. Multi-channel confocal image stacks were first analysed to identify and isolate the highest contrast z-position. Subsequently, the Echinoid channel at this z-location was isolated and noise and background were suppressed. Greylevel thresholding was then used to generate a binary image, which was subsequently skeletonised and pruned.
The resultant inter-skeleton regions, following an erosion operation, were assumed to be reasonable approximations of the cell interiors. A FIJI script to automate all of these steps is available online: https://github. com/djpbarry/wing-cell-quant/blob/ main/Rab11_quant. cppipe. For the data presented in this paper, the script default options were used. At least 31 cells per genotype were analysed from three different retinas. The analysis was carried out in single confocal sections (0. 5 μm in depth) at the level of the adherens junctions.
Quantification of Ecad at cell junctions Quantification of Ecad intensity at cell-cell junctions was achieved using a combination of a FIJI script (https://github. com/djpbarry/wing-cell-quant/ blob/main/Blob_Detector. ijm) and a CellProfiler pipeline (https://github. com/ djpbarry/wing-cell-quant/blob/main/Ecad%20in%20F8%20clones. cppipe). Briefly, the locations of cell centres were estimated in FIJI using a blob detection approach based on calculation of Hessian eigenvalues: https:// imagescience. org/meijering/software/featurej/hessian. These centre locations were then used to seed a full cell segmentation using the marker-controlled watershed in MorphoLibJ  (Legland et al. , 2016). The resultant cell segmentations were then analysed using the CellProfiler pipeline. Cells were first filtered based on size and Ecad intensity to remove those likely to be coincident with veins, which have elevated Ecad density and could have biased the results.
Acknowledgements We are grateful to  H. Bellen ,  S. Eaton ,  T. Schwartz ,  Y. Hong ,  A. Wodarz ,  D. Strutt ,  H. McNeill ,  S. Abmayr ,  K. F. Fischbach ,  F. Pichaud ,  E. Bach ,  A. Nakamura , the  Bloomington Drosophila Stock Center  and the  DSHB  for antibodies and fly stocks. We thank  B. Aigouy  for  Packing Analyzer  (v2. 0). We thank members of the  Tapon lab ,  A. Chalmers  and members of his lab for helpful discussions;  P. Jordan  from the  Crick Advanced Light Microscopy science technology platform  for microscopy advice; and  T. Gilbank ,  S. Maloney  and  F. Earl  for fly stock maintenance. We are grateful to  M. McLellan ,  I. Gailite  and  P. Ribeiro  for critical reading of the manuscript. E. H. Y. C. thanks  P. F. Lenne  and  F. Schnorrer  for their support.
Funding E. H. Y. C. was supported by a  Marie Curie  long-term fellowship (IEF2008 number  237404 ) and the  Francis Crick Institute. Work in the Tapon lab  is supported by the  Francis Crick Institute , which receives its core funding from  Cancer Research UK  ( FC001175 ), the  UK Medical Research Council  ( FC001175 ) and the  Wellcome Trust  ( FC001175 ). Open access funding provided by the  Francis Crick Institute. Deposited in PMC for immediate release.
Among the open-source simulation platforms, the first appeared for the study of LTE-V2X Modes 3 and 4 was LTEV2Vsim  [22]. It is written in Matlab and since its publication in 2017, it has been extensively leveraged by several research teams to evaluate the performance of Mode 4 and its extensions, e. g. , in  [23] , and more recently in  [24] -  [30] , just to name a few. Moreover, besides LTE-V2X, the simulator provides support for evaluating IEEE 802. 11p, and it was exploited for the investigation of their co-channel coexistence in  [31] -  [33]. We have enhanced such platform to include 5G-V2X PHY and MAC layers, with special focus on the autonomous mode, for the performance analysis presented in this work, which complements the current literature analyzing the 5G-V2X sidelink performance.
C. SIDELINK PHYSICAL CHANNELS A sidelink physical channel is defined as a set of REs carrying user data and other control information originating from higher layers. The transmission data is organized in transport blocks (TBs), which contain the data and is associated with a sidelink control information (SCI). In 5G-V2X, each TB is associated to a SCI, which is in turn transmitted in two stages to reduce the complexity of resource sensing. The first-stage SCI is primarily used for channel sensing and is decodable by any UE. The remaining scheduling information is carried by the second-stage SCI. Then, in contrast to LTE-V2X, where only broadcast is supported, the advent of the second stage SCI in NR-V2X allows for a customizable SCI architecture that supports unicast, groupcast, and broadcast transmissions.
Again, ref. [5]  discovered that a form of the LN distribution fit the postpartum blood loss data from several geographical areas quite well, implying that the LN distribution may fit postpartum blood loss globally. In real life, the traditional basic distributions often fail to characterize and do not accurately predict most of the real-life datasets arising from complicated phenomena. Since the quality of results by statistical analysis heavily depends on the assumed model, there is huge importance in the selection of an adaptive model for analyzing the data. For this reason, it is necessary to find more allied distributions to get better quality and more accurate results. Since the LN distribution has superior importance in the field of biological sciences, it is inevitable to derive a new extended version of the LN distribution not only for modelling the biological data but also for the variety of datasets from other study areas where the LN distribution has the best fit.
One should get the MLEs (α, μ, σ) of the GDUSLN model parameters (α, µ, σ) by synergistically solving the nonlinear Equations (  7 )-  (9). In this paper, for the numerical optimization, we maximize the log-likelihood function for finding the MLEs. For fixing a lower and upper bound for each parameter, the numerical optimization technique "L-BFGS-B" in fitdistrplus package of the RStudio software is used. The package provides a set of functions such as fitdist and mledist for fitting univariate distributions to various types of datasets. When the log-likelihood is maximized, one should carefully choose the initial values and remove the constraints of parameters (see  [12] ). Fitdistrplus is a very handy package that gives unique solutions for MLEs whenever there are questions about the initial guesses and convergence of the algorithm.