dataesr/scientific-paragraphs-categorization huggingface dataset:
This way a clear trend may appear, as for the probed data at weaning (PND21), or not. Thus, it is possible to safely avoid over-interpreting the simulations. The main practical interest of the modeling effort is to readily help sorting out the most relevant effects of a perturbation of glucose homeostasis. For the experiments at PND21, we observed that, for both groups Cd1 and Cd2, it is the hypothesis of a decreased response to glucose during the slow phase of production that best represents the changes between the control group and the more exposed groups. On the experimental datasets, this is noticeable by an increased plasma glucose concentration over the  [30, 90]  min interval, which previously led to increased AUC  [18]. The fitted simulations under Hypothesis 3. 1 show a good fitting especially on this interval.
However, a few noticeable points of the experimental datasets are not correctly represented by the simulation with Hypothesis 3. 1. In particular for group Cd2, the points at 10 and 120 min are not correctly fitted by this single assumption on γ. The experimental point at 10 min, being higher than the simulation, suggests an additional effect in the first phase of insulin secretion. This is represented in our model by the parameter p 4. However, this parameter should be affected together with γ, as Table  3  shows that Hypothesis 3. 2 (p 4 varies alone) does not correctly fit the data. Insights on the differences for the point at 120 min are more difficult to explain as they can be due to multiple reasons such as: a too coarse approximation of the glucose rate of appearance at 120 min, an effect on a mechanism not represented in the model such as gluconeogenesis, the role of other hormones than insulin, or a competition between multiple opposing effects that are not represented by our single hypothesis.
At PND26, we can already observe a slight behavioral difference with the previous datasets at PND21: whereas at PND21 the glucose concentration of Cd2 is always significantly above Cd1 and control ([18], Figure  5 ), at PND26, all groups share similar results. Looking to the mean values, one observes that the dataset of group Cd2 is closer to control than Cd1, which is confirmed in the simulation by the lower fitting error for Cd2 compared to Cd1 in absence of hypothesis (see Table  8 ). Even with this bias in favor of mean points, it is hard to reach any strong conclusion. The experimental results of group Cd1 are best fitted under the hypotheses of a reduced sensitivity to insulin of the glucose withdrawing tissues, or a faster degradation rate of insulin (Hypotheses 1. 1-2). The experimental results of group Cd2 are so close to the control experimental results that no hypothesis seems much better than another.
The results of this study may be extended by the development of a more complex model to better approximate the glucose appearance rate in the context of an OGTT, as well as the mechanism affected by Hypothesis 3. 1. For this purpose, the comprehensive modeling of metabolism leading to insulin secretion  [33]  should be included when relevant experimental data become available. In this paper, we only propose a single fitting parameter set, but probing more complete datasets with more exhaustive and costly methods should be performed in order to further assert our conclusions. Finally, this model has only fitted the experimental glucose response, but not the evolution of insulin plasma concentration over time during OGTT in young rats: combining such more extensive datasets would be important to develop the modeling approach to be used in a more quantitative manner.
Results Three-dimensional (3D) diffraction data were collected as rocking curves of the rotation angle around the normal of the sample (here, µ-angle-see Supplementary Figure  S1 ) either in step-by-step or continuous scan- ning modes. Table  1  summarises the scanning conditions used for the two modes. In the step-by-step mode, the data was collected in steps of 0. 0085 • in µ-angle with a total of 128 frames and with a typical counting time of 3 s per angle. In the continuous scanning mode, the µ-angle of the sample was scanned at a constant velocity ( 0. 00085 • /0. 3 s), while the same 2D photon-counting detector was measuring the data. In total, 1280 frames were collected. The motor movement and detector reading were performed simultaneously and synchronously by triggers from a reference clock, the detector dead time being negligible with a value of 290 ms  14.
The increase in frames by a factor 10 and decrease in exposure by the same factor means that the sample sees the same number of total photons in principle, if a fast shutter is installed to protect the sample in-between acquisitions. Thus, the two datasets should be completely equivalent. Note that if the beamline does not have a fast shutter (here, no fast shutter has been used), the sample will be exposed to more dose and possibly to beam damage during step-by-step compared to continuous scanning. The continuous scan was executed in 6 min 42 s compared to the step-by-step scan, which lasted 9 min 24 s (see Table  1 ). The overall time was then significantly reduced with a 30% gain, improving the reliability of data acquisition by limiting the time available for sample drift. For the data acquired during continuous scanning, we applied a binning factor of 10 along the rocking direction to match the conditions of the step-by-step data.
Number of voxels (g) (h) Continuous Step-by-step Step-by-step Continuous is often used to give an estimation of the resolution  23, 25. Figure  5  displays the PRTF for reconstructions obtained from the step-by-step and continuous scanning modes for three sets of solutions (i. e. three different runs of 500 reconstructions). We obtain a spatial resolution in the range of 20-22 nm for step-by-step and continuous scanning methods. Even if the resolution looks slightly higher for continuous scanning, the difference is negligible relative to the pixel size. Finally, we have tested the impact of binning along the rocking direction on the spatial resolution of the reconstructed crystal measured by continuous scanning BCDI. Previously, for the data acquired during continuous scanning, we have applied a binning factor of 10 along the rocking direction to match the conditions (i.
These can explain why the reconstruction works even if σ < 2 1/3 in the binning direction. We also observed that for the step-by-step data, reconstruction works for a binning factor of 2 along the rocking direction. This corresponds to an oversampling of ∼ 1. 1 as for the data measured by continuous scanning. It is often needed to resample the raw data afterwards for increasing photon statistics or reducing the array size for more efficient data processing. Here, we binned the data measured by continuous scanning to match the conditions of the step-by-step data. It is also possible to reduce the array size by skipping data points  28. As the continuous scanning mode generates more data, resampling may be applied and as demonstrated here, applying a binning factor up to 20 in this case does not degrade the spatial resolution of the reconstruction and allows faster data processing (reduced size of the dataset).
22 m 31. The in-plane ( γ ) and out-of-plane ( δ ) angles of the detector were 35. 7 • and 10. 2 • , respectively. Three-dimensional (3D) diffraction data were collected as rocking curves of the rotation angle around the normal of the sample (here, µ-angle of the diffractometer). Note that we normalised the data with the electron beam current in the ring. Measurements have been performed, when the sample was at 400 • C in a Ar-based gas flowed at 50 ml/min and at a pressure of 500 mbar. We do not expect dynamical structural evolution at this condition. Phase retrieval. The reconstruction of the Bragg electron density and phase from step-by-step and continuous BCDI were obtained using the PyNX package  32. Phase retrieval was carried out on the raw diffracted intensity data. Defective pixels for experimental data, and gaps in the detector were masked using the bcdi package  33  , and let free during phase retrieval.
Note that it is important to consider partial coherence as it has been demonstrated that continuous scanning can induce a degradation in coherence  17. We have observed that the reconstructed amplitude/modulus of the object is less homogeneous when partial coherence is not taking into account during phase retrieval (see Figure  S7 ). To ensure the best reconstruction possible, we kept only the best 50 reconstructions (with lowest free Log-Likelihood  24  ) from 500 with random phase start and performed the decomposition into modes  24. The reconstruction was then corrected for refraction and absorption using the bcdi package  33. After removing the phase ramp and phase offset, the data was finally interpolated onto an orthogonal grid for ease of visualisation.
Acknowledgements The authors are grateful to  SOLEIL Synchrotron  for allocating beamtime. The measurement was performed at the SixS beamline. We thank  SixS beamline  staff for excellent support during the experiment. This work has been funded by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant Agreement No. 818823 ). E. A. , S. L. , O. T. , M. -I. R and E. R. wish to thank the support by a grant from the  Ministry of Science and Technology, Israel  and from the  "Centre National de la Recherche Scientifique" (CNRS) , France. M. -I. R. also acknowledges financial support from  ANR   Tremplin ERC  ( ANR-18-ERC1-0010-01 ).
Data availability The data reported in this paper is available upon request. When published the data will be uploaded to the CXI database. The pre-and post-processing scripts belong to the BCDI package (https ://doi. org/10. 5281/zenod o. 36324 71), that can be downloaded from PyPI (https ://pypi. org/proje ct/bcdi/) or GitHub (https ://githu b. com/ carni sj/bcdi). The phasing algorithm PyNX is available at http://ftp. esrf. fr/pub/sciso ft/PyNX/.
F9 Description The f9 gene cluster of EHEC Sakai (Figure  S1 ; Genbank # BA000007. 3) does not include a canonical in cis transcriptional regulator, as the open reading frame (ORF) upstream of the main structural subunit, fmlA (ECs2113), is an incomplete allele of hipA without its cognate partner hipB, a toxin/antitoxin system  [31, 32]. However, a cryptic CDS (ECs2114) is located between hipA and fmlA that belongs to the AraC-like regulator family, containing the HTH_18 superfamily domain, and is present as a hypothetical gene in the equivalent EDL933 genome (z2199). The Sakai and EDL933 amino acid sequences of the Z2199 regulators are 100% conserved, as are the 5 -untranslated regions (UTRs), but with two potential start codons.
F9 Description The f9 gene cluster of EHEC Sakai (Figure  S1 ; Genbank # BA000007. 3) does not include a canonical in cis transcriptional regulator, as the open reading frame (ORF) upstream of the main structural subunit, fmlA (ECs2113), is an incomplete allele of hipA without its cognate partner hipB, a toxin/antitoxin system  [31, 32]. However, a cryptic CDS (ECs2114) is located between hipA and fmlA that belongs to the AraC-like regulator family, containing the HTH_18 superfamily domain, and is present as a hypothetical gene in the equivalent EDL933 genome (z2199). The Sakai and EDL933 amino acid sequences of the Z2199 regulators are 100% conserved, as are the 5′-untranslated regions (UTRs), but with two potential start codons.
Recognition of Plant Glycans In order to identify any potential plant-ligand binding targets for F9 fimbriae, a high-throughput screen using plant glycan arrays (polysaccharide and oligosaccharides) was used, as previously for ECP fimbriae  [8]. F9 organelles were overexpressed and purified from E. coli strain JT-1 (Figure  S2 ). Purified F9 fimbriae interacted with galactosylated xyloglucans, but not fucosylated xyloglucans (FLX) (Figure  2A , blue box). They also bound to oligosaccharides XLX and LLX, but not with XXX (Figure  2A , red box). The binding overlapped with glycan specific antibody LM15 (anti-xyloglucan, Figure  2B ), confirming xyloglucan epitopes on the array. 2  for details. (B) Glycan arrays probed with anti-xyloglucan antibodies LM15. All glycan symbol structures were made using software DrawGlycan-SNFG  [33].
Comparisons with Other Published Datasets In order to define the genes associated with microbe/pathogen-associated molecular pattern trigger immunity (MTI/PTI), we compared our dataset to previous publications that had investigated the MTI response to E. coli and Salmonella enterica. Schikora et al. [34]  investigated the transcriptomic response of A. thaliana seedlings to the non-pathogenic isolate of E. coli DH5α (Table  S2 ). Of the 289 genes described as responsive to E. coli K-12 treatment, 23 genes were also responsive to F9, 79 genes to H7, and 38 genes to ECP (Figure  6A ). E. coli isolate DH5α encode flagella serotype H48 rather than EHEC (Sakai) H7 serotype, although the Flg22 epitope is conserved in both  [22, 23]. Comparison with an A. thaliana dataset that focused on the presence of flagella and included E.
coli O157:H7 fliC-  [35]  identified 49 F9-responsive genes out of the 748 genes described, 249 with H7, and 89 genes with ECP, of which 17 were in common (Figure  6B , Table  S3 ). Our dataset showed that the overall change in A. thaliana gene expression was the same, i. e. , 92-98%, either positively or negatively impacted. Of the total number of genes differentially regulated for each treatment, MAMP response genes constitute 11% of the response to F9 fimbriae, 16% to H7 flagella, and 28% to ECP fimbriae. In order to define the genes associated with microbe/pathogen-associated molecular pattern trigger immunity (MTI/PTI), we compared our dataset to previous publications that had investigated the MTI response to E. coli and Salmonella enterica. Schikora et al. [34]  investigated the transcriptomic response of A.
thaliana seedlings to the non-pathogenic isolate of E. coli DH5α (Table  S2 ). Of the 289 genes described as responsive to E. coli K-12 treatment, 23 genes were also responsive to F9, 79 genes to H7, and 38 genes to ECP (Figure  6A ). E. coli isolate DH5α encode flagella serotype H48 rather than EHEC (Sakai) H7 serotype, although the Flg22 epitope is conserved in both  [22, 23]. Comparison with an A. thaliana dataset that focused on the presence of flagella and included E. coli O157:H7 fliC-  [35]  identified 49 F9-responsive genes out of the 748 genes described, 249 with H7, and 89 genes with ECP, of which 17 were in common (Figure  6B , Table  S3 ). Our dataset showed that the overall change in A. thaliana gene expression was the same, i. e. , 92-98%, either positively or negatively impacted.
coli cells is multifactorial, and that different subsets of genes may be affected by the different epitopes present. The vast majority of A. thaliana MTI related genes (as defined by published datasets  [34, 35] ) induced by F9 were also induced by other EHEC surface organelles, H7 and ECP, indicative of a common MTI response (Figure  6B ). These included genes induced by Flg22, such as pectin methylesterase 17 (PME17; AT2G45220)  [46]  and FAD-linked oxidoreductase gene CELLOX (AT4G20860)  [47]. Interestingly, one FAD-linked gene, BBE8 (AT1G30700), induced by all three E. coli organelles, is involved in stomatal opening by S. enterica and Pseudomonas syringae  [48]. F9 induced a specific response for putative plant defensin protein, (PDF1. 4, AT1G19610), which is predicted to encode a PR (pathogenesis-related) protein.
RNA Purification and Microarray RNA was purified from samples using Qiagen RNAeasy Plant Mini kit following the manufacturer's instructions and including the DNase treatment step (Qiagen, Manchester, UK). RNA concentration and quality were checked on BioAnalyzer2100 (Agilent Technologies, Cheadle, UK). Single-colour labelling, hybridization, and washing of the Agilent Arabidopsis (v. 4) Gene Expression 4x44k microarray slides were performed according to the manufacturer's protocols (One-Color Microarray-Based Gene Expression Analysis, v. 6. 5; Agilent Technologies). Microarrays were scanned using a G2505B scanner (Agilent Technologies, Cheadle, UK) as recommended and data were extracted from images using Feature Extraction software (Agilent v. 10. 7. 3. 1) with default parameters. Data are available at ArrayExpress (https://www.
ebi. ac. uk/arrayexpress/; E-MTAB-9732). Data were subsequently imported into GeneSpring GX 7. 3 (Agilent Technologies, Santa Clara, CA, USA). Quality control was applied to remove those probes with no consistent signal in any of the conditions tested, whereby data were filtered on flags present or marginal in at least two out of the twelve arrays. Principal component analysis was performed to identify any outlier samples. Statistical analysis of the datasets was carried out by performing a Volcano plot on each condition with a twofold minimum cut-off for fold change and a Student's t-test with multiple testing correction (Benjamini and Hochberg; p < 0. 05). A heatmap of the significantly changing datasets was generated using the Gene Tree function in GeneSpring, and default parameters for clustering (Pearson's correlation).
Methods General comments. The model has been implemented in python 3. 6 with PyCuda. The code is available here: https://github. com/jdmathias/SES-transition-pathways. We used a combination of existing models that exhibit tipping points for specific values of parameters. Therefore, we chose values of the parameters within this range for having tipping points. We used a discretized model with a unitary time step. The Fig. 2b + = + - x t x t B x t E t ( 1) ( ) ( ( )) ( ) (1) Where the variable x(t) represents the biomass of the ecosystem at time t and depends on the level of exploitation E(t) and the ecosystem dynamics B(x(t)). Many studies  32, [43] [44] [45]  have analyzed variations of the model system we represented in equation 1. Such analysis centered on robust management under different assumptions of ecosystem dynamics (B(x(t))) and ecosystem exploitation (E(t))  21, 22.
The retinal size of the stripes depended on the position of the fly on the platform and ranged from 8. 4°to 19. 6°in width (11. 7°in the center of the platform). Fly tracks were analyzed using CeTrAn  35  and custom-written python code  58. We evaluated several behavioral parameters, including center deviation and absolute distance walked, and focused on absolute stripe deviation as a parameter that gives an estimate of how precise the animals follow an object-orientated path. It is calculated as an average of all points of the fly path away from an imaginary line through the two black vertical bars. For the absolute stripe deviation, it is irrelevant whether the fly deviates to the right or left. The data was statistically analyzed using analysis of variance (ANOVA) and Tukey's honestly significant difference (HSD) as a post-hoc test using R.
Each 5 min trial consisted of consecutive iterations of the bars rotating clockwise for 5 s, stopping for 5 s and rotating counter-clockwise for 5 s. Flies were filmed from below with 60 Hz under infrared illumination (880 nm) and each fly's body axis orientation was tracked offline using Fiji (source). For each fly the median rotational velocities during each 5 s period (CW, CCW, Stop) were calculated using circular statistics in Matlab. Synapse number analysis. All imaging data were analyzed and presented with Imaris (Bitplane). For synapse number analysis, CD4-tomato channel was used to generate Surfaces for individual axon terminals and Brp-positive puncta inside the Surface are filtered using the masking function. Brp-positive puncta in photoreceptor terminals were automatically detected with the spot detection module (spot diameter was set to 0.
3 µm) using identical parameters between experimental conditions and corresponding controls. Synapse numbers were taken and recorded directly from statistics tab of Spot function. Graph generation and statistical analyses were done using GraphPad Prism 8. 2. 0 Analyses of filopodia and synapse distributions. All imaging data were analyzed and presented with Imaris (Bitplane). For synapse distribution analysis, Brppositive puncta were detected following the same steps in "Synapse number analysis" in R7 axon terminals. Start and endpoints of axon terminals were selected manually with the measurements point module using nCad staining as a reference (start point = beginning of nCad staining at the most distal part of medulla (M0), end point = the beginning of M7, serpentine layer in the medulla).
It is noteworthy that M7 layer is devoid of synapses, hence is not labeled by nCad. The length of axon terminals are measured with the measurement point module and normalized as start point = 0 and end point = 100. The actual positions of Brp-positive puncta were exported and relative positions were calculated according to the normalized length of axon terminals. The following equation is used to calculate relative positions of Brp-positive puncta: relative position = (actual position-start point)/ length × 100. For all filopodia and bulbous tip filopodia distribution analysis, the same steps were followed, except that spots were manually placed on the emerging points of all visible filopodia. Graph generation and statistical analyses were done using GraphPad Prism 8. 2. 0 Filopodia tracing.
Acknowledgements We thank all members of the  Hiesinger, Wernet, and Hassan labs  for their support and helpful discussions. We thank  Eric Baehrecke  and  Stephan Sigrist  for reagents. F. R. K. acknowledges  Gizem Sancer  for her help to design "trans-Tango" experiments. This work was supported by the  NIH  ( RO1EY018884 ) and the  German Research Foundation  (DFG,  SFB958 ,  SFB186 ), and  FU Berlin. B. H. was supported by an  Einstein BIH  Fellowship. M. v. K. acknowledges financial support from the  German ministry for education and science (BMBF)  through grant number  031A307  and from the  Einstein Stiftung Berlin  and the  DFG , provided through the excellence cluster Math+. M. F. W acknowledges the  Deutsche Forschungsgemeinschaft (DFG)  through grants  WE 5761/2-1  and  SFB958  (Teilprojekt A23), as well as AFOSR grant  FA9550-19-1-7005.
Data availability Raw (. lif format) and processed (. ims and. am format) imaging datasets are available on request. The filopodia-tracking software is an extension of the commercial software Amira, which is available from Thermo Fisher Scientific. The filopodia-tracking software is available from the corresponding author upon request in source code and binary form. Executing the binary requires a commercial license for Amira. The source data underlying Figs. 1d, e, i, 2f, g, h, 3d, g, 5g, 6h, j, k, 7b-d and Supplementary Figs. 1e, 2d , 2f, 2g, 2i, 3b, 4b, 4d, 4h, 4l, 5b, 7b, 7c, 7e, 12f, 13c are provided as a Source Data file.
Furthermore, patients' interests should be considered at this stage. Once a relevant clinical problem best solved by means of ML is identified, the second step is algorithm development. In the predominant case of supervised learning, this starts with data selection and establishment of ground truth. Obtaining a sufficient amount of high-quality ground truth data, which can be thought of as "gold standard" used for both ML training and evaluation (e. g. , segmentation masks of the left ventricle) is a necessary requirement for creating an algorithm. The amount of data needed depends on the complexity of the problem, the ML algorithm used, and the ratio between the finding of interest and the whole dataset. As a rule of thumb and providing an example from the field of object detection, tasks that are easy to solve for a human reader (e.