dataesr/scientific-paragraphs-categorization huggingface dataset:
We note that the mobile phone data were collected after our study period (2017 vs 1995-2012). Human mobility may have changed and could help explain some of the differences in mobility between the fitted models and that implied from the mobile phone data. Aedes aegypti abundance estimates. We used previously published estimates of the probability of Aedes aegypti presence for 5 × 5-km grid cells around the globe  19. These estimates were generated by incorporating information on temperature, rainfall, vegetation indices from satellite imagery and fitting models to a large dataset of Aedes occurrence records. The fitted models were then used to predict elsewhere. For Bangkok, we extracted the Aedes aegypti estimate using the centroid of each grid cell. For the province level, for each province, we used the simple average across all the raster cells from the Aedes map that were contained within that province.
The element [a,b] of the matrix is π a,b,τ,ser. Characterising human mobility. We use mobile phone data to characterise human mobility. Initially, we extract a matrix from the CDR data that set out the probability that an individual that lives in location a visits location k. CDRs come from adults, whereas dengue is concentrated in children, who are potentially more likely to spend more of their time at home. In addition, sick individuals may travel differently than healthy individuals and spend more time at home. In this way, the mobility of infectors may differ from susceptible information. To allow for different periods at home for susceptible individuals compared to those in the CDR data and to assess whether there exists differential mobility by illness status, we incorporate separate parameters for the probability of being at home for infectors and the susceptible population (θ infector.
The 95% confidence intervals are then calculated using the mean and the standard deviation of the resulting distribution, assuming that they follow a normal distribution. Once we have fitted the θ age parameter, we calculate from the matrix of ϕ a,b , the proportion of transmissions that are between individuals that have <2 y in age between them. We use an equal weight for the age of the infector across all ages and only consider individuals between the ages of 1 and 15 as they represent the majority of the susceptible population. We compare this to the scenario where all individuals have the same probability of contact, irrespective of age (i. e. , β a,b = 1/ 70, for all a and all b), which is the minimum possible value. Simulation study. In order to ensure that our model is able to correctly identify parameters, we built a simulation framework with known parameters using 50 randomly selected grid cells from the Bangkok dataset where the population size, the historic incidence and the probability of between cell movement were taken from the observed data.
SMILI data and analysis. In order to understand the consistency of our estimated differences in human mobility between susceptible individuals and adults, we used data from the Social Mixing for Influenza-Like Illness (SMILI) project. This project asked 2011 individuals about their mobility patterns. Here we used the responses to the question 'what is the farthest distance you have travelled within the last 7 days?'. We dichotomised the results into those that had not travelled >1 km and those that had travelled >1 km. To reconstruct the probability that a susceptible individual had not travelled more than 1 km within the last 7 days, we used data on the population size from the 2010 census and assumed a constant force of infection (foi) of 0. 04 per year. Using a catalytic model, we can calculate the probability that an individual of age a has never been infected by dengue as pnaive a = exp(-4*a*foi).
Acknowledgements H. S. is funded by the  European Research Council  (No. 804744 ). H. S. and D. A. T. C. would like to recognise funding by The  National Institutes of Health  ( R01AI114703 ). A. P. W. is funded by a  Career Award at the Scientific Interface  by the  Burroughs Wellcome Fund , by the  National Library of Medicine of the National Institutes of Health  under Award Number  DP2LM013102  and the  National Institute of Allergy and Infectious Diseases of the National Institutes of Health  under Award Number  R21Al151750. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Material has been reviewed by the  Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication.
6 µM for CTX-M-15-ertapenem [Supplementary Table  8 ] vs. 47. 6 mM for HEWL-GlcNAc  23  ). Successful binding of ertapenem in the 2 s time point data is apparent in the F o (2 s) -F o (resting) isomorphous difference map, since clear difference density adjacent to the nucleophilic Ser70 appears (Supplementary Fig. 9 ), which allows modeling of an acyl-enzyme complex into the electron density (i. e. , with the β-lactam ring open by reaction with the nucleophilic serine, Fig. 2f , Supplementary Figs. 11  and 12 ). This was further confirmed by calculation of polder OMIT maps which show electron density consistent with ring-opened ertapenem bound similarly to that in a control presoaked dataset (Fig. 2g , Supplementary Note 3).
Discussion In conclusion, the data presented here demonstrate that the ondemand, drop-on-drop method developed from the drop-on-tape XFEL sample delivery system, can indeed overcome the limitations associated with ligand diffusion through droplets. Our successful observation of ligand complex formation in our tr-SFX data at timescales below 0. 6 s shows that the drop-on-drop method enables generation of enzyme complexes with small molecule ligands/substrates on timescales similar to the kinetics of enzyme-catalyzed reactions. The versatility and efficiency of drop-on-drop sample delivery substantially expands the currently very limited set of low sample consumption delivery systems able to achieve reaction times suitable for studying enzymatic reactions. Indeed, our six structures required only 84-258 µL of ligand solutions, and 162-420 µL of microcrystal slurry.
For example, the 0. 6 s tr-SFX HEWL dataset consumed a total of only 0. 7 µmol (9. 5 mg) protein and 0. 18 mL of 0. 226 M ligand (41 µmol total). In addition, by adding humidity and temperature control and varying the position of the PEI, the system can offer easy access to a wider range of delay times (from 50 ms to 10 s) relevant for many enzymatic reactions. Also, a large range of crystal sizes (5-100 µm) can be accommodated by this system, as the droplet size can be adjusted, and no limiting orifice is used for generation of the crystal-containing droplets. This allows to choose optimum conditions for each individual protein system, balancing needed diffraction signal strength (often increased with larger crystal size) and desired minimum diffusion equilibration time (decreased with smaller crystal size, also highly dependent on buffer viscosity).
Methods Crystal preparation. Hen egg white lysozyme (HEWL) crystals were grown by the rapid-mixing batch method  27. In brief, 2 mL of 50 mg mL -1 HEWL (Sigma L4919) dissolved in 20 mM sodium acetate pH 4. 6 were pipetted to a 15 mL polypropylene tube and mixed with an equal volume of crystallization buffer (1 M citric acid, 20% (w/v) NaCl, 5% (w/v) PEG 6000, pH 3. 0) with vortexing at 22 °C. Crystals nucleated almost immediately and were left for at least 1 h to reach the maximal size (3-5 µm). The data for 0. 6 and 2 s datasets were collected from the original crystal slurry (~10 7 crystals mL -1 ) whereas the data for the resting and 0. 2 s datasets were collected from the same crystal batch, concentrated threefold. Recombinant CTX-M-15 (in the expression vector pOPINF  28  ) was expressed in SoluBL21 (DE3) E.
In order to reduce their consumption, substrates were added in the form of droplet bursts synchronized with the ADE slurry droplets. A camera viewing system was used to manually adjust the PEI trigger delay time that controlled the overlap between the crystal drop and the substrate burst. The calculated theoretical numbers of substrate solution drops merged with each crystal drop for data collected on HEWL and CTX-M-15 are presented in Supplementary Table  4. Reaction times were varied by changes in Kapton belt velocity, bringing the sample to a fixed beam interaction location. The setup allowed for reaction times between 0. 1 and 6 s (600 and 10 mm s -1 tape speed, respectively). Our control datasets without substrate additions were recorded using 100 or 300 mm s -1 tape speed. The number of substrate drops dispensed per burst (between 10 and 20) slightly exceeded the number required to fully cover the crystal drop.
We observed that this greatly simplified the drop synchronization process and resulted in much more stable ejection. Operation in burst mode (10 droplets at 30 Hz) reduced substrate consumption up to 20-fold as compared to continuous delivery (continued dispensing at 6. 1 kHz) with 84-258 μL of substrate consumed per dataset. A further reduction in substrate consumption could have been achieved by using a smaller cartridge orifice (for example, 30 μm diameter instead of 100 μm) at the cost of lowering the maximal achievable substrate concentration (50 pL droplets instead of 120 pL) and increasing the probability of clogging if unanticipated particulates are present in the substrate solution. Fluorescence measurements of calcium-sensitive dyes. To experimentally assess substrate mixing and equilibration times through the ADE drops, the dropon-drop setup was used to record the fluorescence of calcium-sensitive dyes in solution.
Two extrema are possible: mixing entirely by diffusion and mixing entirely by hydrodynamic flow  31. The open-access software Kinetiscope (W. D. Hinsberg and F. A. Houle, Kinetiscope, www. hinsberg. net/kinetiscope, 2020) permits both regimes to be examined using a stochastic algorithm for both full three-dimensional reaction-diffusion simulations and for simple mass transfer simulations. The stochastic method is a type of kinetic Monte Carlo algorithm that provides a rigorous solution to the master equation for Markov systems  32, 33  and is applicable to reaction-diffusion simulations  34. In brief, the system is described using particles to represent molecules, located in a system of compartments with specified dimensions. The compartments are coupled by diffusion paths in the 3-D system, and by mass transfer paths when multicompartment systems are used.
The existing model was then modified to construct an additional threedimensional reaction-diffusion system using the software Kinetiscope (Supplementary Fig. 2 ). Species concentrations, reaction kinetics, and total drop volumes were kept constant and matched those used experimentally (Supplementary Tables  1  and 2 ). The Weber number of the large ADE droplet was first calculated, and three-dimensional idealizations of the internal mixing jets formed within a millisecond after collision described in literature were built using Kinetiscope  39  (Supplementary Table  3 ). In this altered geometry, the three PEI drops were represented as conical volumes extending into the ADE drop (Supplementary Fig. 4 ). This was to investigate the effect of partial mixing on the diffusion time of calcium and dyes throughout the combined drop volume.
While this system similarly assumes that the movement of species within the drops is entirely diffusive, it allows the simulation to start with the internal cone-like structures formed within milliseconds after impact  39. The formation of the fluorescent complexes was monitored by extraction of concentration data as a function of time averaged over the total drop volume. Mass-flow compartmental simulations of free colliding drops were also performed to obtain a lower bound on the mixing time of a process controlled purely by hydrodynamic flow with no diffusive movement of species. The relative velocity of the PEI droplet was estimated from the speed of ejection from the PEI head and the speed of the traveling tape. Calculation of the transfer rate of the calcium ligand was performed for picoliter-sized droplets traveling at this relative velocity and colliding with a 200 µm diameter droplet (Supplementary Table  3 )  31.
Inertial mixing was used in multicompartment simulations that include proportional mass flow to evaluate the impact of hydrodynamic flow during the droplet collision on mixing time (Supplementary Fig. 3 ). In all three cases, the time-dependent concentrations of the fluorescent species (i. e. , both calcium-bound dyes) were compared to the experimental fluorescence signals. Enzyme assays. All reactions were carried out in 10 mM HEPES pH 7. 5, 150 mM NaCl at 25 °C using Greiner half area 96-well plates and a POLARstar Omega (BMG LabTech) plate reader  28. Kinetic parameters were calculated and analyzed using GraphPad Prism 6. 0. 0 for Windows, GraphPad Software, San Diego, CA, USA, www. graphpad. com  28, 40. Steady-state parameters k cat and K M for ertapenem hydrolysis were calculated by measuring initial rates of ertapenem hydrolysis with 5 µM CTX-M-15 and plotted against ertapenem concentration.
4 µm FWHM at the sample position. Data were recorded at 30 Hz on a Rayonix MX300-HS detector operating in the 4 by 4 binning mode. Over the course of the SACLA data collection, the average ratio for crystal lattices integrated to images collected ranged from ~6 to 26%. Each dataset was collected within 30-80 min consuming between 162 and 420 µL of microcrystal slurry per one dataset, which corresponds to estimated average sample consumption of 1. 40 and 0. 27 µg of HEWL and CTX-M-15 per integrable detector frame, respectively. Room-temperature (21 °C) datasets on reference crystals as well as crystals exposed to substrate for 10 min were collected on beamline I24, Diamond Light Source. Data were recorded on a Pilatus 6 M detector with 10 ms exposure time (5. 2 × 10 12 ph s -1 ) using a beam size of 8 by 8 µm 2 and an X-ray energy of 12.
4 keV. Sample was mounted in the fixed-target setup  42  with 100 µL of the microcrystal slurry or 100 µL of 1:1 (v/v) mixture of the slurry and 100 mM ertapenem solution loaded per single chip. Data processing. Lysozyme data reduction was performed on frames identified as hits by Cheetah 43 and achieved using cctbx. xfel  19, 44, 45 as described previously  19. HEWL data were indexed with dials. stills_process  46 in space group P4 3 2 1 2 with the target unit cell of a = b = 79. 3 Å, c = 38. 2 Å, α = β = γ = 90°(subsequently refined as detailed below). Data were merged using cxi. merge  19, 44, 45 with PDB entry 4ETA  47  serving as the initial reference model. The resolution cut-offs for the final datasets merged with cxi. merge were determined in a standard procedure based on a combination of several criteria, including where the data falls below tenfold multiplicity, where CC 1/2 no longer decreases monotonically, and where the values of I/σ(I) do not uniformly decrease any more  14.
The resolution cut-offs were also confirmed in the 'paired refinement' test done using the PDB_REDO platform  48  , which includes the implementation of the original algorithm from Karplus and Diederichs  49. In the first round of merging, integrated frames from all datasets were pooled together and the unit cell parameters were allowed to refine. This produced a 'composite' MTZ file with the unit cell parameters representing all data (a = b = 78. 8 Å, c = 38. 0 Å, α = β = γ = 90°). Next, a new reference model was obtained by running molecular replacement using the same coordinates against the 'combined' MTZ file with Phaser  50. The new reference model was then used in the second, final merging round in which the target unit cell parameters were fixed and only frames with unit cell parameters lying within 1% of these values were accepted.
The reference model used in the final merging step was used as the starting point in structure refinement. The structures were refined via iterative cycles of refinement performed using Phenix 51 and manual model rebuilding using Coot  52. Insights into ligand binding were obtained by examination of structure-factor amplitude Fourier difference maps, calculated with Phenix by subtracting observed structurefactor amplitudes for the resting dataset from those of the time point dataset, using phases calculated from the resting state model. GlcNAc was placed in the models according to the initial F o (time point) -F o (resting) and mF o -DF c maps. Restraints for GlcNAc were generated using the Grade Web Server (http://grade. globalphasing. org, 2020) using the identifier NDG. Data collection details and refinement statistics can be found in Supplementary Table  5.
CTX-M-15 data were indexed and integrated using DIALS  46  (synchrotron data) and cctbx. xfel  19, 44, 45 (XFEL data). Images were indexed with dials. stills_process in space group P2 1 2 1 2 1 with the target unit cell a = 45. 3, b = 45. 9, c = 118. 5 Å, α = β = γ = 90°(subsequently refined as detailed below). Next, datasets were merged with cxi. merge  19, 44, 45  using PDB entry 6QW8  28  as the initial reference model to provide starting unit cell parameters, which were allowed to refine. Resolution cut-off criteria were assessed in the same way as for the HEWL data. For the second and final round, reference models were obtained in Phaser 50 by running molecular replacement with the same coordinates against the MTZ files from the first round of merging (unit cell parameters were allowed to refine further).
2 s time point), 7BHM (HEWL SACLA 0. 6 s time point), 7BHN (HEWL SACLA 2 s time point), 7BH3 (CTX-M-15 SACLA resting state), 7BH4 (CTX-M-15 SACLA 0. 6 s time point), 7BH5 (CTX-M-15 SACLA 2 s time point), 7BH6 (CTX-M-15 DLS I24 resting state), and 7BH7 (CTX-M-15 DLS I24 10 min time point). Protein structures used as search models in molecular replacement are accessible in the Protein Data Bank under accession codes 4ETA 47 (HEWL) and 6QW8 30 (CTX-M-15). Source data are provided with this paper.
Acknowledgements The authors acknowledge the financial support of this work from the  National Institutes of Health (NIH)  grants  GM117126  (to N. K. S. ),  GM55302  (to V. K. Y. ),  GM110501  (to J. Y. ),  GM126289  (to J. K. ), and  NIH  training grant  GM133081  (to K. D. S. ). Support was provided by the  Biotechnology and Biological Sciences Research Council  Grant  102593  (to A. M. O. );  Wellcome   Investigator Award in Science   210734/Z/18/Z  (to A. M. O. );  Royal Society Wolfson  Fellowship  RSWF\R2\182017  (to A. M. O. ) and by the Director,  Office of Science, Office of Basic Energy Sciences (OBES) ,  Division of Chemical Sciences, Geosciences, and Biosciences of the Department of Energy (DOE)  (to J. K. , J. Y. , and V. K. Y. ). F. A. H. was supported by the  Laboratory Directed Research and Development Program of the Department of Energy 's  Lawrence Berkeley National Laboratory  under DOE OBES under Contract No.
DE-AC02-05CH11231. C. L. T. was supported by  BBSRC -funded  South West Biosciences Doctoral Training Partnership  ( BB/J014400/1 ). J. J. A. G. K. was funded by the  EPSRC Synthesis 345 for Biology and Medicine CDT ( EP/L015838/1 ) and a Clarendon Scholarship. R. T. was supported by  Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS) ) from  AMED  under Grant Number  JP20am0101070. XFEL data was collected under proposal 2019A8088 at the BL2 instrument at SACLA, Japan. Fixed-target data was collected under proposal mx19458 and mx25260 at Diamond Light Source  BLI24.
Results: We optimized the phenotyping of sunflower/O. cumana interactions by using rhizotrons (transparent Plexiglas boxes) in a growth chamber to control culture conditions and Orobanche inoculum. We used a Raspberry Pi computer with a picamera for acquiring images of inoculated sunflower roots 3 weeks post inoculation. We set up a macro using ImageJ free software for the automatic counting of the number of tubercles. This phenotyping tool was named RhizOSun. We evaluated five sunflower genotypes inoculated with two O. cumana races and showed that automatic counting of the number of tubercles using RhizOSun was highly correlated with manual time-consuming counting and could be efficiently used for screening sunflower genotypes at the tubercle stage.
Image acquisition and analysis RhizOSun took advantage of the low-cost image acquisition Raspberry Pi/picamera system. Using a Raspberry Pi/picamera system allowed us to set up a low-cost dedicated image acquisition apparatus for rhizotron imaging for sunflower/O. cumana research studies in a confined laboratory environment required for experiments with this parasitic plant to prevent dissemination. It also allowed us to standardize the image acquisition procedure (image focus and field of view) to facilitate image analysis, thanks to the dedicated rhizotron laboratory lift support. In the context of sunflower/Orobanche interactions, the images obtained by the picamera were of sufficient definition and quality to enable image analysis and count the number of tubercles. Using image acquisition of all inoculated root systems represents a great advantage over manual counting, as it allows archiving of all data for additional analyses, as well as image analysis shifted in time if necessary, relaxing the experimental organization.
We chose ImageJ software for image analysis, as it is one of the most commonly used software programs for scientific image analysis, in addition to being free and open source, thus facilitating the transfer of this method to other laboratories. Image analysis of the tubercle could be realized thanks to the natural orange color of the tubercle, discriminating tubercles from white-colored   4. The crop size was reduced (compared to the macro used in Fig. 4 ) to adapt to this batch of images roots and the white glass-fiber paper used as a background. Choosing appropriate color threshold values for hue, saturation and brightness on a few representative images needed to be set up at the beginning of the experiment. However, once these values were validated, the gain of time for image analysis was very high, as manual counting required almost 1 min/image compared to approximately 4 s/image using the automatic ImageJ macro, which was 15 times faster.
Raspberry Pi/picamera setup for image acquisition We used the Raspberry Pi 2 model B, with a microSD of 16 Gb. It was coupled to a picamera model V2 and completed with a power supply, an SD card reader and a wireless adaptor. The elements were assembled in a picamera box bundle-B + /2/3. References of the various elements are listed in Additional file 2: 2a, and the setup is shown in Fig. 2a, b. The file Raspberry Pi OS (32 bit) with desktop and recommended software (previously named Raspbian-Stretch) was found on the official Raspberry Pi site (https:// www. raspb errypi. org/) and was flashed on the micro SD card using Etcher software (balena. io/etcher). Once the file was installed on the micro SD card, it was inserted in the dedicated SD port on the Raspberry Pi as well as the other peripherals (computer screen via HDMI connection, mouse, key-board and power supply lastly).
ImageJ macro-mediated image analysis For analysis, images were transferred from the Raspberry Pi computer to a standard computer with a USB key, and ImageJ software  [40] , version 1. 52a, was used. Analysis of the number of tubercles was performed taking into account the natural orange color of tubercles, which allowed their discrimination from white roots and the white filter paper. In brief, 5 successive steps were performed (Fig. 3 ): (i) set a scale, using a ruler, in the image and define the unit as mm, (ii) crop the images by defining the region of interest (the part of the image with inoculated roots), (iii) isolate particles thanks to a color threshold defined on hue, saturation and brightness values corresponding to tubercle color, (iv) convert particles with a binary mask and (v) analyze the number of particles according to a defined size range.