dataesr/scientific-paragraphs-categorization huggingface dataset:
This trend is evident not only for chloroplast-related genes, but also for at least one TF controlling their expression, WRKY40  29, 30  , which by itself is also found to be up-regulated in our dataset. The parallels between leaf morphogenesis and chloroplast development, beginning already in the SAM, raise the possibility that the two processes are intimately connected. This is also suggested by the similar expression patterns of chloroplast-related genes and genes related to leaf development (Tables  1  and 2 ). However, tomato leafless (lfs) mutants possess a naked shoot apex that is green  31. Furthermore, some albino mutants that have only proplastids, nonetheless develop normal-looking leaves  32, 33. It therefore seems that the two processes are independent of each other. Finally, our data show that there is no sequential order of expression of chloroplast-targeted gene products along the chloroplast developmental pathway.
Altogether, three biological replicates, each containing three pooled sections, were obtained and subsequently analyzed for each region along the developmental gradient. cDNA synthesis, library preparation, RNA sequencing, read alignment, clustering and differential expression analysis. Low input RNA libraries for sequencing were prepared using the CEL-Seq method  17. Single cDNA library containing all samples was sequenced on a single lane at the Technion Genome Center (Technion -Israel Institute of Technology) with HiSeq2500 (Illumina) using paired-end sequencing. An 18-bp fragment, Read1, was generated for reading barcode and UMI. At the other end, a 36-bp fragment, Read2, was generated for insert cDNA. The quality of sequences was evaluated using FASTQC v0. 10. 1. Mapping was performed with Bowtie2 v2.
1. 0 in "Local mode". Counting of reads to genes was performed with a script based on the HTSeq-counts (provided as part of the CEL-Seq pipeline). Tomato reference genome (SL 2. 5) and annotations from Solgenomics, with the addition of the chloroplast genome, were used for counting reads per gene. Differential expression analysis was performed using the R-based software 'DESeq2' v1. 4. 0. Differentially expressed genes were those having ≥6 reads in at least one of the tested zones (CZ, PZ or LP), as well as a fold change (increase or decrease) of ≥1. 5 in at least one of the three pair-wise comparisons. Additional criteria were either two-tailed FDR ≤0. 05, or individual p-value ≤0. 05, validated by subsequent qPCR analysis, as detailed below. The differentially expressed signals were log 2 -transformed and normalized by Z-score transformation before PCA and cluster analyses.
Unsupervised clustering was performed using Euclidean distance metric and average-linkage agglomeration method. The heat map was based on K-means clustering of the genes, using Pearson correlation coefficient as a distance metric. The optimal number of clusters in the heat map was computed using the gap statistic method and included 1000 Monte Carlo iterations  36, 37. The RNA-Seq data have been deposited in NCBI's Gene Expression Omnibus  38  and are accessible through GEO Series accession number GSE102070 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE102070).
Real time PCR (qPCR). RNA isolation and amplification (for two rounds) were performed as described  3  , except that the primers used were from the MessageAmp II aRNA Amplification Kit (Invitrogene). Three biological replicates were used for each developmental stage, derived from 5-6 SAMs each. cDNA synthesis was carried out using 20 ng of the aRNA. qPCR was performed using the BioMark ™ HD System (Fluidigm). Data was ana- lyzed with the Fluidigm Real-Time PCR Analysis software, using the Linear (Derivative) Baseline Correction and the Auto Ct Threshold Method. Differential expression was determined using two-tailed Student's t-test (p ≤ 0. 05). 2 ), the majority of which were up-regulated and encode for plastidial proteins.
Acknowledgements This work was supported by a postdoctoral fellowship from The  PBC Program of the Council for Higher Education  to V. D. , grants from the  Israel Science Foundation  ( 1034/12 ), and  National Science Foundation-United States-Israel Binational Science Foundation (NSF-BSF) Molecular and Cellular Biosciences Program  (2015839) to Z. R. , the  Israel Science Foundation  ( 121/13 ) and the  Israel Ministry of Agriculture  ( 837-0123-12 ) to Z. A. , and a joint grant from the  Human Frontier Science Program  ( RGP0005/2013 ) to R. B. and Z. R. We thank  Dr. Tal Katz-Ezov  and  Olga Karinski  for their invaluable help with the RNA sequencing procedures.
HD 219134 hosts several planets, with seven candidates reported, and the two shortest period planets are rocky (4-5 M ⊕ ) and transit the star. Here we present contemporaneous multiwavelength observations of the star HD 219134. We observed HD 219134 with the Narval spectropolarimeter at the Observatoire du Pic du Midi, and used Zeeman Doppler Imaging to characterise its large-scale stellar magnetic field. We found a weak poloidal magnetic field with an average unsigned strength of 2. 5 G. From these data we confidently confirm the rotation period of 42 days, measure a stellar inclination of 77±8 • , and find evidence for differential rotation. The projected obliquity of the two transiting super-Earths is therefore between 0 and 20 •. We employed HST STIS observations of the Lyα line to derive a stellar wind mass-loss rate of half the solar value (10 -14 M ⊙ yr -1 ).
Each spectral order was normalised separately. Synthetic spectra were generated using the Zeeman spectrum synthesis code  (Landstreet 1988; Wade et al. 2001) , and fit to the observations through a Levenberge Marquard χ 2 minimisation Table  2. Stellar atmospheric parameters derived from direct fitting of the Narval spectra. Limits on υ sin i and υ macro are derived by attributing all line broadening to either υ sin i or υ macro. T eff (K) 4756 ± 86 log g 4. 44 ± 0. 27 υ sin i (km s -1 ) 1. 91 υ mic (km s -1 ) 0. 87 ± 0. 19 υ macro (km s -1 ) 1. 55 metallicity 0. 10 ± 0. 07 routine  (Folsom et al. 2016). Atomic data were extracted from the Vienna Atomic Line Database (VALD;  Ryabchikova et al. 1997; Kupka et al. 1999; Ryabchikova et al. 2015)  and MARCS model atmospheres  (Gustafsson et al.
ZEEMAN DOPPLER IMAGING Understanding the strength and geometry of the large-scale stellar magnetic field is essential for modelling the stellar wind (e. g. Vidotto et al. 2015). Thus observational constraints on the strength and geometry of the stellar magnetic field are needed to understand the wind of HD 219134 and its impact on the nearby planets. In order to detect magnetic fields through the Zeeman effect in our Narval Stokes V spectra, we used the Least Squares Deconvolution (LSD;  Donati et al. 1997; Kochukhov et al. 2010 ) technique. This is a multi-line cross-correlation method that produces a pseudo-average line profile with much a higher SNR. For LSD, we used a line mask based on atomic data from VALD, using an 'extract stellar' request for a star with T eff = 4500 K and log g = 4.
This differential rotation rate is smaller than the values found by  Folsom et al. (2018) , although they focused on younger and somewhat hotter stars than HD 219134, and their datasets covered a smaller time span. The differential rotation rate is consistent with that predicted for a 4800 K star by  Barnes et al. (2017) , although their sample is biased towards very rapid rotators. The final fits to the observed Stokes V LSD profiles are shown in Fig. 2  and the magnetic map we derive is presented in Fig. 3  (fit to a reduced χ 2 of 0. 9). We find the unsigned magnetic field, averaged over the surface of the star, is B = 2. 5 G. We find that 93% of the total magnetic energy is contained in the poloidal component and only 7% is toroidal. Of the poloidal magnetic energy, 31% is in the dipole component, 62% in the quadrupole, and 4% in the octupole component.
ASTROSAT ULTRAVIOLET OBSERVATIONS We observed HD 219134 with AstroSat  (Singh et al. 2014 ) simultaneously employing the twin of 38 cm Ultraviolet Imaging Telescopes (UVIT;  Kumar et al. 2012). The observations were performed on the 22nd of December 2016 and spanned about 3. 7 hours, divided into three portions each covering about 15, 12, and 8 minutes. The gaps are due to Earth occultations and instrument overheads. The observations were conducted during the very early phases of AstroSat science operations, while there were still scheduling problems and hence unfortunately the data were not well centred around the primary transit of HD 219134 b (Fig. 5 ). As a result, only the first observation was obtained during the primary transit of HD 219134 b, while the second and third observations occurred right after the transit.
The data do not cover the transit of HD 219134 c. Since AstroSat is a relatively new facility, and this is the first dataset of exoplanet transit observations conducted with AstroSat, these data are a good test-bed to assess the possibility of using this space observatory for exoplanet science. This is particularly important because AstroSat was not designed to detect the small signals typical of exoplanet transit observations. This is highly desirable in light of the limited resources currently available, and foreseen in the near future, to observe at UV wavelengths. Furthermore, the satellite would be, in principle, capable of simultaneously obtaining optical, UV, and X-ray data of a target, which has been shown to be extremely useful for interpreting exoplanet transit observations, thanks to the possibility of disentangling stellar and planetary signals (e.
Ultraviolet and optical photometry The UVIT data were obtained in photometric mode 1 employing the neutral density filter in the optical channel, the NUVN2 filter in the near-UV (NUV) channel, which is centred on the Mgii h&k resonance lines (275-283 nm), and the CaF2 filter in the far-UV (FUV) channel, which is the FUV filter with the broadest available transmission function (for more details, see the UVIT instrument webpage: http://uvit. iiap. res. in/). All detectors were employed in photon-counting mode (the equivalent of TIME-TAG mode available on the COS and STIS instruments on-board of HST), allowing us to chose the integration time over which to do the photometry. The star is not detected in the FUV channel and, Figure  5. UVIT NUV light curve obtained with two minute exposure times (black asterisks).
ACKNOWLEDGEMENTS We are grateful to  Joshi Santosh  for having provided the link towards obtaining AstroSat observations. AAV and CPF acknowledge joint funding received from the  Irish Research Council and Campus France through the Ulysses funding scheme. GV thanks the  Russian Science Foundation  (project No. 14-50-00043 , "Exoplanets") for support of his participation in the analysis of the Astrosat UV-observations.
4  and Supplementary Movie 5, see Methods). After successful image registration, we used a semi-automated approach to annotate walking and grooming behaviors (Supplementary Movie 6, see Methods) and regressed these two datasets to identify VNC regions whose activity patterns correlated with walking and grooming (Fig. 2 ). We anticipate that further improvements in image registration will make it possible to build similar behaviorfunction maps from dense neural population imaging data in which the activity patterns of individual neurons can be identified. Imaging the activity of sparse sets of neurons in the VNC. Using Drosophila, it is possible to repeatedly and systematically investigate the functional properties of sparse sets of genetically identifiable neurons. In a recent study, a thermogenetic activation screen was used to identify a pair of descending neurons-Moonwalker Descending Neurons (MDNs)-that cause flies to walk backwards  25.
Optic flow measurements were passed to a microcontroller board (Arduino Mega2560) to be recorded using custom Python code. Simultaneously, video recordings of animals behaving on the ball were made using an IR-sensitive firewire camera (Basler, Ahrensburg, Germany) at approximately 30 fps. We performed 2-photon microscopy using a Bergamo II microscope (ThorLabs) outfitted with two GaAsP PMT detectors for GCaMP6 and tdTomato imaging and coupled to a Ti:Sapphire laser (MaiTai DeepSee, Newport Spectra-Physics, Santa Clara, CA USA) tuned to 930 nm. We used an Olympus 20× waterimmersion objective lens with 1. 0 NA (Olympus, Center Valley, PA USA). The microscope was controlled using ThorImage software (ThorLabs). Coronal section imaging experiments were performed in Galvo-Galvo imaging mode at 6-9 Hz.
This framerate varied with image size which ranged between 26. 58 × 26. 58 µm and 53. 15 × 53. 15 µm. Laser power ranged between 3 mW and 5. 7 mW. Volumetric imaging is also possible with appropriate hardware (e. g. , Galvo-Resonance scanner and Piezo-driven objective collar). Occasionally, a puff of air was used to elicit walking behaviors. These puffs were digitally encoded (Honeywell AWM 3300 V, Morris Plains, NJ USA). Custom ROS software interfaced through an analog output device (Phidgets, Calgary, Canada) to ThorSync software (ThorLabs) was used to synchronize optic flow measurements, behavior videography, air puff measurements, and 2-photon image acquisition. For coronal section imaging, a Piezo collar (Physik Instrumente, Karlsruhe, Germany) was used to control rapid z-axis movements of the microscope objective lens.
A pre-established criteria of low signal-to-noise fluorescence signals resulted in the removal two MDN experiments from our dataset. No randomization or blinding was used. For antennal laser stimulation, data were not normally distributed, thus Friedman and Mann-Whitney U-tests were performed. Estimates of variation are presented as mean and bootstrapped 95% confidence intervals. Data analysis. We analyzed all data using custom Python scripts. Because the data acquisition frequency differed for optic flow, behavior videography, and 2-photon imaging we interpolated signals to match those of the highest frequency. Subsequently, optic flow data were smoothed using a running average (window = 200 ms) and then translated into rotations s -1 for anterior-posterior, medial-lateral, and yaw axes  22.
Values above 0. 31 mm s -1 were considered moments of forward (rightward) walking and values below -0. 31 mm s -1 were considered moments of backward (leftward) walking. Optic flow values between these thresholds were considered moments of standing still. The percentage of time walking was calculated as the proportion of data points in which an animal was not considered standing still. Similarly, thresholds of 10. 8 and -10. 8 degree s -1 were used to defined moments of turning. A bout was defined as a continuous period of walking or turning. Large tissue deformations could occur during behavior. Therefore, we performed post-hoc pan-neuronal image registration (Fig. 2 ). We registered all frames of an imaging experiment to one reference image. Because the complexity of deformations could not be captured using simple parametric motion models (e.
Sample unregistered images, transformation vector fields, and registered images of the three optimized examples are shown in Supplementary Movie 5. We solved the optimization problem in Eq. (  1 ) with an alternated direction method of multiplier (ADMM) algorithm  45. We introduced two splitting variables, associated with the regularization and the feature matching terms, respectively. Each sub-problem of the algorithm was solved analytically. We used parts of the inverse problems library described in ref. 46. A post processing based on weighted median filtering was applied using the method from  47. In Fig. 2 , behaviors were semi-automatically annotated, using a custom Python module. This module allows the user to select two regions-of-interest (ROIs) on the video's first frame. The first ROI is used to detect walking and must be positioned over the metathoracic and mesothoracic legs.
F is a baseline fluorescence signal measured as the average pixel value for the first ten sequential GCaMP6s images in which no cellular activity was observed (i. e. , minimal and unchanging GCaMP6s fluorescence). The regressor weights were calculated using Eq. (  6 ). w w ¼ X T w X w À Á À1 X T w y w g ¼ X T g X g À1 X T g y; ð6Þ where y is the pixel-wise ΔF/F trace. Figure  2b , c shows heat maps of the regressor weights, w w for walking and w g for grooming, normalized to their respective maxima. ROI 1 was chosen as a region of the heat map with a high weight for grooming but a low weight for walking. ROI 2 was chosen as the spatial location with the highest value of w w. Each ROI encompasses a region with a 15 pixel radius. To identify process sparse neural imaging data (Figs. 3 4 5 ), ROIs were first selected using custom Python scripts that depended on OpenCV and Numpy libraries.
To do this, a reference frame was selected for which the software identified all potential ROIs. To do this, the GCaMP6s image was smoothed to reduce background noise and then an Otsu filter threshold was applied to the image. An erosion factor was then applied on all objects detected within the image. Contours of all detected objects were then presented to the user for manual selection. Once these reference ROIs were selected for left and right neurons, we used a cross-correlation-based image registration algorithm  48  to identify the most likely left and right ROIs for each image frame based on those manually selected on the reference frame. A second script was used to manually verify automatically selected ROIs and, if incorrect, to display all potential ROIs within the frame for manual selection.
If erosion values yielded malformed ROIs, another script was used to manually position elliptical ROIs of arbitrary orientation on any given frame. Finally, binary ROI images were used as an image mask to extract mean fluorescence signals from the original GCaMP6s or tdTomato images. These signals were reported as %ΔR/R as in ref. 49  to reduce the effects of motion on our measurements. Due to the absence of stimuli, the baseline R was calculated as the minimum ratio of GCaMP6s/tdTomato within a 2. 5 s bin. To detect transient increases in activity, we developed an algorithm based partly on ref. 50. We first determined when the first derivative of the %ΔR/R signal crossed a threshold, which was determined by examining all derivative values for a given neuron class (MDN, MAN, or A1). We reasoned that threshold values should be characteristic and potentially different for each type of neuron because fluorescence dynamics are related to intrinsic physiological properties that can differ across neuron classes but not across experiments for a single class.
Optic flow and %ΔR/R measurements were downsampled to 500 values s -1 for this analysis. To increase clarity, %ΔR/R traces were baseline-subtracted to make them zero at the time of the event in the summary panels (Figs. 3d, 4d  and 5d, e ). Control, shuffled data (gray traces) were computed by instead assigning random time-points in place of real, identified events. These random time points were treated as real events and their mean and bootstrapped 95% confidence intervals were computed and plotted for comparison. Covariance analysis (Supplementary Fig. 5 ) was performed using a custom Python script that depended on the Matplotlib and Numpy libraries. Scatter plots were computed to compare left and right neuron %ΔR/R values from all experiments for each fly separately. Pearson's r values are reported as mean ± standard deviation.
Acknowledgements We thank  B. J. Dickson  ( Janelia Research Campus, VA ) for MDN-1-Gal4 and MAN-Gal4 fly strains. We thank  G. Rubin  ( Janelia Research Campus, VA ) for  DNa01-Gal4 ,  DNb06-Gal4 ,  DNg13-Gal4 , and  DNg16-Gal4  fly strains. A. C. acknowledges support from the  National Institutes of Health  ( R01HL124091 ). M. H. D. acknowledges support from the  National Institute of Neurological Disorders and Stroke of the National Institutes of Health  ( U01NS090514 ). P. R. acknowledges support from the  Swiss National Science Foundation  ( 31003A_175667 ).
Cell lines All cells were grown at 37 °C with 5 % CO 2 with 1 % penicillin/streptomycin (PS; 5000 U/ml and 5000 µg/ml, respectively; Life Technologies) and supplemented with 1% non-essential amino acids (Life Technologies) in media as specified below. Baby hamster kidney (BHK-21; ATCC number CCL-10), human hepatocellular carcinoma (HUH7. 5  38  ; RRID CVCL_7927) and human embryonic kidney (HEK-293; ATCC number CCL-1573) cells were grown in Dulbecco's modified Eagle's medium high glucose (4500 mg/l) (Life Technologies) with 7. 5% heat-inactivated fetal bovine serum (FBS; Life Technologies). Vero (ATCC number CCL-81) and Vero-E6 (ATCC number CRL-1586) cells were grown in minimal essential medium (Life Technologies) with 7. 5% FBS.
Viruses ZIKV Asian lineage strains PF (H/PF/2013, GenBank accession number: KJ776791) and Mart2015 (MRS_ OPY_Martinique_PaRi_2015, GenBank accession number: KU647676), ZIKV African lineage strain Dak84 (A. tayloritc/SEN/1984/41662-DAK, GenBank accession number: KU955592), YFV 17-D strain (produced by reverse genetics as described below; GenBank accession number: EU074025), and YFV strain BOL 88/1999 (isolated in 2009 from human serum and kindly provided by the National Center of Tropical Diseases (CENETROP), Santa-Cruz, Bolivia, GenBank accession number: KF907504) were used in this study. All these viral strains are available for the scientific community via the European Virus Archive goes Global (EVAg) project, a non-profit organization (https://www. european-virus-archive. com). For each viral strain, we prepared a stock solution of clarified cell culture medium that was subsequently used for all analyses.
Complete genome sequencing Complete and partial genome sequencing of chimeric viruses were performed as previously described  40. Viral RNA extraction was performed as described above. A set of specific primer pairs (Supplemental Table  5 ) was used to generate amplicons by RT-PCR using a Superscript III One-Step RT-PCR Platinum TaqHifi kit (Life Technologies). For each passage sequenced, purified PCR products were pooled and analyzed using an Ion PGM Sequencer (Life Technologies) according to the manufacturer's instructions. The resulting reads were analyzed using CLC Genomics Workbench 6 (CLC Bio). The sequences were trimmed based on quality scores by removing the primer sequences at their termini and systematically removing 20 nt at the 5′ and 3′ termini. The remaining reads with lengths greater than 99 nt were mapped using the designed sequence of the chimeric virus as a reference to obtain a consensus sequence.
Indirect immunofluorescence assay Confluent Vero-E6 cells were inoculated at an MOI of 0. 01 in an eight-well cell culture Lab-Tek II Chamber Slide System in duplicate. At 2 and 5 days post-infection, cells were washed twice with HBSS and fixed with 4% paraformaldehyde for 2 h. Viral antigens were detected as previously described  19, 42  using a specific ZIKV immune serum as the primary antibody (dilution: 1/50) collected from a Syrian Hamster immunized with the ZIKV strain Mart2015 (see below). This serum was shown to neutralize more than 90% of ZIKV PF replication up to a 1/3000 dilution (data not shown). The secondary antibody used was a goat anti-hamster Alexa 488 antibody (Invitrogen), which was used at a 1/500 dilution. Slides were observed using an Eurostar II fluorescence microscope with the Europicture software (Euroimmune).
Acknowledgements We thank  Ernest A. Gould  from the  UMR UVE (Marseille, France)  for his valuable review of the manuscript. We also thank  Geraldine Piorkowski  and  Karine Barthelemy  from the  UMR UVE (Marseille, France)  for performing viral sequencing. This work was supported by the  French "Agence Nationale de la Recherche"  (grant agreement no. ANR-14-CE14-0001 ) and the  European Virus Archive goes global project  ( EVAg ;  European Union - Horizon 2020 programme  under grant agreement no. 653316 ; http://www. european-virusarchive. com/).