dataesr/scientific-paragraphs-categorization huggingface dataset:
TESS light curves The transit signatures were initially detected by TESS during the first year of the survey (July 2018-July 2019) in the Full Frame Image (FFI) observations with a cadence of 30 min and flagged as promising TOIs for follow-up observations. Afterward, they were reobserved at 2-min cadence during the third year of the mission (July 2020 -July 2021). The photometry of the short cadence data is extracted by the Science Processing Operations Center (SPOC;  Jenkins et al. 2016 ) and consists of the Simple Aperture Photometry (SAP) flux and the Presearch Data Conditioning SAP (PDCSAP;  Smith et al. 2012 , Stumpe et al. 2012 , 2014)  flux. For our analysis, we made use of the PDCSAP light curves since they are corrected for long term systematic trends, such as instrumental artifacts and dilution.
The observations at 30-min cadence were processed by the MIT Quick Look Pipeline (QLP;  Huang et al. 2020). When visually comparing the various light curves, we noticed that the detrended QLP light curves showed a slighly increased flux during transit compared to the un-detrended data. Therefore, we decided to use the raw normalized simple aperture photometry from the best aperture (SAP flux) and account for systematic noise using Gaussian Process regression. All data are publicly available and were downloaded from the Mikulski Archive for Space Telescopes (MAST 2  ). TOI-1107 (TIC 394561119) was observed in Sectors 11-13 with 30-min cadence and Sectors 38 and 39 with 2-min cadence. We detected 17 transits in the FFIs and 13 transits in 2-min cadence light curves with a transit depth of ∼0.
54% and an average period of 4. 08 days. TOI-1107 passed all the Data Validation (DV) tests  (Twicken et al. 2018 , Li et al. 2019 ) and the difference image centroiding test that placed the transit signature source within 0. 2 ± 2. 5 of the target star. TOI-629 (TIC 293853437) was monitored in Sector 6 at 30-min and Sector 33 at 2-min cadence. Three transits with a depth of ∼0. 23% and a period of 8. 72 days were identified in both QLP and 2-min data. The candidate passed the DV test and the difference image centroid test located the source of the transits to within 2. 0 ± 2. 5 of the target star. TOI-1982 (TIC 437329044)  was observed in Sector 11 at 30-min and Sector 37 at 2-min cadence. The 30-min cadence data were processed by the QLP that identified two ∼0. 53% deep transits 17. 1 days apart located at the beginning and at the end of the light curve.
During perigee passage while downloading data, the data collection was paused for 1. 18 days near the middle of Sector 11. As a result, the period of the target was initially reported to be half (8. 58 days). Later, the 2-min cadence data confirmed the 17. 1 days period with only one transit visible in Sector 37. Finally, TOI-2543 (TIC 270604417) was observed in Sector 8 at 30-min cadence and in Sector 34 at 2-min cadence. Two transits were detected in the QLP data and three transits in the 2-min data with a depth of ∼0. 28% and an orbital period of 7. 54 days. The transit signature passed the DV test and the difference image report located the source of the transits to within 0. 8 ± 2. 5 of the target star. The Target Pixel Files (TPFs) of our targets and the aperture masks used by the pipeline to extract the photometry (orange pixels), generated with tpfplotter, can be found in Fig.
LCOGT photometric follow-up We observed full transits of TOI-1107b in B-band and Pan-STARRS z-short band on March 25, 2021 from the Las Cumbres Observatory Global Telescope (LCOGT;  Brown et al. 2013)  1. 0-m network node at South Africa Astronomical Observatory (SAAO). We observed ingresses of TOI-1982b in B-band and Pan-STARRS z-short band on April 19, 2021 from the LCOGT 1. 0-m network node at Cerro Tololo Inter-American Observatory (CTIO). The data that are affected by poor sky transparency have been removed from the light curves. We use the TESS Transit Finder, which is a customized version of the Tapir software package  (Jensen 2013) , to schedule our transit observations. The 1. 0-m telescopes are equipped with 4096 × 4096 SINISTRO cameras having an image scale of 0. 389 per pixel, resulting A94, page 3 of 21 in a 26 × 26 field of view.
The images were calibrated by the standard LCOGT BANZAI pipeline  (McCully et al. 2018) , and photometric data were extracted using AstroImageJ  (Collins et al. 2017 ). The images were focused and have mean stellar point-spread-functions with a FWHM of ∼2 , and circular photometric apertures with radius 3. 9 (SAAO) and 5. 8 (CTIO) were used to extract the differential photometry. A larger aperture was used for the CTIO observations due to the poor skies and highly variable seeing. The photometric apertures exclude all flux from the nearest Gaia EDR3 neighbors. Achromatic transits were detected on-target in both systems.
El Sauce photometric follow-up We observed a full transit of TOI-1982 on April 02, 2021 in Johnson-Cousins R c -band using the Evans 0. 36-m telescope at El Sauce Observatory in Coquimbo Province, Chile. The telescope is equipped with a STT1603-3 CCD camera with 1536 × 1024 pixels binned 2 × 2 in-camera resulting in an image scale of 1. 47 pixel -1. The photometric data for TOI-1982 were obtained from 455 × 60 sec exposures using a circular 8. 8 aperture, processed in AstroImageJ  (Collins et al. 2017 ).
The calibrated light curve is reported via email and the raw light curves of about 1000 stars of the field are transferred to Europe on a server in Rome, Italy and are then available for deeper analysis. These data files contain each star's flux computed through 10 fixed circular apertures radii, so that optimal light curves can be extracted. For TOI-1107, an 11. 2 pixel radius aperture gave the best result. TOI-1107 was observed under good weather conditions with a non-windy (∼3 m s -1 ) clear sky, and air temperatures ranged between -65 • C and -75 • C.
Joint transit and RV analysis We retrieved the companion parameters of the four systems using a joint photometric and radial-velocity modeling with juliet  (Espinoza et al. 2019). juliet is a publicly available tool that allows to fit both transit models (via batman package,  Kreidberg 2015)  and RVs (via radvel package,  Fulton et al. 2018)  simultaneously and model the noise using Gaussian processes (GPs) (via celerite package, Foreman-Mackey et al. 2017). The code uses nested sampling algorithms in order to explore the parameter space with dynesty (Speagle 2020) and performs a proper model comparison via Bayesian evidences (lnZ) with MultiNest  (Feroz et al. 2009 ) using the PyMultiNest  (Buchner et al. 2014 ) Python software package. For the transit model, juliet performs an efficient parametrization by fitting for the parameters r 1 and r 2 to ensure uniform exploration of the p (planet-to-star ratio) and b (impact parameter) space.
Also, juliet fits for the stellar density ρ * that is combined with the orbital period to provide the scaled semi-major axis (a/R * ). For our joint analyses we use the TESS light curves, the ground based photometry and the follow-up RV measurements from CORALIE, CHIRON, TRES, FEROS, and MINERVA-Australis. As first step we isolate each TESS transit by applying a cut of 1. 5 days before and after each transit midpoint. For our analysis we use the stellar density and its uncertainty derived from our SED fit as a Gaussian prior. The quadratic stellar limbdarkening coefficients and their uncertainties are derived using the LDCU 3 code, a modified version of the python routine implemented by  Espinoza & Jordán (2015) , for each photometric filter used. LDCU computes the limb-darkening coefficients and their corresponding uncertainties using a set of stellar intensity profiles accounting for the uncertainties on the stellar parameters.
Acknowledgements. We thank the  Swiss National Science Foundation (SNSF)  and the  Geneva University  for their continuous support. This work was carried out in the frame of the  Swiss National Centre for Competence in Research (NCCR) PlanetS  supported by the  Swiss National Science Foundation (SNSF). This publication makes use of The Data & Analysis Center for Exoplanets (DACE), which is a facility based at the  University of Geneva (CH)  dedicated to extrasolar planet data visualization, exchange, and analysis. DACE is a platform of  NCCR PlanetS  and is available at https://dace. unige. ch. This paper includes data collected by the TESS mission. Funding for the TESS mission is provided by the  NASA   Explorer Program. JSJ gratefully acknowledges support by  FONDECYT  grant  1201371  and from the  ANID BASAL  projects  ACE210002  and  FB210003.
2016). The identification of protoclusters has so far been challenging, due to the low number density and the faintness of distant galaxies  (Muldrew et al. 2015). Over the past few years, only a few protoclusters have been confirmed through multiwavelength and spectroscopic analysis (e. g. ,  Diener et al. 2015; Wang et al. 2016; Lemaux et al. 2018; Polletta et al. 2021). On the galaxy observation side, the recently completed Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP;  Aihara et al. 2018 ) on the Subaru telescope, using the HSC imager  (Miyazaki et al. 2018) , reaches m i ∼ 27. 1 (5σ in 2″ apertures) at deep fields. Although a few of the group catalogs using HSC data have been constructed with different methods, such as red-sequence galaxies  (Oguri et al. 2017)  or weaklensing techniques  (Miyazaki et al.
2018; Hamana et al. 2020; Oguri et al. 2021 ), and a system of galaxy protoclusters at z ∼ 4 has been searched using a sample of g-dropout galaxies selected from the wide fields  (Toshikawa et al. 2018) , only a few group or cluster catalogs have been constructed for the deep fields in particular (e. g. , the updated data version of  Oguri et al. 2017). Ando et al. (2022)  searched for cores of protoclusters at 1 < z < 1. 5, using photometric data from the HSC-SSP wide and deep fields. In addition to the grizy fiveband photometries, the U band contained in the CFHT Large Area U-band Deep Survey (CLAUDS;  Sawicki et al. 2019)  allows the bracketing of the Balmer and 4000 Å breaks at intermediate redshift, which improves the performance of the photometric redshift obtained from spectral energy distribution (SED) fitting (e.
The Photometric Surveys We use the joint CLAUDS and HSC-SSP data set  (Sawicki et al. 2019) , which has been applied for studies including UV and U-band luminosity functions  (Moutard et al. 2020 ) and source classification  (Golob et al. 2021 ). This joint data set is an SExtractor-based multiband catalog, as described in  Sawicki et al. (2019). The detection to an object uses the signal-to-noise (ΣSNR) image, which is constructed from all available CLAUDS u/u * and HSC-SSP grizy images. Once objects are detected by the SExtractor software  (Bertin & Arnouts 1996)  in the ΣSNR image, the multiband catalog is then created by running SExtractor in dual-image mode, with various measurements recorded for each object, such as positions, fluxes (in Kron, isophotal, and fixed-radius circular apertures), fiducial radii, and ellipticities.
Here, we only give a brief description of the combination of the two data sets, while more details can be found in  Sawicki et al. (2019)  and  Moutard et al. (2020). The CLAUDS and HSC-SSP data set contains U + grizy sixband data, distributed in four roughly equal-sized (∼4-6 deg 2 ) fields: E-COSMOS, XMM-LSS, ELAIS-N1, and DEEP2-3. CLAUDS provides the U-band data with a median depth of U AB = 27. 1 (5σ in 2″ apertures), covering a total of 18. 60 deg 2 in the HSC-SSP deep layer, and a 1. 36 deg 2 subarea reaching a depth of U AB = 27. 7 within the ultradeep layer  (Sawicki et al. 2019). CLAUDS uses two U-band filters: the new u filter is applied in the ELAIS-N2 and DEEP2-3 fields, while the older u * filter is adopted in XMM-LSS. The E-COSMOS field uses both the u and u * filters in the central region, and only the u filter in other areas.
The median seeing in the entire deep field of CLAUDS at the U band is 0 92. The HSC-SSP data contains five grizy wave bands, with depths of g AB ∼ 27. 3, r AB ∼ 26. 9, i AB ∼ 26. 7, z AB ∼ 26. 3, and y AB ∼ 25. 3 (5σ in 2″ apertures) in the deep and ultradeep regions (Aihara et al. 2019), respectively. The average seeing in the i band is the best of the five wave bands, reaching ∼0 62. The HSC-SSP data set in total has 14,789,205 objects over 34. 41 deg 2 , within which 18. 60 deg 2 have CLAUDS Uband observations. We note that our data set uses an updated version of the HSC-SSP sample, which is based on the second public data release (PDR2;  Aihara et al. 2019). This data version increases the galaxy number, but not significantly in the deep and ultradeep fields. In the following, we describe the procedures used to select our galaxy samples from these data sets.
Galaxy Selection Our galaxy sample selection begins with the rejection of objects with mask flags, to avoid the light influence from bright stars. While the objects in the data set have been divided into stars, galaxies, and QSOs using the gradient boosted trees method  (Golob et al. 2021) , we only select the objects classified as galaxies. We also exclude galaxies with grizy apparent magnitude less than 0, to avoid spurious photometric redshift determinations. As the i band is priority observed, and has the smallest seeing of the HSC-SSP observations, we choose to use galaxies observed in the i band with an apparent magnitude limit m i < 26 and a magnitude error σ i < 1. We discard two small but bad areas at the edges of ELAIS-N1 and XMM-LSS. With these initial cuts for the CLAUDS and HSC-SSP data set, we have 7,752,546 galaxies spanning an area of 33.
97 deg 2. We then try to exclude the unreliable galaxies, due to the pollution of bad pixels or problems still existing in the image processing. However, the original data set provided in  Sawicki et al. (2019)  does not give such information about these galaxies. We thus construct a new independent galaxy sample based on the HSC-SSP PDR2 database, and apply several pixel flags of images to exclude these unreliable sources. The chosen flags refer to the criteria adopted in  Oguri et al. (2017)  and  Mandelbaum et al. (2018). We first require the flag isprimary to be True, to identify a single version of each astrophysical object. We throw out sources close to bright object pixels at grizy bands. The object type is determined as a galaxy by setting the stargalaxy separation parameter i_extendedness_value = 1 at the i band.
Survey Edge Effect As we can see in Figure  1 , the survey geometries of our galaxy catalogs are quite complicated. We follow  Yang et al. (2007)  to provide a parameter, f edge , to quantify the survey edge effect of the groups. For this purpose, we randomly distribute 200 points within the radius (r 180 ) of each halo. Then, we remove those random points that are outside the survey region, according to the masks of the CLAUDS and HSC-SSP data set. For each group, we calculate the number of remaining points, N remain , and define the fraction f edge ≡ N remain /200 to gauge the volume of the group that lies within the survey edges. About 84% of the groups in our catalog have an f edge value larger than 0. 9. For the groups with M h > 10 13 h -1 M e , ∼72% of the groups have f edge > 0. 9.
While our catalog of groups and protocluster candidates has mostly been produced by using photometric redshifts, it is interesting to note that some samples already have a number of spectroscopic and multiwavelength observations. Besides, as the sky area used to study the science theme "Galaxy Evolution" by PFS will completely overlap the CLAUDS and HSC-SSP data set, once PFS starts operating we will continuously update our group catalogs using the massive spectroscopic data as they become available. Our catalog can be combined with the galaxy observations from PFS to investigate the properties and evolutions of galaxies at high redshifts. Our group and protocluster candidate catalogs can be obtained online 12  or on Zenodo (doi:10. 5281/ zenodo. 6516482). This paper makes use of software developed for the Vera C.
Rubin Observatory. We thank the Rubin Observatory for making their code available as free software at http://pipelines. lsst. io/. This paper is based on data collected at the Subaru Telescope and retrieved from the HSC data archive system, which is operated by the Subaru Telescope and Astronomy Data Center (ADC) at NAOJ. Data analysis was in part carried out with the cooperation of the Center for Computational Astrophysics (CfCA), NAOJ. This work has made use of the Gravity Supercomputer at the Department of Astronomy, Shanghai Jiao Tong University.
We sincerely thank the anonymous referee for the useful comments. This work is supported by the  National Science Foundation of China  (Nos. 11833005 ,  11890691 ,  11890692 ,  11621303 ,  11890693 ,  11933003 and12173025 ), 111 project No. B20019 , and  Shanghai Natural Science Foundation  grant No. 19ZR1466800. We acknowledge the science research grants from the  China Manned Space  Project, with Nos. CMS-CSST-2021-A02  and  CMS-CSST-2021-A03. Y. S. D. acknowledges the science research grants from the  National Key R&D Program of China , via grant No. 2017YFA0402704 ,  NSFC  grant  11933003 , and the  China Manned Space  Project, with No. CMS-CSST-2021-A05. W. C. is supported by the  STFC.
35 , reflecting the multiple competing ruffling fronts observed in these cells. To further exploit the quantitative aspect of our model, we also analyzed our NZ and Golgicide A datasets (Figure  6 -figure supplement 3). For those conditions, unfortunately, the alignment index cannot be calculated, because the Golgi complex is dispersed. Yet, from the persistence time and protrusive unicity, the model predicts that NZ has a stronger effect on P polarized and κ than Golgicide A, confirming the central role of MTs in the feedback.
Optogenetics For local subcellular activation of a RhoGTPase Cdc42, an optogenetic dimer iLID-SspB was used as described in  Guntas et al. , 2015. It was activated by illumination with blue light (440 ± 10 nm). RPE1 cells used in the optogenetic experiments were engineered to stably express the optogenetic dimer and selected for average-high fluorescence level by FACS (the highest expressing cells were discarded, as they were not responsive to optogenetic activation). Experiments were performed in live cell imaging conditions described in the paragraph 'Imaging', using a digital micromirror device (DMD) projector and a blue (440 ± 10 nm) LED illumination source. The projection of blue light was controlled with an interface of a Matlab script and a microscope controlling MetaMorph software by sending a static pattern of light, or using the imaging routine described below.
Live cell imaging All imaging except MT dynamics experiments was performed at 37° C in 5% CO 2 with an IX71 inverted fluorescence and differential interference contrast (DIC) microscope (Olympus, Melville, NY), controlled with MetaMorph software (Molecular Devices, Eugene, OR). The microscope was equipped with a 60× objective (NA = 1. 45), motorized stage and filter wheel with SmartShutter Lambda 10-3 control system (Sutter Instrument Company, Novato, CA), a stage-top incubation chamber with temperature and CO 2 control (Pecon, Meyer Instruments, Houston, TX), ORCA-Flash4. 0 V3 Digital CMOS camera (Hamamatsu Photonics K. K. , Japan), z-axis guiding piezo motor (PI, Karlsruhe, Germany), CRISP autofocus system (ASI, Eugene, OR), a laser control system with azimuthal total internal reflection microscopy (TIRF) configuration (iLas2, Roper Scientific, Tucson, AZ), and a DMD pattern projection device (DLP Light Crafter, Texas instruments, Dalas, TX), illuminated with a SPECTRA light engine (Lumencor, Beaverton, OR) at 440 ± 10 nm.
Before imaging, cells were dissociated using Versene Solution (Thermo Fisher Scientific, Waltham, MA) and seeded for adhesion on previously mentioned prepared coverslips. Live cell imaging for MT dynamics experiments was performed at 37° C in 5% CO 2 with an IX71 inverted fluorescence microscope (Olympus, Melville, NY) with a spinning disk confocal unit CSU-X1 (Yokogawa Electric Corporation, Tokyo, Japan), controlled with MetaMorph software (Molecular Devices, Eugene, OR). The microscope was equipped with a 100× objective (NA = 1. 45), motorized stage, and filter wheel with SmartShutter Lambda 10-3 control system (Sutter Instrument Company, Novato, CA), a stage-top incubation chamber with temperature and CO 2 control (Tokai Hit, Shizuoka, Japan), Prime BSI Scientific CMOS camera (Teledyne Photometrics, Tucson, AZ), z-axis guiding piezo motor (PI, Karlsruhe, Germany), and a laser control system (Gataca Systems, Massy, France).
Fixed cell imaging Static imaging of IF stained MTs was performed with a Leica DMi8 microscope (Leica Camera AG, Wetzlar, Germany) with a CSU-X spinning disk unit (Yokogawa Electric Corporation, Tokyo, Japan), controlled with MetaMorph software (Molecular Devices, Eugene, OR). The microscope was equipped with a 63× oil objective (NA = 1. 40), motorized stage, and Rotr filter wheel (Andor, Belfast, UK), ORCA-Flash4. 0 V2 Digital CMOS camera (Hamamatsu Photonics K. K. , Japan), NanoScanZ z-axis guiding piezo motor (Prior Scientific, Cambridge, UK), and an ILE laser control system with enhanced illumination and imaging upgrade Borealis (Andor, Belfast, UK). Before imaging, cells were fixed following a previously described IF protocol.
Feedback routine and DMD illumination Feedback routine Imaging feedback routine to follow migrating cells with high magnification (60×) was established by using a combination of scripts in MetaMorph and Matlab. It ensures that the microscope stage moves together with a moving cell, always keeping it in the field of view. The main script was written in Matlab, which commands MetaMorph through calling its macros called 'journals'. It enables imaging of multiple stage positions (i. e. multiple cells) in multiple wavelengths in one experiment. It can be controlled with a GUI, which displays a selected position and its coordinates and pattern of activation for every cell. The amount of acquisition channels and timing can be selected globally for the full set of cells in the experiment. One specific wavelength is chosen as segmentation channel.
Image analysis Image segmentation Live cell imaging data obtained using our cell tracking feedback routine was analyzed using a custombuilt Matlab script (which you can find in Figure  4 -source data 1), which allowed segmentation of the shape of the cell, the Golgi complex, and the nucleus, tracking of their position and visualization of their trajectories. All three structures of interest (cell membrane, the Golgi complex, and the nucleus) were fluorescently labeled, so the shape segmentation was done by using a Matlab function 'graythresh' to select the pixels over a certain threshold of fluorescence intensity. Next, the image was binarized with a function 'im2bw', structures touching the image border were deleted with 'imclearborder', small objects were removed from the image with 'bwareaopen', stuctures were closed by dilate erode with 'imclose', using a disk with a radius of 10 pixels as a structuring element, and the holes in the structure were filled with 'imfill' function.
Nucleus tracking (semimanual) Excitation with blue light had to be avoided in optogenetic experiments because of optogenetic system's sensitivity to blue light, so the nuclei in these experiments were tracked semimanually in the DIC channel, using another custom-made Matlab routine (which you can find in Figure  2 -source data 1). The estimated center of the nucleus was manually chosen by single clicking on the image, the centroids of the nuclei were recorded, concatenated into a trajectory, and corrected according to the stage movement.