dataesr/scientific-paragraphs-categorization huggingface dataset:
Border cell cables and broken cable sections were labelled by our semi-automatic methods as mentioned above. Border cell area was quantified from the calculation of the area from both protrusions and main body region of border cell group, both of which were automatically produced by our semi-automatic method as mentioned above. We used the Matlab code for Particle Image Velocimetry (PIV) analysis developed in the Stramer's team  36. The detailed information for PIV, divergence and flow directions are as follows: 1. Cell segmentation: Before the PIV analysis, we used Ilastik software to do the processing of cell segmentation. We used the function, project of pixel classification, in this Ilastik software.
To filter anomalous tracking data, only displacements that had a cross-correlation coefficient above a certain threshold, c0, were kept. For the present work, the threshold was set at c0 = 0. 5. Finally, a spatial convolution with a Gaussian kernel (size of 6 μm, sigma of 1. 2 μm), and temporal convolution with temporal kernel of 20 second (sigma 10 second) were used to interpolate the measured displacements to cover all the pixels within the cell outline. The complete algorithm for this analysis was implemented in Matlab.
Statistics All data are presented as mean ± SEM. Statistical analysis to compare results among groups was carried out by the Mann-Whitney test (GraphPad Prism software). A value of P>0. 05 was considered to be not significant (ns); a value of P<0. 05 (*), P<0. 01 (**) or P<0. 001 (***) was considered to be differently statistically significant, while a value of P<0. 0001 (****) was considered to be remarkably statistically significant. Rel. flow number FIGURE LEGENDS Figure 4.
Determining the cell-to-cell contacts in border cell group. We adapted the Matlab code in step 2 to do the semi-automatic labelling of contacts between border cells. For the 3D reconstructed images of border cell groups, if we can easily see the contact regions of border cells, we marked the starting and ending point of each border cell contact before running the Matlab code. The program automatically labelled the whole contact region between two border cells. Considering that some 3D reconstructed images cannot show the inner contact regions well, we only did the 3D reconstruction of images at several Z-stack layers near the center of border cell groups, which allowed to get the clearer view of each border cell contact. And we used this reconstructed image for the Matlab analysis by the same processing setting.
ACKNOWLEDGEMENTS We thank  Adam Martin ,  Sven Bogdan ,  Bloomington Drosophila  stock center and  Vienna Drosophila RNAi  center for flies. We thank  Stefania Marcotti  and  Brian Stramer  to share the Matlab code for actin flow analyses. We thank  Karine Belguise ,  Aurelien Guillou ,  Mureil Grammont  and  Jocelyn McDonald  for discussion of manuscript preparation. This work was supported by  Scientifiques de la Fondation ARC  (grant number  PJA 20171206526 ,  PJA20191209714 ), to XW; the  National Natural Science Foundation of China  (grant number  82070630 ) to B. Y. PhD  fellowship from  China Scholarship Council (CSC)  to support the PhD study of S. Z. and L. C.
3. 3 Determining the cell-to-cell contacts in border cell group. We adapted the Matlab code in step 2 to do the semi-automatic labelling of contacts between border cells. For the 3D reconstructed images of border cell groups, if we can easily see the contact regions of border cells, we marked the starting and ending point of each border cell contact before running the Matlab code. The program automatically labelled the whole contact region between two border cells. Considering that some 3D reconstructed images cannot show the inner contact regions well, we only did the 3D reconstruction of images at several Z-stack layers near the center of border cell groups, which allowed to get the clearer view of each border cell contact. And we used this reconstructed image for the Matlab analysis by the same processing setting.
Currently time-domain astronomy can scan the entire sky on a daily basis, discovering thousands of interesting transients every night. Classifying the ever-increasing number of new transients is one of the main challenges for the astronomical community. One solution that addresses this issue is the robotically controlled Spectral Energy Distribution Machine (SEDM) which supports the Zwicky Transient Facility (ZTF). SEDM with its pipeline PYSEDM demonstrates that real-time robotic spectroscopic classification is feasible. In an effort to improve the quality of the current SEDM data, we present here two new modules, BYECR and CONTSEP. The first removes contamination from cosmic rays, and the second removes contamination from non-target light. These new modules are part of the automated PYSEDM pipeline and fully integrated with the whole process.
Employing BYECR and CONTSEP modules together automatically extracts more spectra than the current PYSEDM pipeline. Using SNID classification results, the new modules show an improvement in the classification rate and accuracy of 2. 8% and 1. 7%, respectively, while the strength of the cross-correlation remains the same. Improvements to the SEDM astrometry would further boost the improvement of the CONTSEP module. This kind of robotic follow-up with a fully automated pipeline has the potential to provide the spectroscopic classifications for the transients discovered by ZTF and also by the Rubin Observatory's Legacy Survey of Space and Time.
The Spectral Energy Distribution Machine (SEDM)  (Blagorodnova et al. 2018 ) and its pipeline PYSEDM  (Rigault et al. 2019)   5  demonstrates that such spectroscopic classification is feasible in close to real-time. SEDM is a very low-resolution (R ∼ 100) integral field unit (IFU) spectrograph, whose output is a datacube including spatially resolved spectra, with a 28 × 28 arcsec 2 field-of view, mounted on the Palomar 60 inch telescope. The main purpose of SEDM is to classify transients discovered by ZTF. PYSEDM is a pipeline that extracts a spectrum of the SEDM targets within minutes of the exposure completing. With this pipeline, SEDM is fully automated, and hence a flux-calibrated spectrum is available in 5 minutes after the end of the exposure. The transient spectrum is classified mainly through the Supernova Identification code (SNID;  Blondin & Tonry 2007)  and SNIascore  (Fremling et al.
The first module, named BYECR, is developed to remove the cosmic ray hit contamination, while the second, named CONTSEP, removes the non-target light contamination. These two modules are presented in Section 2. Then, in Section 3 we re-extract all the SEDM data obtained from 2018 August to 2020 December and run SNID with the extracted spectra by applying the new modules. Section 4 presents the performance improvement regarding the classification rate and accuracy determined by SNID. A discussion on the next steps for the SEDM data and its classification can be found in Section 5. We summarize the paper in Section 6.
New PYSEDM Modules: BYECR and CONTSEP Here we introduce two new modules for the PYSEDM pipeline to remove contamination from the SEDM datacube. These new modules are executed just before the "PSF spectrum extraction" step in the pipeline and are fully automated and integrated with the whole pipeline process (see Figure  1  of  Rigault et al. 2019 , for the flow chart of the PYSEDM pipeline).
BYECR: For Removing Cosmic Rays Without correctly removing cosmic rays, spurious features will be associated with the SN leading to incorrect classifications. Cosmic rays in the SEDM CCD image are rejected by the lacosmicx 6  python package, after a master bias is subtracted. The lacosmicx is based on the L. A. COSMIC (van Dokkum 2001), which performs well on imaging or undersampled IFU data (see  Husemann et al. 2012 , for the detailed comparison of several cosmic ray detecting methods). As pointed out by  Husemann et al. (2012) , currently, cosmicray detection algorithms for IFU data are missing. Therefore, we implement our own algorithm for SEDM, based on a technique developed for the previous Micro-Lens Array-based IFU (e. g. , the SAURON project, see  Bacon et al. 2001 , or the SuperNova Integral Field Instrument, see  Copin 2013).
CONTSEP: For Removing Non-target Contamination The current PYSEDM, documented in  Rigault et al. (2019) , fits a 3D PSF that has a fixed angular elliptical size. Hence, the extracted target spectrum contains light from neighboring sources. For an SN, this is mostly from its host galaxy. Therefore, some extra pre-processing is required to ensure that the transient spectrum is as clean as possible for more accurate classification (see  Blondin & Tonry 2007 , for more discussions about this). To mitigate this issue, we have developed the CONTSEP module that removes spaxels dominated by nontarget sources. Because the PS data has magnitude information, the CONTSEP module can draw isomagnitude contours on the PS image and project them onto the SEDM image. From the isomagnitude contours, the module finds the faintest contour which separates the target and other sources.
Then, the CONTSEP module selects spaxels inside the target contour for the target (red in Figure  2 ), and the other sources' contour for non-target sources (black). The selected non-target sources' spaxels are removed from the default PYSEDM aperture when we extract the target spectrum using the 3D PSF procedure, described in  Rigault et al. (2019). As a result, the final spectrum processed by CONTSEP is expected to be cleaner than the default PYSEDM spectrum as shown in the right bottom right panel of Figure  2.
Re-extraction and Run SNID In order to test our new modules, we have re-extracted all the SEDM spectra obtained from 2018 August to 2020 December 7  with different module combinations: pysedm: a default PYSEDM, byecr: PYSEDM + BYECR, contsep: PYSEDM + CONTSEP, byecont: PYSEDM + BYECR + CONTSEP In total, 6784 spectra for 4630 individual transients were reextracted (Table  1 ). When the CONTSEP module is applied, the number of extracted spectra is 128 (∼2%) fewer than when not applied. Among those not-extracted spectra, 65 are due to an SEDM pointing failure, 43 are a PS image issue (e. g. , saturation), 15 are aligned with the host core (a strong host or active galactic nucleus (AGN) case), and 5 are CONTSEP failures. Next, we apply a cut based on the signal-to-noise ratio (S/N) of the extracted SEDM spectra.
We calculate S/N (per ∼25 Å bin) in the wavelength range between 4000 and 8000 Å that will be adopted for SNID, and take the median. After examining the spectra, we select S/N > 3 as the cut criterion where   (i. e. , voxel). Bottom left: Spectra of the cosmic ray test spaxel (black) and its six neighbors selected above. There is a strong cosmic ray around 4900 Å. Middle: Detected cosmic rays by the BYECR module (red spaxels). For this sample, there are 593 cosmic rays in 257 spaxels. Black spaxels show the default aperture size of PYSEDM, from which the target spectra will be extracted. Right: Extracted spectra before (upper) and after removing cosmic rays (lower). spectral features are shown. The number of final spectra with the mean and median S/N for each extracted method is presented in the last column of Table  1.
Because pysedm includes all the light from contamination, it has the highest value of S/N, while since byecont removes all the contamination, it has the lowest. Interestingly, the final number of spectra. We note that the offset = (2. 0, -0. 3) in the unit of spaxel has been applied manually for this target (see Section 5. 1). Right: Selected other spaxels are removed from the default PYSEDM aperture (black + red) when we extract the target spectrum. Therefore, the spectra only in red spaxels are extracted. In the bottom panel, extracted flux-calibrated spectra with (black; byecr) and without (red: byecont) contamination light are presented. Note. % diff. is the relative fractional percent difference compared to the default pysedm method. is higher when the CONTSEP module is applied (35 and 29 more for contsep and byecont methods, respectively), even though it extracted 128 (∼2%) fewer spectra at first.
Comparing to the default pysedm method, byecont, which includes both new modules, provides 52 (∼1%) more spectra. We next ran SNID on this final sample of spectra to compare classification results. When running SNID, we used SEDM spectra in the wavelength range between 4000 and 8000 Å, where the quantum efficiency of the SEDM CCD is over 60% and includes most of the important spectral features of the transients. We applied rlap 5 min  for a classification criteria, as suggested by  Blondin & Tonry (2007). It also removed spectra where the date of maximum was unknown. In total, for SN templates, 5170 unique spectra from 460 individual SNe, and for non-SN templates, 67 spectra from 23 stellar objects are used for our analysis. The resulting SNID template type distribution consisted of 3288 spectra from 312 SNe Ia, 1055 spectra from 80 SNe Ib/c, 620 spectra from 33 SNe II, 207 spectra from 35 SLSNe, 29 spectra from 7 TDE, 15 spectra from 3 LBVs, 11 spectra from 11 galaxies, 11 spectra of M-stars, and 1 spectrum from 1 AGN.
Classification Rate First, we calculate how many SEDM spectra are classified by SNID. We calculate the rate based on all the 6784 SEDM spectra. As shown in the columns between second and fourth of the table, each extraction method has a different classification rate. Removing cosmic ray hits (byecr method) improves the rate by 1. 6% (165 more spectra) compared to the default pysedm method. Removing non-target contamination shows a 0. 5% (33 more spectra) improvement. Combining the two new modules improves the result with 2. 8% (188 more spectra) in total compared to the default pysedm method.
rlap The SNID rlap value is a measurement of the strength of the correlation between a template spectrum and the input spectrum  (Blondin & Tonry 2007). The distribution and the mean (median) of rlap of the best-matching template are presented in Figure  4  and the fifth column of Table  2 , respectively. Their distributions and values are similar, however we observe a slight improvement when applying the new modules. Removing cosmic ray hits improves the mean rlap by 0. 29 (∼3%). However, removing non-target contamination produces an rlap value similar to that of pysedm. This is because the CONTSEP module requires an accurate astrometric solution, which sometimes fails in the automated pipeline (see Section 5. 1 for more discussion about this issue). In addition, the CONTSEP module does not work well when the target is aligned with the core of the host galaxy.
Accurate Astrometry for the CONTSEP Module The CONTSEP module works with the PS image based on the target coordinate from SEDM (see Figure  2 ). This means that the astrometry solution from SEDM plays an important role when we utilize the CONTSEP module. For example, for the target in Figure  6 , we manually applied the offset of ΔX = +2. 0, ΔY = -0. 3 spaxels (corresponding to +1. 5″, -0. 4″). This offset aligns the target and the other sources in the SEDM image with those in the reference PS image. However, there is currently no way to apply this offset automatically, even though it critically improves the results from the CONTSEP module. As shown in the left column of Figure  6 , when the SEDM astrometry is not accurate and the offset is not provided, the positions of sources in the SEDM image are not well-aligned with those in the PS image.
Hence, the isomagnitude contours derived from the PS image cannot be placed accurately on the SEDM image. With poorly aligned contours, the CONTSEP module automatically selects contaminated spaxels. Consequently, this affects the shape of the target spectra and thus the classification determined by SNID (right column of Figure  6 ). SNID returns an rlap value of 10. 64 without the offset and 11. 43 with the offset. From this example, we can (partially) explain the relatively low value of rlap when we use the contsep method without the offset presented in Table  2. This astrometry offset is most likely due to bad weather that affects the derivation of an accurate astrometric solution from "the guider-astrometry position" method currently used in SEDM, as detailed in  Rigault et al. (2019). Briefly, SEDM uses the "Rainbow Camera" for guiding during IFU observations (see  Blagorodnova et al.
Removing Strong Host Contamination The CONTSEP module cannot remove host galaxy contamination when a target is located at/near the center of the galaxy (about 2 spaxels or ∼1. 5″ away). Based on Fremling et al. (2020, their Figure  6 ), which presents the cumulative distribution of the angular offsets in arcsec between SNe and their hosts, we expect that ∼20% of all the objects would fall within this radius from the nucleus of its host. In this case, the host contamination is stronger than when the target is at a larger distance. To remove this strong host contamination, work on modeling host spectra and the projection onto the SEDM datacube is ongoing (J. Lezmy et al. 2022, in preparation). When this work is applied to the SEDM data, we can separately obtain SN and host spectra to improve the SN classification and also to measure the host galaxy properties.
Deep Learning Classifier There are advances in the use of deep learning techniques to determine the type of newly discovered transients  (Charnock & Moss 2017; Moss 2018 ). Among them, few attempts use spectroscopic classification (e. g. ,  Muthukrishna et al. 2019; Fremling et al. 2021 ). However, SNIascore  (Fremling et al. 2021)  was designed specifically for spectroscopic classification focusing on SNe Ia with a training set using SEDM data. It reaches the classification accuracy up to 90% while maintaining a very low false positive rate (<0. 6%). This high performance is achieved even though SNIascore was trained on SEDM data extracted with the pysedm method. Hence, we expect that when it will be trained with cleaner data extracted with byecont, SNIascore will show even better performance.
Summary and Plan In this paper, we have introduced two new modules for the PYSEDM pipeline: the BYECR and CONTSEP modules. The purpose of the new modules is to remove cosmic rays and nontarget contamination, respectively, for obtaining cleaner SEDM data. In order to test the performance improvement, we reextracted all the SEDM data obtained from 2018 August to 2020 December, which consists of 6784 spectra for 4630 individual transients. Employing the new modules allows the extraction of up to 52 (∼1%) more spectra than the current PYSEDM pipeline. Then, we ran SNID on the extracted spectra from different extraction methods as a comparison. When we use both modules (byecont extraction method), the classification rate is improved by 2. 8% (188 spectra) and for the classification accuracy a 1.
7% improvement is observed, compared to the current PYSEDM pipeline. However, the average (median) rlap value remains the same, showing a marginal improvement by 0. 31 (0. 32). Work on improving SEDM data and thus the spectroscopic classification is ongoing by improving the astrometric accuracy of SEDM and by removing strong host contamination in SEDM data. Along with this, more robust spectroscopic classification methods will be required through adding more templates for SNID and applying advanced deep leaning techniques. The classification of SEDM data using SNID is currently annotated and reported to registered clients via the GROWTH Marshal  (Kasliwal et al. 2019)  and Fritz  (Duev et al. 2019; van der Walt et al. 2019 ). 8 Only SNe Ia classified by SNIascore (threshold of 0. 9) are reported to the Transient Name Server (TNS) 9   (Fremling et al.
2021 ). From the above efforts to improve the SEDM classification, we are looking forward to reporting all types of transients classified from SEDM data to TNS. Furthermore, we plan to launch SEDMv2 mounted on the Kitt Peak National Observatory 2. 1 m telescope. We expect that SEDMv2 will observe a similar number (15 per night) of but fainter targets (down to ∼19. 0 mag) as SEDM. Robotic followup with SEDM and SEDMv2 will provide spectroscopic data for.
transients brighter than 19. 0 mag, discovered by ZTF and also by the  Rubin Observatory's LSST. We thank the referee for his/her careful reading of the manuscript and many helpful comments. This project has received funding from the  European Research Council (ERC)  under the European Unionʼs  Horizon 2020 research and innovation program  (grant agreement No. 759194-USNAC ). The SED Machine is based upon work supported by the  National Science Foundation  under grant No. 1106171. Based on observations obtained with the  Samuel Oschin Telescope 48  inch and the 60 inch Telescope at the  Palomar Observatory  as part of the  Zwicky Transient Facility  project. ZTF is supported by the  National Science Foundation  under grant No. AST-1440341  and a collaboration including  Caltech , IPAC, the  Weizmann Institute for Science , the  Oskar Klein Center at Stockholm University , the  University of Maryland , the  University of Washington ,  Deutsches Elektronen-Synchrotron and Humboldt University, Los Alamos National Laboratories , the  TANGO Consortium of Taiwan , the  University of Wisconsin at Milwaukee , and  Lawrence Berkeley National Laboratories.
Predicting the functional potential of microorganisms in environmental samples from cultivation-independent techniques is a major challenge. A persistent difficulty lies in associating taxonomic profiles obtained from metabarcoding experiment with accurate functional profiles, particularly for poorly-resolved taxonomic groups. In this paper, we present EsMeCaTa a python package predicting shared proteins from taxonomic affiliations. EsMeCaTa relies on the UniProt database to retrieve the public proteomes associated with a taxon and then uses MMseqs2 in order to compute the set of proteins shared in the taxon. Finally, Es-MeCaTa extracts the functional annotations of these proteins to provide an accurate estimate of the functional potential associated to taxonomic affiliations.
Approach EsMeCaTa takes as input a tabulated file containing two columns. The first column is an identifier and the second contains a taxonomic affiliation (starting with the highest taxonomic rank, such as kingdom, to the lowest taxonomic such as species) as defined by the NCBI Taxonomy database  (Schoch et al. , 2020). The outputs of the workflow are, for each taxon (1) fasta files of all proteomes selected by EsMeCaTa, (2) a fasta file of the shared proteins clustered by MMseqs2 from these proteomes, and (3) a tabulated file containing the functional annotations associated with these proteins (Gene Ontology Terms (GO), Enzyme Commission (EC)). The first part of the workflow selects the lowest taxonomic rank of each input taxonomic affiliation for which the UniProt Proteomes database (The UniProt Consortium, 2021) contains at least one proteome exhibiting a BUSCO score higher than 80% and considered "non-redundant" and "not-excluded" by UniProt (column 'Proteomes selection' in Table  1 ).
More precisely, the taxonomic affiliation is processed using the ete3 python package  (Huerta-Cepas et al. , 2016)  in order to associate a taxon ID (from the NCBI taxonomy database) to each taxon from the affiliation. Using this ID, queries against the UniProt Proteomes database find the lowest-ranking taxon for which there is at least one reference or non-reference proteome in the database and download those proteomes. If the number of proteomes is greater than a threshold (100 by default), only a subsample is downloaded. The second part of the workflow aims at identifying proteins shared by proteomes associated with a taxon. With the downloaded proteomes, EsMeCaTa performs protein clustering using MMseqs2  (Steinegger and Söding, 2017)  and selects clusters such that proteins are shared by at least X% of the proteomes (see the column 'Protein clusters (MMseqs2)' in Table  1 ).