dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to Sitch et al. 25 for the careful description of the equations in the LPJ-DGVM which allowed us to base our model on their work. K. G. , K. J. F. , S. A. B. and B. J. W. M. are funded by the  UK Natural Environment Research Council  ( NE/S009663/1 ). K. J. F. is supported by a  Philip Leverhulme Prize  ( PLP-2017-079 ), a  BBSRC Translational Fellowship  ( BB/ M026825/1 ) and a  European Research Council   Consolidator Grant  ( MYCOREV -865225 ). S. A. B. is supported by a  Philip Leverhulme Prize and UK Nature Environmental Research Council Independent Research Fellowship  ( NE/M019497/1 ). P. P. 's contribution to this work was partly funded by  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-408092731.
Computational simulations Aiming to produce more information about the mechanisms of action of synthetic peptides, we performed a docking analysis to try to explain more about the action of peptides. The protein chosen was the membrane receptor PHO36 from C. neoformans. First, the sequence of PHO36 from Saccharomyces cerevisiae was used to find the sequence of PHO36 from C. neoformans. After finding the protein sequence, the three-dimensional (3D) model was built using the Swiss model server. Then, ClusPro 2. 0 Web Server was used to perform the docking analysis. PHO36 is a transmembrane protein. Based on that, it was only considered for docking analysis the peptides that did not interact in the transmembrane domain, as shown in figure  7  (red dashed lines). Among all peptides tested, RcAlb-PepIII was the only peptide that interacted in the transmembrane domain, so the result was not considered (data not shown).
neoformans. However, Neto et al. [20]  reported that a chitinbinding protein purified from Moringa oleifera seeds also caused perturbation in the redox enzymes leading to the accumulation of ROS. In that case, the authors only measured the accumulation of H2O2. Our data revealed that ROS plays an important role in the antimicrobial activity of synthetic peptides. However, one question arises: Is the antimicrobial action of peptides fully or partially dependent on ROS? An experiment with the antioxidant AsA provided new clues to this question. In the presence of 10 mM of AsA, all peptides have the activity affected (Fig. 4A ). The most affected peptide, RcAlb-PepII, completely lost its activity. Similar results were posted by Neto et al. [20]  for an anticandidal protein that had its activity reduced by 60% in the presence of AsA.
Molecular modeling The three-dimensional (3D) models of the PHO36 from C. neoformans were by comparative modeling using the A chain of the revised crystals of the adiponectin receptors (PDB code: 5LXG and 5LWY) was done by Lopes et al. [29] , using the SWISS-MODEL (https://swissmodel. expasy. org/interactive) to generate the models for PHO36 from C. neoformans. The Macromolecular X-Ray Crystallography Software was used to fix the and Psi angles of the 3D models. Then, the 3D model was refined using the ModRefiner atomic-level protein structure refinement algorithm was used to minimize the energy of the models (https://zhaglab. ccmb. med. umich. edu/ModRefiner/). The stereochemical quality of the predicted models was evaluated with Molprobity (http://molprobity. biochem. duke. edu/) by Ramachandran plot analysis.
Statistical Analysis All experiments were performed three times and the values are expressed as the mean ± standard error. GraphPad Prism 5. 01 (GraphPad Software company, Santa Clara, CA, USA) for Microsoft Windows was used to run the statistical analyses. All data obtained in the assays were compared using ANOVA, followed by the Tukey test (p < 0. 05). Funding and Acknowledgments: Special thanks to CAPES for providing the postdoctoral grant to Pedro F. N. Souza (grant number 88887. 318820/2019-00). We are also grateful to the staff of the central analytical facilities of UFC, Brazil.
| DWI preprocessing All images were visually inspected for obvious artifact by one of the authors (FN) and all were deemed acceptable. To increase signal-tonoise ratio, usually low in DWI because of random noise, DWIs were first denoised using the local principal component analysis (LPCA) algorithm (Manj on, Coupé, Martí-Bonmatí, Collins, & Robles, 2010); then the eddy current induced distortions and head movements were corrected using FMRIB Software Library  (Jenkinson, Beckmann, Behrens, Woolrich, & Smith, 2012). We also calculated the framewise displacement (FD) from the realignment parameter as suggested in  Power, Barnes, Snyder, Schlaggar, and Petersen (2012). For all subjects no volume showed a framewise displacement (FD) relative to the previous one of more than 1. 5 mm. The mean average FD (i.
| Diffusion metrics computation and normalization The images acquired with the b-value of 1,000 s/mm 2 were used to calculate the DTI metrics (MD, FA, AD and RD) and the single-shell FW images. The entire diffusion acquisition (i. e. , b = 500 s/mm 2 and b = 1,000 s/mm 2 ) were used to calculate FW multishell. For each of the diffusion metrics computation, the unique b = 0 s/mm 2 image was used. Thus, in the rest of the article, the references to acquisitions with b = 1,000 s/mm 2 and/or b = 500 s/mm 2 implicitly take into account the acquisition of this b = 0 s/mm 2 image. The DIPY toolbox  (Garyfallidis et al. , 2014) , available on python, was used to compute the DTI indices  (Basser, Mattiello, & LeBihan, 1994)  and the FW multishell indices  (Henriques et al. , 2017; Hoy et al. , 2014).
The performance of the model and the most frequently selected indices were similar, albeit slightly worse, when using TBSSderived images (see Data S1). Colorcode is relative to the proportion of folds in which a certain voxel has been selected over the whole 100 folds 84. 9], r =. 88 [0. 87, 0. 89]. This performance was significantly different from chance level. The results from the leave-one-out cross-validation were slightly worse, with a MAE of 91 and an r of 85. Figure  4  reports the scatterplot illustrating the relationship between predicted and actual age. FW was chosen in all folds, 75 times in association with MD and 55 times in association with RD (Figure  S9 ). The spatial distribution of the most selected voxels for FW overlapped with those from the FW only models (both multishell and single-shell), in the right superior longitudinal fasciculus, in the right corticospinal tract, in the forceps and in the right thalamic radiation.
ACKNOWLEDGMENTS We thank the  Inserm/UPS UMR1214 Technical Platform  for the MRI acquisitions. The authors would like to thank  Marc Golub  for sharing the DIPY extension code and assistance with its use. We thank also  Abdel-Kader Boulanouar  and  Perrine Bertrand  for their contributions in the acquisition of data. For funding, we thank the  Institut des sciences du cerveau de Toulouse (ISCT).
Aims. With an observing time span of more than 20 yr, the CORALIE radial-velocity survey is able to detect long-term trends in data corresponding to companions with masses and separations accessible to direct imaging. Combining exoplanet detection techniques, such as radial velocities from the CORALIE survey, astrometric accelerations from HIPPARCOS and Gaia eDR3, and relative astrometry from direct imaging, removes the degeneracy of unknown orbital parameters. This allows precise model-independent masses of detected companions to be derived, which provides a powerful tool to test models of stellar and substellar mass-luminosity relations. Methods. Long-term precise Doppler measurements with the CORALIE spectrograph reveal radial-velocity signatures of companions on long-period orbits. The long baseline of radial-velocity data allows the detectability of the companion candidates to be assessed with direct imaging.
We combine long-period radial-velocity data with absolute astrometry from HIPPARCOS and Gaia eDR3 and relative astrometry derived from new direct imaging detections with VLT/SPHERE to fit orbital parameters and derive precise dynamical masses of these companions. Results. In this paper we report the discovery of new companions orbiting HD 142234, HD 143616, and HIP 22059, as well as the first direct detection of HD 92987 B, and update the dynamical masses of two previously directly imaged companions: HD 157338 B and HD 195010 B. The companions span a period range of 32-279 yr and are all very low-mass stellar companions, ranging from 218 to 487 M Jup. We compare the derived dynamical masses to mass-luminosity relations of very low-mass stars (<0. 5 M ⊙ ), and discuss the importance of using precursor radial-velocity and astrometric information to inform the future of high-contrast imaging of exoplanets and brown dwarfs.
+165 -97 log g (cgs) 4. 14 (e)  4. 5 (d)  4. 47 ( f )  4. 41 (g)  4. 45 ( f )  4. 60 (g)   Notes. ( a) Parameters taken from the HIPPARCOS catalog  (Perryman et al. 1997 ). ( b) Parameters taken from the Tycho-2 catalog  (Høg et al. 2000). ( c) Parameters taken from Gaia early data release 3 (Gaia Collaboration 2021). In order to increase the efficiency and accuracy of the CORALIE spectrograph over the years, it underwent two major upgrades in June 2007  (Ségransan et al. 2010)  and in November 2014. While these changes improved the overall performance of the CORALIE spectrograph, they also introduced small offsets in the measured radial velocities; therefore, we treat the radial-velocity data from the CORALIE spectrograph as three separate instruments corresponding to these upgrades. We refer to the original CORALIE as CORALIE-98 (C98), the upgrade in 2007 as CORALIE-07 (C07), and the latest upgrade in 2014 as.
[Fe/H] (dex) 0. 07 (e) -0. 47 ( f ) -0. 02 ( f ) -0. 06 (g) 0. 02 ( f ) -0. 35 (g) v sin i (h) (km s -1 ) 2. We also include some additional data for a few of the targets from other spectrographs, namely HARPS  (Mayor et al. 2003)  and HIRES  (Vogt et al. 1994). All of the data products presented in this paper are available at the Data and Analysis Center for Exoplanets (DACE) 1. Fig. 1. Contours showing the astrometric predictions of each companion relative to the host star in ascension (∆α) and declination (∆δ) using the orvara orbit fitting package  (Brandt et al. 2021c) , with the measured companion positions shown by the overplotted gray stars. Relative astrometric positions indicate whether a companion can be directly detected with imaging given its projected separation between the host star and the companion itself.
High-contrast imaging The radial-velocity data taken from the CORALIE spectrograph  (Queloz et al. 2000) , as well as HARPS  (Mayor et al. 2003)  and HIRES  (Vogt et al. 1994) , are used as precursor observations to assess the detectability of any companion candidates through direct imaging. We combined the radial-velocity data with the astrometric information from the HGCA to determine the predicted relative astrometry in ascension and declination (∆α, ∆δ) of each companion on the sky, using the orbit-fitting code orvara  (Brandt et al. 2021c). The results of the predicted positions, which show full orbital fits of each system using the radial velocities and the astrometric accelerations from the HGCA, are shown in Fig. 1. The detectability of these objects was assessed by translating these values into a predicted contrast ratio and a projected angular separation for each system.
Orbital solutions In order to obtain orbital fits of our observed systems, we made use of orvara  (Brandt et al. 2021c) , which is an orbit-fitting code specifically designed to combine radial-velocity data, relative astrometry from direct imaging, and absolute astrometric data from the HIPPARCOS and Gaia. The orvara code  (Brandt et al. 2021c ) utilizes a comprehensive Markov chain Monte Carlo (MCMC) approach, incorporating astrometric accelerations from HIPPARCOS-Gaia eDR3 using the HIPPARCOS-Gaia catalog of accelerations (HGCA; Brandt 2021) and the intermediate astrometry fitting tool htof  (Brandt et al. 2021b  Using orvara, we were able to obtain robust orbital fits by combining the radial-velocity data with relative astrometry obtained from our VLT/SPHERE observations, as described in Sect.
7  and 8 , which helped further refine our derived orbital parameters and dynamical mass. HD 157338 was then observed with VLT/SPHERE program 105. 20SZ. 001 (PI: Rickman) on 2021 June 03, with a total observation time of 30 min, with a 32-s integration time on the coronagraphic frames using IRDIS, as well as several sky, center, and flux frames taken which was necessary for the postprocessing of the data. A follow-up observation of HD 157338 was taken on 2021 June 30, providing an additional relative astrometric point for our orbital fit. The direct detection of HD 157338 B is shown in Fig. 2. We ran orvara for the orbital fit with 500 000 steps in each chain, using the radial-velocity data from CORALIE, HARPS, and HIRES, along with relative astrometry from VLT/NACO and VLT/SPHERE, and astrometric accelerations from the HGCA.
The direct detection of HD 195010 B from the VLT/SPHERE observations is shown in Fig. 2. We ran the orbit-fitting code orvara with 500 000 steps in each chain using the radial-velocity data from the CORALIE survey, the relative astrometry from high-contrast imaging with NACO in 2006, as well as the relative astrometry from highcontrast imaging with VLT/SPHERE in 2021, and the astrometric accelerations from the HIPPARCOS-Gaia catalog of accelerations (HGCA; Brandt 2021). The best orbital fits, with 500 orbits randomly selected from the posterior distribution and a burn-in phase of 700 multiplied by every 250th step that is saved on the chain, are plotted alongside the RVs, and proper motions from HIPPARCOS and Gaia as shown in Fig. 9. From this we derived a companion mass of 217. 8 +3. 5 -3.
HIP 22059 HIP 22059 is a K5V star that has been observed with CORALIE at La Silla Observatory since January 2001. Twenty-nine measurements were taken with CORALIE-98, 9 additional RV measurements were obtained with CORALIE-07, and 30 additional RV measurements were obtained with CORALIE-14. HIP 22059 was also observed with HARPS  (Mayor et al. 2003)  with 11 RV measurements from December 2003 to August 2007. HIP 22059 was then observed with VLT/SPHERE program 0104. C-0724(A) (PI: Rickman) on 2019 November 27, with a total observation time of 1 h 30 min, with a 32-s integration time on the coronagraphic frames using IRDIS, as well as several sky, center, and flux frames taken which was necessary for the postprocessing of the data. The direct detection of HIP 22059 B is shown in Fig. 2. The orbital fit was run using orvara with 500 000 steps in each chain by combining CORALIE and HARPS radial velocities, with the relative astrometry outlined in Table  3 , and epoch astrometry from HIPPARCOS and Gaia eDR3.
By combining these techniques as described in this paper, we are able to detect companions by probing the giant planet to brown dwarf to low-mass stellar regime, and ultimately it will help us define the blurred boundaries between these different types of companions. In conclusion, we report the discovery of new directly imaged companions orbiting HD 142234, HD 143616 and HIP 22059. Furthermore, we report the first direct detection of HD 92987 B, and update the orbital parameters and dynamical masses of two previously reported companions, HD 157338 B and HD 195010 B. For each system we perform an orbital fit using orvara  (Brandt et al. 2021c ) by combining long baseline radial-velocity data from the CORALIE survey for extra-solar planets  (Udry et al. 2000) , along with long baseline proper motion anomalies from HIPPARCOS data  (ESA 1997; van   HD 142234  C are likely to be in a tightly bound orbit, orbiting around the primary star HD 142234 A.
Acknowledgements. This work has been carried out within the framework of the  NCCR PlanetS  supported by the  Swiss National Science Foundation  under grants  51NF40_182901  and  51NF40_205606. The authors acknowledge the financial support of the  SNSF. This publications makes use of The Data & Analysis Center for Exoplanets (DACE), which is a facility based at the  University of Geneva (CH)  dedicated to extrasolar planets data visualisation, exchange and analysis. DACE is a platform of the  Swiss National Centre of Competence in Research (NCCR) PlanetS , federating the Swiss expertise in Exoplanet research. The DACE platform is available at https://dace. unige. ch. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.
cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This research made use of the SIMBAD database and the VizieR Catalogue access tool, both operated at the  CDS, Strasbourg, France. The original descriptions of the SIMBAD and VizieR services were published in Wenger et al. (2000) and Ochsenbein et al. (2000). This research has made use of  NASA's Astrophysics Data System Bibliographic Services. This publication makes use of data products from the  Two Micron All Sky Survey , which is a joint project of the  University of Massachusetts  and the  Infrared Processing and Analysis Center/California Institute of Technology , funded by the  National Aeronautics and Space Administration  and the  National Science Foundation.
Unsupervised gene expression clusters recover genetic programs shaped by physiology The environmental gradients triggered changes in the expression of gene cohorts. We wanted to understand their concerted action independent of any prioritization guided by homology to any land plant genes-solely from the molecular programs that operated in the algae. To do so, we applied weighted gene co-expression network analysis (WGCNA) for unsupervised clustering (Figure  4 ). To then understand the driving forces behind these changes, we turned to the highly connected genes (nodes) in the network-the hubs (Figure  5 ). The 17,905 genes expressed in our samples (and that passed the minimum expression threshold) were clustered into 26 modules, which we refer to with colors (Figure  4a ). Orange is the smallest module (39 genes), the largest modules are Turquoise, Blue, and Brown with 3568, 3101, and 1746 genes, respectively.
Plate reader In vivo Abs480nm, Abs680nm, and Abs750nm of all 42 plates was measured after 65h exposition (4-6 hours after light on) with an absorbance microplate reader Epoch (BioTek Instruments, VT, USA). 9 data points per well were analyzed and averaged using software Gen5 2. 0 (Biotek, USA), resulting in 108 measurements per 12-well plate per wavelength. For downstream analyses these values were averaged resulting in one value per 12-well plate per wavelength (Suppl. Fig. 1 ). After 89 h exposition 16 plates were chosen from the prominent gradients (the four most extreme conditions in the corners and a cross of vibrant growth along the two gradients) for analyzing a full absorption spectrum (300-900nm) using the same setup. (Suppl. Fig. 9 , Suppl. Table  10 ).
Photophysiological measurements For maximum-quantum yield measurements (F v /F m ) the maxi version of the IMAGING PAM (ImagMAX/L, M-series, Walz, Germany) with an IMAG-K5 CCD camera, controlled with the ImagingWinGigE (V2. 32) software, was used. The Mesotaenium cultures in the 12-well plates were dark adapted for 10-30 min before measurement. Before measurements, the lid was removed. For the F v /F m measurement a short saturation pulse (Intensity 3) was applied. The measurement settings on the IMAGING PAM were the following: measuring light 1, gain 3, damping 2, mean over AOI (area of interest) was turned off. No special SP-routine was applied to modify the signal to noise ratio of the chlorophyll fluorescence measurement.
Statistical analysis of absorption and F v /F m values and temperature/light cluster analysis Statistical analysis of the absorption and the Fv/Fm values was done using Kruskal-Wallis test with post hoc test Fisher's least significant difference  (Conover, 1999)  using R (version 4. 1. 3). P-values were Bonferroni corrected and grouped into significant groups using R packages 'agricolae' version 1. 3-5 and 'dplyr' version 1. 0. 9. For heatmap generation of physiological values plotted against temperature/light R package 'pheatmap' version 1. 0. 12 was used. For cluster analysis the R package 'factoextra' version 1. 0. 7 was used. Clusters were generated using the eclust function with clustering function 'kmeans', with number of clusters set to six and for hierarchical clustering 'euclidean' was used as distance measure.
Genome annotation The original annotation of M. endlicherianum  (Cheng et al. , 2019)  had a lower number of genes compared to other Zygnematophyceae algae. We took advantage of our newly generated RNAseq dataset to improve genome annotation. Trimmed reads were mapped via HISAT2  (Pertea et al. 2016, 2)  and assembled via StringTie  (Pertea et al. 2016, 2). StringTie results showed many novel isoforms as well as novel transcripts. We also used BUSCO V5  (Manni et al. 2021)  to measure the completeness of the gene models in annotation V1 independent of StringTie. Although the gene prediction method which used by BUSCO at the genome level is very efficient, it is not unexpected if it misses some proteins that were annotated in a genome via experimental, based on bioinformatic methods and NGS data, or ab-initio based gene prediction methods.
Therefore, we expect that the BUSCO score based on the proteins of a gene model should be equal to or greater than the BUSCO score of the genome. When we compared the BUSCO score between the genome and protein sequences for M. endlicherianum with "viridiplantae. odb. 10-2020-09-10", we noticed that they show similar numbers (Suppl. Fig. 2 ). Therefore, we decided to re-annotate the genome of M. endlicherianum with our comprehensive RNA-Seq datasets as well as public protein and genome sequences published for its close relatives. We annotated the M. endlicherianum genome using REAT (v0. 6. 1). Various gene models were predicted based on different types of evidence and methods. The final gene models and annotation V2 were based on agreement with the experimental evidence. At the end, we tried to quantify "completeness" and quality of the new annotation V2 and the old V1.
First, we used RNAseq evidence with REAT's "Transcriptome Workflow" with HISAT2 (v2. 2. 1), Scallop  (Shao and Kingsford 2017) (v0. 10. 5)  and  StringTie (v2. 1. 5). We also used Portcullis  (Mapleson et al. 2018) (v1. 2. 4)  to identify genuine junctions based on short reads alignments. This workflow uses Mikado  (Venturini et al. 2018) (v2. 3. 4 ) to identify the "best" set of transcripts from multiple transcript assemblies. Then, we used gene homology information from representative streptophytes in REAT's "Homology Workflow". SPALN  (Gotoh 2008a; 2008b) (v2. 4. 7)  was used to align representative protein sequences onto the M. endlicherianum genome. The representative dataset consisted on genome, gene models, and protein sequences of Anthoceros agrestis (Oxford strain)  (Li et al. 2020) , Arabidopsis thaliana (C.
-Y. Cheng et al. 2017) , Azolla filiculoides  (Li et al. 2018) , Chara braunii  (Nishiyama et al. 2018) , Chlorokybus melkonianii  (Wang et al. 2020)   (Banks et al. 2011) , and Spirogloea muscicola (S. Cheng et al. 2019 ). We also used the junction file produced by Portcullis. Since there were no close relatives of M. endlicherianum on the SPALN species-specific parameter set, we used three different closest possibilities (Angiosp, Chlospec, and MossWorts) and built three models. These alignments are filtered using a predefined set of criteria (cf. code on GitHub) including exon length, intron length, internal stop codon, among others. The final gene models of V2 were prepared by Mikado. Afterwards, we used REAT's "Prediction Workflow" to predict gene models ab initio and based on RNAseq and homology evidence.