dataesr/scientific-paragraphs-categorization huggingface dataset:
Assessing the coherency of the climate space In every study involving estimating relationships between biological entities and environmental parameters, the first step is to ensure that the defined study area and associated calibration dataset are coherent. This includes ensuring that (1) all the essential taxa observed in the past are present in the study area, and their distributions are not truncated, (2) the climate values to reconstruct are likely to be covered by present-day climate values (the reconstructions are bounded by the lowest and highest values observed in the modern climate space) and (3) there is no large sampling or representativity bias (e. g. along country borders due to different sampling efforts). The "climateSpace" graphical diagnostic tool (Fig. 6 ) was designed for a rapid assessment of all these characteristics: Ideally, the climate values sampled by the calibration data should be as homogeneous as possible to ensure proper representation of all the possible climate values, even if the extreme climate values will always be under-represented com-pared to the median ones.
However, deviations from a theoretical equivalence between the observed climate distribution and the climate values sampled by the calibration data are not necessarily a bad characteristic. In our case study, the variability of the sampling density represents actual patterns in regional species diversity with the presence of several biodiversity hotspots across the mountainous regions of eastern and southern Africa  (Myers et al. , 2000). This higher diversity in the colder areas explains why the black histogram (i. e. the climate values associated with occurrence data) in Fig. 6  is skewed towards the left compared to the grey histogram (i. e. the distribution of the climate space in the study area). All these elements should be checked and accounted for while designing the final calibration dataset.
As illustrated in Fig. 7 , Ericaceae is preferentially observed in the colder environments of the study area, its higher percentages occur during glacial periods, and a coherent response of all its composing species can be observed despite a high diversity (141 species with at least 20 unique occurrences across the study area). All these elements indicate that Ericaceae can be considered a cold environment Differences between the two histograms can be used to identify biases in the calibration dataset. Here, the small shift of the black histograms towards colder values (towards the left) is another way of seeing that more data are available from South Africa than other countries and reflects, in part, the regional patterns of biodiversity. If more variables had been selected in this study, additional rows would be added to the figure with a similar climate map and histograms and a scatterplot of the climate variables to highlight potential local or regional modern correlations.
indicator in eastern and southern Africa. Sensitivities to other variables can also be expected but are not considered in this study. Similar sensitivity inferences can be made to define a list of temperature-sensitive taxa to reconstruct MAT (Chevalier and  Chase, 2015; Chevalier et al. , 2021a)  or support qualitative interpretations of palaeoecological datasets  (Chevalier et al. , 2021b; Quick et al. , 2021). A complementary diagnostic plot to assess climate sensitivities is the "violinPDFs" (Fig. 8 ): tax <-sample(rcnstrctn$input$taxa. name, 15, replace=FALSE) The violin plot represents the PDFs of a selection of taxa on the same plot, which helps compare the shape and spread of the different responses. All the violins have the same area (all the probabilities sum to 1), and the taxa are ranked by increasing values of their temperature optima (i.
reconstruct() function: rcnstrctn <-crest. reconstruct( rcnstrctn, presenceThreshold = 0, taxWeight = "normalisation", verbose = TRUE) A minimum "presence threshold" below which the taxa will always be considered absent can be provided to reduce the noise of the fossil dataset (e. g. pollen percentages lower than 1 % or 2 % are commonly excluded from reconstructions,  Chevalier et al. , 2020b). When presenceThreshold is set to zero, as is the case here, all the strictly positive pollen percentages are considered as actual presences and used accordingly to reconstruct MAT. To weigh the taxa as described in Eq. (  7 ), four options are available in crestr: -The data can be converted to presence/absence with all the values above and below presenceThreshold being changed to ones and zeros, respectively.
A wrapper function To simplify the use of the package, the three stages of the reconstruction process -data acquisition (crest. get_modern_data() or crest. set_modern_data() if the gbif4crest dataset is not used), calibration (crest. calibrate()) and reconstruction (crest. reconstruct()) -can be called in one line of code using the wrapper function crest(). This function takes the same parameters described in this "step-by-step" guide with the same default values and may be more practical when reconstructing several records in one run.
Exporting the reconstructions All the data stored in the crestObj can be easily exported from the R environment as spreadsheets and RData files using either export_pdfs() to save the climate responses of the studied taxa or export() to save the reconstructions and many associated data in a publishable format. The latter also saves the crestObj as an RData file for easy reloading and sharing of the data: export(rcnstrctn, loc='path/to/folder', fullUncertainties = TRUE, loo = TRUE, weights = TRUE, pdfs = TRUE).
Citing building elements Finally, all the reconstructions derived from crestr are built on numerous independent research efforts, including data compilations, modelling projects, statistical developments and software engineering. To support the long-term growth and visibility of all these building elements, it is crucial always to acknowledge them, even if their processing is invisible to the users. The list of references that must be cited for each use of crestr is automatically included in the summary tab of the spreadsheet generated by the export() function. In addition, the citation information can also be directly obtained from R: cite_crest(rcnstrctn) The function looks at the type of data used in the analysis (e. g. the subset of the GBIF data were used and the climate variables) and returns a corresponding list of references.
For example, a simple way of crediting all the contributors for the MAT reconstruction from marine core MD96-2048 presented here could be the following: To create this MAT reconstruction from the pollen record from marine core MD96-2048  (Dupont et al. , 2019) , we employed the CREST method  (Chevalier et al. , 2014; Chevalier, 2019). The PDFs were estimated by combining the MAT field of  Fick and Hijmans (2017)  and plant occurrence data from GBIF (GBIF, 2020m, k, g). The numerical analyses were realised with the crestr R package v1. 0. 1 (this reference).
Perspectives and conclusion CREST is a probabilistic framework designed to model proxy-climate relationships from modern presence observations and to use these relationships to reconstruct past climate  (Chevalier et al. , 2014). The method's mathematics and assumptions enable an easy application everywhere, even in data-sparse regions. While developments to complexify the method are possible, the current version of the algorithm has proven to be reliable at producing high-quality reconstructions. The first version of the crestr R package is thus dedicated to implementing the original version of the algorithm. It also replaces the original point-and-click CREST software, which was challenging to maintain over time. A critical novelty of crestr is the inclusion of the CREST algorithm in a programmatic environment.
The benefits of this transition are significant and include improved scriptability (i. e. the possibility of analysing many records automatically and sequentially), reproducibility (i. e. the capacity to reproduce an analysis) and better inter-operability (i. e. R packages are compatible with all computer systems). However, maintaining the highest level of accessibility remained at the core of the development process and is illustrated in the final product by the small number of functions necessary to run a complete analysis and the suite of detailed graphical diagnostic figures. In line with the objectives of the original software, the crestr package is aimed at all researchers interested in using CREST to reconstruct climate from palaeoecological datasets, including those with limited coding expertise.
In addition, its broad applicability will allow taking advantage of the recent growth of curated, open-access fossil datasets that offer unprecedented opportunities to reconstruct climate from a wide range of proxies, particularly in regions where quantified climate reconstructions are urgently needed. This package will thus also contribute to the current transition from single-site to multi-site studies, which is necessary to better understand past climate dynamics. However, it is essential to remember that running such techniques on several datasets should always be done carefully, as many factors can impact the reconstruction process. In practice, calibration datasets, variable selection and reconstructions should be assessed, ideally against independent evidence when they are available, even if there is no single way to validate a reconstruction (but see  Chevalier et al.
, 2020b , for some discussions on the generic principles). Over time, the crestr package will be enriched with new functionalities to facilitate reconstruction validation. The online documentation will also be updated with diverse examples and tutorials based on real applications and assessments (https://mchevalier2. github. io/crestr/, last access: February 2022). Future package updates will include Bayesian modules and propose more complex strategies to estimate proxyclimate relationships from data-dense regions. Finally, bug reports, feedback, and suggestions for newer functionalities and graphical diagnostic tools are encouraged and can be transmitted to the author directly or through GitHub's bug report portal (https://github. com/mchevalier2/crestr/issues, last access: February 2022).
Code and data availability. The crestr package is currently accessible from CRAN (https://CRAN. R-project. org/package= crestr, last access: 13 April 2022,  Chevalier, 2022b). The version used in this paper (v1. 0. 1) is available on Zenodo (https://doi. org/10. 5281/zenodo. 6458405, Chevalier, 2022a) and future development versions will be available on GitHub (https: //github. com/mchevalier2/crestr, last access: February 2022), where I also welcome feedback and strongly encourage contributions via the issue tracker (https://github. com/mchevalier2/crestr/issues, last access: 13 April 2022). The source data used for the example application (pollen record from marine core MD96-2048 and associated temperature reconstructions) can be accessed from https://doi. org/10. 1594/PANGAEA. 915923  (Chevalier et al.
, 2020a). NOAA High-Resolution SST data were provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, from their website at https://psl. noaa. gov/data/gridded/data. noaa. oisst. v2. highres. html (last access: September 2021,  Reynolds et al. , 2007). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-18-821-2022-supplement. Competing interests. The author has declared that there are no competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgements. I thank Brian Chase, Yoshi Maezumi, Lynne Quick, Kale Sniderman and, last but not least, Mine Altinli for their invaluable feedback on early versions of the package and manuscript.
And finally, I would also like to address a special thanks to the rticles contributors for their Climate of the Past R Markdown template! Financial support. This research has been supported by the Swiss National Science Foundation (grant no. CRSK-2_195875), with additional support from the University of Lausanne. Manuel Chevalier is also supported by the German Federal Ministry of Education and Research (BMBF) as a Research for Sustainability initiative (FONA; https://www. fona. de/en, last access: 15 December 2021) through the PalMod Phase II project.
(2007) icec_ond Mean fall sea ice concentration (%) Reynolds et al. (2007) diss_oxy Dissolved oxygen concentration (µmol L -1 ) Garcia et al. (2018a) nitrate Nitrate concentration (µmol L -1 ) Garcia et al. (2018b) phosphate Phosphate concentration (µmol L -1 ) Garcia et al. (2018b) of marine variables available for reconstruction in the gbif4crest database. Each one can be selected in crestr using its associated code. SST -sea surface temperature, SSS -sea surface salinity. silicate Silicate concentration (µmol L -1 ) Garcia et al. (2018b) Figure 4. Structure of the gbif4crest PostgreSQL database. By default, the package extracts data from the TAXA, DISTRIB-QDGC and DATA-QDGC tables. The DISTRIB table contains the raw occurrence data obtained from GBIF and can be used to curate the distribution data at a different spatial resolution.
When possible, data regarding the sgcb -/-line have been reported anyway, mainly presented in the Supplementary Materials. When the survival rate of the sgcd -/-line was determined, it was clear that the mutated and wild type fish behaved similarly up to 10 dpf. Conversely, when fish started to feed autonomously, the curves diverged significantly, with an average of 40% of sgcd -/- fish reaching adulthood in comparison to about 60% of the wild type ones (Figure  S3 ). On the whole, these findings indicate a milder phenotype of the null mutants, particularly at the early stage of development. This is rather different from what was observed in fish resulting from the knocking down of δ-SG with morpholino oligos (MO), in which a severe phenotype was evident since 24 hpf  [24, 25]  (Supplementary Materials Figure  S4 ).
This was observed at first by recording free fish swimming for more than 24 h. In this condition, the initial increase in movement of both genotypes in response to the switching on of the light  [29, 62]  was followed by a rapid decline in the swimming activity in the mutants only, suggesting that these animals performed less than the controls. The evidence that the sgcd -/-line suffered a higher fatigability was confirmed by the challenging experiment in the swim tunnel  [37] , where the sgcd -/-fish, forced to swim countercurrent, stopped swimming significantly before the wild type. These data suggest a severe skeletal muscle impairment, where damaged contractile tissue was partially replaced by fibrotic and adipose tissue, as demonstrated by the histological and TEM analyses of one-year-old zebrafish.
Generation of Mutated Zebrafish Lines by CRISPR/Cas9 Genome Editing The sgRNAs targeting either the sgcd or sgcb gene at the exon2 level were designed by using the Chop-chop software v3 (http://chopchop. cbu. uib. no/ accessed on 10 January 2020). RNA guides with the lowest predicted off target events and the highest matching score for the desired sequences were selected and are reported in Supplementary Materials Table  S1. The sgRNAs were produced by using the MEGAScript Kit SP6 (Thermo Fisher Scientific, Ambion, Waltham, MA, USA). Zebrafish WT zygotes were injected with 8 nL of a solution containing 70 ng/µL of sgRNA, 300 ng/µL of Cas9 NLS (Nuclear Localization Signal) protein (New England Biolabs, Ipswich, MA, USA), 1× Phenol red dye, and 1× Danieau injection buffer by using a WPI pneumatic PicoPump PV820 injector.
Genotyping of sgcd and sgcb Mutants Genomic fragments of the targeted regions were amplified by PCR with the 2X Dream-Taq Hot Start PCR Master Mix (Thermo Fisher Scientific, K9012) and 0. 25 µM of each specific primer designed by the software Primer blast (https://www. ncbi. nlm. nih. gov/ tools/primer-blast/index. cgi accessed on 10 January 2020) that are listed in Supplementary Materials Tables  S1  and S3. Amplification conditions were as follows: 5 min denaturation at 95 • C, followed by 30 cycles of 30 s at 95 • C, 30 s at 60 • C, 30 s at 72 • C, final extension of 5 min at 72 • C. Mutations in F0 were detected by the heteroduplex mobility shift assay. After DNA amplification, denaturation at 95 • C for 15 and slow renaturation, the PCR products were run on a 10% 29:1 polyacrylamide gel to observe the presence of heteroduplex bands in comparison to the WT single band.
For verification, PCR products from fish harboring indel mutations were sequenced. Poly Peak Parser software (http://yosttools. genetics. utah. edu/PolyPeakParser/ accessed on 10 January 2020) was used for the identification and sequence characterization. Alleles from heterozygous and homozygous fish (F1 and F2) were cloned in pGem-T easy vector (Promega Corporation, Madison, WI, USA) by TA cloning, according to the manufacturer's instructions, and subsequently used for Sanger sequencing.
Body Length Measurement Body length of the KO and control zebrafish was measured at 3 and 5 dpf and adult animals. Zebrafish were anesthetized by tricaine and digital images were captured using a Leica M80 stereomicroscope fitted with a Leica MC170HD digital camera. Body length was measured from the mouth tip to the tail base along the major body axis in the larvae while both axes were considered in adult fish by using the ImageJ software (version 1. 53t).
Larvae Locomotion Assays Continuous recording at basal conditions: larvae at 3 dpf were placed in a 96-well plate (1 larva per well, 300 µL of FW medium) in the observation chamber of the DanioVision tracking system (Noldus Information Technology, Wageningen, The Netherlands). Locomotor activity was tracked for about 2 consecutive days and then analyzed by Ethovision 11 software (Noldus Information Technology, NL, Wageningen, The Netherlands). The IR-sensitive camera was set to 25 frames per second. Swimming activity of each larva was calculated as the distance moved during 6 min time windows. A minimal distance movement of 0. 2 mm was used. Fish were kept under 12:12 LD cycles (lights on at 08:00, lights off 20:00). Light startle test: larvae at 5 dpf were placed in a 24-well plate (1 larva per well, 1 mL of FW medium) in the observation chamber of the DanioVision tracking system (Noldus Information Technology, NL).
Adult Fish Locomotion Assay From the 7-month-old fish, locomotor activity was video-tracked for 2 days and then analyzed by Ethovision 11 software (Noldus Information Technology, NL). The IR-sensitive camera (Monochrome GigE camera, Basler, Ahrensburg Germany) was set to five frames per second. Locomotor activity of each fish was calculated as the total distance moved during a 6 min time window. A minimal distance movement of 2 mm was used. The IR backlit table (λ > 980 nm) had dimensions of 120 × 120 cm and four fish were placed in an arena of 40 × 40 cm. Fish were kept in 12:12 LD conditions. For the light source, an array of white LED strips (Superlight Technology Co. , Ltd. , Shenzhen, China) was used, and irradiances were set at 0. 17 W/m 2. Irradiance was measured with a radiometer (DO9721, Probe LP9021 RAD, spectral range 400-950 nm, DeltaOHM, Padova, Italy).
Statistical Analysis Data were expressed as the mean ± SD. Statistical differences among groups (control vs. mutated) were determined using Pris, Version 10. 0. 2 GraphPad software (San Diego, CA, USA) by the non-parametric Mann-Whitney test. If more than two groups were compared, statistical differences were determined by the one-way ANOVA test, followed by Tukey's multiple comparisons test. A level of confidence of p < 0. 05 was used for statistical significance.
Acknowledgments: Special thanks to the  zebrafish facility  at the  Department of Biology  for the valuable advice and help during this study. We thank  F. Caicci  and  F. Boldrin  ( EM Facility, Department of Biology, University of Padova ) for the electron microscopy sample preparation and image acquisition. The authors also thank  A. Margutti ,  Department of Life Science and Biotechnology, University of Ferrara , for the technical support.
Context. The Solar Orbiter mission completed its first remote-sensing observation windows in the spring of 2022. On 2 April 2022, an M-class flare followed by a filament eruption was seen both by the instruments on board the mission and from several observatories in Earth's orbit, providing an unprecedented view of a flaring region with a large range of observations. Aims. We aim to understand the nature of the flaring and filament eruption events via the analysis of the available dataset. The complexity of the observed features is compared with the predictions given by the standard flare model in 3D. Methods. In this paper, we use the observations from a multi-view dataset, which includes extreme ultraviolet (EUV) imaging to spectroscopy and magnetic field measurements. These data come from the Interface Region Imaging Spectrograph, the Solar Dynamics Observatory, Hinode, as well as several instruments on Solar Orbiter.
Conclusions. The extensive dataset covering this M-class flare event demonstrates how important multiple viewpoints and varied observations are in order to understand the complexity of flaring regions. While the analysed data are overall consistent with the standard flare model, the present particular magnetic configuration shows that surrounding magnetic activity such as nearby emergence needs to be taken into account to fully understand the processes at work. This filament eruption is the first to be covered from different angles by spectroscopic instruments, and provides an unprecedented diagnostic of the multi-thermal structures present before and during the flare. This complete dataset of an eruptive event showcases the capabilities of coordinated observations with the Solar Orbiter mission.
M-class flare on 2 April 2022 On 2 April 2022, two M-class flares were recorded between 12:00 and 22:00 UT from the region containing AR 12975 and 12976, as shown by the GOES X-ray Sensor (XRS) light curves in Fig. 3. This figure also shows the availability of the FUV/EUV datasets presented in the paper at different times during the flares. We focus on the first flare, which featured a filament eruption and CME and had a GOES classification of M3. 9. The filament was located between the two active regions, as indicated with the yellow contour shown in Fig. 2d. The flare started around 12:50 UT, and reached a peak at 13:56 UT in the low energy bands of GOES-16. The impulsive phase shows a twostep increase in the GOES/XRS light curve rather than a monotonic increase usually seen in flares. The peak is followed by a long decrease, as often seen in eruptive flares with long-lasting flare loops (e.
5 Å and, in particular, images formed from the core of the line, which is referred to as k3 (e. g. ,  Pereira et al. 2013). We analysed the level-2 data, downloaded from the IRIS database 4. To find the extrema of the Mg ii spectral line (intensities) for the whole raster, we used the SolarSoftWare (SSW) routine iris_get_mg_features_lev2  (Pereira et al. 2013). Finally, the routine computed the intensity raster map of Mg ii k3. The raster maps of the Mg ii k3 line core also show the filament seen in the AIA images before the flare peak (Fig. 5a ). Interestingly, we also see that the lowest portion of the northern Notes. All timings are given at Earth time. The white arrows highlight the hook part of the filament, as well as the dotted contour lines in the AIA and EUI/FSI observations. part of the filament is still present after the flare, as indicated by the white arrow in Fig.