dataesr/scientific-paragraphs-categorization huggingface dataset:
Data processing Each densitogram of a track on the HPTLC plate set at 254 nm defines a fingerprint associated with a retention time for each peak correlating with an integrated area. All data points were exported into an Excel sheet to build a matrix table. All peaks were integrated automatically (minimum slope: 5; minimum height: 10 AU; minimum area: 40 AU; maximum height: 990 AU) and then normalised based on their retention time and area, using butein as internal standard.
Statistical analysis The matrix generated from the HPTLC data was used for multivariate analyses. A fourth-root transformation of the values was applied, centering and autoscaling as null values and great differences in concentrations according to chemical profiles were observed (van den  Berg et al. 2006). Principal Component Analysis (PCA) was performed on the normalised values to discriminate the extracts obtained from plants harvested at the two altitudes on the basis of their metabolic profiles (function dudi. pca, package "ade4"). All statistical analyses were made using R software V. 3. 3. 1 (R Core Team, 2016).
Isolation and identification of differential compounds present in both extracts Preparative TLC plates 30 x 30 cm, with a thickness of 1 mm, were prepared using the stationary phase silica gel 60GF 254 and developed in the mobile phase chloroform / ethyl acetate (75:25). Relevant areas were scraped from the plates and compounds eluted from the silica with methanol. NMR spectra were recorded with a Bruker Avance I 300 MHz spectrometer, and the residual solvent signals were used as internal references: MeOD δH = 3. 3 ppm, δC = 49 ppm. Additional spectra were recorded with a Bruker AVANCE III HD 500 MHz spectrometer in C 6 D 6 (δH = 7. 16 ppm, δC = 128. 06 ppm). The ESI HRMS data were recorded in positive mode using two spectrometers, an AGILENT 6510 and a THERMO FISHER SCIENTIFIC Q-Exactive.
Acknowledgements We acknowledge funding from  CAPES (Capes Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) , a Brazilian federal government agency under the  Ministry of Education and FAPESC: A Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina. The authors are also grateful to  Aurélie Bernard  for her technical help. We would like to express our thanks to  Centre Régional de Mesures Physiques de l'Ouest, Université de Rennes 1, France  for MS measurements. We are most grateful to the  PRISM core facility (Rennes-Angers, France)  and to the  SPECTROSCOPIES CDTP core facility (Rennes, France)  for their technical support.
94, median=1. 87, estimated in TPM, Figure  2B , raw data provided in Table  S3 ) compared to the control line. There is thus evidence of overexpression of the duplicated genes. As no DC could be observed at the transcriptional level, we investigated whether post-transcriptional processes may temperate this twofold excess of transcripts. This second hypothesis was tested in the same B. prasinos MA line by sequencing mRNAs associated with ribosomes (polysomes) in order to compare the translation efficiency (TE) of genes located on duplicated and non-duplicated between lines. The translation efficiency of each transcript was estimated by the ratio of mRNAs in polysomes versus total RNA between Bp37B and control. We found that the average TE for the genes located on the duplicated chromosome 4 was 0.
Acknowledgements We are grateful to  Claire Hemon  and  Elodie Desgranges  for technical assistance with the MA experiments. We acknowledge the  GenoToul Bioinformatics platform (Toulouse, France ) for bioinformatics analysis support and cluster availability, the  BIOPIC  platform for support with the cytometry, and the sequencing facility of the  Université de Perpignan Via Domitia BioEnvironnement platform. This work was funded by  ANRJCJC-SVSE6-2013-0005  to GP and SSB. This study is set within the framework of the  "Laboratoires d'Excellences (LABEX) "  TULIP  ( ANR-10-LABX-41 ) and of the  "École Universitaire de Recherche (EUR) "  TULIP-GS  ( ANR-18-EURE-0019 ).
Data availability statement All genomic raw reads of ML and mutation accumulation lines are available under the bioprojects PRJNA531882 (Ostreoccocus tauri, O. mediterraneus, Micromonas pusilla, Bathycoccus prasinos), PRJNA453760 and PRJNA389600 (Picochlorum costavermella), and PRJNA478011 (Phaeodactylum tricornutum). Transcriptomic raw reads of the Bathycoccus prasinos MA lines 37 (Bp37B) and control line RCC4222 are available under the bioproject PRJNA715163. A summary of all data is provided in Table  S5.
The emergence of multidrug-resistant bacteria and fungi requires the development of antibiotics and antifungal agents. This review identified natural products isolated from Asian angiosperms with antibacterial and/or antifungal activities and analyzed their distribution, molecular weights, solubility, and modes of action. All data in this review were compiled from Google Scholar, PubMed, Science Direct, Web of Science, ChemSpider, PubChem, and a library search from 1979 to 2022. One hundred and forty-one antibacterial and/or antifungal alkaloids were identified during this period, mainly from basal angiosperms. The most active alkaloids are mainly planar, amphiphilic, with a molecular mass between 200 and 400 g/mol, and a polar surface area of about 50 Å 2 , and target DNA and/or topoisomerase as well as the cytoplasmic membrane.
The present work attempts to provide a comprehensive review of the main findings regarding the antibacterial and antifungal alkaloids from Asian angiosperms. All data in this review were compiled from Google Scholar, PubMed, Science Direct, Web of Science, ChemSpider, PubChem, and a library search from 1979 to 2022 and were analyzed to address the following points: (i) distribution; (ii) strongest principles identified; (iii) spectrum of activity; (iv) influence of molecular mass, water solubility, and polar surface; (v) the mode of action; (vi) structure-activity; and (vii) efflux pump inhibition.
In some databases, one can find a Log P of 3 for the ionic alkaloid berberine, suggesting a lipophilic substance, which is not sensible. Since compounds destined for pharmaceutical development will mainly be exposed to physiological pH and often a weak base or a weak acid, we defined, at pH 7. 4, lipophilic compounds for a negative Log D value of 4, amphiphilic (mid-polar) compounds for a Log D up to about 4. 5, and lipophilic for a Log D above 4. 5. Note that the Log D values given here are predicted values. Following this classification, we noted that principles with very strong activity against bacteria and fungi were mainly amphiphilic, whereas very strong antimycobacterial agents were mainly lipophilic (Table  S2 ). Most antibiotics with very strong antibacterial and/or antifungal effects have a polar surface area around 50 Å (Table  S2 ).
Experimental Sites, Plant Material, and Cultivation Flower initiation and plant performance were studied in six June-bearing strawberry genotypes in the years 2016-2019. Each year, the experiment started in August and ended after anthesis the next spring. Plants were grown at five locations from north to south of Europe: in Kapp, Norwegian Institute of Bioeconomy Research (NIBIO), Norway (60 out in an open field, whereas in Italy and France, they were performed in polytunnels that were opened sideways from the beginning of anthesis. Details of the respective latitude, altitude, yearly mean temperature, and type of cultivation as well as soil type are given in Table  1. Air temperature (T mean , T max , and T min ) and global radiation were recorded at each location. Additional climatic data for the experimental periods are presented in Table  A1.
Plant Traits and Environmental Factors In order to evaluate the impact of latitude-related environmental factors on the time of FI and anthesis as well as the number of crowns and flowers per plant, daily temperature (T mean , T max , T min ), global radiation, and photoperiod were calculated based on recorded data for all locations and cultivars and expressed as means of the relevant period: (a) for the 3-week period before FI and (b) for the 5-week period after FI. For the 3-week period before anthesis, growing degree days (GDD) were calculated according to the equation GDD = [(T max + T min )/2] -T Base , where T max and T min are the mean daily maximum and minimum temperature and T base is the threshold temperature below which the plant development is markedly reduced. In the present study, we checked results using 3, 4, 5, and 6 • C as T base , and based on the strongest output, only results with 5 • C is presented here.
Statistical Analyses Since plant traits were registered at 10-day intervals, the cubic spline smoothing function within the R 4. 2. 1 statistical software (R Core Team, 2021) was applied to the registered data after the removal of outlying samples to interpolate apex flowering stages. The root mean square error of the spline function, calculated on the days of the actual registration, averaged at 0. 54 (floral dev. stage), which was considered acceptable, taking into account that the resolution of the registrations was 10 days and that the nominal registration step was 1. The date of the actual flower initiation was then extracted from the interpolated data (Figure  A1 ). To test whether climatic parameters and genotype are affecting plant physiological responses, statistical variance analysis (ANOVA) was used to detect the differences between physiological traits, and simple linear regression evaluated the relationship between the environmental conditions of the different locations and cultivar-specific needs of temperature, global radiation, and photoperiod before and after FI and before anthesis.
The analyses were performed using SPSS version 26. Significant differences were calculated using post hoc Tukey's test at a 5% probability level. Principal component analysis (PCA) was performed using the Minitab ® Statistical Software program package (Release 19, Minitab Inc. , State College, PA, USA). PCA was built using a correlation matrix and R-mode. PC1, PC2, and PC3 represented 80. 2% of the total variance. The selection is based on the size of eigenvalues and the Kaiser criterion (>1).
To test the hypothesis that climatic conditions are affecting the number of crowns and flowers in strawberry plants, temperature (T mean ) and global radiation were related to the phenological observations across the experimental locations. The effect of the environment before and after FI was assessed as a factor. The observed relation between FI and climatic conditions within the locations was negligible as all cultivars experienced relatively similar conditions in each geographical site (Table  4 ). In France, the number of flowers and crowns was evaluated in 2 years only, and showed a strong year effect (Table  3 ). Therefore, the observed significant data for France are not comparable to the other locations. However, when the physiological response of individual cultivars across the locations was studied (Figures  4  and 5 ), a stronger relationship was observed due to the extended variability of the climatic conditions across the continent.
Statistical Analysis Did Bees Have an Innate Preference for Odd or Even Numerosities? To determine whether bees had any prior preference to odd or even numerosities before training we estimated the mean of the "even number" choices, the intercept of a generalized linear mixed model (GLMM) with a binomial response, using individual bees as a random, categorical variable to account for the repeated measurements. Choice (even or odd number) was coded as a binary response (even or odd) modeled by a binomial distribution. The statistical tests and models were performed on the R environment for statistical analysis using the routine "glmer" available as part of the "lme4" package written for the R statistical language, run in R version 4. 0. 3 analysis (R Core Team, 2020).
Did Bees in Both Groups Learn Equally? To determine if the bees in the two groups (trained to even; trained to odd) learnt equally, we statistically compared the number of trials required for bees to achieve criterion in both groups by means of a GLM model. The response variable included the number of trials taken to reach criteria by bees belonging to each group and stimuli group as a categorical predictor with two levels: even and odd. We initially assumed a Poisson distribution to model the response variable but the resulting model was overdispersed (overdispersion = 2. 98). We thus assumed a negative binomial distribution for the response variable as usually done to alleviate overdispersion of Poisson models  (Zuur et al. , 2013). We used the routine "glm. nb" available in the package MASS  (Venables and Ripley, 2002)  for the R statistical program to fit the negative binomial model.
A total of 26 bees were divided across the two levels of the experimental group factor (13 in each group), and each bee was then tested under all levels of the test factor. The model was fitted using the routine "glmer" available in the lme4 library  (Bates et al. , 2015)  available for the R language. Statistical significance of the model terms was tested by means of Type III Wald Chi square test using routines available in the package car  (Fox and Weisberg, 2019)  for R.
Analysis of the Stimuli To determine whether there was a significant difference in the spatial information content for the odd and even stimuli, we calculated the rotational average of the power spectrum of each image to produce a frequency spectrum for each image using the fast Fourier transform routine available in MATLAB release 2016b. We then calculated the area under each spectrum corresponding to the stimuli of the odd and even sets, and compared them by means of a Wilcoxon rank-sum test. Experiment 2: Discrimination of 11 vs. 12 Using a similar protocol as detailed above in Experiment 1, we trained and tested bees on their ability to discriminate between stimuli containing 11 and 12 elements. This experiment was performed in 2018, as their ability to categorize those numerosities during the transfer test in the Experiment 1 was unexpected considering the honeybee's previous limit of counting and discriminating numerosities.
FUNDING SRH acknowledges the  Australian   Government Research Training Program (RTP) Scholarship, HDR Candidate Funding  from the  School of Media and Communication at RMIT University , the  Fyssen Foundation , the  L'Oréal-UNESCO for Women in Science Young Talents French Award , and the  Alfred Deakin Postdoctoral Research Fellowship from Deakin University. AGD  acknowledges financial support from the  Australian Research Council  ( FT160100357 ).
Rheological measurements Rheological measurements of the coacervates were carried out using a stress-controlled rheometer (DHR2, TA Instruments, Guyancourt, France) with a cone -plate geometry (angle of 2 • , diameter = 20 mm, truncature = 51. 5 μm) and a parallel plate geometry (diameter = 20 mm) with gap values of 250 μm and 500 μm. Whatever geometry is used, around 200 mg of coacervates samples were loaded on the Peltier plate at 20 • C and allowed to rest for 2 min before measurement. All rheology measurements were performed at least twice and plotted as means and standard deviations and fitting of data was performed with python (version: 3. 9. 5) and excel software.
Cryo-EM single particle analysis of the different compaction states of the Mimivirus genomic fiber In order to shed light on the Mimivirus genome packaging strategy and to determine the structure of the purified genomic fibers, we performed cryo-EM single-particle analysis. The different conformations of the genomic fiber initially observed by negative staining (Fig. 1 ) and cryo-EM resulted in a highly heterogeneous dataset for single-particle analysis. In order to separate different conformations, in silico sorting through 2D classification (using Relion  (28, 29) ) was performed. Next, we carried out cluster analysis relying on the widths of the helical segments and correlations (real and reciprocal spaces) between the experimental 2D class patterns (Figs. S4 & S5). Three independent clusters (Cl) could be distinguished, corresponding to the compact (Cl1), intermediate (Cl2) and relaxed (Cl3) fiber conformations (Fig.
Models of the three helical assemblies and asymmetric unit were further refined using the real-space refinement program in PHENIX 1. 18. 2  (33). In the 3. 3 Å resolution map of the asymmetric unit, most side chains and notably the FAD co-factor are accommodated by density suggesting that the oxidoreductase enzyme could be active (Fig. 3D ). Density that can be attributed to the FAD cofactor is also present in the Cl2 and Cl3a maps. The atomic models of Cl1a and Cl3a dimers are superimposable with a core RMSD of 0. 68 Å based on Cα atoms. 143 dimers fitted into one of the Cl2 6-start helix strands in the respective 4. 0 Å resolution map (C). The maps are shown at a threshold highlighting the periodicity of contacts between the dsDNA and the protein shell. D] Zoom into the 3. 3 Å resolution focused refined Cl1a map illustrating the fit of the side chains and the FAD ligand.
Data acquisition Grids were imaged using a Titan Krios (Thermo Scientific) microscope operated at 300 keV and equipped with a K2 direct electron detector and a GIF BioQuantum energy filter (Gatan). 7,656 movie frames were collected using the EPU software (Thermo Scientific) at a nominal magnification of 130,000x with a pixel size of 1. 09 Å and a defocus range of -1 to -3 μm. Micrographs were acquired using EPU (Thermo Scientific) with 8 s exposure time, fr act i onat ed i nto 40 fr a mes and 7. 5 e -/pixel/ s (total fluence of 50. 5 e -/Å²) (Table  S3 ).
S5 ). A custom 2-step clustering script was written in python with the use of Numpy  (60)  and Scikit-learn (61) libraries. First, a few main clusters were identified by applying a DBSCAN  (62)  clustering algorithm on the previously estimated fiber external width values (W1). The widths values, estimated by adjusting a parameterized cross-section model on each 2D stack, range from roughly 280 Å to 340 Å. The crosssection model fitting process is based on adjusting a section profile (S) described in Equation 1 on the longitudinally integrated 2D-class profile (Fig. S5A ). The cross-section model is composed of a positive cosine component, (parameterized by the center position μ, its width σ 1 and amplitude a 1 ) associated with the fiber external shell, a negative cosine component (parameterized by the center position μ, its width σ 2 and amplitude a 2 ) associated with the central hollow lumen (W2), and a constant p 0 , accounting for the background level, as 𝑆(𝜇, 𝜎 1 , 𝑎 1 , 𝜎 2 , 𝑎 2 , 𝑝 0 , 𝑥) = 𝑎 1 cos 𝑥 -𝜇 𝜎 1 -𝑎 2 cos 𝑥 -𝜇 𝜎 2 + 𝑝 0 Equation 1 Then, as a second step, each main cluster was subdivided into several sub-clusters by applying a KMEANS  (63)  clustering algorithm on a pairwise similarity matrix.
This similarity metric was based on a 2D image cross-correlation scheme, invariant to image shifts, and mirroring  (64). The number of sub-clusters was manually chosen by visual inspection. For the most populated 2D classes corresponding to the most compact conformations, the number of sub-clusters was small: n=2 sub-clusters, Cl1a and Cl1b, and n=1 sub-cluster for the intermediate class, Cl2. However, the number of sub-clusters was higher (n=5) for the relaxed conformation (Cl3 from green to purple in Fig. S5 ), highlighting the overall heterogeneity of our dataset with compact, intermediate, relaxed states and even loss of one protein strands (Cl3b) and unwound ribbons.
Focused refinement of the asymmetric unit The Cl1a map, prior to solvent flattening, was used to obtain the mask corresponding to a single asymmetric unit of the structure (just one dimer). It was then used to subtract everything else from the corresponding experimental images, keeping only information from that asymmetric unit for a following round of focused refinement. The subtracted dataset was then used as input for Relion in order to reconstruct a reference for 3D refinement of the asymmetric unit with a last solvent flattening step. Further CTF refinement steps were performed before last 3D refinement with solvent flattening and post-processing (B-factor applied -45). This led to the best resolved 3. 3 Å map (FSC threshold 0. 5, masked, Fig. S6 ) that was used to refine the GMC-oxidoreductase dimer and its ligands (Table  S1 , Fig.
Automatic picking of the three different conformations (Cl1a, Cl2, Cl3a) Projections from the different helical maps were used as new input for automatic picking of each Cl1a, Cl2 and Cl3a clusters to get more homogeneous datasets for each conformation  (68). For each dataset a final round of extraction (box size Cl1a: 380 pixels, 121,429 segments; Cl2: 400 pixels, 8479 segments; Cl3a: 400 pixels, 11,958 segments) and 3D refinement with solvent flattening (Central z length 30%) was performed using the appropriate helical parameters and additional symmetries (none for Cl1a, C3 for Cl2 and D5 for Cl3a). This led to the improved maps presented in Fig. 2 3 4 and Fig. S6 -S8 (Cl1a: 3. 7 Å; Cl2: 4. 0 Å; Cl3a: 3. 7 Å; FSC threshold 0. 5, masked). Post-processing was performed with B-factor -80 (Table  S1 , Fig.
DNA focus refinement The Cl1a and Cl2 maps without solvent flattening were used to obtain the mask corresponding to each DNA strand independently. Each strand was then used for subtraction keeping only information from the corresponding DNA on the corresponding experimental images. The subtracted datasets of each DNA strand were then merged. The resulting dataset was used to perform 2D classifications in order to assess the quality of the subtracted dataset (Fig. S9 ). It was then used to perform a 3D refinement followed by a 3D classification and final refinement of the best 3D classes (Fig. 3E , Fig. S10 ). For the Cl3a compaction states, a cylinder with a diameter corresponding to the lumen of the relaxed structure was used as mask for subtraction, in order to try to enhance the information corresponding to the sole DNA.
Structures refinement The resolution of the EM map enabled to fit the R135 dimeric structure (30) (PDB 4Z24) into the maps using UCSF Chimera 1. 13. 1  (69). The qu_143 and qu_946 models were obtained using SWISS-MODEL (70) (closest PDB homologue: 4Z24). It is only at that stage that the best fitted qu_946 model was manually inspected and additional N-terminal residues built using the extra density available in the cryo-EM map of the 5-start compact reconstruction (Fig. 3 ). The entire protein shell built using the corresponding helical parameters were finally fitted into the Cl1a and Cl3a maps and were further refined against the map using the real-space refinement program in PHENIX 1. 18. 2 (33) (Fig. S6-7 ). The qu_143 model was selected to build the entire shell using the Cl2 helical parameters and symmetry and was further refined using the real-space refinement program in PHENIX 1.
18. 2 (33) (Fig. S8 ). Validations were also performed into PHENIX 1. 18. 2 (33) using the comprehensive validation program (Table  S1 ). The qu_946 model was manually corrected and ultimately refined and validated into PHENIX 1. 18. 2 (33) using the highest resolution focused refined Cl1a map. In that map the 2 first amino acid are disordered including the cysteine.
Data acquisition Tilt series were acquired using SerialEM (57) on a Titan Krios (Thermo Scientific) microscope operated at 300 keV and equipped with a K3 direct electron detector and a GIF BioQuantum energy filter (Gatan). We used the dose-symmetric tilt-scheme  (71)  starting at 0° with a 3° increment to +/-60° at a nominal magnification of 64,000x, a pixel size of 1. 4 Å and a total fluence of 150 e-/Å² over the 41 tilts (i. e. ~3. 7 e-/Å²/tilt for an exposure time of 0. 8 s fractionated into 0. 2 s frames (Table  S3 ).
Data processing Tilt series were aligned and reconstructed using the IMOD  (72). For visualization purposes, we applied a binning of 8 and SIRT-like filtering from IMOD (72) as well as a bandpass filter bsoft  (73). The tomograms have been deposited on EMPIAR, accession number 1131 and videos were prepared with Fiji (Fig. 1E -F, Fig. S11  & Tomo.