dataesr/scientific-paragraphs-categorization huggingface dataset:
Analyses of M. francoismoutoui Diversification of an island endemic bat Using mitochondrial D-loop data, genetic diversity indices, including haplotype number, haplotype diversity (Hd), and nucleotide diversity (π), were measured at the roost-level using DnaSP v6. 12. 03  (Rozas et al. , 2017) , for the entire Reunion Island dataset and then separately for each sex and season. Differences among roosts, sexes, and seasons were tested using analyses of variance (ANOVA) in  RStudio 1. 4. 1106 (RStudioTeam, 2021). Spatial structure was assessed by calculating genetic distances (Φst) among roosts for the entire Reunion Island dataset and then separately for each sex and season using Arlequin 3. 5. 2. 2  (Excoffier & Lischer, 2015). The significance of multiple tests was corrected with the Holm method using RStudio.
To test for temporal differences in the spatial structure, Φst values were compared among seasons using ANOVAs and Tukey's post-hoc tests. Subsequently, to test for the presence of isolation by distance (IBD), we performed a Mantel test with 1,000 permutations using Arlequin and calculated the correlation between genetic and geographic distances. IBD was first tested for the whole Reunion Island dataset, and then separately for each sex and season. Finally, we also used an AMOVA test (analysis of molecular variance) in Arlequin and defined "population" as individuals from a single roost to test for a roost-associated genetic structure in the Reunion population. The significance of this test was assessed by 1,000 permutations of individuals among roosts. We determined the most appropriate nucleotide substitution model of Reunion Island D-loop sequences based on AIC criterion  (Akaike, 1974)  using JModelTest v2.
Global population size change through time was reconstructed using a coalescent Bayesian skyline model (CBS,  Drummond et al. , 2005)  with the same parameters as described above in BEAST v. 2. 6. 4  (Bouckaert et al. , 2019). We performed BEAST analyses changing the dimension group parameter from 3 to 10 groups, according to the results of the Yule Bayesian tree. We ran three analyses for each group and followed the same method described for Yule Bayesian tree and choose the best k according to the higher ESS. For the microsatellite data, genotype determination was performed using GeneMapper 6 (ThermoFisher). We tested the dataset for scoring errors, out of range allele, and null alleles for each roost using Microchecker v. 2. 2. 3  (Van Oosterhout et al. , 2004). Using GenAlex 6. 503  (Smouse & Peakall, 2012) , the global number of genotypes was calculated, and for each roost, deviations from Hardy-Weinberg equilibrium were tested for each locus.
A permutation test with 1,000 permutations was used to perform linkage disequilibrium analysis between each pair of loci using Genetix v. 4. 05. 2  (Belkhir et al. , 2004). We used Fstat v. 2. 9. 4  (Goudet, 2003)  to calculate the inbreeding coefficient (FIS) within each roost  (Weir & Cockerham, 1984)  and tested the significance by randomizing alleles among individuals within roosts (5,000 permutations). We estimated the observed  (Ho)  and expected (He) heterozygosity in each roost, first using the whole dataset, subsequently estimated these two parameters for each sex and season, and tested for sex and temporal differences using an ANOVA in RStudio. Effective population size (Ne) was estimated for the global population because of a lack of genetic structure (see results) with the linkage-disequilibrium model and assuming random mating using NeEstimator v2.
3  (Pritchard et al. , 2010) , and performed two analyses with and without the LocPrior model, which uses location to test for a weak signal of population structure  (Hubisz et al. , 2009). We used the admixture Diversification of an island endemic bat model with correlated allele frequencies among groups, and 10 replicate runs were performed with a burn-in of 10 6 steps and 10 6 recorded steps for the Monte Carlo Markov Chain (MCMC). We ran K from 1 to 19 groups (corresponding to the number of studied roosts, plus one). We applied the Evanno method  (Evanno et al. , 2005)  to estimate the best K, but results were inconclusive (see results). To determine the optimal number of genetic clusters, we performed a k-means clustering analysis, tested K from 2 to 19 over 26 indices according to the "majority rule", using the NbClust package in Rstudio.
Genetic relationships among regional Mormopterus In order, to investigate genetic relationships among the three regional Mormopterus species, we reconstructed a Bayesian tree based on Yule model (Yule, 1925), using the island as a trait to resolve the spatial origin of the nodes using BEAST v. 2. 6. 4  (Bouckaert et al. , 2019). HKY model  (Hasegawa et al. , 1985)  with invariant and gamma distribution was used according to the best substitution model on AIC criterion using JModelTest  (Darriba & Posada, 2016). We used the same parameters in BEAST as described above for the Reunion Island dataset. Diversification of an island endemic bat Microsatellite analysis of regional samples was performed using STRUCTURE v2. 3  (Pritchard et al. , 2010) , with LocPrior  (Hubisz et al. , 2009)  and no admixture model, using uncorrelated allele frequencies among group.
Data quality Altogether, we obtained good quality D-loop sequences for 603 M. francoismoutoui (alignment of 985 pb) and 30 sequences for each Mormopterus species (Mauritius and Madagascar, 811 pb). We genotyped 1,136 individuals of M. francoismoutoui using the 12 microsatellite loci, and 30 individuals from Mauritius (M. acetabulosus) and Madagascar (M. jugularis). One locus (MF_loc11) was removed because of a high percentage of uninterpretable weak signals in the Reunion Island dataset (~ 41% of individuals). Also, we removed individuals from Reunion for which at least six loci were not genotyped (n = 22), leading to a final microsatellite data set containing 3. 7% of missing alleles. In the Reunion dataset, a majority of loci significantly deviated from Hardy-Weinberg equilibrium, especially MF_Loc03, MF_Loc04, MF_Loc05, and MF_Loc15.
Acknowledgements We are grateful to personnel of  Eco-Med Océan Indien, Biotope, the   Direction de l'Exploitation et de l'Entretien des Routes (DEER) of Région Réunion , the  Direction des Routes et des Transports (DRT) of Département Réunion , and the  Salazie city hall  for their help in identifying and accessing bat roosts. We are thankful to  David Wilkinson  for fruitful discussions and help with analyses. We also thank  Yann Gomard  and  Julien Mélade  for previous laboratory work on Mauritius and Malagasy samples. We also thank  Guillaume Verchère  for his assistance in the field. This research was supported by the  French National Research Agency (ANR JCJC SEXIBAT) , by the  European Regional Development Funds ERDF PO INTERREG   V ECOSPIR  number  RE6875. Samantha Aguillon  was supported by a " Contrat Doctoral de l'Université de La Réunion ".
Data accessibility and benefits-sharing section Mitochondrial sequences (D-loop) has been deposited in Genbank under ID's from OR081945 to OR082607 and microsatellites genotypes and metadata are available at Zenodo (https://doi. org/10. 5281/zenodo. 8069702). D-loop sequences for the Reunion Island dataset are coded with the roost name, the field identification number and the sex (F = female and M = male). Regional sequences are coded with the island (MADA = Madagascar and MAU = Mauritius) and field identification number.
This second task was used to probe attention at each of the two stimulus locations and at various times during attentional orienting (valid trials; sustained attention) and reorienting (invalid; exploratory attention), assessing potential rhythms in attentional sampling. We computed d-primes for this detection task independently for the valid and invalid conditions and for each ISI between the discrimination and probes onset. Across ISIs, d-prime exhibited multiple peaks and troughs, and individual data makes clear this was highly consistent across participants (Figure  2A ). We then performed fast-fourier transforms (FFT) of the averaged dprimes for the valid and invalid conditions separately. Peak frequencies were identified: 12. 3Hz in valid and 6. 1Hz in invalid (permutations tests: 100,000 surrogates, p = 0.
Discussion The rhythmic sampling theory of attention has become an important topic of research, yet remains largely debated due to methodological considerations, as well as a lack of clear understanding regarding the sensory mechanism underlying performance fluctuations. Here we addressed this critical gap using a psychophysics paradigm with a large amount of data collected for each participant, and applying reverse correlation analysis correlating stimulus energy of irrelevant noise with behavioral detection performance. We assessed the fluctuations in sensory representations for two voluntary attention modes, i. e. , sustained and exploratory. First, we showed that performance fluctuated periodically at the alpha frequency (12. 3Hz) for sustained attention and theta frequency (6. 1Hz) for exploratory attention, replicating previous reports  8, 9, 32, 33.
instructed to detect the presence/absence of the Gabor inside each patch of noise (50%). These patches presented at multiples delays after the previous Landolt-Cs were used to probe the state of attention across time. At the end of the trial, participants were first asked to report the position of the gap in the target Landolt-C (press C key for a gap on the left with the left hand, and press B key for a gap on the right with the right hand; maximum response time of 1500ms), and then to report the presence or the absence of the Gabor inside each patch (for left patch, press C key for Gabor present and D key for Gabor absent with left hand; for right patch, press B key for Gabor present and H key for Gabor absent with right hand; maximum response time 2000ms). Staircases. Two independent staircases were implemented via a Best PEST procedure 54 using the Palamedes toolbox  55  in Matlab, to adapt first the size of the Landolt-C gap in the discrimination task, and second the SNR (signal-(Gabor) to-noise ratio) for the detection task.
Phase angles were extracted from FFTs performed on each participants data for 12. 3Hz in valid and 6. 1Hz in invalid. We selected high-and low-performance trials by taking local maxima (above d-prime average) and minima (below d-prime average) for each of the d-prime time courses (ISI Valid high: 145, 285, 390 and 460ms; low: 75, 180, 320, 425 and 495ms; ISI invalid high: 110, 320 and 425ms; low: 40, 145, 390 and 460ms). Two local maxima were removed, one in each condition, because they were below the averaged d-primes. All analyses described below were done separately for the high-and low-performance trials. Reverse Correlation analysis. To perform this analysis, we subsampled (100 repetitions) the number of high-(or low-) performance trials (separately for valid and invalid conditions) to equalize to the number of low-(or high-) performance trials.
Apparatus and stimuli Participants sat in a dark room at 57cm from a calibrated and linearized CRT monitor (800*600 pixels, width = 37. 8cm, height = 28. 4cm, refresh rate 120Hz). Their heads were positioned on a chin rest to maintain the distance between the monitor and the eyes. Stimuli were generated using PsychToolbox 3 in Matlab R2018b (MathWorks). Background was gray (127. 5, 127. 5, 127. 5 RGB). A black central fixation annulus with inner eccentricity of 0. 2° (degree of visual angle) and thickness of 0. 16° was presented throughout the experiment. The cue and response cue were white rectangles (255, 255, 255 RGB, 0. 5° long, 0. 16° large) adjacent to the fixation (0. 65° eccentricity from the center) and pointing down with a 45° angle toward the bottom left or bottom right. Stimuli were two conventional Landolt-C optotypes (2° high, 2° wide, 5.
RNA concentration and quality were measured using a NanoDrop spectrophotometer and an Agilent Bioanalyzer (Santa Clara, CA, USA). The samples were subjected to reverse transcription using Super Script II reverse transcriptase (LifeTechnology), as previous described  [39]. RT-qPCR was performed using a SYBR green-based detection protocol (Life Technology) with an Opticon 2 real-time PCR detection system (Bio-Rad) and MyIQ software (Bio-Rad). Primers (Table  1 ) specificity and PCR efficiency were analyzed on genomic DNA range prior to quantification. The tuf gene was used as internal standards for normalization. Variations of gene expression between strains were calculated with NCDO2727 (condition without chloride) as reference with the ΔΔCt method  [40]  and expressed as fold changes (FC).
Sequence analysis All the genome sequences of the representative strains used in this study (except those of strains MG1388, S170, CIRM-BIA 1564 and EIP3I) were obtained from the NCBI database. For strains MG1388, S170, CIRM-BIA 1564 and EIP3I, genomic DNA was extracted using the GenElute Bacterial Genomic DNA kit (Sigma-Aldrich) according to manufacturer instructions. Short-read whole genome sequencing was performed on an Illumina MiSeq sequencer (Illumina, Inc. ,San Diego, US-CA) using 2 × 150 bp paired end reads with 100x average coverage. The raw data were analyzed using tools from the GALAXY website (https://usegalaxy. eu/). Briefly, the reads were assembled to contigs using Unicycler (version 0. 4. 8. 0) with bold assembly and default parameters. Contigs shorter than 100 bp were excluded.
The GAD cluster, a region of approximately 4200 bp encompassing the gadR, gadC, gadB genes and their specific promoters, was extracted from the sequences of the representative strains (21 L. lactis strains, 8 L. cremoris strains with the lactis phenotype and 7 L. cremoris strains with the cremoris phenotype) using BLAST software (https://blast. ncbi. nlm. nih. gov/Blast. cgi). The nucleotide sequences were then aligned using the ClustalW algorithm with the software MEGA X (https://www. megasoftware. net/home). All raw and processed sequencing data generated in this study have been submitted to the NCBI BioProject database under accession number PRJNA960850.
Acknowledgements The authors thank the  Galaxy Project Team Freiburg  for their technical expertise and help with NGS data generation and analysis. The authors also thank the  International Centre of Microbial Resources -Food Associated Bacteria (CIRM-BIA ,  STLO UMR 1253 INRAE-Agrocampus ,  65 rue de saint Brieuc ,  F-35042 Rennes Cedex, France ) for providing their strains.
In 1990, in its Publication 60  (ICRP, 1991) , the ICRP revised the detriment quantification method on the basis of new scientific data produced in particular by epidemiology and follow-up of the cohort of survivors of the Hiroshima and Nagasaki atomic bombs (Life Span Study, LSS). The ICRP also introduced the DDREF (Dose and Dose Rate Reduction Factor) which accounts for the extrapolation to low doses and dose rates of observations made for high doses and dose rates. In 2007, the ICRP published new general recommendations  (ICRP, 2007) , which are the basis for international radiological protection standards. In this publication, the radiological detriment was revised and is expressed as follows: D ¼ X T R T Â 1 À k T ð Þ Â 1 À q min ð ÞÂk T þ q min ½ þ R T Â k T ; where, -D: radiological detriment; -T: organ or tissue; -R T : nominal risk of cancer incidence of organ T; k T : lethality fraction of cancer T; q min : 0.
These data are available free of access via the INERIS portal (https:// substances. ineris. fr) or the ANSES database (https://www. anses. fr/fr/content/liste-des-valeurs-toxicologiques-de-référ ence-vtr-de-l'anses). The thresholds TRVs are expressed in the form of reference dose (RfD) or reference concentration (RfC). The threshold TRV then corresponds to "the quantity of a product, or its airborne concentration, to which an individual may be exposed without any adverse effect being observed over a set period"  (INERIS, 2016). For no-threshold substances, the TRV is defined as "unit risk factor (URF) and corresponds to the slope of the straight line of the dose-effect relationship"  (INERIS, 2016). For illustrative purposes, Tables  2  and 3  present several TRVs proposed by the INERIS (https:// substances.
Expressing radiological detriment in DALY For ionizing radiation, the detriment calculation relies upon nominal risks, which reflect excess cancer incidence for an exposed population. For each cancer under consideration, it is possible to assign a DALY to express radiological detriment in DALY. Sv À1. In order to illustrate this method on the basis of the nominal risks outlined in ICRP Publication 103, the following assumptions are made: the incidence of skin cancer is not considered. The nominal risk for skin cancer corresponds to a very high incidence, but its contribution to radiological detriment is near null (lethality fraction set at 0. 002). Further, the data for this cancer have not been updated recently 1. in the absence of precise information for a type of cancer in  Huijbregts et al.
, 2007; 2010; 2017; Edmonds and Herd, 2007; Marini et al. , 2011; Oppenheimer et al. , 2011; Pino et al. , 2011; Moretti et al. , 2013a; Moretti et al. , 2013b; de Moor et al. , 2016; Moretti et al. , 2018; Aiuppa et al. , 2022). The program can be downloaded from https://github. com/charlesll//chosetto (DOI: 10. 5281/zenodo. 5554941). To calculate the stratospheric aerosol optical depth (SAOD) perturbation and radiative forcing produced by the studied eruptions, we used EVA_H (Easy Volcanic Aerosol_Height), a simple model of volcanic sulphate aerosol forcing  (Aubry et al. , 2020). EVA_H is an empirical box model predicting the temporal and spatial variation of aerosol optical properties given the characteristics of a volcanic SO 2 injection (mass, latitude, altitude, and season). It was calibrated for the 1979-2015 eruption sequence using recent satellite based volcanic SO 2 emission inventories  (Carn et al.
Volatiles For a full report of major element analysis for MI and GM please refer to  Metcalfe et al. (2022)  and  Metcalfe et al. (2022). A summary of key geochemical data is provided in Table  1. MIs in this dataset have not experienced any post-entrapment crystallization, and are in equilibrium with their host mineral  (Putirka, 2008). Additionally, no bubbles were observed in the MIs. To understand whether the volatile elements in the MI are representative of the undegassed magma, particularly if any H 2 O loss has occurred from the inclusions, we measured the D/H ratio of the MI. The D/H ratio is expressed as δD (the permil deviation from Standard Mean Ocean Water (δDSMOW)). Slab fluids related to the average upper MORB mantle fall in the range δD -20 and -80‰ and values below -150‰ are the upper limit of terrestrial mantle values  (Shaw et al.
Here, we report the MI volatile dataset plotted against the incompatible element K 2 O and also report the average GM value for each eruption to understand the effects of crystallization and degassing (Figure  3A ). The H 2 O values measured for this study range from 1. 05-4. 42 wt% (excluding values from 341 CE). The H 2 O values for 1530 CE calculated by difference extend this range to 5. 6 wt%  (Pichavant et al. , 2018)  however, based on the SIMS data from the other eruptions discussed here this is perhaps an overestimate. CO 2 for this study ranges from 2070 ppm to below the detection limit, with the highest CO 2 values recorded in the 341 CE eruption (Figure  3B ). The upper values above 1700 ppm may be outliers with most of the 341 CE CO 2 data falling below 1,500 ppm, the H 2 O loss observed from these inclusions also makes it unclear if CO 2 has also been affected.
We ran the same compositions and conditions at multiple oxidation states (NNO+0. 8, NNO and NNO-0. 5) under open and closed system conditions. This is important when investigating S as the partitioning of sulphur depends on redox conditions, with the sulphur partition coefficient increasing with increasing fO 2 (e. g. ,  Scaillet et al. , 2012). Although the degassing paths under open conditions at NNO-0. 5 conditions (Figure  5 ) fit closer to the dataset, this does not produce the steep decrease in sulphur over a small range of S contents (Figure  5 ) as long as the highest S-content is taken as the initial (total) value (Figure  5A ). (Papale et al. , 2006). Open (dashed red line) and closed (solid red -0. 5% excess vapour, solid blue-1% excess vapour and solid green-3% excess vapour) degassing paths were also calculated using the same code.
The groundmass glass plus microlites viscosity, calculated by  Metcalfe et al. , 2022 , correlates with the total volatile emissions, with a higher groundmass glass viscosity correlating with lower total volatile emissions (Figure  7C ). This suggests lower viscosity in the groundmass glass allows for more degassing and higher volatile emissions. Faster ascent rates (calculated by  Metcalfe et al. , 2022  to range from <1-12 m/s at La Soufriere) correlate with higher total volatile emissions (Figure  7D ). However, this is not observed in all the eruptions due to top-down effects, such as a reduction of permeability at the surface due to hydrothermal activity (e. g. ,  Cassidy et al. , 2018) , which have allowed slow ascending magmas to result in large eruptions. In order to contextualise our results, we integrate the La Soufrière emissions into a global database of subduction zone volcanoes (Figure  8 ).
(2003)  provide a global database of SO 2 emissions calculated using the petrologic method but also include a thermodynamic estimation to estimate the co-existing S-rich vapor phase at depth for intermediate to silica magmas (improved petrological method; Figure  8 ). Scaillet et al. (2003)  demonstrate the SO 2 emissions calculated closely agree with independent estimates obtained from analysis of ice cores, opticaldepth measurements, and remote-sensing spectroscopic techniques. The VEI 4 5680 BCE and 1010 CE eruption SO 2 emissions (0. 3 and 0. 2 Mt of SO 2 , respectively) are very comparable to eruptions with similar magma volumes (e. g. , Mount Unzen 1991 which emitted 0. 25 Mt of SO 2 ). Data available from the Lesser Antilles eruptions Montagne Pelée 1902 (0. 3 Mt of SO 2 ) and Soufrière St Vincent 1979 (0.
Acknowledgments The authors thank the  OVSG-IPGP team  for logistical support and help with data collection. We are grateful for help and information from  Laurence  and  E. Barret  ( Vert Intense ). We thank  Y. Legendre  for assistance in the field and discussions of the geology and the eruptive past reconstruction of La Soufrière. We are grateful to the  Parc National de Guadeloupe  for allowing us to undertake research and obtain geological samples. Radiocarbon dates were obtained from Beta Analytical. TJA acknowledges support from the  Cambridge Sidney Sussex College  through a  Junior Research Fellowship  during completion of this work. Finally, we would like to thank the two reviewers. This study contributes to the  IdEx Université de Paris   ANR-18-IDEX-0001  and is  Laboratory of excellence ClerVolc  contribution number  604.
Funding This work was supported by the  Institut National des Sciences de l'Univers (INSU-CNRS)  for radiocarbon dating ( AO Artemis ,  LMC14-CEA Saclay ), and the  CASAVA  Project funded by  Agence Nationale de la Recherche ,  2009-2015  ( ANR-09-RISK-02 ). The PhD scholarship of AM was co-funded by the  "Make our Planet Great Again" initiative (Campus France)  and the  IPGP Ecole Doctorale. We thank  IPGP  for general funding to the  Observatoires Volcanologiques.