dataesr/scientific-paragraphs-categorization huggingface dataset:
Results All statistical analyses and graphical representations were generated with the software R version 4. 0. 3 (R Core Team, 2013), and the packages ggplot2, ggpubr, and lmerTest (scripts available in  (Anonymous, 2022) ). Our search resulted in 84 articles that could be included in the analyses, with inclusion and exclusion criteria as detailed in Fig. 1. Although a majority of samples were based in North America, the geographic coverage was considerable, and data on diverse operationalizations of parental bias (sex ratio, investment or both) comes from a wide range of sites: Bangladesh, Canada, China, Cuba, Czech Republic, Dominica, England, Ethiopia, Gambia, Germany, Ghana, Hungary, Ifaluk, India, Japan, Kenya, Mexico, Namibia, Nepal, Netherlands, Papua New Guinea, Philippines, Poland, Rwanda, Slovakia, Spain, Sweden, Tanzania, Uganda, UK, USA, and Venezuela, in addition to 10 with data Fig.
1. PRISMA flow chart describing database searches results, number of abstracts screened, number of full text screened, and exclusion criteria. V. Thouzeau et al. collected in several places, and 2 based on historical data. Fig. 2  describes how the literature is distributed in temporal and disciplinary terms. The number of publications has increased in line with the overall increase in scientific publications, with no clear association between the year of publication and the operationalization of parental bias (sex ratio or investment). A majority of the included articles appeared in interdisciplinary, anthropology and biology journals, with a few articles appearing in journals classified as bearing mainly on demography, economics, psychology and sociology. We also observe a trend for an association between the venue and parental bias measure: A majority of articles in anthropology, psychology, and sociology journals use parental post-birth investment measures, whereas sex ratio tests tend to be published in biology or interdisciplinary journals.
Exploratory analyses of subtypes of parental condition measures and investment measures Fig. 4  shows the frequency at which different types of measures were used and highlights the variety of ways in which parental condition and investment are operationalized. The most common measures of parental condition were those associated with economic and educational status, followed by biological condition and those related to social status. Only a few studies measured parental condition on a macroscopic scale (i. e. region-wide, like a country-wide famine) or induced them experimentally. To better inform future work, we performed exploratory analyses to dig deeper into our database and test whether there is evidence mostly consistent with the Trivers-Willard hypothesis regardless of the study's methodology.
g. , a two-by-two matrix using chi-square tests, but a lot less for other tests (see e. g. ,  Borenstein, Hedges, Higgins, & Rothstein, 2009). Additionally, experts criticize the use of transformations to include studies using diverse statistical analyses in the same meta-analysis (Lakens, 2013). We found there was too much heterogeneity and too few results to hope that the results of a meta-analysis on investment may be useful. We thus turned to sex ratio, where there was a small but not negligible group of studies employing two-factor chi-square tests. We computed the effect size from 33 tests from 10 different studies using the chies function of the R package compute. es and integrated them via a random-effects meta-regression using the R function robu from the robumeta package, which allows consideration of correlated structures (due to several effects coming from the same study).
In order to allow collaborative work (as proposed by  (Tsuji, Bergmann, & Cristia, 2014 )), we archived our database, so that the scientific community can update it as new evidence is produced, and propose alternative categorisations according to their interests. This deposit may be useful for future attempts at estimating effect sizes, although as obvious in Supplementary Information material, a meta-analytic approach may be impossible given that standardized effect sizes are not definable for a majority of the analyses  (Borenstein, Hedges, Higgins, & Rothstein, 2021). Third, the initial Trivers-Willard hypothesis has recently been complemented by theoretical work that clarifies what can be expected according to the ecology of the populations  (Schindler et al. , 2015). In particular, some conditions may lead to predictions contrary to the initial Trivers-Willard hypothesis.
Acknowledgments We thank  Laudine Carbuccia  for her help in validating the categorization criteria and for helpful discussions. We thank the support of  FrontCog   ANR-17-EURE-0017  for its participation in funding this research. AC acknowledges the support of the  J. S. McDonnell Foundation Understanding Human Cognition Scholar Award ;  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Programme (ExELang , Grant agreement No. 101001095 ). CC acknowledges the support of the  ANR  ( ANR-21-CE28-0009 ).
between enough amorphous phase stability against crystallization, good infrared (IR) transparency, and high nonlinearity. [12] igure  3f  shows the calculated and experimental values of nonlinear refractive index n 2 of the four chalcogenide compounds deposited by magnetron co-sputtering as detailed in reference. [12] or each material, the reported n 2 values were both calculated and measured experimentally. The calculated n 2 values were derived from the linear optical constants of the films determined by modeling of spectroscopic ellipsometry (SE) data acquired on the films using a Cody-Lorentz model (see Experimental Section). The measured n 2 values were determined by means of a nonlinear phase heterodyne interferometry experiment (for details on heterodyne technique see the Experimental Section and reference).
Wavelength Dispersive X-Ray Fluorescence (WDXRF) was used to control Ge, Sb, and Se contents of the films. A first set of samples with thicknesses of ≈100 nm and checked by X-ray Reflectivity was deposited for purpose of structural and optical characterization such as Raman, FTIR spectroscopies, and spectroscopic ellipsometry. For the other films composition of this work shown in Figure  1 , all details were given in reference. [12]  second set of thin film samples with same compositions had been deposited with thicknesses of ≈400-500 nm on a 3 μm thick thermal SiO 2 layer on top of the Si 200 mm substrate to permit fabrication of waveguiding structures required for experimental Kerr index determination by means of heterodyne experiment. Spectroscopic Ellipsometry (SE) Measurements and Data Modeling: Spectroscopic ellipsometry measurements on the 100 nm thick films deposited on Si using a J.
A. Woollam M-2000 equipment operating in the 190-1700 nm range was used to determine the real and imaginary parts of linear optical constants (refractive index n and extinction coefficient k) of the chalcogenide films as described in references. [5, 12] SE data were collected at three incidence angles in range [55, 65, 70, and 75°] and the raw data were analyzed using WVASE 32 and CompleteEASE software. For ellipsometry data modeling, the SiN x or GeN x capping layers had been first measured and modeled separately to extract their optical constants, the latter being then injected in the model used for modeling of data acquired on the whole layers stacking. A Cody-Lorentz (CL) model was used to fit the experimental SE data and to extract linear optical constants of the chalcogenide films. Finally, the nonlinear Kerr indices of the films were also deduced from their linear optical constants by means of the Sheik-Bahae model as extensively described in a previous paper.
Acknowledgements P. N. and B. C. acknowledge funding from the  ANR (Agence Nationale de la Recherche)  through  OCTANE  project (grant agreement  ANR-20-CE24-0019 ). Nonlinear phase experiments were conducted on the facilities of the  SMARTLIGHT  platform funded by the  ANR  and  Région Bourgogne Franche-Comté EQUIPEX + contract  " ANR-21-ESRE-0040 "). P. N. and B. C. also thank the  French research group  " GdR CHALCO ", recently created for fruitful discussions (https://gdrchalco. cnrs. fr/). J. Y. R. acknowledges the  FNRS CDR  project  ABIGLO (J. 0154. 21 )as well as support from  FNRS  and computational resources provided by the  CÉCI  (funded by the  F. R. S. -FNRS  under Grant No. 2. 5020. 11 )and the Tier-1 supercomputer of the  Fédération Wallonie-Bruxelles  (infrastructure funded by the  Walloon Region  under grant agreement  n°1117545 ).
, 2014). The low spectral resolution modes were calibrated with stars  (Chaffin et al. , 2018). Both the high-and low-spectral resolution modes have been checked for consistency and calibrated using the Solar and Heliospheric Observatory (SOHO) mission's Solar Wind Anisotropy (SWAN) instrument  (Mayyasi, (Bohlin et al. , 2019)  and utilizes observation modes that have similar spectral resolution to the MAVEN Echelle instrument  (Sohn et al. , 2019). Additional insights into the MAVEN Echelle mode optical characteristics and performance, obtained since early mission, include improved determinations of the instrument line spread function, in-orbit characterizations of the detector, and a clearer understanding of the instrument background level. The lessons learned from science-phase mission observations as well as the need to obtain an alternative standard for calibration motivated an upgrade to the Echelle data reduction pipeline.
Method Early in the MAVEN mission, the IUVS instrument Echelle data reduction pipeline included preliminary characterizations of the detector using in-lab and in-cruise observations. Since orbit insertion, experimental optical settings have stabilized, and a significant number of on-disk observations have been made to facilitate improved characterization of the IUVS Echelle detector. Incorporating lessons learned from several years in orbit, the data reduction pipeline has been upgraded to include a more accurate background level mitigation technique, an updated and empirically derived line spread function, and a robust integration process that accounts for multiple binning schemes, voltage settings, and that accommodates the new line spread function. These improvements result in more accurate detections of faint emissions and improved assessments of measurement uncertainties pertaining to detector background.
The number of spectral and spatial bins have varied throughout the mission, and sample different regions on the detector, but the resolution of the spectral bins has remained fixed. The only un-binned (full spectral resolution across the entire detector) observations made with the MAVEN/IUVS echelle mode were obtained early in the mission for diagnostic purposes and are limited. In these full-frame observations, the wavelength resolution, dλ is 0. 00071 nm. For all other observations, the spectral range has been consistently binned ×2, resulting in a wavelength resolution, dλ of 0. 00143 nm. The upgraded data reduction pipeline includes spatial integration across the full aperture at the Lyman-α signal region that spans a consistent region of the detector across all existing binning schemes.
Cross-Calibration The HST/STIS data was obtained from Program# 15595 Visit 04 Target Mars-exosphere-STIS observations. The MAVEN/IUVS data were obtained from orbits 08010, 08014, and 08018 (per Table  1 ) that best matched the HST/ STIS observational conditions. The HST data were analyzed using a pipeline developed at Boston University for raw STIS E140H echelle spectra with a long aperture to provide brightness values for the Martian D Lyman-α emission (e. g. ,  Clarke et al. , 2004; Quémerais et al. , 2009). Since this is an unsupported mode of the STIS The resulting Lyman-α spectrum (black) derived with the new background best fits (red). In both the bottom middle and bottom right panels, a horizontal dotted line indicates the zero-signal level, and an insert is shown to zoom-in on the D emission and its fit.
, 2005; Woodgate et al. , 1998). Reduction of the data includes detector dark subtraction, flat fielding of the response, correction for geometric distortion, and then a correction for the angled and curved images to make echelle dispersion linear in one direction and the image of the aperture linear in the other direction. The spectra are then derived by adding rows in the spectral image, and the absolute calibration for sensitivity is derived by comparison of count rates with another calibrated mode (for STIS G140L). The resulting integrated flux observations were compared to derive a new calibration standard for the MAVEN data. When applied to all the observations to date, the updated pipeline and calibration factor resulted in a decrease of ≤15% in the fluxes of previously derived H emission brightness values.
Updating the Line Spread Function and Integration Scheme Early in the MAVEN mission, the instrument line spread function (LSF) was derived from laboratory data. The in-orbit FUV and Echelle performance was degraded compared with the lab data and so, with MAVEN in orbit, a newer LSF was derived using thousands of Lyman-α emission spectra, measured when the line of sight was disk-pointed. All available disk-pointed observations were used to derive the new LSF in order to average out orbit-to-orbit variations (e. g. , illumination conditions, latitude, local time, season, etc. ). The resulting LSF shape differed from the theoretical shape only in the wings of the emissions, where the newer LSF showed the signal in those regions to be reduced, as shown in Figure  3. In an echelle spectrum, the same LSF is used to fit both the H and D Lyman-α line emission line shapes and could be used for fitting additional thermal UV emissions detected at Mars with the echelle instrument (e.
To validate this further, the algorithm was applied to the MAVEN/IUVS Echelle data for observations that were not exclusively disk pointed. A comparison of old versus new brightness values is shown in Figure  8  for limb-pointed observations where the tangent point altitude along the instrument line of sight is between 0 and 300 km. This altitude cut-off was chosen to include realistic comparisons of D emissions that become negligible above 300 km of the Martian surface (e. g. Clarke et al. , 2017; Mayyasi et al. , 2019). The bottom panels of Figures  7  and 8  show the most significant effect of the improved background correction in the new pipeline for faint D Lyman-α emissions. For on-disk observations, faint emission derivations of brightness values are systematically reduced by ∼0. 12 kR, and off-disk observations of faint emissions are reduced by ∼0.
This updated data pipeline will be applied to all integrations (individual observations) within an observational scan (that can include anywhere from 8 to over 30 integrations). A full-mission reprocessing of the level1a MAVEN/IUVS/Echelle data will be conducted with this pipeline in order to produce the higher level1c data products on the NASA PDS and will be provided with an upgraded version number to distinguish archived data reduced with the previous pipeline.
The Echelle brightness values are therefore on the higher end of the FUV brightness values for this orbital block of orbits. Similarly, the data shown for MY33 has a narrower range for Echelle than for FUV and the former SZA range is on the lower end of the latter. The brightness values corresponding to this orbital block are consistent with Echelle values on the higher end, as expected. The SZA ranges for the MY34 data block are more closely aligned for both modes, and the resulting brightness values compare similarly. The Echelle data display similar trends in time as the FUV data and are in consistent agreement. When the effects of SZA are binned for each 10°, the FUV and Echelle (ECH) data compare favorably, with difference ranging between 1% and 7% at SZA <90° and between 1% and 30% at SZA ≥ 90°.
Implications for Science This work summarizes a new data reduction pipeline for MAVEN/IUVS Echelle mode data that includes recalibration of the instrument with HST/STIS and includes an improved algorithm for faint-emission fitting. The new data reduction pipeline has been compared with the previous pipeline to show consistency in the results for brightness values and their trends with time while delivering more accurate D Lyman-α emission retrievals. The algorithms and data comparisons are implemented on a frame-by-frame basis for data collected in each orbit. An IUVS Echelle orbital data product may have 8-20 frames, and an integration time of 1. 4-29 s for each frame  (Mayyasi, Clarke, Quémerais, et al. , 2017). For scientific studies that require D Lyman-α emission derivation and interpretation, individual frames are likely to be co-added in a manner that suits the science implementation in order to further improve the signal-to-noise ratio in observed spectra (e.
Data Availability Statement The MAVEN data used in this study are available on the NASA PDS Atmospheres Node at: https://pds-atmospheres. nmsu. edu/data_and_services/atmospheres_data/MAVEN/ maven_main. html. IUVS echelle level1a and level1c v13 data were used. At the time of this writing, the MAVEN/IUVS/Echelle PDS data are generated using the old pipeline and cover mission observations through delivery #31, MAVEN orbit 17004, 15 August 2022. A full mission reprocessing of IUVS data products, including updates to the ECH pipeline described here, are presently underway. The resulting level1a and level1c v14 data are targeted for public access in the next 3-6 months. The HST data used in this study are available on the MAST archive at: https://archive. stsci. edu/hst/.
Individual metabolic models identify specific metabolic pathways in a milk environment The microbial community starter was composed of Propionibacterium freudenreichii CIRM-BIA122, Lactiplantibacillus plantarum CIRM-BIA465 and Lactococcus lactis biovar diacetylactis. For each of them, full-length total DNA was obtained. Assembling bacterial genomes from a combination of short and long reads produced accurate and complete assemblies revealing one circular chromosome and the presence of 7 plasmids for the L. lactis strain and 4 plasmids for the L. plantarum strain. The P. freudenreichii strain did not harbour any plasmids. Annotated genome can be retrieved at ENA under 5 accession ENA: PRJEB42478. The number of protein encoding genes, ribosomic sequences, tRNA and GC percent and accession numbers are gathered in Table  1.
, 2020). Table  2  describes the characteristics of the three models. We observe that L. lactis has a smaller number of reactions than the other two strains, a qualitative difference that is also observed for other strains of the same species in other databases  (Noronha et al. , 2018). We built genome-scale metabolic models (GEMs) from the three metabolic reconstructions using Flux Balance Analysis (FBA)  (Palsson and Varma, 1994; Orth et al. , 2010)   nutritional environment reflecting milk composition (see Materials and Methods). We obtained growth rates ranging from 0. 187 mmol. gDW -1. hr -1 to 2. 17 mmol. gDW -1. hr -1 , the former obtained for P. freudenreichii and the highest value obtained for L. lactis. Particular attention was given to the role of oxygen in the three metabolisms.
Other predicted exchanges include mostly additional amino-acids (isoleucine, proline, glycine, alanine). We used metatranscriptomic data in order to assess the validity of 25 the most plausible interactions according to the SMETANA score (see Methods): H 2 S (from L. lactis and L. plantarum to P. freudenreichii ), ribose (from L. lactis and plantarum to P. freudenreichii ), glycerol (from L. lactis and L. plantarum to P. freudenreichii) and phenylanaline (from L. plantarum to P. freudenreichii). We verified the expression of genes associated to the production of the metabolite in the donor species, and to the consumption of the compound in the receiver species (see Methods and Supp. Fig. S2 ). Results suggest that interactions for H 2 S, ribose and glycerol are plausible at several steps of cheese making.
, 2021) : molding stage (Cm, t=19. 5h), demolding stage (Cdm, t=40h), start of ripening (C0w, t=60h) and after 4 and 7 weeks of ripening (C4w, t=732h and C7w, t=1236h). Cheeses were replicated four times (biological repetitions realised different days with independant inocula and milks) and sampled each times for numeration and biochemistry and twice (biological repetitions) for RNA sequencing. Single strain culture experiments. In order to calibrate metabolic models, a training dataset is built using single strain culture experiments. Single strain growth experiments of the three bacteria were performed in UHT full fat milk (Delisse) in tube under microaerobic conditions, i. e. without shaking in an incubator, at 30 • C, with two replicates. For P. freudenreichii , the milk was supplemented with lactate (20 g/L) and with casein peptone (10 g/L).
A second growth experiment was performed for P. freudenreichii in order to measure metabolite levels (acetate, lactate, propionate and succinate) at the final time. L. lactis was inoculated at 1 × 10 6 and 4 × 10 6 CFU/g, L. plantarum at 3 × 10 6 and 6 × 10 6 CFU/g and P. freudenreichii at 1. 4 × 10 7 and 9 × 10 6 CFU/g. Pure culture growth data are available in Table  S1. Metatranscriptomic data. Ten grams of cheese were dispersed in 90g of a 2% sodium citrate solution during 1 min at max speed followed by 1 min at low speed in stomacher (Humeau, Treillière, France) at room temperature. Ten milliliters of the dispersion were centrifuged at 10 000 g at 4 • C during 5 min. Cell pellets were stored at -80 • C for RNA extraction. After defrost, the samples were centrifuged again for 5 min at 4 • C, 10 000 g to discard the supernatant.
RNA concentrations were quantified using Qubit Fluorometric quantitation (Thermofisher, France). Lack of DNA contamination was also checked using Qbit and DNA quantitation protocol according to manufac-34 turer instructions. RNA samples with a RIN value greater than 8, indicating a good RNA integrity, were kept for further analysis. Metatranscriptomic data were obtained at five time steps of cheese manufacturing. Illumina libraries and sequencing were performed by GeneWiz (Leipzig, Germany). Reads are available at ENA (Cambridge) under accession number ENA: PRJEB42478. Sequenced reads were trimmed using Trim Galore and mapped against reference genomes with Bowtie 2  (Langmead et al. , 2009). No mismatch was allowed and only reads that mapped to unique sequences were further analysed. Reads mapping to each coding sequence were counted using HTSeq-count (-stranded=reverse, -a 0, -t CDS, -i db_xref, -m union),  (Putri et al.
, 2022). Statistical analysis were conducted using R statistical software version 4. 0. 3 (R Core Team, 2021). RNA-Seq raw count data were normalized in two steps. First, a species-specific scaling factor was applied to eliminate composition biases between libraries. For each of the three species, they were calculated using all the genes of the considered species and two others rows for the other species calculated as the sum of the counts attributed to each of these two other species, using the method TMM (Trimmed Mean of M-Values) as implemented in the package edgeR version 3. 32. 1  (Robinson et al. , 2009). A supplementary within-sample normalization step was performed to correct for gene length and enable comparison. Finally replicates were averaged.