dataesr/scientific-paragraphs-categorization huggingface dataset:
Unlike the other datasets, where the POC vs. b bp700 relationship reflected mostly sea-surface variability in a given biome, this was the only dataset that included data collected between the sea surface and 600 m. Bol et al. (2018)  reprocessed this dataset by computing the linear regression between POC and b bp700 in different depth bins, forcing the intercept through zero. The slope of the POC vs. b bp700 regressions, and therefore the POC / b bp700 ratio, decreased more than 2-fold between the surface and the 600 m bins. These results suggest that the POC vs. b bp700 relationship may be better modeled with nonlinear equations, as done for surface data in some previous studies  (Balch et al. , 2010; Johnson et al. , 2017; Stramski et al. , 1999). However, one must keep in mind that the ecosystem processes that define POC / b bp700 ratios in the surface layer may be different from those occurring in the mesopelagic (see  Sect.
c p and POC vs. b bp700. The observed POC / b bp700 variability in the surface layer across biomes, reflected in our POC estimation algorithm, seems analogous to that found for the relationship between POC and c p by  Cetinić et al. (2012)  (Fig. A1 ). The POC content per unit b bp700 decreases with the maximum b bp700 of each dataset, which may be related to the structure and species composition of upper-ocean ecosystems (see  Sect. 4. 3). It is also plausible that the fraction of POC (in terms of particle size or type) that contributes to backscattering varies across sites, further enhancing POC / b bp700 variability. The number of studies that tackled the interconversion between POC and bio-optical proxies in the mesopelagic layer is much smaller than those that focused on the epipelagic layer.
Besides  Cetinić et al. (2012)  in the subpolar North Atlantic, we are only aware of the studies of  Bishop (1999)  and  Bishop et al. (1999). The latter two studies found a POC vs. c p slope of around 16 mmol C m -2 (= mmol C m -3 m). Given that modern transmissometers accept more forward scattered light that those used by  Bishop (1999) , the corresponding slope would be approximately 27 mmol C m -2 (Jim K. B. Bishop, personal communication, 2021). Bishop (1999)  found this slope to be nearly constant in contrasting areas over the 0-1000 m depth range, as depicted in Fig. A1  with the datasets labeled "Bishop1999eqpac" (Equatorial Pacific) and "Bishop1999all" (Equatorial Pacific, NE Pacific, and North Atlantic together). The linear relationship between POC and c p is compatible with the nonlinear relationship between POC and b bp700 along the vertical profile if the backscattering ratio also increases with depth, as found by  Cetinić et al.
More collocated measurements of POC, b bp700 , and c p in the mesopelagic and below are needed to reduce these uncertainties. Beyond the natural variability, the interconversion between POC and bio-optical proxies is also confounded by methodological issues  (Cetinić et al. , 2012; Strubinger-Sandoval et al. , 2021) , most of which were not fully addressed in the studies compiled here. In particular, filtration of large sample volumes in the cubic meter range with in situ pumps yielded much stronger POC-c p relationships than small-volume sampling  (Bishop, 1999). Data availability. The simulated and observed datasets analyzed in this article are available at https://doi. org/10. 5281/zenodo. 5139602 (Galí et al. , 2021b). The code and documentation of NEMO and PISCES are available at https://www.
The supplement related to this article is available online at: https://doi. org/10. 5194/bg-19-1245-2022-supplement. Author contributions. MG and RB designed the study. MF produced and/or reprocessed global climatological datasets. RB produced the NEMO dynamical fields used to force PISCES 1D offline simulations. OA provided the global PISCES simulation. MG processed BGC-Argo coherent annual time series, ran PISCES 1D simulations, analyzed data and produced the figures, and wrote the paper with contributions from all coauthors.
Acknowledgements. The authors acknowledge  Antoine Poteau  for guidance with  BGC-Argo data ;  Margarida Samsó  and  Pierre-Antoine Bretonnière  for downloading and storing the Argo data; and  Thomas Arsouze ,  Vladimir Lapin , and  Joan Llort  for advice on the PISCES 1D configuration. Argo data were collected and made freely available by the  International Argo Program , which is part of the Global Ocean Observing System, and the national programs that contribute to it. The authors thank  Jim K. B. Bishop  and an anonymous reviewer for their constructive criticisms that improved the paper. Financial support. Martí Galí  has received financial support through the  Postdoctoral Junior Leader Fellowship Programme from "La Caixa" Banking Foundation  ( ORCAS  project;  LCF/BQ/PI18/11630009 ) and through the  OPERA  project funded by the  Ministerio de Ciencia, Innovación y Universidades  ( PID2019-107952GA-I00 ).
Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-19-1245-2022-supplement. Author contributions. MG and RB designed the study. MF produced and/or reprocessed global climatological datasets. RB produced the NEMO dynamical fields used to force PISCES 1D offline simulations. OA provided the global PISCES simulation. MG processed BGC-Argo coherent annual time series, ran PISCES 1D simulations, analyzed data and produced the figures, and wrote the paper with contributions from all coauthors.
In silico protein sequence analyses For comparison of TOGL domains among CLASPs (Ce: Caenorhabditis elegans, Dm: Drosophila melanogaster, Hs: Homo sapiens, Mm: Mus musculus, Xl: Xenopus laevis), protein sequence alignment and phylogenetic tree were generated using Clustal Omega (Version 1. 2. 4, https://www. ebi. ac. uk/Tools/msa/). The tree was rooted using the sequence of the TOG3 of human chTOG. The phylogenetic tree figure was generated using the FigTree software (v1. 4. 4, http://tree. bio. ed. ac. uk/software/figtree/). Sequence alignment of the TOGL2 domains (At: Arabidopsis thaliana, Ce: Caenorhabditis elegans, Dm: Drosophila melanogaster, Hs: Homo sapiens, Mm: Mus musculus, Xl: Xenopus laevis) and the TOGL3 domain of C. elegans was done with MAFFT using the Snapgene software (https://www.
ACKNOWLEDGEMENTS We thank all members of the  Pintard and Dumont labs  for support and advice. We are grateful to  Patricia Moussounda ,  Téo Bitaille  and  Vincent Maupu-Massamba  for providing technical support. We thank  Dhanya Cheerambathur  for worm strains and  Benoit Palancade  for yeast two hybrid strains. We thank  Jérémie Gaillard  and members of the CytoMorphoLab for technical advices and assistance with TIRF microscopy and microtubule-based assays. Some.
Results The hypothesis studied was that the maternal Met deficiency affected the hepatic transcription level of genes involved in one-carbon metabolism and epigenetic mechanisms in ducklings from the Met-restricted dams. We thus sought to compare the normalized relative expression of 70 genes (Additional Table  2 ) related to these metabolisms in 38 livers from ducklings of the two groups (50% of males and 50% of females). However, 8 of the 70 genes studied and 2 of the 38 liver cDNA samples showed more that 25% of missing expression data and were removed from the study. Moreover, another cDNA sample was considered as outlier and was also removed from the data set (see Methods section). The 35 remaining liver cDNA samples are from 10 male and 8 female ducklings from the Met-restricted dams and 9 male and 8 female ducklings from the control dams.
Primer design and qPCR validation We targeted 70 genes known to be related to one-carbon metabolism or epigenetic mechanisms and their sequences were obtained from the NCBI  [73]  and/or Ensembl  [74]  databases either in Anas platyrhynchos if available or in Gallus gallus. The two primers used for each gene (Additional Table  2 ) were each designed on either side of an intron, and for a hybridization temperature of 60 °C, using either Primer3Plus  [75]  or LightCycler ® Probe Design 2. 0 software (Roche Applied Science). Primer sequences were blasted to databases to confirm that they were specific to the gene in question and PCR products were subjected to 2% agarose gel electrophoresis to confirm amplicon size. Next, primer pairs showing a specific band and the absence of primer dimers were selected for qPCR testing, using SYBR green fluorescence detection (Applied Biosystems) and a QuantStudio6 (Thermo Fisher Scientific).
Each primer pair was tested on four serial dilutions of a cDNA pool (cDNA from all animals used in the study) to obtain a standard curve and to check PCR efficiency, with each point being performed in duplicate. The conditions were: 50 °C for 2 min, denaturation at 95 °C for 10 min, 40 cycles of 15 s at 95 °C and 1 min at 60 °C. A gradual increase in temperature from 60 °C to 95 °C was added to analyze the melting curves and detect primer dimers. Cq values and PCR efficiency were obtained directly from QuantStudio Real-Time PCR v1. 3 software.
Identification of potential reference genes, quantitative PCR and gene expression analysis The stability of expressions of 9 genes (ALB, GAPDH, HMBS, HPRT1, NDUFA10, POLA1, RPS13, TBP and TUBA1C) was tested in the livers of newly hatched mule ducklings as already described  [32]. These 9 primer pairs were tested by qPCR on liver cDNA from 8 ducklings of both sexes and both maternal diet groups (R and C groups) with SYBR green fluorescence detection, on a QuantStudio6. The selection of the 5 most stable genes (GAPDH, HMBS, NDUFA10, RPS13, TBP) was performed with the SLqPCR package on RStudio  [76]. These 5 genes are highlighted in dark grey in Additional Table  2. Gene expression was quantified using 96. 96 Dynamic Array Integrated Fluidic Circuits (IFCs) and the Fluidigm BioMark HD system, as described previously  [77].
The resulting cDNA samples were then processed as previously reported  [32, 77]. Fluidigm Digital PCR Analysis software (version 4) was used to analyze the data, using the linear (derivative) baseline correction method and the automatic (global) cycle threshold (Ct) method. Pre-processing of the data was carried out by removing cycle threshold (Ct) values recorded from amplifications whose melting curves showed either an abnormal Tm (melting temperature), double peaks (corresponding to a mixture of expected and aberrant PCR products), or a high baseline. The slope of the standard curve obtained with serial dilutions of the 168 cDNA pool was used to measure the PCR efficiency (E) for each gene and genes with less than three dilution points were eliminated. Finally, all genes with efficiencies greater than 2.
Statistical analyses For the remaining 62 genes, the few missing values were imputed within each group of similar sex and maternal diet using the imputPCA function with 3 principal components from the missMDA package of R software  [79]. These normalized and imputed relative expressions were then transformed using the function qqnorm(Y)$x to make the data follow a centered reduced normal distribution and it is these transformed data that were then used to describe the data set. First, a Partial Least Square Discriminant Analysis (PLS-DA) was performed with the package MixOmics of R software  [80]  on the 62 genes and the individuals were plotted on the two first latent variables of the PLS-DA score plot. ANOVAs were then conducted on the qqnorm transformed normalized relative expressions of the 62 genes using a linear mixed model fitted with ASReml software  [81]  as previously described  [32].
Additional analyses were performed on the DEGs for the effect of diet. First, a PCA was performed on the DEGs with the MixOmics package of the R software  [80] , using the qqnorm transformed normalized relative expressions. Using the Factoextra package of R, the biplot of the variables and samples of the first two principal components was obtained and then the ellipse package was used to plot the concentration ellipses around the mean points of each group with a confidence level of 0. 75. Next, correlation matrices were performed with the ducklings' phenotypic traits and the DEGs for the effect of diet, using the imputed but not qqnorm transformed normalized relative expressions, to be consistent with the phenotypic data which were also not transformed. The correlation matrices were plotted with the package Hmisc of R using the functions rcorr and corrplots  [83, 84]  and only the correlations with a P-value < 0.
Acknowledgements The authors thank the entire staff of the  INRAE-Ducks and Goose Experimental Facility  who took care of animal breeding and the  PEAT, INRAE ,  2018. Poultry Experimental Facility , (https:// doi. org/ 10. 15454/1. 55723 26250 88729 2E12 ) for the preparation of experimental diets. The authors are very grateful to the  GET ,  INRAE ,  2018. Genome & Transcriptome Facility , for technological support. They warmly thank  Caroline Molette  and  William Massimino  for sharing 5 pairs of unpublished primers.
VI. SIMULATION AND RESULTS In this section, we demonstrate the performance of our proposed Q-learning SBS controller using MATLAB. Our simulator mimics a scenario of one macro BS serving 100 users (unless stated otherwise) covering a cluster of 4 SBSs with 30 users. The macro and cluster users are randomly distributed inside the macro cell and the cluster, respectively. We obtain the results over 200 independent runs, simulating 10 seconds of network activity. The network parameters for simulations were set according to 3GPP specifications  [37]. We detail in Table  IV  the parameters used in our simulation.
While the relationship between loneliness and psychological distress is well documented, the mechanisms underlying this relationship are less clear. One factor known to be related to loneliness as well as psychological distress, is social support, with some studies suggesting that support-both received and provided-can serve as a mechanism to reduce the distress associated with loneliness. In this paper we examine the mediating role of both aspects of support in the relationship between loneliness and psychological distress in the COVID-context. We used a multi-country dataset collected at two timepoints during the pandemic; the first during the early stages (N = , , countries) and the second collected for a subset of countries (N = , , countries) months later. Across all eleven countries, results revealed significant positive associations between loneliness and distress.
The present research The COVID-19 pandemic created the context for a potential double jeopardy: social restrictions put people at risk of increased loneliness and reduced their capacity to engage prosocially with others (to support others and benefit from support provided). We argue that social support, received and given, lies at the heart of this double jeopardy, offering a means to overcome the effect of loneliness on psychological distress. To test this relationship, we used a multi-country dataset to explore relationships between loneliness and psychological distress, with social support as a possible mediator. We first examined the more established route via feeling supported by others, and then examined the lesser-known pathway through provision of support to others. Within this study there were two waves of data, both collected during the pandemic (March and June 2020; for a description of the pandemic conditions of each country at the time of data collection, refer to Supplementary material).
In the first wave, residents from eleven countries took part in the study, providing a sample of 6,842 participants. Three months later we collected a second wave of data in three countriesthe UK, Australia, and the US-surveying 1,299 of the same participants. Using the first wave of data, we first examined the relationships between loneliness, psychological distress, and social support, focusing particularly on the hypothesized mediating role of social support in the relationship between loneliness and psychological distress. Using the second wave of data, we explored these same relationships longitudinally. We expected to find that at Wave 1 higher levels of loneliness would be related to higher levels of psychological distress (H1a), and that higher levels of support received would be associated with lower levels of both loneliness and psychological distress (H1b).
Method Participants A total of 6,842 participants were sampled across eleven countries: Australia, China, France, Germany, Indonesia, Italy, the Netherlands, Spain, Thailand, the UK and the US. Participants were sampled via either the Prolific crowd sourcing platform (Germany, Italy, the Netherlands, Spain, the United Kingdom and the United States), recruited through social media (Thailand and Indonesia), or both platforms (France and Australia). Countries were selected to represent as broad a dataset as possible but were also dictated by logistical constraints such as researcher access and funding restrictions. This first wave of data was collected during the pandemic in March 2020. For a description of the pandemic conditions of each country at the time of data collection, refer to Supplementary material.
Within this second wave, for reasons of convenience, data were collected from only three of the eleven countries, and comprised responses from 1,299 participants residing in Australia (n = 468), the US (n = 373) and the UK (n = 469). A total of 32 participants were excluded from Wave 2 after having failed an attention check, leaving a final sample of 1,267 (53. 40% female; 45. 50% male, 1. 10% non-binary/other; M age = 35. 04, SD = 12. 36). Additional sample characteristics for each country are presented in Supplementary Table  1. A Monte Carlo sensitivity power analysis for indirect effects  (100)  indicated that the final sample had 99% statistical power (a = 0. 05) based on the strength of associations between our predictor, mediator, and outcome variables.
Procedure The study received ethical clearance via its university Ethics Committee (clearance number 2020000485). For Wave 1, data was collected between March 17 th and April 10 th , 2020, and for Wave 2 between June 24 th and July 2 nd , 2020. Surveys conducted in Chinese, Dutch, French, German, Indonesian, Italian, Thai, and Spanish were translated from English by the authors (all native speakers in their respective languages). Participant data collected on Prolific was advertised as a study looking at the effects of COVID-19 on people's thoughts and behavior, and participants were paid according to the platform's best practice guidelines. Once the participants had read a brief introduction to the study and were informed of their data anonymity and right to withdraw, they were asked for consent to proceed.
Longitudinal relationships To explore changes in loneliness and distress at Wave 1 and Wave 2, we used SPSS (v28) to conduct a regression between loneliness at Wave 1 and psychological distress at Wave 2, controlling for psychological distress, age and gender at Wave 1. We found that loneliness at Wave 1 significantly predicted psychological distress at Wave 2 (R 2 = 0. 54, F (4,1,259) = 364. 960, p < 0. 001); providing support for H3.
In our research, we examined the role played by social support in the relationship between loneliness and psychological distress, particularly testing the pathway from loneliness to distress via both received and provided social support. The latter pathway via support provision is currently under-investigated in the literature but was particularly relevant during the COVID-19 pandemic in which providing support to more vulnerable groups became highly salient  (11, (17) (18) (19) (20). We interrogated a large multi-county dataset across eleven countries and found a significant association between loneliness and distress. Using longitudinal data from three countries, we found evidence that this relationship unfolded over time in the hypothesized direction such that increased loneliness predicted increased distress.
Of note however, when examining both forms of support together, it appeared that receiving support had more impact on the relationship between loneliness and distress than provision of support. This might be a reflection of the difficulties people had in providing support to others due to enforced social distancing measures. It might also be a reflection of the importance of feeling supported by others through a highly stressful event, and which fits with other data collected during the pandemic demonstrating the relationship between lower levels of support received and psychological distress  (108). The current findings have several theoretical implications. Using a large, multi-country dataset, our results provide an empirically tested model of the directional relationship between loneliness and distress during COVID-19.
Strengths, limitations, and future directions This study analyzed data from eleven different countries across the globe. Such a large dataset provided us with a significant source of information with which to understand loneliness, psychological distress, and provision of social support. However, a limitation of using crowd sourcing platforms, as well as convenience samples for some countries, was that the sample is unlikely be fully representative. A further limitation was that the measures used were deliberately brief due to the data forming part of a much larger survey. As such, it would have been preferable to have more measures with which to validate the constructs of interest, using clinical measures of other related constructs such as depression or anxiety. Methodological limitations also resulted from the crosssectional nature of the Wave 1 data.
This is expressed on a per-plant basis: PDCD RAS (g C) = PDCC RAS (g C kg -1 soil) × mass of RAS (kg). The plant-derived C deposited / plant biomass was calculated as follows: plant-derived C / plant biomass = PDCD RAS / plant biomass. Statistical analysis was performed using the R statistical environment (version 4. 0. 3). The normality of the data was tested using the Shapiro test (p<0. 05). To test the effects of the pearl millet line on the different parameters (SOC, δ 13 C, F 14 C), general linear models (GLMs) were constructed using the "quick linear regression" (glm) function in R. Each model was fitted by considering the distribution mode and using the corresponding link function, i. e. Gaussian (link = "identity") for normally distributed data and Poisson (link = "log") for not normally distributed variables.
Acknowledgements. Papa Mamadou Sitor Ndour would like to thank the  French National Research Institute for Sustainable Development (IRD)  and the  Make Our Planet Great Again (MOPGA)  initiative of the French government for co-funding his postdoctoral fellowship. We thank  Oscar Pascal Malou  for communicating data on soil texture. Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant no. ANR-17-CE20-0022 ) and the  Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CarbonSeq, DRF impulsion). Review statement. This paper was edited by  Paul Hallett  and reviewed by  Paul Hallett  and one anonymous referee.
K. Barré et al. Environmental Pollution 292 (2022) 118394  Since it is impossible to determine the number of individual bats from their echolocation calls, we calculated an activity metric commonly used in Europe, hereafter named 'bat passes', calculated as the number of bat passes per night  (Kerbiriou et al. , 2019 (Kerbiriou et al. , , 2018;; Stahlschmidt and Brühl, 2012). A bat pass was defined as a single or greater echolocation call separated by at least a 2-s gap without calls, and a maximum duration of 15 s. We did not separated foraging from commuting bat passes. We then automatically identified bat passes to the most accurate taxonomic level possible, using the SonoChiro© software (Biotope, France). To account for potential errors in automated identifications, we followed the  Barré et al.