dataesr/scientific-paragraphs-categorization huggingface dataset:
The sediment core simulation (seamus_run) The seamus_run module uses the required and optional input parameters specified by the user (see the documentation in the script) to synthesise n number of single specimens being net-added to the historical layer of the sediment core per simulation time step, whereby n is scaled to the capacity of the synthetic sediment archive being simulated (input variable fpcm) and to the SAR of the time step as predicted by an inputted age-depth relationship. The simulation creates large single-specimen arrays of matching dimensions for age (corresponding to the time step) and "unbioturbated" sediment depth (according to the age-depth input), as well as a 14 C age (in 14 C yr) and 14 C activity (in F 14 C). The user also has the option to input a 14 C blank value.
The required input parameters should be inputted into the command line as follows. seamus_run  (simstart, siminc, simend, btinc, fpcm, realD14C, blankbg, adpoints, bdpoints, savename)  Optional parameters can be additionally specified as follows, e. g. in the case of including the array arrayname containing temporal changes in reservoir age for Species A. seamus_run  (simstart, siminc, simend, btinc, fpcm, realD14C, blankbg, adpoints, bdpoints, savename, 'resageA', arrayname)  The seamus_run module outputs a. mat file containing a number of very large one-dimensional arrays of the same size, whereby each position in each array corresponds to the same simulated single specimens. Output arrays are described in the script documentation. To improve simulation performance and data retrieval, all output arrays for all species are of the same dimension.
Errors (in 14 C yr) for intermediate dates are linearly interpolated to F 14 C. The MatCal  (Lougheed and Obrochta, 2016)  calibration software is used to calibrate 14 C ages and associated errors within the simulation after the application of a user-prescribed calibration curve and downcore reservoir age. The seamus_pick function is called from the MATLAB command line interface. seamus_pick(matfilein, matfileout, calcurve, pickint, Apickfordate, Bpickfordate) Optional parameters can be additionally specified as follows, e. g. in the case of including the array arrayname containing downcore changes in the fraction of broken specimens in Species A. seamus_pick(matfilein, matfileout, calcurve, pickint, Apickfordate, Bpickfordate, 'Abroken', arrayname) After running seamus_pick, one could consider, if wanted, adding Gaussian noise to the outputted discrete-depth carrier signals, thus simulating the uncertainty associated with machine measurement.
Comparison with TURBO2 In order to evaluate the performance of the SEAMUS model, it is compared here to the output of the established TURBO2 bioturbation model  (Trauth, 2013) , which was also authored in the MATLAB environment. The most notable difference between SEAMUS and TURBO2 is that the latter outputs data in the form of the perturbation of the mean downcore signal, whereas SEAMUS takes advantage of recent increases in available computer memory to store and output a very large array of single elements (foraminifera specimens). The two models can be compared, therefore, by comparing the mean downcore output from TURBO2 with the SEAMUS downcore mean value derived from discrete-depth single-specimen populations. To achieve this comparison, the NGRIP δ 18 O record on the GICC05 timescale (North Greenland Ice Core Project members, 2004;  Rasmussen et al.
Processing speed and computing requirements Where possible, the processing of arrays for simulation time steps has been vectorised (i. e. not processed within an iterative loop), in order to maximise processing speed. For example, the per time step assignment of single-specimen arrays corresponding to ages and carrier signals all occurs within fully vectorised code. However, the bioturbation simulation (i. e. the bioturbation of the assigned depth values)   (Trauth, 2013) , based on the same NGRIP input data and using a SAR of 10 cm kyr -1 and a constant BD of 10 cm. Table  1. Approximate run times and memory use in MATLAB and Octave in the case of a 70 kyr simulation run with 10-year iterations and sediment archive capacity of 10 2 , 10 3 and 10 4 specimens per centimetre depth. The runs were carried out on a 64-bit system (Ubuntu 18.
04) with 16 GB of RAM and an Intel i7-2600 processor, using MATLAB 2019a or Octave 5. 1. 0. Reported memory use is the additional memory load on the system (rounded up to the nearest 10 Mb) while running the simulations (i. e. excluding the general background memory use by MATLAB or Octave). 10 2 specimens cm -1 10 3 specimens cm -1 10 4 specimens cm is not vectorised and is carried out within a single-thread iterative loop, due to each iteration of the bioturbation simulation being dependent upon the results of the previous iteration. In order to optimise the processing time on 64-bit computers, all arrays are stored as 64-bit. Should the user wish to save memory, it is possible to select the do32bit option when accessing seamus_run from the command line (see the function documentation). Indicative run times and memory use are shown in Table  1.
The SEAMUS model was developed in MATLAB 2017b and has been tested as compatible with Octave 5. 1. 0. The seamus_run module can be run using the basic MATLAB environment, with no extra toolboxes. The seamus_pick module runs more efficiently when the Statistics and Machine Learning toolbox (specifically, the prctile function) is installed, but when it is detected that users do not have access to that toolbox, seamus_pick will revert to using a modified version of an equivalent function  (Kienzle, 2001) , which has been embedded into the script. The seamus_pick function also uses the Matcal  (Lougheed and Obrochta, 2016)  14 C calibration script, which has been embedded in the SEAMUS download package. As outlined in the introduction, advances in stable isotope mass spectrometry have allowed for routine single-specimen analysis, which has led to increased interest in using geochemical analysis of single-specimen populations from discrete depths as a potentially powerful tool with which to reconstruct past changes in climate variability.
Analysing 14 C calibration skill As outlined earlier, it is possible to assign 14 C activities to single specimens in the sedimentation simulation by using suitable records of the Earth's 14 C history (e. g. , IntCal). Subsequently, SEAMUS uses the 14 C activities of the specimens contained within each discrete depth to calculate an expected laboratory 14 C determination and measurement uncertainty. Using the MatCal software, it is subsequently possible to calibrate the aforementioned 14 C age, in combination with a calibration curve and reservoir age estimate, to produce an expected calibrated age distribution. The calibrated age distribution for the discrete depth can be compared with the true age distribution for the discrete depth, as recorded by the simulation, to evaluate the skill with which current 14 C dating and calibration processes can reproduce the true age distribution of a particular sediment core slice.
A MATLAB script enabling users to produce a figure similar to Fig. 3  is included within the tutorial script (tutorial. m) that is bundled with SEAMUS. Users can subsequently explore downcore changes in the effectiveness of 14 C dating to accurately estimate true age under various dynamic simulation conditions, including abundance changes, SAR changes, bioturbation depth changes, reservoir age changes, as well as during periods of dynamic 14 C. Figure  3. Example of using output from a SEAMUS simulation to estimate 14 C calibration skill for a particular discretedepth subsample. The green histograms represent the SEAMUS simulation output: on the x axis the true age distribution of the discrete-depth single specimens (with the green diamond corresponding to the median true age) and on the y axis the 14 C age distribution of the single specimens (with the green diamond corresponding to the mean 14 C age).
Investigating noise created by the picking process When picking discrete-depth samples from discrete-depth specimen populations, palaeoceanographers randomly pick whole specimens to produce a downcore mean signal. The seamus_pick module can be used to test for random noise introduced upon the mean signal by the picking process. The module can be repeatedly run with a set number of randomly picked whole specimens per sample, and the resulting picking runs can be compared to an ideal picking run that picks all available whole specimens for each discrete depth. Such an approach is investigated here, once again using the same SEAMUS bioturbation simulation that was carried out in Sect. 3. 1, for picking scenarios each with 1 specimen per sample (Fig. 4a ), 2 specimens per sample (Fig. 4b ), 3 specimens per sample (Fig.
Acknowledgements. Thanks to  Laboratoire d'Océanologie et de Géosciences  for kindly hosting me as a guest researcher at  Université du Littoral Côte d'Opal. Brett Metcalfe  is thanked for various discussions about the state-of-the-art of single-specimen foraminifera analysis (see also the resources at http://www. brett-metcalfe. com,last access:  15 January 2019 ). Two anonymous reviewers are thanked for their helpful input. Financial support. This research has been supported by the  Swedish Research Council (Vetenskapsrådet ; grant no. 2018-04992 ). Review statement. This paper was edited by  Paul Halloran  and reviewed by two anonymous referees.
Bioinformatic analysis The STAR aligner  (Dobin et al. , 2013) v. 2. 7. 3a  was used to align RNA-seq read against the mm10 mouse genome assembly and GENCODE vM22 mouse transcripts  (Frankish et al. , 2019). The software program featureCounts v. 1. 6. 4 was used to summarize reads onto genes  (Liao et al. , 2014). Testing for differential expression was performed using edgeR 4 v. 3. 26. 8 using the quasi-likelihood framework with a fitted model of the form ~ 0 + group where group encoded both genotype and treatment  (Robinson et al. , 2010). Contrasts were constructed as described in the edgeR manual. Gene Ontology 5 enrichments were found using the CAMERA function 7 which is part of the edgeR package (The Gene Ontology, 2019;  Wu and Smyth, 2012). Only gene ontologies with between 5 and 500 genes were investigated.
Acknowledgements A. M. L. and A. M. F. were supported by a research grant from the  Danish Diabetes Academy , which is funded by the  Novo Nordisk Foundation , grant number  NNF17SA0031406. Furthermore, A. M. F. was supported by the  Alfred Benzon Foundation. CC is supported by research grants from the  Lundbeck Foundation  (Fellowship  R238-2016-2859 ) and the  Novo Nordisk Foundation  (grant number  NNF17OC0026114 ). Novo Nordisk Foundation Center for Basic Metabolic Research  is an independent Research Center, based at the  University of Copenhagen, Denmark , and partially funded by an unconditional donation from the  Novo Nordisk Foundation  (www. cbmr. ku. dk) (Grant number  NNF18CC0034900 ).
Data availability The raw sequencing data and aligned read counts generated as part of this study has been deposited to the NCBI Sequence Read Archive. Accession number: GSE146336; https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE146336. The scripts used for the analysis of the sequencing data and figure generation is available at https://github. com/lars-worksund/GSE146336.
Standard statistical analyses were performed using R language "R version 3. 5. 2" available at http://cran. r-project. org/ and Mplus 7. 11 version 7. 1 software available on the website https://www. statmodel. com/ for factor analysis and SEM using robust weighed least squares due to the potential non-normality of item scores  [32]. The R packages used were MplusAutomation  [33] , psy for Cronbach coefficient and pROC for ROC analysis. A significance threshold of 0. 05 was chosen unless otherwise indicated.
Children Ten items of the BISQ and 2 items about screen exposure were correlated with the total SDSC score. This produced a 12-item questionnaire (the Sleep Hygiene Scale for Children, SHSC) (see Appendix B). The exploratory and confirmatory factorial analysis of the SHSC was reported in the Supplementary Data on pages 8-9. The 3-factor model was retained because it was the smallest model with acceptable fit (RMSEA = 0. 055 < 0. 06; CFI = 0. 939 > 0. 90) and it explained 66% of the total variance of the SHSC (Supplementary Table  2  and Table  3 , page 8). The 3 cross-loading items (6, 10 and 12) were assigned to the factor with which they had the highest factor loading. The three factors were " Attachment parenting" (items 1 to 6, 8 and 10), " Transitional coping" (items 7 and 9) and "Screen exposure" (items 11 and 12).
Acknowledgements 12 Many thanks to all the families who participated in this study, to the nurseries of the  Auvergne-Rhône-Alpes region  and the  Mother and Child Welfare units (Protection Maternelle Infantile) in Ain , and a special thanks to the  Petite Enfance network in Bourg-en-Bresse. We would like to thank  Dr. Marie-Joseph Challamel  for her sound advice. We also thank  Dr. Oliviero Bruni  for authorizing the adaptation of his scale.
Invertebrate glia performs most of the key functions controlled by mammalian glia in the nervous system and provides an ideal model for genetic studies of glial functions. To study the influence of adult glial cells in ageing we have performed a genetic screen in Drosophila using a collection of transgenic lines providing conditional expression of micro-RNAs (miRNAs). Here, we describe a methodological algorithm to identify and rank genes that are candidate to be targeted by miRNAs that shorten lifespan when expressed in adult glia. We have used four different databases for miRNA target prediction in Drosophila but find little agreement between them, overall. However, top candidate gene analysis shows potential to identify essential genes involved in adult glial functions. One example from our top candidates' analysis is gartenzwerg (garz).
The experimental advantage of performing a miRNA-based screen followed by in silico identification and ranking of predicted miRNAs target transcripts  11, 12  has, however, its bottleneck in the validation of the action of the genes of interest. In principle, the specific knockdown of predicted target genes should mimic, to some extent, the phenotype obtained upon corresponding miRNA overexpression. In fact, using databases of predicted miRNA-target genes previously allowed us to identify repo as an important player for maintaining glial function and, consequently, homeostasis in the adult brain  11. We have shown that while the miR-1-repo axis is physiologically relevant only in the embryo during the glia versus haemocyte cell fate choice  13  , the miRNA-target relationship can be exploited as a discovery tool to identify the functions of a target gene in a different context, namely adult glial functions  11.
Scientific benefits: • This screen has provided a thorough analysis of glial functions in ageing • Potential to shortcut gene discovery through miRNAs effect. The screening of only ~200 mutant lines potentially targets >6000 genes. • Potential to identify complex regulatory networks that include miRNAs and target genes. • Validated the identification of essential genes for the adult nervous system and their functions specifically in motor control. • An open-access searchable database for future discoveries upon improved precision of miRNA-target predictions. 3Rs benefits: • This screening method provides an alternative approach for studying genes important in glial biology, without the need for animal experiments. • Example validation that Drosophila can be used to study the biology of GBF1, instead of in vivo vertebrate animal models, such as zebrafish or mouse.
Amendments from Version 1 We have now modified some aspects of our text to explain better some points highlighted by the reviewers. This includes revisions to Figure  1  and Figure  3  to incorporate one of the reviewer's comments on the visibility of labels and of the items identified by arrows. Any further responses from the reviewers can be found at the end of the article This is followed by experimental validation of the function of these targets in adult glia in the same paradigm used in the miRNAs screen. We conclude that this approach is valid but has issues of efficiency given the large number of predicted targets that do not recapitulate the expected phenotype. We also establish that there is no significant synergy generated by focusing on the common predictions between all available miRNAs target databases.
Online resources and in silico algorithms for target identification and ranking The following databases were used for the prediction of miRNA targets: • MicroCosm (https://www. ebi. ac. uk/enright-srv/microcosm/ htdocs/targets/v5/) • microRNA. org (http://www. microrna. org/microrna/home. do) • TargetScan (http://www. targetscan. org/fly_72/) • PicTar (https://pictar. mdc-berlin. de/) Each of the databases provides for every miRNA a numerical prediction of the likelihood of targeting a given gene (Score). For MicroCosm and PicTar this was used without additional steps. In the case of miRNA. org this score is a negative value and we have squared it to obtain a positive number. In the case of TargetScan a numerical score was calculated on the basis of the information provided by the database as follows: conserved 8mer = 10 points, conserved 7mer-m8 = 6 points, conserved 7mer-1A = 4 points, poorly conserved 8mer = 8 points, poorly conserved 7mer-m8 = 4 points and poorly conserved 7mer-1A = 2 points.
• Step 2 -column Σ(Score)*Av(χ 2 )] or Σ(Score 2 )*Av(χ 2 )]: For each target gene, as defined by its CG number/ accession ID, all values resulting from all miRNAs predicted to target the same gene were summed in a final ranking value. Information regarding different mRNAs from the same gene, where available, was grouped under the same gene name. Table  1. Average strength of miRNAs that shorten lifespan in adult glia. To determine the strength of miRNAs in our lifespan assay we have used the averaged χ 2 values from each transgenic line used in our previously published analysis  11. When only one line was tested for a given miRNA, the value was divided in half, i. e. assuming a neutral value of 0 for a second putative untested line. For the TargetScan database, some miRNAs are grouped in families requiring an amendment to our approach.
In this case, we have averaged all miRNAs in the given families. Additionally, some of the lines tested for these grouped miRNAs had, in the original screen the opposite effect of what is here considered, i. e. extending lifespan with respect to the control used. To account for this opposite effect the χ 2 values for these miRNAs have been given negative values and have been effectively subtracted, when calculating the Av(χ The algorithm for comparing the ranking between different databases and providing a final common ranking consists of two steps: • Step1 -column Normalised Σ[(Score)*Av(χ 2 )] or Normalised Σ[(Score 2 )*Av(χ 2 )] For each database the Σ(Score)*Av(χ 2 )] was normalised to 100 and then weighted for the fraction of miRNAs present in the database, out of the total tested in our miRNAs screen.
0 (freely distributed upon request to info@bfklab. com) in order to evaluate the relative speed and activity before, during and after the stimuli event. The speed analysis was used for the "Stimuli Response Trace" and the general activity used to deduce "Active Speed", "Mean Bout Length" and "Inter-Bout Interval", using a custommade modification of the DART software  36. Raw data were analysed with GraphPad Prism for statistical significance and DART-derived graphs were edited with Adobe Illustrator CC2017 (RRID:SCR_010279).
Results Development of an algorithm for ranking miRNA target genes for their relevance in adult glia in lifespan and ageing Firstly, such algorithm should prioritise the information for the miRNAs that had the strongest effect on the fly lifespan in our miRNA screen. To achieve this, we have quantified the average strength of each miRNA using the Chi square (χ 2 ) of each Kaplan Mayer analysis (Table  1 ). To identify potential target genes, we used four different databases available online: EBI MicroCosm, PicTar, micro-RNA. org and TargetScan. Each database weights the likelihood of every miRNA to target a given gene with a numerical score. Where this is different, for TargetScan, we calculated a numerical score on the basis of the sequence information provided by the database (see Methods).
Therefore, to rank target genes within each database taking into account both the likelihood of being targeted by a given miRNA and the strength of the effect of this miRNA in adult glia, we first multiplied the average strength of each miRNA from our screen (values in Table  1 ) by the strength of the target prediction (Score) given by the database, obtaining the parameter (Score)*Av(χ 2 ). This was done for all miRNAs tested in our screen that were present in each database. Because a given gene can be targeted by more than one miRNA, to rank its overall importance in adult glia, we have summed all the values obtained for a given gene that were calculated for different miRNAs, obtaining the parameter Σ[(Score)*Av(χ 2 )]. In the case of TargetScan, some miRNAs are grouped in families and we have considered them as a single unit value.
This underweights these miRNAs in comparison to others and the genes targeted by them (for instance a gene targeted by miR-9a, miR-9b and miR-9c would obtain a Σ[(Score)*Av(χ 2 )] that is the sum of three (Score)*Av(χ 2 ) in the other databases, but for TargetScan it would only reflect one (Score)*Av(χ 2 ). Our reasoning was that grouped miRNAs in TargetScan was not taking into account valuable information and this should be reflected in a penalisation in the ranking. In conclusion we have ranked target genes according to Σ[(Score)*Av(χ 2 )] for EBI MicroCosm (Extended data Table  1 )  38  , PicTar (Extended data Table  2 )  38  , microRNA. org (Extended data Table  3 )  38  and TargetScan (Extended data Table  4 )  38. Surprisingly, this revealed that there was very little agreement among the four databases.
The top-ranking genes obtained using the same algorithm were very different and only 5. 6% (i. e. 520 genes) of target predictions were common to all four databases (Figure  1A ). To rank these common targets for their predicted overall relevance in adult glia in ageing, we have devised additional steps. First, to make the numerical rankings from each database comparable, we have calculated the Normalised Σ[(Score)*Av(χ 2 )] parameter by normalising the maximum value to 100. Additionally, we have weighted this number for the fraction of miRNAs present in each database, out of the total tested in our miRNAs screen. Out of 44 miRNAs screened, 31 were present in EBI Microscosm, 28 in PicTar, 43 in microRNA. org and 40 in TargetScan. The rationale for this weighting was to prioritise the databases carrying more information that was relevant to our screen.
Systematic experimental testing of the prediction, ranking and effectiveness of different databases To test these predictions, we decided to screen for the lifespan effect, a number of RNAi lines from Vienna Drosophila Resource Center (VDRC) that were already present in our stock collection. These corresponded to a random selection of approximately 10% (51 out of 520) of commonly predicted target genes. Adopting a similar strategy used for the miRNA screen, we have used the repo-Gal4, tub-Gal80 ts inducible system to trigger the RNAi expression in all glial cells in adult flies. As negative control, we used the offspring of crossing repo-Gal4, tub-Gal80 ts to w 1118 throughout the screen. The expectation was that RNAi against these target genes in adult glia, would phenocopy the effect of the miRNAs that are predicted to target them, therefore shortening lifespan.