dataesr/scientific-paragraphs-categorization huggingface dataset:
03Z ) we have shown that CUBES could obtain spectra with S/N ∼ 50 of the candidate O-type star in Leo P at 1. 6 Mpc in approximately half a night of observations. Our qualitative consideration of the models here was intended as a first study of the potential diagnostics available in the CUBES domain. Data from the ongoing XShootU ESO Large Programme (in support of the HST ULLYSES Legacy Survey) will soon enable quantitative investigation of O-type spectra in the CUBES region from high S/N (> 100) X-Shooter observations in the Magellanic Clouds. Analysis of the XShootU data will provide an important test of the temperatures estimated using the He I λ3188/He II λ3203 ratio and logg from the high Balmer lines compared to diagnostics in the visible. Our expectation is that for the extragalactic metal-poor dwarfs envisaged as future CUBES targets, the impact of stellar winds on the determination of physical parameters from the near-UV region alone should be relatively minor, and the XShootU data in the Clouds will provide an important test of this.
More generally, the greater throughput and resolving power delivered by CUBES compared to X-Shooter will give a unique capability for studies of massive stars in the ground-UV region. Moreover, the absence of cross-dispersion in the CUBES design will avoid the challenges of blaze correction and order recombination that affect the analysis of data from echelle instruments such as UVES and X-Shooter; the CUBES design will give a smooth, continuous spectral response for the two optical channels. This will enable more robust definition of the stellar continuum for normalization, particularly for broad features in the Balmer line series, as well as analysis of weaker absorption lines if they would otherwise be in the reconnection regions. The latter will be important for more rapidly-rotating massive stars (vsini 100 km s -1 ), which will also benefit from the greater resolving power compared to X-Shooter.
Acknowledgements We thank the reviewers for their suggestions on the manuscript, which helped clarify details as well as helping place this study in the wider context of previous work and some of the challenges of ground-based observations at these short wavelengths. MG knowledges financial support from grants  ESP2017-86582-C4-1-R  and  PID2019-105552RB-C41 , and from the  Unidad de Excelencia ?Mariá de Maeztu? -Centro de Astrobiologiá (CSIC-INTA)  project,  MDM-2017-0737.
This has potential to increase understanding of the links between molecular and genetic mechanisms of autism with macroscopic measures such as cortical thickness, aiding in developing markers for subtyping and developing targeted treatment options in autism  [19]. In the current study we want to integrate parts of the E/I puzzle by taking a multimodal approach focusing on aggregated (common) genetic variation, different autism phenotypes and their association with brain structure. One relatively understudied part of the autism phenotype comprises sensory symptoms. These are especially interesting as they have been suggested promising in an attempt to unscramble the autism heterogeneity  [31]  as well as for their shown link with E/I imbalance using MR spectroscopy  [32]. Additionally, previous investigations within our dataset have shown differences in CT between those with severe and low sensory processing deficits in brain regions enriched for genes that are expressed in excitatory neurons in the developing cortex  [19].
1 in windows of 500 kb were selected. This resulted in 546 participants with genotypic data (n = 304 autistic, n = 242 NTC). Selection of the glutamate (n = 72 genes) and GABA (n = 124 genes) gene-sets was based on Ingenuity Pathway Analysis software (http:// www. ingenuity. com), a frequently updated database for genetic pathway analysis. Supplemental tables S1 and S2 show an overview of the included genes. In case of any significant associations between the aggregated genetic variation in the gene-sets and the phenotypes of interest, we explored smaller gene-sets containing genes encoding glutamate/GABA receptors and transporters specifically because of their more direct role in neurotransmitter signaling  [47]. Those we refer to as glu-RT (n = 32) and GABA-RT (n = 26) and are defined in the supplemental Tables  S1  and S2.
Neuroimaging data Structural brain images were acquired on 3 T MRI scanners at all sites, with T1-weighted MPRAGE sequence (TR = 2300 ms, TE = 2. 93 ms, T1 = 900 ms, voxels size = 1. 1 × 1. 1 × 1. 2 mm, flip angle = 9°, matrix size = 256 × 256, FOV = 270 mm, 176 slices). For a summary of scanner details and acquisition parameters at each site, see Table  S3  in the supplemental information. The processing of all neuroimaging data was conducted at one site for all available data. For each image a model of the cortical surface was computed using FreeSurfer v6. 0 (https://surfer. nmr. mgh. harvard. edu/), using a fully automated and validated procedure  [48] [49] [50] [51]. Subsequently, each reconstructed surface went through strict quality assessments, described in detail in  [19]. This quality assessment included visual inspection of reconstruction errors by independent raters, manual editing where needed, and examination of the Euler number of each FreeSurfer surface reconstruction resulting in the conclusion that there were no differences in the complexity of the reconstructed cortical surface between participant groups.
446). Secondly, we used data from the BrainSpan Atlas, where gene-expression data in a wide age-range of donors are available (www. brainspan. org). Donors were selected within the age range of our LEAP dataset (6-30 years), which gave us 9 donors (male/ female = 5/4). We calculated correlations to the median expression values in the 11 cortical regions in the AHBA-to-FreeSurfer data that were also included in the BrainSpan Atlas, using methods as described by  [24]. We then selected genes that correlated between the profiles of the two atlases higher than r = 0. 52 (one-sided test p < 0. 05), which resulted in 2293 genes available in total. The overlap with the gene-sets left 29 genes in our glutamate pathway gene-set, and 42 genes in our GABA pathway gene-set. The median expression profiles across regions for these genes constitute the interregional gene-expression profiles used in our analyses.
Analyses All analyses included the linear effects of age, sex, IQ and site as covariates. All tests were corrected using the false discovery rate (FDR; q < 0. 05 was considered significant) unless otherwise described. Gene-set analysis. To investigate associations between aggregated genetic variation within the glutamate and GABA gene-sets and the autism phenotypes of interest (SRS-2 total score, RBS-R total score, SSP total score, and ADOS-2 and ADI-R (the last two for the autism group only)) and cortical thickness, we performed competitive gene-set analysis using MAGMA (Multi-marker Analysis of GenoMic Annotation) software (version 1. 10,  [33] ). The analysis was performed in two steps. First, gene-based p-values were calculated for each gene (excluding genes located on the X-chromosome, see supplemental tables S1 and S2) on our phenotypes of interest, using a multiple linear principal components regression using F-tests.
In order to verify any of these associations, analyses were replicated using structural imaging data of CT from the multi-site opensource ABIDE database  [60]. We included participants in the same age range as our own sample (6-30 years), which resulted in data from 874 participants matched for age, sex and IQ (n = 437 in both groups). Details on these analyses and results can be found in the supplemental information and Fig. S1. Building upon previous results from our group  [19, 31]  and to parse some of the autism heterogeneity, we additionally performed these geneexpression analyses with interregional CT profiles in separate sensory subgroups (low, n = 375; moderate, n = 37; severe, n = 37). These subgroups were defined previously  [31]. The interregional expression profiles of the genes in our glutamate and GABA pathway gene-sets were then correlated with the CT-difference interregional profiles (autism minus NTC across different age-groups and CTaverage interregional profiles in sensory subgroups), which provided a distribution of correlation coefficients per gene-set.
Furthermore, here we focused on behavioral autism characteristics and genetic information, not in vivo brain concentrations of glutamate and GABA. We did however find links between repetitive behaviors (RBS-R) and CT in the frontal pole, where increased RBS-R scores were associated with increased CT. Measures from the ADI-R and ADOS-2 diagnostic tools (in the autism group only) were differently associated with CT in the precuneus and insula, although this was only at trend-level. We did not find direct associations of CT with SSP scores, although previous work on this dataset did find associations of differences in CT between sensory subgroups in right premotor cortex and supplementary motor areas, regions enriched for genes expressed in excitatory neurons in developing cortex  [19]. In support of this, we found that regions with greater expression of genes from both gene-sets also showed greater CT in all sensory subgroups, although there were no significant differences between sensory subgroups.
Another strength was the relatively large number of participants available giving us more confidence in our results. There were also some limitations. Firstly, there were fewer females than males included in this study, a common problem in autism research. Furthermore, the gene-expression data were only used in the left hemisphere. However, the gene-expression data used in the expression profile analyses was robust and only included if the interregional profiles were similar across another dataset (BrainSpan), increasing the confidence in the robustness of these profiles. Another limitation is that the AHBA donors were all neurotypical controls, and we do not know whether genes are expressed differently in autism. Additionally, there were differences in ages of participants recruited at different sites, which has been investigated in an initial analysis of the LEAP cohort  [38].
We also did not fully replicate our geneexpression profile results in the independent ABIDE data set (see the supplemental information and Fig. S1 ). However, the results were largely overlapping showing similar effects, although in opposite direction in the adult group compared to the adults Fig. 1  Distributions of correlation coefficients between cortical thickness difference and gene-expression. Distributions of the interregional correlation coefficients between differences in cortical thickness (CT) and profiles of gene-expression in adults (A) and adolescents (B). The CT-difference profiles were obtained from our LEAP data, and the expression profiles from the Allen Human Brian Atlas (AHBA), in our glutamate-pathway and GABA-pathway gene-sets. The x-axes show the correlation coefficient between CT-difference and expression profile for all genes in the gene-set; the y-axes show the estimated probability density for the correlation coefficients; the vertical dashed-lines indicates the average expression-CT difference correlation coefficient across all the marker genes in a gene-set; and the edges of the gray boxes indicates the 2.
FUNDING This work has been supported by the  EU-AIMS  (European Autism Interventions) and AIMS-2-TRIALS programmes which receive support from  Innovative Medicines Initiative Joint Undertaking  Grant No. 115300  and  777394 , the resources of which are composed of financial contributions from the  European Union 's  FP7 and Horizon2020 Programmes , and from the  European Federation of Pharmaceutical Industries and Associations (EFPIA)  companies' in-kind contributions, and AUTISM SPEAKS, Autistica and SFARI; by the  Horizon2020  supported programme  CANDY  Grant No. 847818  and a  Veni grant  from the  Netherlands organization for scientific research (NWO)  under grant number  VI. Veni. 194. 032  awarded to JN. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
All statistical analyses were conducted in R version 4. 2. 2. Incidence of ILI and 95% confidence intervals (CI) were estimated using complex survey analysis methods, adjusting for the demographic structure (age and sex) of the Vientiane urban population and clustering in the survey design, as detailed previously (9, 13). The prevalence of pneumococcal detection among ILI cases, and the proportional distribution of serotype categories among pneumococcal-positive ILI cases, were calculated and assessed for associations with age-group, surveillance year, and climatic season (wet vs. dry months) using chi-squared tests. Binomial generalized linear mixed models (GLMMs), adjusting for random effects at household-level, were used to further investigate risk factors for pneumococcal positivity among ILI cases using the "lme4" R package  (14).
Bivariable GLMM analyses tested for associations with each independent variable separately, while multivariable GLMMs included age group, sex, and any covariates which showed associations with P < 0. 05 in the bivariable models. We were unable to adjust for individual level random effects in these GLMMs as the majority of study subjects with ILI experienced only a single episode throughout the surveillance period (Models specifying two nested levels of random effects, i. e. , for both households and individuals, displayed poor convergence and singularity issues, indicating that these were overly complex for the dataset). However, to assess whether the results of our GLMM analyses may have been influenced by individual-level random effects which could not be adjusted for in the models, we also repeated these analyses with a subset of the data which included only a single ILI episode per subject.
Lastly, observations are represented as 9 x 9 grids of vision centered on the agent. Parameterization. Both the Q-Learning baseline and the actor-critics employ tabular models; that is, they have distinct parameters for each observation. Policies and Q tables have four parameters per observation (one for each action), while critics only have one (for the state-value). All parameters are initialized to zero. Evaluation. Curves are made with data from 100 evaluation episodes evenly spaced throughout training, that is, every 300 update steps. The evaluation data is collected with deterministic policies. Hyperparameters All hyperparameters not mentioned in our work are set to their default value in the original setup. The supplementary code 1  contains a complete implementation of the algorithms, environment and plotting scripts.
The size of the final layer is the same as the size of the action space for the policy and one for the critic and prior weights. The weights of the last layers are initialized with 100 times smaller scale for the policy and prior weight networks. Evaluation. We evaluate agents every 50 thousand timesteps by freezing the policy and reporting the mean undiscounted reward of the stochastic policy on 50 evaluation episodes. Hyperparameters. The hyperparameters used in our PPO implementation are presented in Table  3. Data collected during a rollout is randomly assigned to minibatches at the start of each epoch. Computing Infrastructure. Deep RL experiments were performed on a 32-core system equipped with Intel Xeon Gold 6134 CPUs at 3. 20GHz and NVIDIA GeForce RTX 2080Ti GPUs. The training time varied from 30 minutes to 2 hours per run, depending on the environment and algorithm.
Acknowledgments We acknowledge: the support of the  Métropole Européenne de Lille (MEL) ,  ANR ,  Inria ,  Université de Lille , through the  AI  chair Apprenf number  R-PILOTE-19-004-APPRENF ; the  AI PhD@Lille program  that funds MC's grant; the outstanding working environment provided by  Inria  in the  Scool research group.
In this work, we exploited the XQ-100 quasar legacy survey  (López et al. 2016)  to collect a sample of 54 absorption systems at redshift z ∼ 3-4 selected by the presence of the Mg II absorption doublet. Among these systems, we identified a subsample of 37 diffuse optically thick LLSs and sub-DLAs absorbers that we have studied in detail. We performed Voigt profile fitting of metal absorption features and hydrogen Lyman lines in the quasar spectra to measure column densities. To derive relative abundances of different elements, we applied photoionization model corrections to the measured ionic abundances. In Section 2 we present our data set, including the line profile fitting of the absorption systems and the determination of the chemical abundances. In Sections 3 and 4 we present and discuss the results.
Data Analysis The Large Programme "Quasars and Their Absorption Lines: A Legacy Survey of the High-redshift Universe with (VLT)/ X-shooter"  (López et al. 2016 ) has produced a homogeneous and high-quality sample of echelle spectra of 100 quasars (QSOs) with emission redshift z ∼ 3. 5-4. 5. The targets were observed with the X-shooter spectrograph  (Vernet et al. 2011 ) mounted at the ESO Very Large Telescope (VLT, Cerro Paranal, Chile). X-shooter is characterized by three arms that allow us to cover in one observation the full spectral range between the atmospheric cutoff at 300 nm and the near-infrared K-band at 2500 nm, at an intermediate resolving power. The full spectral coverage, along with a well-defined target selection and the high signal-to-noise ratio (S/N) achieved (median S/N = 30), clearly makes XQ-100 a unique data set to study the rest-frame UV/optical spectra of high-z QSOs in a single, homogeneous, and statistically significant sample.
When a target is observed more than once at different epochs, there is one spectrum for each epoch and a combined spectrum, putting together all epochs. The telluric corrections are calculated on the spectra corresponding to the single epochs, while the continuum is calculated only on the combined spectrum. Since for our purposes we needed telluric-corrected spectra, we created a coadded spectrum when multiple observations were present, and then we redetermined the intrinsic QSO continuum in the framework of the Astrocook 13  Python software package  (Cupani et al. 2020). In Astrocook, the emission continuum is estimated by first masking the most prominent absorption features and then interpolating the nonmasked regions with a univariate spline of chosen degree. The final spectrum for each QSO was created by cutting the noisy edges of each arm and "stitching" the three arms together.
Line Fitting The analysis of the spectra has been carried out with the Astrocook software package. In Astrocook, absorption lines are detected as prominent local minima in the flux density spectrum, and then they are identified by cross-matching their wavelengths with a list of ionic transitions commonly observed in QSO spectra and finding coincidences among the obtained redshift values. For our study, we need to identify absorption systems with H I column densities in the range   N 17. 2 log cm 20. 3 H 2 I - ( ) , i. e. , LLSs and sub-DLAs, which can trace diffuse gas, such as the one in the outskirts of galaxies and in cosmic filaments  (Lofthouse et al. 2023). To this aim, we searched the XQ-100 spectra for singly ionized magnesium doublets, Mg II, which are good probes of the optically thick, low-ionization gas.
Mg II is one of the best known examples of strong resonance-line doublets: it has rest-frame wavelengths (see Table  3 ) longer than H I Lyα (Lyα), and therefore it appears on the red side of the Lyα emission line in the QSO spectrum. For this reason, it is relatively easy to identify as it is not embedded in the thick Lyα forest. The search for Mg II doublets was carried out using an automatic recipe of the Astrocook software. Subsequently, the detection of the absorption lines was confirmed visually. In our analysis we have not made any attempt to reach or determine the completeness of the absorber sample since it was not relevant for our scientific purposes. In each line of sight, we restricted our search to a redshift range that avoids the proximity region of the quasar (5000 km s -1 from the quasar emission redshift) and that allows us to cover the H I Lyα and Lyβ transitions of a given absorption system, for a more reliable determination of the H I column density.
(2019)  for the sub-DLAs. Our analysis was then focused on a sample of 16 sub-DLAs and 21 LLSs. Three of the QSOs in our sample (J0247-0556, J1111 -0804, and J1723+2243) have a reduced UVES spectrum available from the SQUAD database  (Murphy et al. 2019 ). We verified with Astrocook that for our systems there is a good agreement between column densities measured in the X-shooter and UVES spectra.
CLOUDY (ver. 17; Ferland et al. 2017 ) is an open-source photoionization code that maps each set of input parameters into the corresponding column densities of all metal ions that we considered in this work. The MCMC sampler searches Z and n H for models with the column density pattern that best matches the neutral hydrogen column density, N H I , and the metal ion column densities fitted by the software Astrocook. The output of the MCMC modeling is a probability distribution of the sets of parameters Z and n H. In this work, we adopt the minimal model parameters  (Fumagalli et al. 2016) , which assumes a slab of gas at constant density illuminated on one side by both the UV background  (Haardt & Madau 2012 ) and the cosmic microwave background. All z min R (UVB) R (VIS) R (NIR) Quasar z max z min R (UVB) R (VIS) R (NIR) metals are assumed to be in the gas phase with a solar abundance pattern  (Asplund et al.
Other Chemical Elements To investigate more deeply the chemical enrichment history of our C-enhanced very metal-poor absorbers, in Figure  4  we compare the relative abundances of oxygen, magnesium, and silicon with respect to iron with those of ancient very metal-poor stars. The stellar data we used for these elements were taken from the SAGA database  (Suda et al. 2008). The sample of stars is different from the one used for the carbon-to-iron ratio (Figure  3  and top left panel of Figure  4 ). For this reason we do not distinguish between CEMP-no and CEMP-s/r stars. In Figure  4  we see that there is a general agreement between the chemical abundances of our absorption systems and the abundances of the stars in the MW and dwarf galaxies. Furthermore, at [Fe/H] < -2, we note the same trend for all the chemical elements in both stars and gas: the relative Other panels: comparison between the chemical abundances of the very metal-poor gaseous absorption systems (circles) and of the stars (squares) in the MW (gray), UFDs (light blue), and dwarf spheroidal galaxies (blue).
Red and yellow circles are C-enhanced and C-normal very metal-poor absorbers, respectively. Orange circles are iron-rich absorbers. Stellar data are taken from the SAGA database  (Suda et al. 2008). In all the panels the orange shaded area identifies the region where chemical abundances could be affected by dust depletion. abundances increase as the [Fe/H] decreases. In particular, we see that C-enhanced very metal-poor absorbers are always overabundant also in the other chemical elements. The same is observed in CEMP-no stars (I. Vanni et al. 2023, in preparation). Oxygen (Figure  4 , top right panel) shows a similar trend to that of carbon even if we have fewer measurements. Indeed, we could measure the oxygen abundance only for 15 of the 30 systems for which we have the C abundance. In Figure  4 , however, we report only 12 measurements since for three absorbers we have upper limits for both iron and oxygen.