dataesr/scientific-paragraphs-categorization huggingface dataset:
Final catalogues for SED fitting In order to ensure consistency and reliability across the different SED-fitting codes used in this paper, it was important to ensure that the input dataset was as robust as possible, and that all photometric errors were uniformly treated. For each field, a catalogue was produced combining the (aperturecorrected and Galactic extinction corrected) fluxes from UV to mid-IR wavelengths with the far-IR fluxes determined by XID +. Next, the small zero-point magnitude corrections determined during the photometric redshift fitting were applied: these are tabulated in Appendix B of Paper IV. Specifically, the corrections derived using the extended Atlas library (referred to as 'Brown' in that paper) were applied; this template set was chosen because it extended out to the longest IRAC wavelength and also incorporated the full range of SED types expected within the LoTSS Deep Fields sample.
Even in these deep datasets, ho we ver, fainter galaxies often lack measurements in one or more channels, prev enting an y classification by the Stern, Lac y, or Donle y criteria. To help o v ercome this,  Messias et al. ( 2012 )  derived a series of redshift-dependent colour-cuts based on K band to Channel 2, Channel 2 to Channel 4, or Channel 4 to 24 μm flux ratios (hereafter, the Messias regions). These allow classification of a larger fraction of galaxies, but with the same issues regarding completeness and contamination. Furthermore, simple application of colour-cuts takes no account of low signal-to-noise measurements that can scatter data across the colour criteria, and can also miss some types of AGN (e. g. G ürkan,  Hardcastle & Jarvis 2014 ). The wide array of data available in the LoTSS Deep Fields allows a classification scheme to be developed, which uses much more than just the mid-IR colour bands.
0 < z < 1. 5 is due to an aliasing effect in the photometric redshifts, particularly in the ELAIS-N1 and Lockman Hole fields, probably due to the lack of H -band data; this is discussed in more depth in  Cochrane et al. ( 2023 )  but is not a significant issue for the analysis in the current paper. The upper panels of Fig. 10  show the simulation versus data comparison for a simple split into the two T-RECS source populations: SFGs plus radio-quiet AGN, against radio-loud AGN (HERGs + LERGs). Note that as well as allowing a comparison against both T-RECS and SKADS, this population split is arguably the most robustly determined in the LoTSS Deep dataset, as it depends only on the presence or absence of a radio excess rather than the (more difficult to establish) evidence for a radiative AGN.
These upper panels show that both T-RECS and SKADS describe fairly well the transition between these two populations with decreasing radio flux density. T-RECS also provides an accurate match to the redshift distributIon out to redshift z ∼ 4, beyond which the simulated source counts fall below those measured in the data; it is not clear whether this is a shortcoming of the simulation, or whether the photometric redshifts of the highest redshift sources become less reliable. The SKADS simulations also match the data reasonably well out to redshift z ∼ 2, but thereafter they overpredict the number of radio-loud AGN and underpredict the SFG population. The lower panels of Fig. 10  provide further analysis of the SKADS simulations, split into the four subpopulations. Here, signficant differences are observed between the simulated and observed datasets.
7; recent works (e. g  Kondapally et al. 2022 )  show this to be a reasonable assumption out to z ∼ 2, but with indications of a decline between 2. 0 < z < 2. 5, suggesting a breakdown of the SKADS assumptions. In conclusion, while the SKADS simulations have been very successful in producing simulated radio skies, datasets such as LoTSS Deep that probe new parameter space are revealing the shortcomings in our understanding 15 years ago when those simulations were first produced. The more modern T-RECS simulations provide a better match to the current dataset but would be enhanced by the explicit inclusion of a radio-quiet AGN dataset, since the assumption that the radio emission of these sources is entirely produced through star formation is known not to be true (see e. g. Macfarlane et al.
The observed populations are compared against the prediction of the SKADS and T-RECS radio sky simulations. SKADS is shown to underpredict the SFG population by a factor ≈2 across all redshifts. It o v erpredicts the proportion of radio-quiet AGN in the sample. This is likely to be due to the assumption of a radio spectral index of α = 0. 7 for these sources: a flatter spectral index, as indicated by recent LOFAR observations of radio-quiet quasars, would reduce the pre v alence of these sources in these lo w-frequency observ ations. Finally, SKADS o v erpredicts the numbers of LERGs at redshifts z > 2, as it does not account for the ne gativ e cosmic evolution of this population at high redshift beginning to be observed in the latest datasets. T-RECS provides a good match to the star-forming and radio-loud AGN populations, but its lack of a separate radio-quiet AGN population is a significant shortcoming.
AC K N OW L E D G E M E N T S PNB, JS, and RK are grateful for support from the UK STFC via grant ST/R000972/1 and ST/V000594/1. RK acknowledges support from an STFC studentship via grant ST/R504737/1. MJH and DJBS acknowledge support from STFC via grant ST/V000624/1. BM acknowledges support from STFC under grants ST/R00109X/1, ST/R000794/1, and ST/T000295/1. WLW acknowledges support from the CAS-NWO programme for radio astronomy with project number 629. 001. 024, which is financed by the Netherlands Organisation for Scientific Research (NWO). KJD acknowledges funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. getto di Grande Rile v anza ZA18GR02. MJJ ackno wledges support from the Oxford Hintze Centre for Astrophysical Surv e ys.
no. 400501), both from BioLegend. After washing with ice-cold PBS (300g, 10 min, 4 °C), cells were re-suspended in 500 μl PBS and analyzed by flow cytometry on a BD FACSCanto II instrument (BD Biosciences). Data were visualized on BD FACSDiva Software (v. 6. 1. 3) and analyzed by FlowJo (v. 10. 5. 2). The gating strategy was based on the isotype control.
qRT-PCR analyses Total RNA was isolated from whole tissue with TRIreagent (cat. no. T9424, Sigma-Aldrich) as detailed elsewhere  56  and from IECs with the Promega kit (cat. no. Z6012, Promega), according to the manufacturer's instructions. After purity and quality checks, mRNA was converted into complementary DNA with a High-Capacity cDNA Reverse Transcriptase kit (cat. no. 4368814, Applied Biosystems). The cDNA was diluted 1:20 (v/v) by RNase-free water (Aqua ad iniectabilia, B. Braun). Relative mRNA expression was quantified by qPCR analyses on a qTOWER3 Real-Time PCR Thermal Cycler instrument (Analytic Jena) equipped with qPCRsoft (v. 4. 0) software, using iTaq Universal SYBR Green Supermix (cat. no. 1725121, Bio-Rad Laboratories). Each biological sample was analyzed in triplicate, using the ribosomal protein L32 as the housekeeping gene.
Single-molecule fluorescence in situ hybridization RNAscope Multiplex Fluorescent V2 assay (cat. no. PS-00003027. 1, Bio-Techne) was performed on 5-μm paraffin sections (for paraffin embedding and cutting with microtome see above) according to the manufacturer's instruction. Gli1, Ptch1 and Hhip transcripts were https://doi. org/10. 1038/s42255-023-00828-5 ordered from the company and probed using TSA Vivid Fluorophore 650, 520 and 570, respectively. Cell nuclei were counterstained with DAPI. Distal small intestine of GF versus CONV-R and Tlr2 ΔIEC versus WT-floxed littermates were analyzed, using three mice per group. The probes were visualized with a confocal microscope (Leica TCS SP8). Data analysis was accomplished with the Leica Application Suite X (LAS EZ) software (v. 3. 7. 5. 24914).
qPCR array The RT2 profiler PCR array kit (cat. no. PAMM-078Z, QIAGEN) was used according to the manufacturer's instructions. RNA was isolated from the distal small intestine of seven mice per group and adjusted to a concentration of 200 ng μl -1. cDNA was synthetized using the RT 2 First Strand kit (cat. no. 330404, QIAGEN). Per group, one 96-well plate was used. The obtained data were analyzed using the online software RT2 Profiler PCR Array Data Analysis (v. 3. 5)  58.
VEGF-A ELISA Small intestine tissues or IECs were processed as described before to obtain protein lysates. VEGF-A ELISA was performed according to manufacturer instructions using the VEGF-A-Cell Lysate Mouse ELISA kit (cat. no. EMVEGFACL, Invitrogen, Thermo Fisher Scientific). Raw absorbance values were read on a Dynex Opsys MR Reader (Dynex Technologies), equipped with the Revelation Quicklink software (v. 4. 25). The standard curve was constructed by plotting the mean absorbance of each standard on the y axis against the concentration on the x axis and then used for the calculation of the VEGF-A concentration in the samples.
Statistical analysis GraphPad Prism v. 9. 3. 1 was used for all statistical analyses and graphs except for the 16S rRNA gene-sequencing data. Statistical analyses of the metagenomics data were performed in R  64. We assessed variations in microbiota structure and diversity across genotypes using several diversity measures, within tissues and content samples, distinctively. First, we evaluated variation in abundance of the most abundant (major) phyla and genera across genotypes using linear mixed-effects models with genotype, sex and sex:genotype included as fixed effects and breeding cage as a random effect in the 'nlme' R package. Best models were fitted by maximizing the restricted log-likelihood and evaluated and validated by (1) checking residuals distribution; (2) plotting fitted and residual values; and (3) inspecting residuals and explanatory variables.
Additionally, we calculated different diversity measures including Shannon and Bray-Curtis, based on operational taxonomic units at a 97% sequence similarity threshold in the 'vegan' R package  65, 66. Comparison of Shannon measures across genotypes was assessed through linear mixed-effects models as explained above. The Bray-Curtis measure was assessed across genotypes using the 'adonis' function with 10 5 permutations and principal coordinate analyses. P < 0. 0001) from WT littermates versus Tlr2 ΔIEC. (f) Relative mRNA expression of Ptch1 and Hhip in the distal small intestine of WT littermates versus Tlr2 ΔIEC mice (n = 7 versus 6, Ptch1: P = 0. 0047; Hhip: P < 0. 0001). (g-i) Relative mRNA expression of the Hh target bone morphogenetic protein 4 (Bmp4) in the distal small intestine of (g) GF versus CONV-R mice (n = 8 versus 5, P = 0.
Acknowledgements We are grateful to  K. -P. Derreth ,  N. Hörmann  and  C. Karwot  ( CTH, University Medical Center Mainz ) for expert technical assistance. We thank  U. Walter  ( CTH, University Medical Center Mainz ) for helpful discussions. We are thankful to  S. M. Reba ,  R. Rojas ,  C. Herding  and  W. H. Boom  ( Department of Medicine, Case Western Reserve University & University Hospitals and Cleveland Clinic and Cleveland Medical Center ) for providing the Tlr2-flox mouse line. We thank  M. Radsak  ( Department of Medicine III, University Medical Center Mainz ) for providing global Tlr4-deficient mice. We thank  A. T. Gewirtz  ( Georgia State University ) for providing small intestinal tissue specimens from GF and CONV-R Tlr5 -/-mice. We express our gratitude to  D. Kaiserlian  ( Inserm Rhône-Alpes, CEDE ) for the MODE-K small intestinal epithelial cell line.
We are grateful to  T. Nikolova  and  T. G. Hofmann  ( Institute of Toxicology, University Medical Center Mainz ) for the use of their microscope. We thank  K. Farquharson  ( Pulmonary Center, Department of Medicine, Boston University School of Medicine ) for reading the final version of the paper. This work was supported by the computing infrastructure provided by the  Core Facility Bioinformatics  and by the  Histology Core Facility  at the  University Medical Center Mainz. The project was funded by a research grant from the  Stiftung Pathobiochemie und Molekulare Diagnostik , the  CTH Junior Group Translational Research in Thrombosis and Hemostasis (Federal Ministry of Education and Research (BMBF)   01EO1003  and  01EO1503 ),  Deutsche Forschungsgemeinschaft (German Research Foundation)  individual grants to C.
If no deletion is found,a molecular genetic testing should be performed to confirm the clinical diagnosis and identify the pathogenic variants allowing phenotype-genotype correlations (and possible eligibility for future clinical trials). It is to be noted that NGS-based diagnostic protocols have the potential to become the one-for-all test for molecular diagnosis of aniridia, replacing the standard strategy represented here (see Section 4. 3 and 4. 4). This diagnostic algorithm is the currently strategy used because the implementation of NGS is not available in all molecular laboratories worldwide because of the cost involved, the infrastructure required and the bioinformatic analytical challenges. Fig. 4  shows the phenotype and genotype of the congenital aniridia cohort from BaMaRa, the French national database for rare diseases.
, 2019; Hanson and Van Heyningen, 1995; Shaham et al. , 2012). The human PAX6 gene for congenital aniridia was identified by positional cloning and discovered to be deleted in aniridia patients in 1991  (Ton et al. , 1991) , 65 years after the disease was first described by  Rush in 1926 (Rush, 1926). During the same year, murine Pax6 was cloned  (Walther and Gruss, 1991)  and then demonstrated to be the causal gene explaining the small eye phenotype  (Hill et al. , 1991). Very soon after, intragenic mutations in PAX6 were reported in additional aniridia patients, strongly suggesting that mutations in PAX6 might be the most common genetic causes for aniridia in humans  (Glaser et al. , 1992; Hanson et al. , 1993; Jordan et al. , 1992). In the following years, hundreds of PAX6 pathogenic variants of various types were reported, and now, at least more than 700 unique variants in PAX6 are listed in PAX6 databases (see section 4.
PAX6 mutational spectrum Two major PAX6 databases, including the public LOVD PAX6 database (http://lsdb. hgu. mrc. ac. uk/home. php?select_db = PAX6) and the professional "PAX6 Human Gene Mutation Database (HGMD) Pro Mutation Database" catalog all reported PAX6 variants, and more than 700 unique variants have been registered according to the last update in 2022. Here, we provide a summary of PAX6 molecular changes which have been reported in detail in several recent comprehensive reviews (E. S. E. S. Landsend et al. , 2018; Lima Cunha et al. , 2019; Wawrocka and Krawczynski, 2018). The genetic landscape of PAX6 is characterized by a wide range of molecular defects including single nucleotide variants as well as balanced and unbalanced structural variants. The most common pathogenic variations are single nucleotide variants of various classes, scattered throughout the PAX6 gene, while approximately 15% of the remaining molecular defects are structural variants including large chromosomal aberrations and microdeletions of chromosome 11p13 first reported in 1995, and which are now increasingly reported in the literature  (Fantes et al.
As the functional consequences of PAX6 variants depend on both its location and cellular environment, the best way would be to use induced pluripotent stem cells (iPSCs) and 3D organoid-based approaches to explore a variant of interest in its "native" cellular context. Skin fibroblasts of PAX6 patients can be reprogrammed toward iPSCs, which in turn can be differentiated toward specific ocular-like cells, accordingly to the associated-phenotype, modeling most faithfully the human PAX6 disease in its "native" context  (Doss and Sachinidis, 2019). In conclusion, centralized PAX6 variant databases, robust genotype-phenotype correlations, and streamlined approaches to functional studies will pave the way for novel treatment opportunities for aniridia.
The discrepancy between Kits' study and those by the Petersen and Daruich groups concerning the retinal phenotype might be explained, in part, by a difference in the mutational PAX6 spectrum and the mutation subgroup classification resulting in underrepresented categories of variants, notably the absence of deletions restricted to the 3 ′ regulatory regions and missense variants mimicking premature termination codon loss-of-function variants in Kits' study, resulting in a sampling bias for retinal phenotype-genotype correlation with all mutation categories representative of those reported in PAX6 databases. In conclusion, ophthalmologists also should be aware that almost all aniridia patients, including those with PAX6 missense variants, remain at high risk for severe foveal hypoplasia, regardless of the severity of the iris phenotype  (Daruich et al.
, 2014) , suggesting that developmental eye defects can be efficiently corrected following postnatal treatment (X. Wang et al. , 2017a). A Phase-2 clinical trial (STAR Study) was designed to evaluate the safety and efficacy of an oral ataluren suspension (named premature termination codon 124 or Translarna)  (Koenig et al. , 2009). However, the STAR trial did not meet its primary endpoint, apparently due to patient age and phenotypical discrepancies (https://clinicaltrials. gov/ct2/show/NCT02647359). Inclusion of children, adults and the elderly, presumably with varying grades of AAK, may hinder the assessment of the therapeutic effect on AAK, which may be more sensitive to treatment in earlier stages. Nevertheless, topical administration of the drug could improve availability of the drug for the eye and cornea, in particular  (Djayet et al.
Acknowledgments We would like to thank the  CRMR (Centre de Reference Maladies Rares en Ophtalmologie) OPHTARA, Gêniris association, Aniridia Europe , the  European union Cost Action CA18116 ANIRIDIA-NET , as well as the patients and their families for their support. We would also like to thank  TG Arch  for the graphics. ISM for english editing. DBG received grant support from  Gêniris Association. MKD  received grant support from the  National Eye Institute of the USA  via  EY028597-01  and  Aniridia Foundation International. ,.
Procedure Participants were seated ∼50 cm from a computer screen. They were informed that they would read a succession of sentences, in pairs, and that they would have to determine as quickly as possible whether the second sentence was a good or bad continuation of the first one. The experiment was run on E-Prime 2. 0 software (Psychology Software Tools, Pittsburgh, PA). The first sentence (context sentence) remained on the screen for 3 s. It was then replaced by the second one (continuation sentence). The participant then had to respond as quickly as possible by using one of the two keys on the keyboard. They were asked to press the "yes" key with their preferred hand. The intertrial interval was 3 s. The experiment began by 10 practice trials followed by a list of 88 (66 fillers and 22 experimental) trials that were randomly presented.
Analysis Data were analyzed through Bayesian hierarchical mixed effects regression models. This choice was motivated by the need to examine the influence of the predictor variable, grammatical gender, while accounting for the random variation between participants and items. By including the factors participant and item as random intercepts, we could capture the baseline response times for each participant and item separately, allowing for a more precise estimation of the effect of grammatical gender. Firstly, reaction times for all positive responses were examined. The z-scored response time data, RTz, was fitted with a model including a fixed effect of grammatical gender (0: feminine; 1: masculine) and two random effects of factors participant and item, entered as random intercepts: RTz = α p,i + β gender • gender + ǫ (1) where ǫ ∼ N (0, σ ).
All analyses were carried with R version 4. 3. 0 (R Core Team, 2020), using the RStan interface to STAN for Bayesian modeling  (Carpenter et al. , 2017; Stan Development Team, 2020). Seven chains of 7,000 samples each were run independently (3,000 burnin samples, estimates based on 4,000 samples). Their convergence was monitored through standard summary statistics R-hat and Effective Sample Size, absence of divergent transition, and visual inspection of posterior distributions. Throughout this article, Bayesian estimates will be reported along with their 95% credible intervals, providing an assessment of the reliability of the results. All the codes and data supporting this analysis are openly available on Github at https://github. com/LeoVarnet/GenderFair/.
Then, the intensity and pressure fields were evaluated for each temperature map using eqns (11) and (  12 )  [61]. Because the temperature profiles were observed as Gaussian and radially symmetric, a simulated Gaussian intensity and pressure field were also used. The maximum values of these intensity and pressure profiles were extracted in each frame using the equations where p is the pressure (Pa), and c o is the speed of sound in the fluid (m s -1 ). T a g g e d E n d T a g g e d P A custom MATLAB program allowed the simulation of a single-element focused ultrasound transducer with a 130 mm radius and 1 MHz frequency. The acoustic field was normalized and then scaled to the calculated intensity maximums. The intensity field was input as the heat source with the k-Wave BioheatExact function to solve for the simulated temperature fields [36,45].
The cannula was left in place for an additional 1 min and then removed before VTA DA neuron recordings. For acute withdrawal, extracellular recordings were performed from 18 h up to 72 h after the last cocaine self-administration session. For protracted withdrawal, recordings were performed from 26 days to 45 days after the last session. Naive rats were also used for recording of BLA (3 naïve rats) and DA (7 naïve rats) activity. Since there was no difference in BLA and DA activity between naive rats and saline WD1-3 and saline WD26-45, data were pooled.
, 2021). Single-unit activity recorded from the VTA was amplified 10 times, filtered (low pass: 30 Hz; high pass: 16 kHz), and further amplified 50 times (Multiclamp 700b, Axon Instruments, Union City, CA). The signal was digitized at 16 kHz (Cambridge Electronics Design, Cambridge, UK, CED 1401) and acquired on a computer using Spike 2 7. 0 software (Cambridge Electronics Design). Three parameters were analysed: the number of neurons/electrode track, firing rate, and the percentage of spikes in bursts. For BLA recordings, the electrode was lowered through the right BLA at the following coordinates: AP À2. 8 mm to À3. 2 mm (from bregma), ML +4. 6 mm to 5. 2 mm (from midline) and DV À7. 0 mm to À9. 5 (from brain surface). Encountered cells in the targeted area meeting BLA putative pyramidal neuron criteria (action potential from peak to trough ≥0.
5 ms;  Bienvenu et al. , 2012)  and with a signal-to-noise ratio of 3:1 (or greater) were recorded for at least 5 min. Single-unit signals were amplified 10 times, filtered using a high-pass filter at 30 Hz and a low-pass filter at 10 kHz, and further amplified 50 times (Multiclamp 700b, Axon Instruments). The signal was digitized at 16 kHz (CED 1401) and acquired on a computer using Spike 2 7. 0 software (Cambridge Electronics Design). Bursts were detected using characteristic parameters of pyramidal neurons  (Vitrac et al. , 2014)  (maximum interval to start a burst: 40 ms, maximum interval to end a burst: 10 ms, minimum interval between burst: 20 ms, minimum duration of a burst: 5 ms, and minimum number of spikes in a burst: 2) and analysed using NeuroExplorer ® (Nex Technologies, Colorado Springs, USA).