dataesr/scientific-paragraphs-categorization huggingface dataset:
Previous estimates to meta-analyze administration error rates were limited by the high statistical heterogeneity, restricting their use. This study aimed to investigate sources of heterogeneity in pooled administration error rates in hospitalized adults. We systematically searched scientific databases up to November 2017 for studies presenting error rates/relevant numerical data in hospitalized adults. We conducted separate meta-analyses for the numerators: One Medication Error (OME) (each dose can be correct or incorrect) and Total Number of Errors (TNE) (more than one error per dose could be counted), using the generic inverse variance with a 95% confidence interval. Heterogeneity was assessed using the I 2 and Cochran's Q test. We meta-analyzed 33 studies. The global pooled analyses based on the OME and TNE numerators showed very high heterogeneity (I 2 = 100%; p < 0.
| Inclusion and exclusion criteria Included studies had to present a medication administration error rate or provide numerical data to calculate a medication administration error rate, be conducted in hospital setting, on adult patients over 18 years of age, and published in English or French, between January 1, 2000, and November 21, 2017. We included crosssectional studies, prospective studies, and interventional studies for which only pre-intervention data were retained. We excluded retrospective studies, studies that are not original articles (editorials, letters, comments, opinions, narrative, and systematic reviews), as well as qualitative studies, case reports, and case series, and studies that are only available as a conference abstract. We excluded studies that only target errors related to a specific medication, and studies that rely only on reporting systems to detect errors, because of the underreporting  [15, 16].
| Selection of studies Two reviewers (CA/PR) reviewed the titles and abstracts retrieved to identify potentially eligible studies, using EndNote X6 software. Then, three pairs of reviewers (CA/PR; CA/CB; PR/CB) reviewed independently and in duplicate the full texts of these studies against the inclusion criteria. A calibration exercise was performed before starting the selection process. Discrepancies were resolved by consensus between the two reviewers after discussion. If no consensus was reached, a third reviewer was consulted.
| Data synthesis Author-reported study characteristics were summarized narratively in textual and tabular forms. The numerators were presented based on: number of doses (or administrations) with at least One Medication Error (OME) (i. e. , each dose may be either correct or incorrect) and the Total Number of Errors (TNE) (i. e. , more than one error per dose could be counted). We contacted authors of studies where the numerators were unclear regarding whether they represented the numerator as OME or TNE. In case they did not respond, we considered the numerator as OME  [17] [18] [19] , since no information on whether one or more errors were counted per dose. The prevalence was calculated by dividing the numerator by the denominator. The denominators were used, as reported in the studies, including the number of doses, administrations, observations, and the TOE.
| Strengths and limitations Our study was first to provide quantitative evidence on the sources of heterogeneity in pooled medication administration error rates and induced that the calculation methods, specifically the denominator, constitute a primary factor in this regard. We used an exhaustive search strategy, conducted on several databases and validated by a medical information specialist. Inclusion of studies and data extraction were done in duplicate. Finally, we followed best practices for reporting systematic reviews and used a validated checklist to assess the reporting quality of included studies  [24, 25]. However, our systematic review has certain limitations, specifically regarding the categorization of studies. The definitions of denominators used in included studies were not always sufficiently detailed or precise; also, in some studies, the same term corresponded to different definitions.
DNA Quantification DNA quantification was performed to assess cell proliferation and maintenance. DNA was extracted from an average of 24 spheroids or five GelMA-embedded spheroids according to the blood and tissue DNA extraction kit (Qiagen) manual. Spheroids were washed with D-PBS and lysed ALT/proteinase K buffer (200 μL) overnight under mixing (800 rpm). To ensure DNA purity, RNase A (Qiagen) was added to the samples and incubated for 2 min at RT before the addition of a 1:1 AL/100% ethanol mix. DNA was detected with the 1x QubitTM High sensitivity ds-DNA kit according to the manufacturer's instructions. Fluorescence intensities were measured with Qubit 4. 0 fluorometer (Invitrogen, CRCT, Toulouse). Data were expressed as DNA quantity/spheroids (ng).
After D-PBS washes, nuclei were stained with DAPI (2 μg mL -1 ), 1 h at RT (Sigma, USA). For 2D culture, the same protocol was used but incubation durations were shortened for the different steps. For 3D culture imaging, samples were cleared for at least 48 h in Scale S4 solution  [39]  composed of (w/v) D-(-)-sorbitol (40%, Sigma, USA), glycerol (10% w/v, Euromedex, France), urea (4 m, Sigma, USA), Triton X-100 (0. 2%), dimethylsulfoxide (20% v/v, Sigma, USA). Samples were analyzed by confocal imaging (LSM 880, Carl Zeiss, France) and images were processed using Fiji software (National Institutes of Health, USA).
Briefly, protein extracts (1. 5 ng well -1 ) were mixed with dithiothreitol (40 mm, DTT) and master mix (Protein-Simple). Samples were heated at 95 °C for 5 min and dispensed into a designated plate along with blocking reagent, primary antibodies Horseradish peroxidase (HRP)-conjugated secondary antibodies, chemiluminescent substrate, and total protein detection reagents. All electrophoresis and immunodetection steps were automatized within the capillary system (ProteinSimple Jess). Chemiluminescence intensities were quantified with Compass software (ProteinSimple) and normalized to the total protein signal.
Lipolysis Assay A lipolysis assay measuring glycerol release was conducted on embedded spheroids after differentiation using Free Glycerol Reagent kit (Sigma, USA) according to the manufacturer's recommendations. At the end of the differentiation, protocolembedded spheroids were transferred into 96 wells of ULA plate in phenol red-free DMEM medium (90 μL) supplemented with glucose (5 mm). After 24 h of lipolysis stimulation with 8 cpt-AMPc (200 μm), media (20 μL) was collected and added to the reaction mix. After 15 min of incubation, absorbance was read at OD 540 nm, and glycerol released was calculated using a standard curve. Four days prior to the experiment, the adipogenic medium was depleted of insulin to prevent lipolysis inhibition. Data are normalized by DNA quantity.
Lactate and Glucose Measurements Extracellular levels of lactate and glucose were measured after treatment with UCP1 inducers to assess the change in metabolic activity of embedded spheroids. Lactate and glucose levels were measured with the Lactate Pro II test meter (Arkray) and Contour XT TS (Bayer), respectively. To account for increases in molecular concentration that could arise from medium evaporation, differences in glucose and lactate levels were calculated in comparison to evaporation in control wells without cells. Data are normalized to DNA quantity.
Measurement of Organoid Protein-Secreted Factors Levels of secreted interleukin-6 (IL6), meterorin-like, growth/differentiation facto-r-15 (GDF15), and chemokine C-X-C ligand-14 (CXCL14) were measured using ELISA. Medium was collected at the end of the culture process and stored at -80 °C. IL6, meterorin-like, GDF15 and CXCL14 levels were determined using commercial ELISA kits (R & D Systems, Minneapolis, USA, # D6050, # DY7867-05, # DY957; Ray Biotech, Peachtree Corners, USA, # ELH-CXCL14, respectively). Medium that had not been in contact with cells was used as a detection control for ELISA assays. Data are normalized to DNA quantity.
Acknowledgements The work was supported by the  French National Agency for Research  ( hiPSC- Adipospheres  ANR-18-CE18-0006 ,  Printiss  Project, grant  ANR21 CE1941 ) and the  LAAS-CNRS  micro and nanotechnologies plat-form, a member of the  French Renatech  network. It was partly supported as part of the  MultiFAB  project funded by  FEDER European Regional Funds  and  French Région Occitanie  (grant agreement number  16007407/MP0011594 ) and by the  HoliFAB  project funded by the  European Union 's  Horizon 2020 research and innovation program  (grant agreement No. 760927 ). This work bearing the reference  CARe -Graduate School   N°ANR-18-EURE-0003  benefited from a State grant managed by the  Agence Nationale de la Recherche  under the  Programme "Investissements d'Avenir ". This work was partly supported by Grant  PID2020-114112RB-I00  from  MCIN/AEI/10.
Demographics of participants The present study analysed participants enrolled in the IMAGEN project, a prospective, multicentre longitudinal imaging genetics study in 2000 healthy adolescents  33. Participants' neuroimaging and behavioural data were assessed at ages 14 (baseline), 19 (the follow-up, FU) and 23 (the newly released follow-up data as validation, FU-age-23). Cigarette smoking for each time point (i. e. , of age 14, 19, or 23) was measured by the item "How many occasions during your lifetime have you smoked cigarettes?" from the self-rated European School Survey Project on Alcohol and Other Drugs (ESPAD). Participants with scores greater than 0 were considered smokers at the interview. After quality control, this study included 807 (36. 4%, 444 female participants [55%]) participants with complete structural images and behavioural scores at both BL and FU.
e. , the valid-PRS (see Methods for more details)  35, 36. We first conducted GWAS for GMV in the left vmPFC, rule-breaking scores and quantity of smoking at BL in the remaining subjects from the IMAGEN project (n = 1026), who were not included in the above longitudinal analyses due to the lack of neuroimaging data at FU (see Methods for more details). We then calculated the PRSs of above phenotypes in the independent longitudinal sample, and these PRSs all showed consistent positive associations with the corresponding phenotypes (Table  S5 ). We observed consistent negative associations of valid-PRS left-vmPFC with rule-breaking scores and the quantity of smoking as well as positive associations of valid-PRS rule-breaking with the quantity of smoking as expected. On the contrary, the reversed prediction did not stand (Table  S5 ).
Participants The dataset used for the present study was selected from the IMAGEN project, which was a prospective, multicentre longitudinal imaging genetics study that recruited 2000 healthy adolescents. The standard operating procedures for the IMAGEN project are available at https:// imagen-project. org/, which contain details on ethics, recruitment, neuropsychological tests and scanning protocols of magnetic resonance imaging (MRI) data  33, 57. Characteristic information is assessed using multiple questionnaires, assessments and tasks. In brief, socioeconomic and family stress scores were rated according to the Development and Well-being Assessment (DAWBA, parent-rated) family stresses total score and socioeconomic item, with greater scores indicating poorer family environments. The Life Events Questionnaire (LEQ) assesses positive and negative life events in childhood and young adulthood.
MRI data acquisition and processing MRI data were acquired at eight IMAGEN assessment sites with 3 T MRI scanners of different manufacturers (Siemens, Philips, General Electric, Bruker). The scanning variables were specifically chosen to be compatible with all scanners. In addition, imaging protocols were harmonised across sites and scanners. Based on the ADNI protocol, high-resolution 3-dimensional T1-weighted images (1. 1 mm isotropic voxel size) were acquired with a gradient-echo sequence (http://adni. loni. usc. edu/methods/mri-analysis/mri-acquisition). Processing of the structural T1-weighted images was performed with the Statistical Parametric Mapping toolbox (SPM, Wellcome Department of Neuroimaging, London, United Kingdom; http://www. fil. ion. ucl. ac. uk/spm) implemented by Matlab (R2020b).
Adolescent Brain Cognitive Development study An independent longitudinal genetic-imaging cohort, the ABCD study (acquired from Annual Curated Data Release 2. 01, https://data-archive. nimh. nih. gov/abcd), recruited 11,875 children between 9 and 10 years of age from 21 sites across the United States  37. The study conforms to each site's Institutional Review Board's rules and procedures. All participants provide informed consent (parents) or informed assent (children). More details of the subjects and the data collection are provided at the ABCD website (https://abcdstudy. org/scientists/ protocols)  37. MRI data in the ABCD study were collected from different 3 T scanner platforms (i. e. , Siemens Prisma, General Electric (GE) MR750 and Philips Achieva dStream). High resolution 3-dimensional T1-weighted images (1.
0 mm isotropic voxel size) were acquired with harmonised imaging protocols (https://abcdstudy. org/images/ Protocol_Imaging_Sequences. pdf). The processing of VBM was the same as that in the IMAGEN project. The rule-breaking score and conduct problem score were rated according to the child behaviour checklist (CBCL, parent-rated)  67. To ensure homogeneity of the datasets, only self-reported white people were included to validate the relationship between the GMV of left vmPFC and rule-breaking behaviour and discover the genetic factors underlying the neurobehavioural circuit (see Fig. S8  for the plot of the first two PCs projected to 1000 Genome data). Therefore, 4417 participants with the quality-controlled brain images at baseline year and complete behavioural data at both baseline year and one-year follow-up interview were included.
GWAS for the GMV of left vmPFC To discover the genetic factors underlying the neurobehavioural circuit from the left vmPFC to rule-breaking behaviour, we performed a meta-analysis of GWAS for GMV of the left vmPFC. For ABCD cohorts, we performed the same quality control and imputation procedures as the above IMAGEN study. After imputation, 4244228 SNPs of 4390 participants (with complete phenotypes and with no siblings) from ABCD were available. GWAS was performed using PLINK with sex, research sites and top 20 ancestry principal components of ABCD cohorts as covariates (see Fig. S8  for the plot of the first two PCs projected to 1000 Genome data). The meta-analysis was performed using METAL software (https://csg. sph. umich. edu/abecasis/metal), and the genome-wide significant threshold was set as 5E-8.
Human Connectome Project Total 330 regular smokers (ages 22-37 years, mean age 29. 49 y, 152 females) from the Human Connectome Project  40  were included in our current study. The HCP consortium is a public shared large-scale neuroimaging dataset and the details on the inclusion and exclusion criteria of HCP consortium were provided in the previous study  40  and the HCP website (https://www. humanconnectome. org). High resolution 3-dimensional T1-weighted images (0. 8 mm isotropic voxel size) were scanned on a 3 T Siemens connectome-Skyra scanner. The processing of VBM was the same as that in the IMAGEN project. Smoking behaviour and the level of nicotine dependence were assessed according to the Fagerström Test for Nicotine Dependence (FTND) and DSM.
Acknowledgements We thank  Professor David Nutt  and  Professor Stephan Ripke  for their helpful comments on the manuscript. We thank the  IMAGEN Consortium  for providing the discover data. This work received support from the following sources:  National Key R and D Program of China  ( 2021YFC2501402  [to T. J. ],  2022CSJGG1000  [to T. J. ],  2019YFA0709501  [to T. J. ],  2019YFA0709502  [to J. F. ],  2018YFC1312900  [to T. J. ] and  2018YFC1312904  [to J. F. ]), the  National Natural Science Foundation of China  ( T2122005  [to T. J. ],  82150710554  [to G. S. ] and  81801773  [to T. J. ]), the  Shanghai Pujiang Project  ( 18PJ1400900  [to T. J. ]),  Guangdong Key Research and Development Project  ( 2018B030335001  [to J. F. ]), the  European Union -funded  FP6 Integrated Project IMAGEN (Reinforcementrelated behaviour in normal brain function and psychopathology)  ( LSHM-CT-2007-037286  [to G.
S. ]), the  National Institute for Health Research (NIHR) Biomedical Research Centre at South London  and  Maudsley NHS Foundation Trust  and  King's College London , the  Bundesministeriumfür Bildung und Forschung  ( BMBF  grants  01GS08152 ;  01EV0711  [to G. S. ]; the  European Union  and  UKRI funded  project ' environMENTAL ' (grants  101057429  [to G. S. ] and  10038599  [to S. D. ]); Forschungsnetz  AERIAL 01EE1406A ,  01EE1406B  and Forschungsnetz  IMAC-Mind 01GL1745B  [to G. S. ]), the  Medical Research Foundation and Medical Research Council  (grants  MR/R00465X/1 ,  MR/ S020306/1  and  MRF-058-0009-RG-DESR-C0759  [to S. D. ]), the  National Institutes of Health (NIH)  funded  ENIGMA  (grants  5U54EB020403-05  and  1R56AG058854-01  [to S. D. ]). Further support was provided by grants from: -the  ANR  ( ANR-12-SAMA-0004  [to M.
-L. P. M. ],  AAPG2019 -GeBra  [to J. -L. M. ]), the  Eranet Neuron  ( AF12-NEUR0008-01 -WM2NA ; and  ANR-18-NEUR00002-01 -ADORe  [to J. -L. M. ]), the  Fondation de France  ( 00081242  [to J. -L. M. ]), the  Fondation pour la Recherche Médicale  ( DPA20140629802  [to J. -L. M. ]), the  Mission Interministérielle de Lutte -contre-les- Drogues-etles-Conduites-Addictives  (MILDECA [to J. -L. M. ]), the  Assistance-Publique-Hôpitaux-de-Paris  and  INSERM  (interface grant [to M. -L. P. M. ]),  Paris Sud University IDEX   2012  [to J. -L. M. ], the  Fondation de l'Avenir  (grant  AP-RM- 17-013 [to M. -L. P. M. ]), the Fédération pour la Recherche sur le Cerveau; the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797 [to R. W. ]). The views expressed in this article are those of the authors and not necessarily those of the national funding agencies or ERANID.
Data availability The IMAGEN project data are available from a dedicated database: https://imagen-project. org. ABCD data are available from a dedicated database: https://abcdstudy. org. HCP data are available from a dedicated database: https://www. humanconnectome. org. All data needed to evaluate the conclusions in this study are present in the paper and/ or the Supplementary Information. Source data are provided with this paper (https://doi. org/10. 6084/m9. figshare. 23508369)  68.
manuscript; C. X. , and W. C. helped in preprocessing the neuroimaging and genetic data; C. X. helped visualization; B. C. , H. G. and B. J. S. helped in interpreting the results; T. W. R. , B. J. S. and J. F. revised the first draft; T. B. , G. J. B. , A. L. W. B. , C. B. , S. D. , H. F. , A. G. , P. A. G. , R. B. , J. -L. M. , M. -L. P. M. , F. N. , D. P. O. , L. P. , S. H, J. H. F. , M. N. S. , N. V. , H. W. , R. W. , H. G. , and G. S. were the principal investigators of IMAGEN Consortium; Imaging, genetic and behavioural data in the IMAGEN project were acquired and provided by the IMAGEN Consortium; All authors critically revised the manuscript. G. S. , B. J. S. , T. W. R. , and J. F. contributed equally to this paper.
Biomass and catch data Standing stock biomass: Reef fish biomass estimates were recorded through underwater visual census (UVC) from surveys collected on a total of 2053 reefs spanning depths from 0 to 26 m and the following reef habitat types: slopes, crest, flat and lagoons/backreefs. Most sites came from our main dataset (e. g. ,  19 ). However, additional sites from other published work  (14, 15 ) that used the same sampling methodology were also included. All surveys used standard belt-transects, distance sampling, or point-counts, and were conducted between 1999 and 2014. Except for the biomass trajectory of reserve reefs, where data from multiple years were available from a single reef, we included only data from the year closest to 2010. Within each survey area, diurnally-active, non- cryptic reef fish above 10 cm length from families that are resident on the reef (Table  S1 ) were counted, identified to species level, and total length (TL) estimated, except for one data provider who measured biomass at the family level.
We chose 20 h travel time because, for lower cut-off travel times (e. g. , 10 h), biomass did not asymptote as a function of travel time (i. e. , estimates of unfished biomass would be biased low when lower travel time thresholds are used; Supplementary information 1). Thus, we ended up having four defined categories (i. e. , remote, high-compliance marine reserves, restricted and openly fished): remote and high compliance marine sites (n=150) were used to estimate the sustainable reference points and fished sites (openly fished and restricted; n=1903) were used to assess the status of individual reef sites open to extraction relative to reference points. See Fig. S4  for a map of our sites. Reef fish catch: Spatially reconstructed reef fish catch estimates (in metric tonnes) per jurisdiction were obtained from the Sea Around Us Project (SAUP) catch database (http://www.
Sensitivity analyses and additional model checks section). We analyzed the entire dataset (s) using different models for three subsets: reserves (i), remote (j) and fished (z) (i. e. , s=i+j+z). However, different components informed distinct parameters. Seascape-level unfished biomass and community biomass growth rate for coral reef fish were jointly estimated  (15)  from the biomass trajectory of high compliance marine reserve sites (n=70) and the reef fish biomass of remote reefs (n=80;13). Data from almost all reserves consisted of only one or a few years of data, precluding estimation of the variability in recovery trajectories among reserves. Instead, for the reserve sub-model, a space-for-time substitution approach between previously-fished high-compliance reserve sites of different ages that had environmental information was used  (14, 15, 34) , assuming that the relationship between reserve age and standing biomass follows a common Gompertz-Fox recovery trajectory, accounting for the human impact (i.
e. , total gravity; 19) of the location (i. e. , allowing for a lower recovery biomass if human impact was above zero, reflecting net movement of biomass from reserves to surrounding fished areas: Fig. S1 ). Note this differs from previous studies, data from which are included in this study (Supplementary information 1). To ensure comparable representation of reserves in the dataset, if 18 a reserve was sampled multiple times (i. e. , at different ages), we randomly chose one year and checked that the randomly selected years did not affect the robustness of our trajectory estimates (Supplementary information 1). For the remote sub-model, reef scale biomass observations contributed to the estimated seascape unfished biomass, bounding the potential values that reserves could reach (Fig. S1 ; see Sensitivity analyses and additional model checks).
We had two additional reef-specific covariates to correct for potential reserve biases: reserve size and gravity  (19) , a measure of human impact to control for potential reserve exports (see Sensitivity analyses and additional model checks). Categorical covariates were treated as dummy variables (1's and 0s), and continuous methodological, environmental, and reserve size covariates were standardized (meancentered and divided by two standard deviations; 55). The human impact covariate was not standardized (i. e. , not mean centered) so that estimated parameters from reserve recovery (e. g. , MMSY and BMMSY) were not biased downwards due to potential exports. 19  We first assessed collinearity among our covariates using variance inflation factors and pairwise correlations. We did this for the entire dataset, but we also tested different subsets because different subsets informed different parameter estimates.