dataesr/scientific-paragraphs-categorization huggingface dataset:
In the experiments involving imaging by optical and confocal microscopy, seeds that were strongly injured during sample preparation were excluded from the analysis. In the piercing experiments, only seeds presenting at least one wall pierced in the center were included. No randomization or blinding was performed. The data were analyzed using Excel (v. 2016), the R software (v. 4. 2. 0), Python (V. 3. 7. 5) or Jasp (v. 0. 16. 3). When experimental data were compared, notably using Student tests, statistical significance was displayed with stars with the following nomenclature: *p < 0. 05, **p < 0. 01, ***p < 0. 001 and ****p < 0. 0001. Exact p values can be found in the Source Data file. In boxplot representations, the midline represents the median of the data while the lower and upper limits of the box represent the first and third quartile respectively.
Acknowledgements We thank  Frederic Berger  and  Claudia Köhler  for providing the iku2-2 and ede1-3 mutant seeds, respectively;  Romain Azais ,  Florian Ingels ,  Yuchen Long ,  Arezki Boudaoud ,  Christophe Godin  and  Oliver Hamant  for helpful discussions and comments on the manuscript;  Lena Beauzamy  and  Simone Bovio  for technical assistance regarding indentation;  Guillaume Cerutti  and  Jeremy Just  for technical assistance regarding the distribution of the data and the code;  Alexis Lacroix ,  Patrice Bolland  and  Justin Berger  for technical assistance regarding plant cultivation;  Isabelle Desbouchages  and  Hervé Leyral  for technical assistance regarding molecular biology work;  Cindy Vial ,  Laureen Grangier ,  Nelly Camilleri  and  Stéphanie Maurin  for administrative assistance; the  PLATIM (SFR128 Biosciences ) for technical assistance regarding the microscopy.
As in  Delis et al. (2017)  and  Ashraf and Galor (2018) , we assume that the population diversity index is not related merely to genetic differences in populations but is a more general measure of interpersonal diversity that embodies traits that have been formed thought time from an initial distribution of genes in ancestry. As such, interpersonal diversity (or simply diversity) can be a proxy for deep-rooted cultural, ethnolinguistic, psychological, and institutional traits that contemporary relevant indices do not capture  (Delis et al. , 2017 and Ashraf and Galor, 2018). We match the country-specific diversity index to microdata about individual immigrants based on their origin countries. We obtain the immigrant data from the Survey of Income and Program Participation (SIPP). We use the immigrant data for several reasons.
Survey of income and program participation To examine how interpersonal diversity affects individuals' financial risk-taking/portfolio decisions, we use individual (micro) data on immigrants to the United States. Looking at international migration has the advantage of holding constant the current level of financial, economic, and social environments in which individuals live (e. g. ,  Osili and Paulson, 2008). Thus, we can study whether individuals moving to the same country carry with them the deeprooted, ancestral characteristics of their origin countries. Our main data source is the Survey of Income and Program Participation (SIPP). Its unique feature is that it has information on individuals' country of origin and their financial behavior (stock market participation, use of financial products, share of these products in their overall wealth, etc.
), as well as several individual financial and demographic characteristics. There are three waves  for 1996, 2001, and 2004 , and we use all three waves. The SIPP data are representative of the U. S. population and are cross-sectional in nature with individuals not tracked across waves. For reference, in Appendix A, we define all the variables extracted from the SIPP that we use in our empirical analysis. We also report summary statistics in Table  1. (Please insert Table  1 here)  The sample includes a wide variety of origin countries: there are 51 countries from which immigrants originate in our sample. For these countries, we have 13,022 observations (immigrants) across three waves for which we also observe all important variables in our analysis. The average number of immigrants per country is 255.
This analysis entails a two-stage model, the first stage being a regression of the mediating variable (e. g. , Trust) on covariates including Diversity and the second stage being equation (1) including the mediating variable. 9 We examine channels that the literature suggests should relate to both diversity and financial risk-taking. Unfortunately, the SIPP does not include immigrant-level data on these channels. Therefore, we use country-level measures from the origin country. We provide detailed definitions of the channel variables in Section A. 5. We find that most of them are significantly correlated (see Table  B1 ). Therefore, one at a time we introduce them into the regression (1).
Our results open new pathways for research in the household finance literature. For example, historical migration, associated conflicts, and empire-building can have important implications for the contemporary financial landscape. Similarly, more attention should be devoted to the prevalence of agriculture versus industrialization and trade in key countries and periods. Moreover, the geographic landscape may also influence the evolution of the financial sector and related institutions. We leave these analyses for future research. variable is called pdiv_aa in the Ashraf and Galor database and is available on the website of the American Economic Association. Y Y Y Y Y Y Y Y Y Control variables Y Y Y Y Y Y Y Y Y Y.
Our recent study focusing on some candidate genes associated with eggshell decalcification and calcium transport highlighted the limitations of such an approach  [18]. In this study, we have gained functional insight into CAM molecular components using an RNA-seq approach to define the transcriptome of the CAM at two stages of embryonic development, EID11 and EID15. These two developmental time points correspond to the morphologically immature (EID11) and mature CAM (EID15), as assessed by transmission electron microscopy. Functional gene annotation was performed to provide an overview of the CAM transcriptome. We explored differential gene expression between these two developmental stages, and also highlighted the highly expressed genes at both developmental stages. Finally, the expression of certain candidate genes selected from the RNA-seq dataset was investigated in two commercial breeds (Ross 308 broiler breed and ISA Brown layer breed) to assess the impact of decades of genetic selection for distinct quality and production traits (meat production versus egg laying) on CAM gene expression.
Library concentration was first quantified using a Qubit 2. 0 fluorometer (Life Technologies, CA, USA). The insert size was checked (Agilent 2100 bioanalyzer, Santa Clara, CA, USA) and quantified using quantitative PCR. Libraries were analysed using Illumina technology (San Diego, CA, USA). The original raw data were transformed to Sequenced Reads by base calling. Raw data were recorded in a FASTQ file, which contains sequence information (reads) and corresponding sequencing quality information. Raw reads were then filtered to remove reads with adapter contamination or reads with low quality. Gene expression level was assessed by transcript abundance. The gene expression levels were estimated by counting the reads that mapped to genes or exons. Alignments to the reference Gallus gallus genome (GRCg6a (GCA_000002315.
5), [22] ) were performed with HISAT2  [23]  that uses a graph-based alignment. HTSeq software  [24]  was used to quantify the gene expression levels using the union mode. Raw data are shown in Supplementary data S1. The validation of RNAseq data was performed using Ross 308 CAM samples at EID11 and EID15 (n = 8 for each developmental stage) by analysing the expression of ten candidate genes. The protocol is as described in 2. 4 and results are shown in Supplementary data S2.
qRT-PCR of ten selected candidate genes in CAM collected from layer breed embryos Total RNA samples (1 μg) from ISA Brown CAM samples collected at EID11 and EID15 (n = 8 for each developmental stage) were reverse transcribed using RNase H-MMLV reverse transcriptase (SuperscriptTM II RT, Invitrogen, Cergy Pontoise, France) without RNaseOUT®. Primers to detect the expression of ten candidate genes and four housekeeping genes (beta-actin, stromal antigen 2 (STAG2), tyrosine 3monooxygenase/tryptophan 5-monooxygenase activation protein zeta (YWHAZ) and TATA box binding protein (TBP)) were designed using Primer-BLAST (NCBI, https://www. ncbi. nlm. nih. gov/) and synthesized (Eurogentec, Seraing, Belgium). The sequences and characteristics of each pair of primers are given in Supplementary data S2.
The gene expression was analysed by RT-qPCR using LightCycler® 480 SYBR Green I Master and LightCycler® 480 instrument II (Roche, Basel, Switzerland). Eight biological replicates, and three technical replicates were performed for each sample. GenNorm® software was used for validation of housekeeping gene stabilities  [25]. The normalized quantities of candidate genes were calculated using the following formula: gene efficiency^(ctcalibratorctsample) / geometric average quantity of housekeeping genes.
Statistical analyses For RNA seq analysis, all statistical analyses were performed on raw data (Supplementary data S1) using the R software, version 4. 0. 2 [27] , the edgeR package  [28]  in Bioconductor project  [29] , and SARTools package  [30]. Read counts were normalized using the trimmed mean of M-values (TMM) method  [27]  from the edgeR package. After estimating the dispersions, we fitted the negative binomial Generalized Linear Model (GLM)  [29]  with day of embryonic incubation and sex factors, while including interaction effect for each gene. P-values were adjusted by controlling the false discovery rate below 0. 05 using a Benjamini-Hochberg correction  [31]. Note that the "C15_83" sample had a very different profile compared to other samples and was removed from the statistical analyses.
04 to 197. 12 (Supplementary data S1, EID15 > EID11). An interaction between sex and developmental stage was found for four genes (Supplementary data S1_Stage x sex interaction). Thus, the expression of tropomodulin 1 (ENSGALG00000002125) was shown to decrease over time in female CAMs while it remained stable in male CAMs. Heterogeneous nuclear ribonucleoprotein K-like (ENSGALG00000040086) and E3 ubiquitin-protein ligase KCMF1-like (LOC431003) were not expressed in male CAMs, while their expression decreased in female CAMs. In contrast, we found that the expression of the ENSGALG00000048050 gene (LncRNA) was increased at EID15 versus EID11 in female CAMs (stable in male CAMs). A panel of 18 genes was selected to validate the RNA-seq results by qRT-PCR. The list of genes encompasses genes encoding proteins related to bone/calcium metabolism (CA2, ATP6V0A4LOC776719, SLC15A1; S100A14; CAB39L, CASR), innate immunity (CATH1, CATH2, AvBD1, AVBD2, AVBD7, CXL14, B2M, TLR21 and IRF2), and highly expressed genes (MGP, TGM4).
Expression of candidate genes in a layer breed at EID11 and EID15 (impact of genetic selection) The expression of a series of 12 overexpressed genes at EID15 in the Ross 308 eggs (Supplementary data S2) was screened in CAM samples originating from layer eggs (ISA Brown) to assess whether they were similarly overexpressed during incubation. From the results obtained (Fig. 5 ), we observed a variability of expression of all genes tested, between the two pedigree breeds. Several patterns of expression were noticed. Similar to the Ross 308 breed, the expression of TGM4 (Fig. 5A ), MGP (Fig. 5B ), S100A14 (Fig. 5D ), CA2 (Fig. 5E ), and CALB39 (Fig. 5H ) (Fig. 5J ) was higher at EID15 compared with EID11 in the ISA Brown breed. In contrast to the Ross 308 breed, there was no significant difference in expression of ATP6V0A4 (Fig.
Indeed, some researchers have reported a longer incubation time (24-h delay), lower chick weight at hatch, lower oxygen consumption and lower levels of blood parameters, although higher heart rate in the layer breed, compared to broiler embryos  [68, 69]. These observations and our results suggest that the developmental maturation of layer embryos may be slower than in broilers, which may explain why layer embryos require more time to reach hatching maturity. qRT-PCR analyses also highlighted the high inter-individual variability, especially for immune AvBD7 and CAMP genes, (Fig. 5 ), which could reflect differences in immune status between embryos, and not only between distinct chicken breeds  [70]. To conclude, this article provides a comprehensive dataset and molecular basis to further investigate the cellular and molecular mechanisms associated with the CAM, which plays a central role in several aspects of chicken embryonic development.
Acknowledgments The authors are grateful to  Joel Delaveau  and  Christophe Rat  ( INRAE, PEAT, Centre Val de Loire ,  DOI: 10. 15454/1. 5572326250887292E12 ) for egg handling. The authors wish to thank the  Region Centre Val de Loire  and the  PHASE Department (INRAE)  for the financing of  M. Halgrain 's PhD. The authors also thank " LE STUDIUM Institute for advanced studies -Loire Valley " for supporting the residency of M. Hincke at  DOVE, BOA, INRAE Centre Val de Loire. Hincke 's participation was additionally supported by the  Canadian Natural Sciences and Engineering Research Council (NSERC, Discovery program ,  RGPIN-2022-04803 ). We thank  Sonia Georgeault  who performed SEM analyses ( Plate-Forme IBiSA des Microscopies, PPF ASB ,  Université de Tours  and  CHRU de Tours, Tours, France).
| Whole exome sequencing WES was performed on the two affected brothers (II. 3 and II. 5), their father (I. 1), and a nonaffected brother (II. 1) using Twist Human Core Exome kit (Twist Bioscience) on an Illumina platform (CeGaT). For more details, see Appendix S1. Sequencing data were analyzed according to an autosomal recessive inheritance pattern. We selected variants that (1) affected the protein sequence, (2) had a minor allele frequency <. 1% in the gnomAD database, (3) were predicted to be pathogenic or likely pathogenic by at least 3 of 5 in silico prediction programs, and (4) were expressed in cerebral tissue. Validation and segregation analysis of the NAPB variant was done using Sanger sequencing (data not shown).
| Sequencing results A targeted gene panel containing 144 genes known to be involved in epileptic syndromes (Table  S1 ) was sequenced    7  This variant was absent from the gnomAD (v2. 1. 1) and ClinVar databases, and was predicted to be damaging by 18 prediction tools out of 20 (https://varso me. com/). 8 Because this variant was absent from her affected siblings, exome sequencing was performed on the two affected boys (II. 3 and II. 5), their healthy brother (II. 1) and father (I. 1) to identify other pathogenic variants. Because of consanguinity, we focused our analysis on a recessive mode of inheritance, and variant filtering was performed as described in the methods. We found a homozygous 7-base-pair deletion in the two affected brothers, located in exon 5 of the NAPB gene (Hg19 chr 20:23375625TGTAAACC>T:NM_022080:c.
347_353del:p. [Arg120Lysfs*19]; Figure  1B , Figure  S2 ). We confirmed using sanger sequencing that this protein-truncating variant (PTV), which affects all NAPB isoforms, was homozygous for their affected sister (II. 2), and heterozygous for both parents and their healthy child (II. 1). It has never been described in the literature  [4] [5] [6]  or gnomAD (v2. 1. 1), ClinVar, or LOVD (v3. 0) databases (Figure  1A, B ). No other homozygous or compound heterozygous variants that could explain the clinical phenotype were identified (Tables  S2  and S3 ).
ACKNOWLEDGMENTS We are grateful to all family members who participated in this study. We thank the  Genomics and Bioinformatics Platform of the Marseille Medical Genetics Center  for their support and  Auriane Caurette  for technical assistance. This work was supported by  Institut National de la Santé et de la Recherche Médicale ,  Aix-Marseille University, and Assistance Publique des Hôpitaux de Marseille. F. R. has received 1-year financial support from the  NeuroMarseille Institute  through the  French government program Investissements d'Avenir, Initiative d'Excellence d'Aix-Marseille Université  via  A*Midex  funding ( AMX-19-IET-004 ), and  ANR  ( ANR-17-EURE-0029 ) for this work.
, 2017]. The key questions that this paper will address are: what is the role of creativity and the connection to co-creation in Living Labs? Is creativity encouraged in co-creation processes or is it undervalued and not properly understood? How can collective creativity be monitored and measured in the context of Living Labs? Is co-creativity (collective creativity) an interesting object of study for supporting co-creation processes in Living Labs? This article addresses these questions using primary data collected in three experiments inspired by the project DIVINFOOD EU described in section 3. The first section of the article focuses on the concept of co-creation: what does it mean, how has it evolved over time and in different sectors, and what are the gaps and difficulties we have today in activating a co-creation process.
Section two will clarify how co-creation and co-learning are two sides of the same coin. We draw upon data from the DIVINFOOD project, 2  collected during the first seven months during the development of the guidelines to facilitate the Living Labs, besides the collection of scientific literature on creative processes in LLs and the conduct of additional empirical experiments (workshop sessions) developed during the ENOLL 3  annual conference. Main insights confirm that co-creativity is an essential component of co-creation because it sustains collaborative learning, has transformational power that can generate change. The scenarios and methodologies used for the experiments are explained in the third section. In the fourth section, the results obtained and some critical issues that exist for considering co-creativity as a potential indicator for monitoring and evaluating transformation in LLs are discussed and, in the conclusions, some proposals for continuing research on this topic are highlighted.
The Figure  2  is a possible visualization of the Living Lab main processes. In the horizontal axis we see Living Labs' main phases of development: after the start-up phase, the Living Lab will develop and grow, until it stabilizes, and if possible, consolidates in a maturation phase. The co-learning and co-creation processes (nonlinear and recursive) take place along the development phases of the LLs (vertical axis). The process starts through stakeholder identification and engagement, definition of the common vision/aims and action planning (for co-creation). This initial phase is followed by analysis (data collection), self-assessment and reflection (for co-learning). A redefinition of actions, revisions of aims and plans may then take place  [Massari, Mattioni & Galli, 2022]. In a collaborative learning process aimed at addressing "real world" problems, diverse actors come together to solve problematic situations that are beyond individual possibilities.
Methodology In the context of the DIVINFOOD project described above, the authors investigated how co-creation is understood by different audiences and whether its creative dimension is considered. The investigation took place in the process of drafting the project's facilitation guidelines aimed at the nine LLs (Deliverable 5. 1-Divinfood Project, Conceptual framework and methodology for LLs activities and data collection 4  ). Here, the authors highlighted some doubts and identified questions to be answered: (a) co-creation might be a term understood and defined differently by project partners with very different backgrounds, and this misunderstanding could affect the overall project as well as the set up and development of the LLs. (b) some partners seem to understand and emphasize more the participatory, collaborative and inclusive component of co-creation (i.
Three empirical experiments were conducted, (including one within the course of the project) as described below, to address these questions. The goal of the first experiment was to collect data on how people with different backgrounds define the concept of co-creation. Specifically, the goal was to find whether there were differences between definitions from people with different backgrounds and geographical origins. Data were collected by engaging participants of three international events in which the authors participated as organizers during the period 16 th September-23 rd October, 2022. Specifically the participants and the events were: 28 participants of the workshop "From Brainstorming to Groupthink" organized inside the annual conference of ENOLL Open Living Labs Days Turin September 16, 2022 (users involved in LLs in different settings); 20 auditors of the workshop conducted at the Science Festival in Genoa (general public not related to LLs); 20 people (general public, but more creative/design-oriented) participated to the international event of WFDD22 (World Food Design Day) organized by FORK.
5  In addition, an online survey was launched as part of the event. In the case of ENOLL, data were collected using post-it notes within an interactive design thinking workshop. In the case of WFDD22 an online questionnaire was administered with open-ended questions in which respondents were asked to describe the terms provided, while at the Festival of Science, the entire audience attending the conference was asked to define the terms and deliver their descriptions at the end of the event. The second experiment was carried out during the kick-off meeting where a collaborative and creative exercise was done involving all DIVINFOOD partners. Everyone was asked to think concretely about their idea of Living Lab, without trying to give a standard and elaborated definition to express it. So, each partner was asked to answer the question: "If the Living Labs in DIVINFOOD project were animals, what animals would they be?".
| Search strategy and eligibility criteria We first searched PubMed, Web of Science, the Cochrane Database of Systematic Reviews and the PROSPERO databases from January 2000 to February 2020 for systematic reviews with meta-analysis of observational studies investigating general cognition (intelligence quotient; IQ) in children and adolescents (<18 years) born VPT compared to term-born controls (the search strategy including search terms was published elsewhere  6  ). We excluded systematic reviews that investigated only subdomains of cognition, that did not include studies with control groups or that did not provide quantitative synthesis of study results for their potential contribution. Incomplete reporting limited the evaluation of the full impact of this methodological diversity.
| Study selection and data extraction Data were extracted in two successive stages. First, two researchers (MS and JZ) extracted data from the systematic reviews. 6, 10 In a second stage, primary studies were independently reviewed by two co-authors (MS, ST, VB, AM and JZ) to extract the following information: country of origin, range of birth years, age at assessment, type of study design, eligibility criteria for VPT regarding GA and/or BW (cut-off and combination of criteria), selection and follow-up of participants, outcome measurement, statistical methods (i. e. imputation methods), sample characteristics (i. e. clinical conditions or disabilities, multiple birth, socio-economic characteristics) and results (sample sizes, mean IQ scores with SD for both groups). A pilot extraction form was developed and tested on a set of studies; disagreements were resolved by consensus.