dataesr/scientific-paragraphs-categorization huggingface dataset:
0-3. 4) fell within the accepted ranges for archaeological and modern samples  (Guiry & Szpak, 2020 , 2021). Since variation in lipid content between samples can potentially drive differences in δ 13 C values  (Guiry & Szpak, 2020) , we studied the relationship between C:N Ratios and δ 13 C which revealed a non-significant correlation (Pearson's r = -. 12, p =. 23, Figure  S3 ). Quality control criteria for sulphur isotopes are relatively poorly defined for fishes. The quality of 104 samples analysed for δ 34 S (which passed the assessment above) was assessed by %S values. Following  Nehlich and Richards (2009)  we calculated the theoretical sulphur content of BFT collagen from its Type 1A and 2A collagen amino acid sequences (NCBI BioProject: PRJNA408269) following  Nehlich and Richards (2009).
We estimated the theoretical sulphur content of BFT collagen at 0. 47% (Table  S3 ), therefore two 9-13th c. Istanbul samples (MRY3285 and MET12545, Table  S2 ) were excluded from the dataset using the range (0. 4%-0. 8%) suggested by  Nehlich and Richards (2009). However, modern BFT fell outside of the C:S (125-225) and N:S (40-80) criteria for archaeological collagen suggested by  (Nehlich & Richards, 2009) , and consequently, this additional criterion was not applied. To confirm that our results were robust to variable.
| Statistical analyses We tested statistical pairwise differences in distribution between Black Sea, modern NE Atlantic and Mediterranean, and ancient NE Atlantic and Mediterranean isotope values, using non-exact pairwise Wilcoxon tests in R. To estimate the probability of a priori defined spatial groups being found within the same niche as each other we applied the overlap function using default settings and 10,000 iterations in the R package nicheROVER  (Swanson et al. , 2015). To avoid conflating temporal and spatial effects of Black Sea foraging, we used sample location to group all archaeological samples named 'Ancient NE Atlantic and Mediterranean',   S4 ). Gaussian models with link functions were thus applied to a different suite of effects for each stable isotope as follows using the restricted maximum likelihood (REML) approach in the R package mgcv  (Wood, 2012).
, 2020). However, BFT is likely to be highly generalist, as supported by relatively its low δ 15 N values (for predatory marine fish), which may have provided resilience against changes in marine food webs. Indeed, an increase in generalist BFT foraging strategies have been hinted at recently, on the importance of gelatinous prey  (Günther et al. , 2021) , which is likely to promote resilience to changes in prey availability or size, as has been shown for marine cetaceans over time  (Samarra et al. , 2022). We acknowledge, however, that the presence of spatial and size relationships added noise to our dataset, which limits temporal observations. This issue may be overcome through the application of compound specific isotope analysis (CSIA) to disentangle source versus trophic effects (e. g.
Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/faf. 12785 by Test, Wiley Online Library on [11/08/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License C:S and N:S values (TableS2), we performed non-exact pairwise Wilcoxon tests in R (R Core Team, 2013) which reported that BFT δ 34 S values do not significantly differ between samples falling inside or outside of the Nehlich and Richards (2009)  C:S and N:S Ratio criteria (Wilcoxon, p >. 05). Finally, we returned to quality checks after final data analyses and found no significant differences were found in N:S or C:S between samples from the preversus post 16th century (Wilcoxon, p >.
It is important to note that splitting data for pre-and post-16th c. inferences increased sample distribution overlap temporally. For spatial (longitude) models, distributions included Black Sea (Istanbul, Turkey) sample locations. GAMs indicated δ 13 C values were significantly influenced by longitude (p <. 001, Table 2) whereby Atlantic locations and central Mediterranean and Black Sea locations had lower δ 13 C values than the western Mediterranean samples (Figure 3a). There was no significant relationship between δ 13 C or δ 15 N values and time (year, Figure 3d,e). δ 15 N was significantly explained by latitude 14672979, 0, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/faf. 12785 by Test, Wiley Online Library on [11/08/2023]. See the Terms and Conditions (https://onlinelibrary.
4. 4 | 4 Consequences for management and conservationWhile limited in sample size, the isotopic data presented here provide the first information on the foraging ecology of the Black Sea niche of BFT, with which to guide their return. They suggest that limiting catches throughout the eastern Mediterranean may promote divergent migration strategies, even if these may have a behavioural rather than evolutionary basis. The potential need to manage Mediterranean BFT as more than one stock has been previously hinted at, in-part due to proposed Mediterranean residency of some individuals (Cermeño et al. , 2015; Cort & Abaunza, 2019 ; 14672979, 0, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/faf. 12785 by Test, Wiley Online Library on [11/08/2023]. See the Terms and Conditions (https://onlinelibrary.
C-V profiles of B devices were measured at room temperature using an Agilent 4294 A precision impedance analyzer with a measurement frequency of 40 kHz across a voltage range of À1. 0 to þ0. 5 V. Despite the different measurement frequencies, each capacitance measurement had a Q-factor >5, indicating accurate capacitance measurements across all devices. [45] ime-resolved photoluminescence (TRPL) was measured with an excitation wavelength of 640 nm and fluence 1. 3 Â 10 12 -1. 0 Â 10 14 photons cm À2 pulse À1 (60 μm laser beam diameter). TRPL decays were first measured at various injection levels to identify injection-independent ranges (low injection), which were implemented for final measurement and analysis. 0. 3-ps pulses and 1. 1 MHz repetition rate were used. Time-correlated single photon counting was used for data collection.
Method 3. 1. 1. 1. Participants: Participants were recruited on Amazon. com's Mechanical Turk and completed the experiment for $2. 5 payment. We used the Turkolizer software from  Gibson, Piantadosi, and Fedorenko (2011). The experimental tasks were available to participants who had (i) an IP address inside the United States, (ii) previously completed more than 100 tasks on Mechanical Turk, (iii) an approval rate above 95%, and (iv) had not participated in similar experiments before. We kept collecting data until we obtained 240 participants who met all the requirements listed as follows. (i) Participants' native language was English (based on self-report). (ii) They completed more than 90% of the target questions (i. e. , the probability rating questions). (iii) Their accuracy on comprehension questions in the target trials was 100%.
Fig. 1 (b) shows the mean rating of the probability of not all items having the property across knowledgeability conditions and in both exposure conditions. Effects of speaker knowledgeability and exposure to numeral alternatives on probability ratings were assessed via linear mixed-effects regression using the lme4 package in R  (Bates, Mächler, Bolker, & Walker, 2015). Knowledgeability (partial = -0. 5, full = 0. 5) and exposure (no numerals = -0. 5, with numerals = 0. 5), along with their interaction, were entered as contrast-coded fixed effects. Varying intercepts were included for participants and items with varying slopes for knowledgeability by participants and varying slopes for all effects of interest (the two main effects and their interaction) by items. 1 The probability ratings were lower in the partial knowledge condition than in the full knowledge condition (β = 18.
Method Participants were recruited on Amazon. com's Mechanical Turk and completed the experiment for $3 payment. We kept collecting data until we had 240 participants who met the inclusion criteria. Data inclusion criteria were the same as those of Experiment 1. Design, materials, and procedure were all similar to Experiment 1, except that eight more exposure trials were added in each contextual numerals condition. Hence, each participant received 32 trials (8 target trials + 24 exposure trials).
Results Fig. 1 (c) shows the mean participants' rating of the probability of not all items having the property across knowledgeability conditions and in both exposure conditions. Effects of speaker knowledgeability and exposure to numerals on probability ratings were assessed via linear mixed-effects regression using the lme4 package in R  (Bates et al. , 2015). Knowledgeability (partial = -0. 5, full = 0. 5) and exposure (no numerals = -0. 5, numerals = 0. 5), along with their interaction, were entered as contrast-coded fixed effects. Varying intercepts were included for participants and items with varying slopes for knowledgeability by participants and varying slopes for all effects of interest (the two main effects and their interaction) by items. 3 Table  3  shows the regression table of the fixed effects (see Table  A1  in the online Appendix for random effects).
Method 3. 3. 1. 1 Participants were recruited on Amazon. com's Mechanical Turk and completed the experiment for $3 payment. We kept collecting data until we obtained 240 participants who met the preregistered requirements. Participant's qualification requirements and data inclusion criteria were the same as those of Experiment 2. 3. 3. 1. 2 Design, materials, and procedure were all similar to Experiment 2, except that large numbers in the exposure trials were replaced by small numbers (i. e. , 1, 2, 3, 4, or 5).
Results Fig. 1(d)  shows the mean rating of the probability of not all items having the property across knowledgeability conditions and in both exposure conditions. Effects of speaker knowledgeability and exposure to numerals on probability ratings were assessed via linear mixed-effects regression using the lme4 package in R  (Bates et al. , 2015). Knowledgeability (partial = -0. 5, full = 0. 5) and exposure (no numerals = -0. 5, with numerals = 0. 5), along with their interaction, were entered as contrast-coded fixed effects. Varying intercepts were included for participants and items with varying slopes for knowledgeability by participants and varying slopes for all effects of interest (the two main effects and their interaction) by items. 7 Table  4  shows the regression table of the fixed effects (see Table  A1  in the online Appendix for random effects).
Method 3. 4. 1. 1. Participants: A power analysis using data from our previous experiments indicated that we needed to recruit around 350 participants to achieve 80% power to detect the hypothesized interaction. Based on the inclusion rate of participants in the previous experiments, 448 participants were initially recruited on Amazon. com's Mechanical Turk and completed the experiment for $3 payment. We used the Turkolizer software from  Gibson et al. (2011). The experimental tasks were available to participants who had (i) an IP address inside the United States, (ii) previously completed more than 500 tasks on Mechanical Turk, (iii) an approval rate above 98%, and (iv) had not participated in similar experiments before. If a participant's accuracy on comprehension questions (including those in the target trials and those in the exposure trials) was less than 60%, we rejected their work and reposted it so that a new participant could receive it.
Results There were 390 participants left after the data exclusion procedure. Fig. 1 (e) shows the mean rating of the probability of not all items having the property across knowledgeability conditions and in both exposure conditions. Effects of speaker knowledgeability and exposure to numerals on probability ratings were assessed via linear mixed-effects regression using the lme4 package in R  (Bates et al. , 2015). Knowledgeability (partial = -0. 5, full = 0. 5) and exposure (no numerals = -0. 5, numerals = 0. 5), along with their interaction, were entered as contrast-coded fixed effects. Varying intercepts were included for participants and items with varying slopes for knowledgeability by participants and varying slopes for all effects of interest (the two main effects and their interaction) by items.
5′P-Seq and RNA-Seq Sequencing files were demultiplexed using bcl2fastq v2. 20. 0. 422 (one mismatch, minimum length 35 nt), and adapters were trimmed using cutadapt 2. 3. [79]  at default settings, allowing one mismatch and minimum read length of 35nt. In addition to standard illumine dual index (i5, i7), the inline sample and UMI barcode was analyzed using Umitools. Reads were mapped to the concatenated genome of S. cerevisiae (R64-1-1) and S. pombe (ASM294v2) using STAR. Second read enables to split reads between oligo-dT or random primer. That information was not used in the current analysis. CDS positions were defined with Ensembl gff version 94 for S. cerevisiae (R64-1-1). Counts in S. cerevisiae were calculated by aggregating RNA-Seq reads and 5′P-Seq 5′-ends, overlapping CDS positions.
ChEC-seq analyses FASTQ files were adapter stripped, using cutadapt (parameters: -a GAT CGG AAG AGC ACA CGT CTG AAC TCC AGT CA -minimum-length = 20 -quality-cutoff = 2) and then mapped using bowtie2  [83]  to sacCer3 (parameters: -v 2 -m 10 -best -strata). Positions of the + 1 nucleosome associated with each gene were taken from the Saccharomyces Genome Database and read counts overlapping the promoter binding region 400 bp upstream and 100 bp downstream of these position were calculated and normalized to RPKMs. To find the Not1 ChEC signal, the log2 fold change (LFC) of the promoter binding region RPKMs were taken over free MNase. These values were mode-centered to zero (the mode of the LFCs was estimated by fitting a log-normal distribution using "fitdistr" from the R package MASS).
Funding Open access funding provided by  University of Geneva. This work was supported by grants  31003A_172999  and  310030_207420  from the  Swiss National Science Foundation  awarded to M. A. C, grants  GINOP-2. 3. 2-15-2016-00020  and  UNKP-21-5-595-SZTE  from the  Hungarian National Research, Development and Innovation Office  and the  János Bolyai Research Scholarship of the Hungarian Academy of Sciences  awarded to Z. V. , a  Helmut Horten Stiftung  grant awarded to O. O. P, grant  FOR1805  ( Deutsche Forschungsgemeinschaft)  awarded to Z. I. and the  Ragnar Söderberg Foundation, Wallenberg Academy Fellowship  [ KAW 2016. 0123 ] as well as the  Swedish Research Council  [ VR 2020-01480  and  2021-06112 ] and  Vinnova  [ 2020-03620 ] to VP. Computational analyses were partially performed on resources provided by the  Swedish National Infrastructure for Computing (SNIC-UPPMAX)  [ VR 2018-05973 ].
Availability of data and materials tRNA microarray data are accessible under the accession number GSE190658 in the Gene Expression Omnibus (GEO) database  [85] , 5′P-Seq data under GSE193912  [86]. All code used in the manuscript is available at https:// github. com/ georg ealle nunige/ NotTr ansla tion [87], released under an OSI compliant GNU General Public License version 2 (GPL-2. 0) with DOI https:// zenodo. org/ badge/ lates tdoi/ 20927 9257.
Irradiation can be used as an insect pest management technique to reduce postharvest yield losses. It causes major physiological changes, impairing insect development and leading to mortality. This technique is used to control the melon fly Zeugodacus cucurbitae, a major pest of Cucurbitaceae in Asia. Here, we applied irradiation to melon fly eggs, and the larvae emerged from irradiated eggs were used to conduct comparative transcriptomics and thereby identify key genes involved in the development and survival. We found 561 upregulated and 532 downregulated genes in irradiated flies compared to non-irradiated flies. We also observed abnormal small-body phenotypes in irradiated flies. By screening the 532 downregulated genes, we selected eight candidate genes putatively involved in development based in described functions in public databases and in the literature.
, 2021). Initial processing of raw fastq data (raw reads) was performed using custom Perl scripts  (Ma et al. , 2022). Lowquality reads and reads containing adapter and poly-N were first removed from the data. The Q30 and GC content were then calculated in the clean data. We used the clean, high-quality data for all downstream analyses. Reference transcriptomic data for Z. cucurbitae (GCF_000806345. 2) was downloaded from the NCBI's Sequence Read Archive database. A paired-end alignment of clean reads with the reference was performed using HISAT2 v2. 1. 0 (Jiang et al. , 2022). The number of reads aligned to each reference gene (read depth) was counted using HTSeq v0. 11. 2. To measure gene expression level, we calculated the FPKM (number of Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced) for each gene using the gene length and its read depth  (Nawaz et al.
Differentially expressed genes (DEGs) and enrichment analysis We used the DEGseq2 R package version 1. 26. 0  (Love et al. , 2014)  to test significant differences in gene expression levels between irradiated replicates and control replicates. The threshold for significantly different expressions was set at p <. 05, a minimum of 1. 5 fold change in expression level (|log2 (fold change)| ≥. 58), and an FPKM >1 in at least one of the six samples  (Kumar et al. , 2020). Gene expression levels were externally validated by qRT-PCR (Supplemenatry Figure  S1 ). GO (Gene Ontology; http://www. geneontology. org/) significantly enriched pathways were identified using the R package ClusterProfiler with a p-value <. 05  (Wu et al. , 2021; Chen et al. , 2021). KEGG (Kyoto Encyclopedia of Genes and Genomes) significantly enriched pathways were identified using the KEGG Automatic Annotation Server (KAAS, https://www.
The present study To summarise, there is a need to better understand issues around measurement invariance of the BAS-2 across national contexts, the stability of the association between BAS-2 scores and psychological wellbeing across nations, and associations between BAS-2 scores and sociodemographic characteristics and national-level indicators, respectively. These issues are best approached from a multinational perspective, particularly given the historic neglect of such a perspective from body image research  (Andersen & Swami, 2021). To address these issues, we utilised data from the Body Image in Nature Survey (BINS;  Swami et al. , 2022) , a collaborative, researcher-crowdsourced project that gathered BAS-2 data between 2020 and 2022 from participants in 65 nations. The BINS dataset presents unprecedented opportunities to advance knowledge on body appreciation on a number of fronts.
Indeed, to our knowledge, the BINS dataset contains the largest bank of information on cross-national body image generally, and body appreciation specifically, that has ever been collated. Thus, drawing on the BINS dataset, we first examined measurement invariance of the BAS-2 across nations, gender identities, and age groups (i. e. , emerging adults: 18-24 years; young adults 25-44 years; middleage and older adults: ≥ 45 years;  Arnett, 2000; Erikson, 1968). Additionally, and to account for the possibility that linguistic structures may impact measurement invariance  (Swami, Todd et al. , 2021b) , we also conducted the first test of measurement invariance of the BAS-2 across languages (i. e. , across the 40 languages represented in the BINS). Based on findings that the BAS-2 minimally achieves partial scalar invariance across national contexts (e.
Overview of the body image in nature survey The Body Image in Nature Survey (BINS) is a researchercrowdsourced project involving 253 scientists working collaboratively across 65 nations (for a detailed, published study protocol, see  Swami et al. , 2022). All data were collected between November 2020 and February 2022 with community sampling, with the majority of recruitment taking place online. The overall project received ethics approval from the School Research Ethics Panel at the first author's institution (approval code: PSY-S19-015) and, unless exempt by national laws, all collaborating teams additionally obtained ethics approval from local institutional ethics committees or review boards. A list of nations, associated sample sizes, data collection methods, ethics approvals, and survey languages is presented in Supplementary Table  S1.
Participants The BINS dataset consists of 56,968 respondents from 65 nations, of whom 58. 9% (n = 33,539) were women, 40. 5% (n = 23,083) were men, and 0. 6% (n = 346) were of another gender identity. In terms of race, the majority (74. 2%, n = 42,269) self-identified as being part of a racialised majority, whereas 11. 3% (n = 6448) identified as part of a racialised/ ethnic minority group, and 13. 5% (n = 7689) were uncertain about their status (race data were not collected in France [n = 562; 1. 0%] due to a legal prohibition banning the collection and storage of race-based data). In terms of self-reported residence, 27. 1% (n = 15,408) of participants lived in a capital city, 13. 7% (n = 7811) lived in a suburb of a capital city, 25. 1% (n = 14,319) lived in a provincial city (more than 100,000 residents), 18.
Body mass index Participants self-reported their height and weight, which we used to compute self-reported BMI as kg/m 2. Following  Swami et al. (2018) , data for participants with improbable BMI values (< 12 or > 60 kg/m 2 ) were recoded as missing values. In keeping with weight-neutral approaches to research practice (e. g. ,  Mensinger et al. , 2016) , we report BMI values for descriptive purposes only.
Demographics Participants were asked to provide their demographic data consisting of gender identity (1 = woman, 2 = man, 3 = describe gender in another way), age (open-ended), highest educational qualification (1 = no formal education, 2 = primary education, 3 = secondary education, 4 = still in fulltime education, 5 = undergraduate degree, 6 = postgraduate degree, 7 = other), marital status (1 = single, 2 = single but in a committed relationship, 3 = married, 4 = other), and ethnicity/race (1 = ethnic/racial majority, 2 = ethnic/racial minority, 3 = not sure). For descriptive purposes at the national level and for analyses, response options for highest educational qualification were collapsed into secondary/tertiary (secondary education, undergraduate degree, postgraduate degree) versus other (all remaining categories) and response options of marital status were collapsed into committed/married (single but in a committed relationship, married) versus other (all remaining categories).
Tests of correlations Correlates of body appreciation across nations were examined with multilevel models. The factor scores of the national invariance model were used as the dependent variable. The groupmean-centred variables of financial security, urbanicity, education, marital status, racialised status, and life satisfaction (using factor scores) were used as Level-1 predictors. The nation-level means of all of these variables were used as Level-2 predictors to investigate the associations of these predictors with the outcome at Level 2 between nations. Bayesian estimation (using diffuse priors as specified in the Mplus software default settings) was used to obtain parameter estimates on a standardised scale. A second model also included nation-level indicators as Level-2 predictors, besides the above-mentioned variables.