dataesr/scientific-paragraphs-categorization huggingface dataset:
MX projects are presented in the form of branched Project Trees, using a logically clear system for naming the data objects produced during the course of a project. Jobs are highlighted with summary statistics where appropriate to qualify or quantify Job success. Altogether, this visual representation makes projects quickly and easily navigable. Project development is made simple by way of growing the Project Tree via adding and removing Jobs with primitive graphical actions. Project branching is made unambiguous by introducing the concept of Structure Revisions, which can be considered as check points corresponding to specific states of the structure-solution workflow. While CCP4 Cloud might be perceived as a mere web-based HPC-powered GUI for the CCP4 software suite, there are significant differences upon closer inspection.
Firstly, the centralized online setup provides a natural basis for combining CCP4, web resources and third-party software. Examples include the PDB, the AlphaFold database at the EBI and software from Global Phasing and AlphaFold2. Secondly, CCP4 Cloud provides an environment for teamwork, where team members can be located anywhere but can work on the same projects, similar to other online systems such as Google Docs or GitHub. This feature is also important for education and training, as CCP4 Cloud can store virtually indefinite volumes of relevant documents and data integrated with demo projects. Thirdly, CCP4 Cloud provides a framework for the long-term storage of structure-solution projects and results, which is maintained to industry standards. This feature is important for research projects that span many years and helps data management in research laboratories and companies.
Thus, the PDB is reaching a state where it captures experimental data in the form of indexed reflections and final interpretation in the form of an atomic model and electron-density maps. However, the PDB does not capture the way in which this interpretation was performed: information about the workflow and modelling decisions made during a structure-solution project is lost. We would like to advocate the idea that retaining structuresolution projects is as important as retaining experimental data. This can be useful for the subsequent understanding of ambiguous structural features, revisiting and revising structural studies, education, facilitating the peer-review publication process, training and software development. We further suggest that CCP4 Cloud provides a convenient framework for archiving structure-solution projects, complementing data repositories at the wwPDB, and we consider the implementation of such archival as one possible future direction.
Acknowledgements The authors are grateful to all CCP4-associated developers and to CCP4 Working Group 2 for their suggestions and useful discussions. Many feedback and bug reports from users have helped to bring CCP4 Cloud to a stable production state. Special thanks to  Global Phasing Ltd  for providing access to the BUSTER refinement software in kind for use by  CCP4  Cloud's academic users. The third-party inclusion framework was developed in cooperation with the ARP/wARP team at  EMBL Hamburg. The  CCP4 Cloud setup at RAL Harwell  is hosted in the  Research Complex at Harwell , with the help of RCaH IT support. Computational facilities are provided by  CCP4 ,  Scientific Computing Department of the Science and Facilities Research Council UK (STFC) ,  STFC Cloud and IRIS Community  Project.
Data access Source code for CCP4 Cloud is included in the CCP4 software suite version 7. 1 and higher, as obtainable from https:// www. ccp4. ac. uk. It can also be found in GitLab repositories at https://gitlab. com/CCP4/jsCoFE. CCP4 Cloud documentation is maintained in GitLab at https://gitlab. com/CCP4/jscofe-doc. CCP4 Cloud and CCP4 Cloud documentation are provided free to academic users under the terms of the general CCP4 Licence (https://www. ccp4. ac. uk/licensing/academic_ software_licence. pdf).
Preregistration: respondents Our sample will consist of 275 respondents recruited via the online platform Prolific Academic (see §2. 7 'Power analysis and sample-size estimation' for details). We will use Prolific's pre-screening criteria to filter respondents by age, nationality, language, approval rate and number of previous submissions. We will only recruit respondents older than 18 years, who are from the UK and speak English as their first language, and who have a minimum approval rate of 90% and more than 10 previous submissions on Prolific. Respondents will be excluded when they do not complete the entire experiment, when they do not provide correct answers during the last of the two comprehension check sections on the first and last session of the experiment, when their reaction time is below 200 ms on at least 90% of the games, or when multiple respondents use the same IP address.
In the eight intermediate sessions, respondents play the Trust game 16 times (detailed instructions and comprehension check sections are again included). During the last session, respondents play the Trust game 16 times again and fill out the self-reported trust questionnaire. After that, respondents are confronted with two scenarios where they can display real-world behaviour related to social trust: data sharing and actual helping. We included these two measures for exploratory purposes. The goal is to see whether people's Trust game decisions and responses to the questionnaire are correlated with actual behaviour. For data sharing, respondents have the option to give permission to share their data from 13 additional questions about their personal life with other researchers. For actual helping, respondents have the option to help the researchers by providing feedback on instructions for a future experiment.
Data sharing A new scenario proposed by Bauer et al. [44]  to measure real-world behaviour related to social trust is whether respondents share, i. e. entrust their data to others. Specifically, they ask respondents for their permission to include additional external private data (i. e. data from social insurance carriers) to be included in the analysis. Respondents are therefore asked to trust the researchers with this additional data and to not abuse it, making it straightforwardly related to social trust. Here we adopt a similar royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 9: 210213 strategy. At the end of the experiment on session 10 (after the Trust games are played and the three trust items are filled out), all respondents will be presented with a set of 13 additional questions about their personal life.
Preregistration: pre-registered analyses All analyses will be carried out in R 4. 0. 4 (https://www. r-project. org/) with R Studio 1. 2. 5042. The data consist of a within-subject, repeated measures design because the study is longitudinal in nature. To test the hypothesis that decisions in the Trust game show substantial variability over time, we will measure the test-retest reliability of the willingness-to-play via the intraclass correlation coefficient (ICC), which is a widely used reliability index. To determine the ICC, we will first fit an ordinal logistic regression model using the function clmm2 from the R package Ordinal. The dependent variable is willingness-to-play, stakes and reciprocation probability will be included as fixed effects and respondents' ID will be included as a grouping factor with a random intercept.
05. To test the main hypothesis that the average behaviour in the Trust game on the first session is not or poorly correlated with social trust questionnaire responses while the average behaviour in the Trust game over all sessions is significantly and more strongly correlated with social trust questionnaire responses, a mixed effects model will be performed with the R package nlme and fitted with a Maximum-Likelihood method. The willingness-to-play averaged on the first session and over all sessions is the dependent variable, labelled AWP (Average Willingness to Play). The single responses that give rise to AWP are measured via a nine-point Likert scale, and in principle could be classified as ordered categorical. However, AWP represents the average of these responses (16 on session 1; 160 over all sessions) and therefore includes a number of ranks that is large enough to consider it as approximately continuous.
Preregistration: power analysis and sample-size estimation To determine sample size, a power analysis was performed using Monte Carlo simulations with the R package simr  [55]. Monte Carlo simulations are particularly useful to calculate power and sample-size requirements when analytical solutions cannot be derived because of the complexity of the target statistical model. Using the parameters of the distributions of the Trust game decisions and the royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 9: 210213 questionnaire responses of Mell et al. [34] , we conducted an a priori power analysis of the effect of the interaction Qavg Ã Session on the AWP variable, mentioned in the description of our main analytic model above, and which tests the difference between the correlation of Qavg with AWP observed at S1 and the correlation of Qavg and AWP observed at Sall.
Preregistration: exploratory analyses In addition to the pre-registered analyses detailed above, additional exploratory analyses will be performed. First, concerning real-world behaviour related to social trust (i. e. data sharing and actual helping) and the Trust game decisions, we will use mixed effects models in a similar manner to that described above. The only difference is that real-world behaviour will be included as a binary variable (0 = not sharing or helping; 1 = sharing or helping) and the average of the questionnaire responses (Qavg) will be removed. To test the relationship between real-world behaviour and the questionnaire responses we will use a one-way mixed effects model, where the average of the questionnaire responses (Qavg) will be the dependent variable and real-world behaviour is an explanatory binary variable (0 = not sharing or helping; 1 = sharing or helping).
Tested participant sample Of the 275 participants included in the study on Prolific. co, 155 completed the 10 sessions of the longitudinal study (84 women, 70 men; mean age = 34. 65, s. d. = 8. 23). The mean bonus payment across the 10 sessions matched the expected range (actual mean bonus payment: 5. 64£, predicted mean bonus payment: 5. 75£). One participant was excluded from the analyses following the pre-registered exclusion procedure. The analyses were thus conducted on 154 participants (1 non-binary person, 83 women, 70 men; mean age = 34. 66, s. d. = 8. 25). This sample size is lower than the targeted sample size of 183 respondents. However, based on our power calculations, this sample size is sufficient to detect a moderate slope difference in the association of participants' behaviour in the Trust game and their responses to the social trust questionnaire between data collected on Session 1 and the data averaged across the 10 sessions with a power of 73.
Heterogeneous trajectories of willingness-to-play Latent Class Mixed Models were then conducted using the 'hlme' function of the lcmm R package  [61]  in order to define subpopulations of participants (N = 154) who displayed various trajectories of willingness-to-play trajectory over the entire experiment  [62]. The metric we used to analyse individuals' willingness-to-play trajectories was the number of days participants spent between the 1st and the 10th economic game session, and was coded as follows: day 1, day 3, day 5, day 7, day 9, day 11, day 13, day 15, day 17, day 19). Importantly, the time interval between two game sessions was held constant along the entire experiment and across subjects. Note in addition that no missing value was present in this longitudinal dataset. The average willingness-to play-collected on each day departed from normality, so the whole vector of willingness-to-play data was z-scored.
Predictors of participants' class membership We then investigated whether our psychometric (Social trust questionnaire average score) and real-world (Extra help and Data sharing) proxies of social trust predicted the class to which participants were more likely to belong. We also studied whether age, gender, and current socio-economic status predicted class membership. We used the nnet, broom, scales and car R packages  [67] [68] [69] [70]  to perform multinomial logistic regressions weighted by the logit-transformed posterior probabilities of class membership  [71]. Because only one participant identified as non-binary, we limited our analyses to participants who identified as either a woman or man to avoid misinterpretation of differences associated with this category. Similarly, the single participant in the 'willingness-to-play null' class was excluded from the analyses.
18; s. e. = 0. 33; z = -3. 55; OR = 0. 31; CI lower = 0. 16; CI upper = 0. 59; p < 0. 001). In addition, the model showed that female participants were more likely to be classified in the 'willingness-to-play increase' profile (RRR = 0. 67; s. e. = 0. 24; z = 2. 82; OR = 1. 95; CI lower = 1. 23; CI upper = 3. 10; p = 0. 004) than in the 'willingness-to-play slow decrease' profile. In order to investigate more precisely how the probability of being assigned into a specific class of willingness-to-play trajectory varied for each questionnaire score, we used the MNLpred R package  [72]. Inspection of predicted probabilities revealed that the likelihood of being classified in the 'willingness-to-play increase' profile linearly increased with scores on the social trust questionnaire, starting at 0.
Computational Results In this section, we will compare the efficiency of the proposed algorithm with Dijkstra's one. There are several criteria for this comparison result: the length of the obtained ESP, the computation speed, the smoothness, and the position error of the solution. The experiments were ran on a machine with an Intel Core i5-8400 CPU @ 2. 80 GHz processor. It has a sixcore CPU and the available RAM was 16 GB. All algorithms were implemented in Matlab. The code is available online at https://github. com/nguyengiathuongphai/ESP_Tubular. git (accessed date: 18 Feburary 2022).
MicroRNAs (miRNAs) are small (22 nucleotides in length) non-coding RNA molecules that can post-transcriptionally regulate gene expression. 10 Mature miRNAs, complexed with facilitative proteins (i. e. the Argonaute protein family), exert their effects by binding mRNA molecules and inducing mRNA degradation or inhibiting translation. 11 Currently, over 2,000 known human miRNAs are recorded on the miRBase database, 12 some of which have been characterised as diagnostic biomarkers for diseases such as cardiovascular disease  13  and cancer. 14 Despite several studies postulating the association of various miRNAs with NAFLD, very few have been unequivocally replicated and validated. 15 The lack of consensus may in part be caused by differences in study design, relatively small numbers of cases being studied, technical approaches, and variances within the chosen techniques.
Patient recruitment and sample collection The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki. Cases were derived from the European NAFLD Registry (NCT04442334) with the collection and use of NAFLD patient data and samples approved by the relevant ethical committees in the participating centres and all patients having provided informed consent. 20 The collection of blood samples from 'healthy' controls (no reported comorbidities or evidence of NAFLD) was approved by the County Durham and Tees Valley Research Ethics Committee (12/NE/012), and informed consent was given by all donors. Disease stage and grade of NAFLD samples were histologically proven by liver biopsy, with the serum samples taken within 6 months of said biopsy. Liver biopsy specimens were scored centrally by 2 expert liver pathologists (DT and PB) to ensure consistency in the analysis and interpretation of histology data.
FASTQ files were processed by the pre-installed HTG EdgeSeq parser software to align the probe sequences to the results, giving an output of raw count data. Quality control was conducted using the raw counts according to HTG guidelines. Delta mean values within 2 standard deviations (calculated by the manufacturer as 4. 8 for serum samples) were considered acceptable. miRNA sequencing data analysis Data for the 2,083 sequenced miRNAs were analysed using RStudio version 1. 0. 143 (RStudio: Integrated Development for R. RStudio, Inc. , Boston, MA, USA, http://www. rstudio. com/). Raw counts were first transformed into counts per million (CPM), and the miRNAs with a CPM < -100 were filtered out of the raw count dataset. DESeq2 version 1. 18. 1 (https://bioconductor. org/ packages/release/bioc/html/DESeq2.
html)  23 as used to generate a datasetcorrected for batch, age, and centrefrom which principal component analysis (PCA) plots were constructed. Outliers were defined by visualisation of the PCA plots and removed from the dataset. Comparisons between each of the histological groupings (control, NAFL, NASH-F0/F1, NASH-F2, NASH-F3, and NASH-F4) and various subgroups were performed using limma version 3. 34. 9 (https://bioconductor. org/packages/ release/bioc/html/limma. html). 24 Statistical significance was determined using a Benjamini-Hochberg-adjusted p value < -0. 05; no log 2 fold change (logFC) threshold was set. Area under the receiver operating characteristic (AUROC) curves were generated using IBM SPSS Statistics for Windows, version 24 (IBM Corp. , Armonk, NY, USA), and significance between 2 AUROC curves was assigned based on the DeLong, DeLong, and Clarke-Pearson method  25  in RStudio version 1.
The reverse transcription reaction products were diluted 1:10 in nuclease-free water and stored at -20 C. The expressions of 3 miRNAs, including the non-phosphorylated spike-in, were quantified using pre-formulated standard TaqMan TM MicroRNA Assays (Thermo Fisher Scientific, Paisley, UK): 000200 (cel-miR-39-3p), 464645_mat (hsa-miR-3687) and 000426 (hsa-miR-34a-5p). Each qPCR reaction mixture contained 2. 5 ll diluted cDNA, 1 × TaqMan TM Fast Advanced Master Mix (Thermo Fisher Scientific, Paisley, UK), and 1× TaqMan TM Small RNA Assay (Thermo Fisher Scientific, Paisley, UK) and was made up to 20 ll with nucleasefree water. Target gene predictions and bioinformatic searches Two online databases were searched for predicted gene targets of miR-193a-5p, namely, TargetScan  27  and miRDB,  28  and the overlapping genes between the 2 were identified.
Common genes were identified between the resulting predicted genes and the differentially expressed genes in NAFLD liver as described by Govaere et al. 29  Normalised and transformed counts of miR-193a-5p, GPX8, and COL1A1 were correlated for the 80 overlapping samples for which miRNA-seq and RNA-seq data were available; a linear model was performed to obtain the slopes and p values of the correlations. Single-cell RNA-seq data for GPX8 and COL1A1 were extracted from the Liver Cell Atlas gene browser on 13 August 2020. 30 Data on gene expression were obtained from the Gene Page of the Genotype-Tissue Expression (GTEx) Portal (GTEx Analysis Release V8; dbGaP Accession phs000424. v8. p2).
Study cohort description The current study comprised cases that were histologically characterised and represented the full spectrum of NAFLD severity from NAFL to NASH F4 (cirrhosis). The samples were divided between discovery (n = 183) and replication (n = 372) cohorts. Discovery cases to cover the range of NAFLD phenotypes were selected from those already recruited within the European NAFLD Registry 20 by July 2018 with replication cases selected from the larger cohort available by June 2019. Demographic and clinical data are reported in Table  1. There were some significant differences between the cohorts. In particular, the discovery cohort included a relatively large number of NAFL cases without detectable fibrosis or inflammation to allow detailed comparisons between early and late disease.
Comparison of NAFLD serum miRNA profiles with controls The serum miRNA profiles of 183 patients across the NAFLD spectrum and 10 population controls were generated using the HTG EdgeSeq and Illumina NextSeq technologies. Complete sequence data are available as GSE185062 on Gene Expression Omnibus (https://www. ncbi. nlm. nih. gov/geo/). Initial PCA confirmed that there was no clustering by centre, plate/batch, or age, indicating that batch correction was effective (Fig. S1 ). PCA showed that the control samples appeared to cluster together, suggesting they had a different miRNA profile to the NAFLD samples, although some overlap was observed (Fig. 1 ). Relative to controls, levels of 275 serum miRNAs were different in NAFLD patients, 165 increasing and 110 decreasing (Table  S1 ). The most significantly different miRNA was miR-   3687 for which decreased levels in NAFLD were seen (fold change [FC] 0.
35, p = 1. 79×10 -03 ). When the grade of steatohepatitis was considered using NAS and SAF activity, 7 and 4 miRNAs respectively showed altered levels, with all but 1 of these changes an increase with increasing disease severity (Table  2 ). MiR-193a-5p again showed the most significant increase (NAS FC = 1. 36, p = 2. 61×10 -04 ; SAF activity FC = 1. 35, p = 2. 70×10 -02 ). In light of its association with disease severity, we evaluated miR-193a-5p as a potential circulating biomarker of disease stage and activity by assessing its diagnostic performance in the sequencing dataset using AUROC statistics. AUROCs were also calculated and plotted for miR-122-5p as well as simple composite scores (the aspartate aminotransferase/alanine aminotransferase [AST/ALT] ratio and the fibrosis-4 [FIB-4] score).
An unpaired Student's t-test was performed for all conditions relative to the control conditions (**p < -0. 01). Data are presented as the mean with error bars representing the SEM. qPCR, quantitative PCR. recently reported hepatic transcriptomics dataset from 206 histologically characterised NAFLD cases to identify genes differentially expressed between mild and severe steatohepatitis and/ or fibrosis. 29 We then projected the list of 78 a priori predicted miR-193a-5p targets onto these data. The resulting differentially expressed genes for each comparison are detailed in Fig. 3. Nine predicted gene targets of miR-193a-5p were significantly differentially expressed in the liver of patients with greater steatohepatitic activity by NAS (COL1A1, CRYBG3, GPX8, OLA1, RAP2A, SLC7A1, XK, ANKS1A, and IGF2) and 3 by SAF activity (COL1A1, GPX8, and IFFO2), and 11 gene targets were differentially expressed in the liver of patients with significant fibrosis (COL1A1, GPX8, IFFO2, NETO2, PCDHA12, SLC7A1, ZNF827, GOL-GA6A, ITSN1, KCNH1, and KMT2A).
Quantification of miR-193a-5p expression in a hepatoma cell line We utilised data from the GTEx Portal to visualise the tissuespecific expression of miR-193a-5p, confirming thatin addition to the renal medulla, omentum and subcutaneous adipose tissue, mammary tissue, and cultured fibroblasts -miR-193a-5p was expressed in the liver (Fig. S5 ). Considering this evidence, we characterised the expression of miR-193a-5p in vitro in a model system of human hepatocytes in the context of lipid accumulation as seen in NAFLD (Fig. 4 ). Hep G2 cells were loaded with fatty acids, and miR-193a-5p expression was quantified by qPCR. After a 24-h treatment with lipotoxic palmitic acid, miR-193a-5p expression was decreased significantly relative to the control condition (p = 0. 009). A less lipotoxic but more steatogenic lipid, oleic acid,  32  had a negligible effect relative to the control condition, and, also in line with a previous report,  32  there was no effect when the 2 lipids were combined.