dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses Phenotypic data analysis. Qualitative data such as fruit shape classes were first converted to binary form. The quantitative traits that were found to deviate from normality were logtransformed. Tests of normality, log transformation of data that were not normally distributed, derivation of descriptive statistics, ANOVA (General Linear Model for unbalanced data) of log-transformed data, and correlation analysis of the collated quantitative phenotypic data were performed using Minitab Version 18. The correlation matrices for the quantitative yieldrelated and anthocyanin pigmentation traits studied were calculated using command cor() and visualised with the 'corrplot' package in R  [47]. Pearson and Spearman correlation coefficients were derived for the continuous and interval data, respectively.
The proportion of phenotypic variance explained by a marker was determined by the square of the partial correlation coefficient (R 2 ). Genomic prediction. The predictive breeding value (GEBV) (along with the predictive error variance (PEV)) for each phenotypic trait was derived using ridge regression in TASSEL 5. 2. 50 software, which performed multiple correlation tests to compute correspondence between genotype and phenotype and accounted for collinearity to avoid bias. The genotypic data (737 SNPs in alpha format) were loaded, and kinship analysis was performed to generate an Identity by Descent (IBD) file. The phenotypes and K matrix were selected, and the genomic selection routine was run with 5-fold cross validation with 100 iterations. Detecting candidate genes located within marker-trait association zones.
It was over a distance of 5 Mb for chromosome 1, 1. 6 Mb for chromosome 4, 0. 87 Mb for chromosome 7 and 0. 86 Mb for chromosome 9. A physical map, showing the position of annotated putative candidate genes that co-localised with SNP markers associated with traits of interest (MTAs), was then constructed using SpiderMap (Rami, 2007 unpublished, Spidermap v1. 7. 1, free software, CIRAD).
They encode traits associated with seed development and lipid accumulation (Table  4 ). Associations based on TASSEL MLM between log seed length and TcSNP 953 (P � 9. 61 × 10 -04 ) as well as with log seed length and TcSNP344 (P � 2. 88 × 10 -05 ), pod index and TcSNP 555 (P � 8. 24 × 10 -04 ), and seed number and TcSNP 1160 (P � 1. 35 × 10 -04 ) as well as those confirmed with FarmCPU (Table  3 ), suggest that chromosome 4 may contain a cluster or 'hotspot' of QTLs for yield-related traits. Several MTAs involving TcSNPs 344 and 555 on chromosome 4, TcSNP 733 on chromosome 5 and TcSNP 642 on chromosome 8 were also confirmed with FarmCPU. There were two putative candidate genes involved with seed development co-localised with TcSNP 344 on chromosome 4, six that encode for seed development that were co-localised with TcSNP 667 (TASSEL MLM), eight responsible for seed protein and development and sugar transport that were linked to TcSNP 555, including Sugar transporter ERD6-like 16, and five with seed development functions co-localised with TcSNP 1160 (TASSEL MLM) (Table  4 ).
Anthocyanin pigmentation relationships In this study, fruit ridge anthocyanin concentration (fruit colour) was significantly associated with TcSNP 401 on chromosome 4, located at 20,485,872, about 199 Kb upstream of the gene Tc00_t058610 (mRNA). The latter encodes a Putative MYB-related protein 308. TcSNP 401 was also 198 Kb upstream of the Transcription repressor MYB 6 (Table  4 ) (https://cocoagenome-hub. southgreen. fr/). This SNP accounted for 7. 1% of the phenotypic variation in fruit colour observed (P � 4. 78 × 10 -06 ) with TASSEL MLM (Table  3 ). The MTA was corroborated with FarmCPU. Four SNP variants on chromosome 4, co-localised with a MYB transcription factor gene (TcMYB113), were inferred to encode 'fruit colour differences' between cacao varieties by Motamayor et al. [70]. Another significant association (P � 6.
Conclusion In this study, carefully collated phenotyping data on traits of economic interest in cacao, such as yield potential (S2 ), generated via transcriptome sequencing, were subjected to GWAS. Twenty-nine cacao accessions represent promising material for breeding in terms of yield potential. The results presented herein indicated oligogenic and polygenic control of yield-related traits in cacao. The stringently significant marker-trait associations related to yield, found in this study, were indications of the presence of QTL on chromosomes such as 3, 4, 5, 7, 8 and 9. Chromosome 4 putatively contains a QTL cluster associated with yield-related traits. Some annotated candidate genes associated with seed development, seed lipid accumulation, metabolism and development and plant stress responses (including to drought and to cadmium) have been identified during this research.
Acknowledgments The Director of  CRC ,  Prof. Pathmanathan Umaharan , is gratefully acknowledged for endorsing this collaborative research. Useful discussions with  Drs. Didier Cle  ´ment,  Christian Cilas  and  Martijn Ten Hoppen ,  CIRAD, France ,  Dr. Michelle End  and  Mr. R. A. (Tony) Lass, UK  and  Tricianna Maharaj , Trinidad, are deeply appreciated. J. Bhola ,  Dr. W. Mollineau ,  V. Badall ,  A. Richardson-Drakes ,  N. Persad ,  S. Samnarine ,  C. Jagroop ,  T. Jugmohan ,  E. Solozano  and other individuals are gratefully recognized for technical assistance in phenotyping at various times during the period of study. The comments of anonymous reviewers are also acknowledged.
1 ). In contrast, Fe had no effect and Zn moderately affected bfrG mRNA levels, suggesting limited cross-regulation. We performed qRT-PCR experiments on each of the three genes of the operon and normalized the results against a housekeeping gene. We also generated chromosomal reporter fusions by inserting lacZ in frame with the first codons of each gene to assess the effect of copper on their expression. bfrG and bp2921 were expressed at moderate levels in copperrestricted medium (Fig. 1c, d ). Addition of copper to the medium abolished their transcription and translation, consistent with RNAseq data  (Rivera-Millot et al. , 2021). Interestingly, bp2923 was hardly translated despite being the first gene of the operon, and it did not appear to be regulated by copper (Fig. 1c, d ). qRT-PCR analyses on bfrG at various times after Cu addition showed a fast decrease in transcript abundance (Supplementary Fig.
We replaced the two Cys residues with Ser and determined the effect of the SXXS sequence on regulation of bfrG-lacZ by copper (mutant C90S+C93S; Fig. 4a ). These modifications did not affect the expression level of bfrG but abolished its control by copper, demonstrating the involvement of the CXXC motif in regulation (Fig. 4b ). In contrast, replacement of a semi-conserved Cys in the DUF2946 family preserved bfrG regulation (mutant C51S; Fig. 4b ). The CXXC and the RAPP motifs are separated from each other by 45 intervening residues. Genome mining identified more than 2000 DUF2946 protein sequences in databases, mostly in Proteobacteria (Suppl. Table  S1 ), and their analysis showed that the spacing between the two motifs is conserved to within one or two residues in the family (Fig. 4c ).
RNA techniques 8 mL of liquid B. pertussis cultures grown in Cu-restricted or Cu-supplemented media to an OD600 of 1-1. 5 were centrifuged at 4000 rpm at 4°C for 10 minutes after adding 2 mL of a 95/5 ethanol/phenol mix. Pellets were flash-frozen in liquid nitrogen and kept at -80°C. For qRT-PCR, RNA extraction was performed by using Tri-Reagent (InVitrogen), followed by a DNAse I treatment (Sigma Aldrich) to remove remaining genomic DNA. Retro-transcription was performed with the Verso cDNA synthesis kit (ThermoFisher). qRT-PCR was performed in a Roche LightCycler 480 Instrument II using the Takyon LowROX SYBR kit (Eurogentec). All qRT-PCR experiments were conducted with 3 biological replicates and 3 technical replicates, and data were normalized using the housekeeping gene bp3416. For the detection of transcripts in the IGR, a PCR was performed on 75 ng of total cDNA, and 75 ng of DNAse-treated RNA or purified genomic DNA as controls.
5'RACE experiments were conducted on total RNA extracted from cultures of BP2923-OCU supplemented with BCS, using the Generacer kit (Invitrogen) and specific RACE primers for bfrG and cruR according to the manufacturer's instructions. For cruR, after PCR amplification of the cDNA with a first primer annealing within the gene, a nested PCR was performed using a second primer annealing immediately before the cruR start codon to enhance specificity. The cDNA isolated in the RACE experiment was used to build a library using the Illumina TruSeq Stranded RNA LT library preparation kit, followed by sequencing on an Illumina NextSeq 500 benchtop sequencer. The GeneRacer adapter sequence was removed from the reads using Cutadapt (https://github. com/marcelm/cutadapt) and the reads were mapped using the CLC Genomics software (Qiagen).
Reaction pellets were resuspended in loading buffer (95% formamide; 5 mM EDTA) and analysed by denaturing 7% polyacrylamide gel electrophoresis and by phosphorimaging with a Typhoon FLA-9500 instrument and ImageQuant TL software (GE Healthcare). Potential Cu 2+ scavenging-by-buffer effects  (Mash et al. , 2003)  were ruled out in control transcription termination experiments where Tris-HCl was replaced by MOPS, pH 7. 9 (not shown).
In silico analyses The non-redundant NCBI database (release of December 2020) was searched for the occurrence of DUF2946 using its PFAM hmm profile (PF11162) and the hmmsearch program (http://hmmer. org/). The results were curated to retain proteins less than 80% identical in sequence using cd-hit (http://cd-hit. org). Using a locally built database containing all the bacterial GenBank files of NCBI, the 5 genes flanking the DUF2946 genes on either side were retrieved irrespective of their distance and translated, and the corresponding protein identification numbers were attached. The sense of transcription of the neighbouring genes and their intergenic distances were determined. Putative operons harbouring genes transcribed in the same direction as the DUF2946-coding gene were retained. The numbers of occurrences of given Pfam domain-coding genes at each position relative to the gene of interest, itself found at position '0' of each locus, were then computed.
Acknowledgements. We thank  Dr Qing Chen  ( FDA, USA ) for the kind gift of the pQC2123 plasmid, and  Dr Axel Innis  ( IECB Bordeaux ) for discussions. This work was funded by the  Institut National de la Santé et de la Recherche Médicale (INSERM)  and the  University of Lille. G. Roy  acknowledges the support of a  doctoral fellowship  from the  University of Lille.
We used bootstrapped selection with 200 repetitions to internally validate the PREPARE risk assessment tool. Results A total of 27 388 children were included in the analysis (mean age 14. 0 months, pneumonia-related case fatality ratio 3. 1%). The PREPARE risk assessment tool included patient age, sex, weight-for-age z-score, body temperature, respiratory rate, unconsciousness or decreased level of consciousness, convulsions, cyanosis and hypoxaemia at baseline. The PREPARE risk assessment tool had good discriminatory value when internally validated (area under the curve 0. 83, 95% CI 0. 81 to 0. 84). Conclusions The PREPARE risk assessment tool had good discriminatory ability for identifying children at risk of hospitalised pneumonia-related mortality in a large, geographically diverse dataset.
INTRODUCTION 5] [6] [7]  These risk assessment BMJ Global Health tools have important limitations, including the use of variables that are not routinely collected in clinical practice,  5  being limited to single sites  [4] [5] [6]  and the reliance on auscultatory findings with variable inter-rater reliability. 4 6 Additionally, these risk assessment tools have not been widely implemented; thus, their potential to reduce hospitalised pneumonia-related mortality among children is unclear. 8 e recently validated three of these risk assessment tools in a large, globally representative dataset using the demographic and clinical features of children at the time of admission. 9 In that external validation, only the Respiratory Index of Severity in Children-Malawi (RISC-Malawi) score demonstrated fair discriminatory value (area under the curve (AUC) 0.
Study design We used the World Health Organization's (WHO) Pneumonia Research Partnership to Assess WHO Recommendations (PREPARE) dataset to derive and validate a novel risk assessment tool for hospitalised pneumonia-related mortality among children 2-59 months of age across many global settings. We have described the details of the construction of the PREPARE dataset previously. 10 Briefly, the PREPARE dataset includes primary data of individual patients from 30 study groups comprising 41 datasets of children evaluated for pneumonia in studies conducted from 1994 to 2014 in over 20 low-income and middleincome countries in Asia, Africa and Latin America, as well as the USA and Australia. We adhered to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines.
RESULTS Of 41 datasets with a total of 285 839 children in the PREPARE dataset, 11 studies with 27 388 children met our inclusion criteria (figure  1 ). Six of the included studies were randomised controlled trials; two were prospective cohort studies; two were retrospective cohort studies; and one was a prospective case series (table  1 ). The included studies were conducted in 20 different low-income or middle-income countries in Asia, Africa, Central and South America, the Caribbean and the Middle East. The mean age was 14. 0±12. 1 months and BMJ Global Health the case fatality ratio was 3. 1%. Children included in the derivation and validation of the PREPARE risk assessment tool were slightly younger than those who did not meet the inclusion criteria (14. 0±12. 1 months vs 17. 1±13. 5 months) but did not differ substantially by sex (male 15 862 (57.
Most variables included in the PREPARE risk assessment tool can be easily assessed by providers of many training levels in various settings. However, the assessment of SpO 2 requires a pulse oximeter with paediatric probes and additional training in the accurate use of pulse oximetry. Prior studies demonstrate that community level health workers and first-level health facility workers can be trained to accurately use pulse oximetry. 28 29 The PREPARE risk assessment tool also had good discriminatory value in our sensitivity analysis excluding pulse oximetry. Therefore, the PREPARE risk assessment tool may be useful in identifying children at risk of hospitalised pneumonia-related mortality in settings with limited access to pulse oximetry. Due to missing data in the included datasets, we were not able to assess if the presence of wheezing was potentially protective.
Limitations Our study is subject to several limitations. As many studies in the PREPARE dataset used tachypnoea as an entry point sign to diagnose pneumonia, tachypnoea as a predictor of mortality may have been overestimated. However, we assessed the contribution of varying severity of tachypnoea to mortality, which may reduce potential bias introduced by including tachypnoea as a predictor. Furthermore, the WHO manual on oxygen therapy for children recommends oxygen therapy for children with severe lower chest wall indrawing, respiratory rate of ≥70 breaths/min and head nodding in settings where pulse oximetry is not available. 20 As we conducted an analysis of previously collected data, not all candidate variables for the novel risk assessment tool were available in large numbers in the PREPARE dataset.
Additionally, six of the datasets included in our analysis came from randomised controlled trials, which tend to be highly selective of patients and may explain part of the lower case fatality rate in our derivation and validation populations compared with children who were excluded from our analysis. An additional limitation is the possibility of confounding by disease severity and management variation across regions. For example, some variables included in the PREPARE risk assessment tool, such as SpO 2 , may be modified through interventions (eg, the use of supplemental oxygen). Lastly, our analysis did not assess the role of the quality of care, supplemental oxygen availability, regional variations in clinical care or antibiotics administered.
Acknowledgements We thank the following Pneumonia REesearch Partnership to Assess WHO REecommendations dataset collaborators:  Angela Gentile ,  Bradford D Gessner ,  Tabish Hazir ,  Luis Marinez Arroyo ,  Rumina Libster ,  Kerry-Ann O'Grady ,  Raul O. Ruvinsky ,  Claudia Turner  and  Syed Mohammad Akram uz Zaman. We have provided detailed information on author contributions and positionality in our reflexivity statement in online supplemental table 5. Contributors SQ secured the funding. CAR, MIN, YBN and SQ conceptualised and designed the study, interpreted the data and wrote the first draft of the manuscript. CAR and YBN are responsible for the overall content and take full responsibility for the work, had access to the data, and made the decision to publish this article. YBN verified the underlying data and conducted the statistical analyses.
ACKNOWLEDGEMENTS We thank  Jean-Philippe Fougère  and  Eric Wattelet  for administrative assistance. We also thank  Sandra Dovero ,  Evelyne Doudnikoff  and  Matthieu Bastide  for technical advice and  Etienne Gontier  from the  Bordeaux Imaging Center (BIC)  for giving us access to his laboratory facility for brain perfusion. Finally, we thank  Dr Michel Engeln  for his helpful comments on a previous version of the manuscript.
Further prospective studies are needed on a larger scale in different populations to confirm if PCSK9 levels provide additional information in predicting CVD beyond the recognized CV factors. The current study has several limitations: first, the children's sampling was not performed in a fasting state. However, circulating PCSK9 seems to be poorly affected by a fasting state since in healthy adults, plasma PCSK9 concentrations vary minimally in response to a short-term high-fat diet  [42]. Second, estradiol was not measured in girls to examine the relationship between PCSK9 and estradiol. Third, no data were collected on the food intake of the recruited children since it has been demonstrated that PCSK9 can be affected by diet  [43]. Furthermore, the cross-sectional nature of our study precludes any causality link between PCSK9 and the other components of the lipid profile.
This work was also supported by the Institut National de la Santé et de la Recherche Médicale (INSERM); the national project CHOPIN (CHolesterol Personalized Innovation) granted by the Agence Nationale de la Recherche (ANR-16-RHUS-0007), and by Fondation-Leducq (FLQ # 13 CVD 03) through the Transatlantic Networks of Excellence in Cardiovascular Research program ("The function and regulation of PCSK9: a novel modulator of LDLR activity"). Yara Azar and Carine Ayoub are supported by grants from the Lebanese National Council for Scientific Research (CNRS-L) and the Conseil de la Recherche de l'Université Saint-Joseph of Beirut, Lebanon; Yara Azar is supported by a grant from an AMGEN funding. Institutional Review Board Statement: The study was conducted according to the guidelines of the declaration of Helsinki and approved by the Ethics Committee of Hôtel Dieu de France university hospital, Beirut, Lebanon (CEHDF 2013 reference 546, CEHDF 2018 reference 1174).
Experimental design The row was divided into 12 blocks and one vine per block was randomly selected. Nine vines were initially selected from these initial 12 vines (three vines were eliminated to growth issues) for this experiment, of which image data was successfully captured for eight vines in 2017/2018 resulting in 287 counts from 89 inflorescences. In 2018/2019 data was collected for three of the eight vines from the previous season (138 counts on 37 individual inflorescences). South-facing canes on the vine were chosen, as it is assumed that these can also represent the north-facing half of the vine (assuming equivalent responses for both directions of the vine and therefore between the south and north-facing canes). Both the upper and lower south-facing canes were tagged and monitored for the presence of inflorescences, as the microclimate of these canes may differ (the lower cane shoots grow into the fruiting zone of the upper cane in the 4-cane VSP configuration).
While automated systems have been developed to count the total number of flowers on an inflorescence  (Diago et al. , 2014; Aquino et al. , 2015a; Aquino et al. , 2015b; Millan et al. , 2017; Liu et al. , 2018; Tello et al. , 2020) , flowers were manually labelled in our experiments using Krita 4. 2. 9 (Krita Foundation and KDE, Germany), to identify the subtle differences between flowers with the cap intact or detached; open flowers were labelled with red dots and closed flowers were labelled with blue dots (Figure  1B ). Labels were placed to avoid overlap so all dots could be accurately counted. These dots were added as an overlay to the original image (a completely separate image of exactly the same size as the original), so the overlay only contained dots (Figure  1B ). The "label connect components" function of the Scipy library (used in the Python programming language, USA) was used to extract each individual dot, whereby all dots were automatically classified from their colour channel alone and counted as open or closed.
WinBUGS software  (Lunn et al. , 2000)  was used for Bayesian estimation, and statistical software R (R Core Team, 2013), specifically the R2WinBUGS package  (Sturtz et al. , 2005)  was used for preand post-processing. For more details on the code please read the Supplementary material. A total of 50,000 iterations were run after the burn-in of 1000 with thinning after every five. The resulting posterior sample of 1000 was used for inference. Convergence was visually assessed.
, 2017)  or using cutting techniques to investigate the phenomenon of interest (e. g. ,  May and Cellier, 1973). While all represent valid methods, the measurements made here reflect the in-situ variability that exists within a vineyard. As a result, the representation of inflorescences at different vine structures was not equal across vines, shoots and inflorescence positions, leading to an unbalanced dataset. The monitoring of flowering repeated measures over time on the inflorescences of interest also represents a repeated measurement data collection. Consequently, the Bayesian approach employed here, worked well to deal with fitting a mixed effect model with repeated measures and hierarchy within the data and unbalanced design as a result of measuring specific within vine structures. Therefore, the approach used to analyse the variability in this study is another example of applying a Bayesian modelling framework to investigate variability in biology.
ACKNOWLEDGEMENTS Sam Whitley  and  Anne Eyharts  are recognised for their contributions to the manual counting of open and closed flowers on images used for this study. The authors thank the field staff from  Lincoln Agritech  Limited who assisted with image capture. The authors acknowledge the  Precision Grape Yield Analyser (GYA) science research programme led by Lincoln Agritech Limited  with project partners of  Lincoln University, University of Canterbury , Plant and Food Research and  CSIRO. The programme receives major funding from the  Ministry of Business, Innovation and Employment (MBIE)  through an  Endeavour programme  ( LVLX1601 ) The  GYA  project also acknowledges the financial support provided through the  Bragato Research Institute (BRI).