dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This study was permitted by the financial support of the  BNP Paribas Foundation  through its  Climate & Biodiversity Initiative for the Tree bodyguards citizen science project. EM was supported by the  Grant Agency of the Czech Republic  ( 19-28126X ). KS was supported by  ERC Starting  Grant  BABE 805189. The authors warmly thank the scientists, teachers and students who took part in this survey.
, 2015). This study set out with the aim of understanding how K. marxianus manages to produce each Bat1 proteoform depending on the metabolic needs. Initially, the structure of the BAT1 locus was examined in the GWIPS-Viz genome browser  (Michel et al. , 2017)  to identify features such as transcriptional start sites (TSS) (TSS), mRNA translation and ORF architecture to examine potential alternative translation start sites. Next, using protein fusions, we demonstrated the presence of an MTS at the N-terminus of Bat1. Expression analysis confirmed that BAT1 uses alternative TSS to produce the two differentially localised Bat1 proteoforms, with the choice of TSS determined by the nitrogen status of the cells. To investigate this further, we individually inactivated eight transcript factors (TFs) and found that several of those involved in regulation of BAT1 and BAT2 in S.
The real-time qPCRs were carried out in a 7300 Fast Real Time PCR System (Applied Biosystems). Primer specificity was confirmed by analysing dissociation curves of the PCR amplification products. The primers used for the amplification of BAT1-Long, BAT1-Total, and ACT1 mRNA are listed in supplementary table 1. Standard curves were done to calculate the primer efficiency of each primer set using genomic DNA of K. marxianus NBRC1777 extracted with (LiOAc)-SDS method adapted from  (Lõoke et al. , 2011). The data was normalized against ACT1 used as control. The relative gene expression was calculated with Pfaffl mathematical model that determines the relative expression ratio (R) of the target genes based on the primer efficiency (E) and the Ct deviation of the genes of interest versus the ACT1 control  (Pfaffl, 2001).
Bioinformatic and statistical analysis Expression of genes at specific genetic loci (BAT1, ALT1, BAP3) was analysed in the K. marxianus GWIPS-viz database  (Fenton et al. , 2022a). The prediction of the presence of a mitochondrial targeting signal (MTS) in Bat1p and the corresponding cleavage site (CS), was done with TargetP 2. 0 software  (Armenteros et al. , 2019). The analysis of the growth kinetics data was performed using R Studio software, version 1. 3. 1093 (RStudio Team. , 2020) with GrowthCurver package  (Sprouffske and Wagner, 2016). T graphs and statistical tests were performed using GraphPad Prism version 8. 0. 2 for Windows (GraphPad Software, San Diego, California, USA). The K. marxianus BAT1 promoter analyses was performed using the web based software YetFasco (De Boer and Hughes, 2012) and the Yeastract database  (Monteiro et al.
Data Availability Strains and plasmids are available on request. All databases uses in this study are freely accessible: GWIPS-viz genome browser (https://riboseq. org/); Yeastract (http://www. yeastract. com/); YetFasco (http://yetfasco. ccbr. utoronto. ca/). All software listed in the relevant sections is freely available and described in the cited publications. No new code was written for this study. The authors affirm that all data necessary for confirming the conclusions of the article are present within the article, figures, and tables, including the supplemental material provided.
K. marxianus BAT1 has two transcription start sites (TSS) To investigate the mechanism by which mitochondrial and cytosolic isoforms of Bat1 could arise from a single gene in K. marxianus, we analysed the BAT1 locus in the recently published K. marxianus GWIPS-viz database  (Fenton et al. , 2022a). It was possible to identify transcription start sites (TSS-seq), the transcribed region (RNA-Seq) and the translated region (Ribo-Seq) (Figure  1 ). The two tall histogram peaks in the TSS-seq panel indicate discrete transcription start sites and the RNA-Seq reads show transcription across all this region. There are two possible in-frame translation initiation codons and, from the ribosome profiling data, it is clear that translation can initiate from the first AUG codon. Since the second identified transcription start is downstream of the first AUG codon, a transcript arising from use of this TSS could only initiate translation from the second putative AUG initiation codon.
Taking these data together, we hypothesised that two BAT1 mRNA isoforms are produced in K. marxianus through the use of alternative TSS (aTSS), each of which may be translated from a different AUG start codon. This would give rise to two different proteoforms that we refer to as Bat1-L (long) and Bat1-S (short). To test if the two versions of Bat1p are predicted to be targeted to different organelles, the subcellular localisation of the two isoforms was analysed using the in silico TargetP 2. 0 tool. The program predicted the presence of a MTS in the long isoform of BAT1, while no signal peptide was detected in the short isoform (Figure  2 ). This is in agreement with the prediction of an MTS in this protein by  Fenton et al. , (2022b). Notwithstanding the presence of two transcriptional starts, an alternative explanation for two isoforms would be leaky scanning, which could lead to bypass of the first AUG.
First, looking at growth with NH4 as the nitrogen source it is seen that BAT1-GFP localises to both the cytosol and the mitochondria. This result was unexpected since it had been predicted that under these biosynthetic conditions, only mitochondrial localisation would be seen. In contrast, and in line with predictions when growing on BCAA, localisation was exclusively to the cytosol. A similar result was seen when cells were growing in the rich medium, YPD, with no mitochondrial localisation. Correlating these data to the potential use of aTSS, it suggests that when amino acids are available as the nitrogen source (BCAA/YPD), only the shorter form of BAT1 is made (using the second TSS, whereas when there is a requirement to synthesise amino acids (NH4), both TSS are used and the cell makes the long (mitochondrial) and short (cytosolic) forms of Bat1.
For this analysis, the BAT1 mRNA levels in each mutant was compared to that of the wild-type growing under the same conditions and there were clear indications of the involvement of multiple TFs. Looking first at ammonium, it is seen that deletion of GCN4 had the greatest effect. The total amount of BAT1 mRNA was reduced by about 50%, with essentially none of the longer form made (BAT1-L). Given the earlier data that there is approximately equal transcription from both the first and the second TSS under these conditions (Figure  5 ), this result indicates that Gcn4 is required for transcription from the first TSS but not the second. Deletion of LEU3 led to a 50% reduction in BAT-L, showing that Leu3 activates expression from the first TSS but it is not absolutely required. On BCAA, there were visible increases in the total amount of BAT1 in some mutants, though this did not pass the threshold for statistical significance.
Looking specifically at use of the first TSS however, a substantial increase in the amount of the long isoform was seen in three mutants: put3, nrg1 and hap2. Although fold-increases were high, they did not dramatically change the total amount of BAT1 mRNA because the level of BAT1-L was coming from a very low base in the wild-type (Figure  5 ). Nevertheless, the conclusion is that all three TFs impair expression from the first TSS on BCAA. Since most BAT1 expression on BCAA is of the short isoform, these data suggest that none of these TFs are required for expression from the second TSS on BCAA.
Function of Bat1p in K. marxianus depending on the presence of the NTE The data are consistent with the hypothesis that when growing on ammonium (biosynthetic conditions), both proteoforms are made, with the longer form localising to the mitochondrion to enable synthesis of BCAA. On VIL as a nitrogen source (catabolic conditions), the mitochondrial proteoform would not be required and cytosolic Bat1 is involved in degradation of the BCAA to provide nitrogen for the cell. To explore this further, we constructed two mutants of BAT1, one completely inactivating the gene (Δbat1) and one precisely deleting the MTS so that only the cytosolic proteoform is made (ΔNTE-bat1). These mutants were then grown on different sole nitrogen sources and the maximum specific growth rates (μmax) were calculated (Figure  7 ).
DISCUSSION The increased availability of omics datasets and on-line databases for non-traditional or nonconventional yeasts are valuable assets for the investigation of the evolution of gene regulatory mechanism. We exploited these resources to perform an in silico analysis of the gene encoding branched chain aminotransferases (BCAT) in K. marxianus, a species that diverged from the Saccharomyces lineage prior to the whole genome duplication (WGD). Previous studies in the related species K. lactis and L. kluyveri had established that BAT1 is bifunctional, encoding both the mitochondrial and cytosolic BCAT, required for synthesis or degradation of branched chain amino acids (BCAA), respectively. It was proposed that synthesis of the required proteoform was dependent on the nitrogen source in the growth medium, but the underlying mechanism was not determined.
This also mirrors S. cerevisiae where Leu3 activates expression of BAT1, though in this case, Leu3 is considered the major activator. The candidate Leu3 binding sites in the K. marxianus BAT1 promoter deviate by a single nucleotide from the consensus, and are quite distal from the transcription start site. One or both of these factors may explain the apparent difference in importance of Leu3 in the two yeasts. Looking at catabolic conditions (BCAA), we found roles for three TFs. Interestingly, none of these was a positive effect since the data indicate that Put3, Nrg1 and Hap2 inhibit transcription from the first TSS on VIL. Nrg1 works by recruiting the Ssn6-Cyc2 co-repression complex and it is possible that this TF represses transcription by recruiting this complex to the BAT1 promoter  (Murad et al.
In forward prediction, previous years are used to predict the following years progeny. This method represents a common breeding situation where previous data are available, but no data is available for phenotypes or environments in the future year/s. Predicting performance of lines for future years is a signi cant missing data problem and is challenging  (Hoffstetter et al. 2016; Jarquín et al. 2017; Juliana et al. 2018 ). Here we explored the potential of genomic selection to predict future performance using data from 2012-2019 for end-product quality traits in ve-fold cross-validation and forward prediction. The main objectives of this study were to 1) estimate the genetic correlations of end-product quality and NIR derived phenotypes, 2) evaluate the in uence of adding NIR derived phenotypes in improving prediction accuracies of end-product quality traits using ve-fold cross-validation, and 3) investigate forward prediction of line performance across years in a multi-environment context.
A plot of rst two components was visualised using the ggplot2 package  (Wickham 2016 ) in R. Narrow-sense heritability (h 2 ) was calculated as the ratio of the additive variance to the total phenotypic variance using the GREML model. Single trait genomic prediction accuracy was evaluated with 5-fold random cross validation. In each cross validation run, one-fold was used as validation set with their phenotype data masked in the analysis (Fig. 1 ). The accuracy of genomic prediction was calculated as the Pearson correlation coe cient between corrected phenotypic values and GEBVs in the validation subset. This process was repeated 10 times and average prediction accuracy across all folds (50) was reported.
Multi-trait genomic prediction A basic multi-trait mixed model was used as follows: Wheat end-product quality traits are costly to assay directly and therefore cannot be rapidly determined for large number of lines in breeding programs. NIR data can be generated easily across many lines and used to predict end-use traits, making them an interesting test case for examining bivariate models. The bene t of using NIR to predict end-product quality traits was investigated using three different crossvalidation scenarios. y EP y NIR = 1 EP 0 0 1 NIR μ EP μ NIR + Z EP 0 0 Z NIR u EP u NIR + e EP e NIR 1. Cross validation scenario S1: End-product quality phenotypes of validation lines were masked and predicted with a reference population with equal numbers of end-product and NIR phenotypes. This aimed to assess the effect of multi-trait analysis in improvement of prediction accuracy of endproduct quality traits where both traits are available on the same lines (i.
e. , their assessment is costly and time consuming). 2. Cross validation scenario S2: Validation lines were masked from both end-product and NIR traits, however, the reference set included all additional lines with only NIR. This aimed to study the effect of extra NIR data on improving prediction accuracy. 3. Cross validation scenario S3: Assessment of adding NIR on validation lines to increase prediction accuracy, assuming that NIR are easy and cost effective to generate on all lines. In this scenario, endproduct phenotypes were masked only in the validation set, while their corresponding NIR measurements were included in the reference set.
Forward genomic prediction In breeding programs, the aim is to predict future years/environments using previous years' data. These independent predictions were performed by training the model on previous years data and predicting future environments using For use in genomic prediction genetic correlations between end-product quality and NIR predicted traits were estimated using a multi-trait model (Table  3 ). All end-product traits were strongly correlated with NIR traits, with the exception of FSV, which had a low correlation (0. 19), and Protein, with a moderate correlation. Phenotypic correlations for the trait sets were high for most traits, except for FlrYld and FSV (Table  3 ).
Population structure A principal component analysis was used to investigate population structure. The two rst components jointly explained 56. 2% of the variation (Fig. 2 ). The plot shows the relationship between groups of individuals used in the analysis. There is slightly denser concentration of lines in the lower right corner of the chart for three groups (i. e. , end-product only, NIR only, both phenotypes). However, there is a complete overlap of lines in all groups. In other words, lines form diverse backgrounds have been recorded for both NIR and end-product performance. This close genetic relationship between lines with end-product assay data and those with NIR data are expected to improve prediction accuracy and result in more accurate genomic breeding values.
Multi-trait prediction We assessed three scenarios that included NIR phenotypes to predict end-product quality traits in a different manner. In the rst scenario (S1), NIR data were limited to the lines which had been assessed for end-product quality traits, while in the second scenario (S2) a large number of additional lines with NIR predicted records were added to the training population. In both S1 and S2, end-product measured phenotypes of the validation set and their corresponding NIR data were removed from training, however in the third scenario (S3), NIR predicted data of lines in the validation set were included. The aim of this approach was to investigate whether early NIR phenotyping of candidate lines improves prediction accuracy of end-product traits. Five-fold cross validation within population was used to estimate prediction accuracy from GBLUP.
In S1, adding NIR data increased the accuracy of predictions in all endproduct quality traits compared to single trait prediction, except for Protein, in which there was a reduction in prediction accuracy and b*, with no signi cant change (Fig. 3 ). In this scenario, PSI showed the highest improvement followed by Wab. To identify whether the inclusion of additional NIR phenotypes in the model could further improve the predictive performance of end-product quality traits, S2 and S3 scenarios were studied. Adding more information through inclusion of NIR data improved mean prediction accuracy of all traits (Fig. 3 ), although traits responded differently depending on the scenario. For most multi-trait scenarios with inclusion of NIR data prediction accuracy increased. Protein content was an exception with multi-trait prediction inferior to the single trait model in all scenarios.
Wab and PSI showed the highest accuracy increase in S2 and S3 when compared to single trait scenarios, with an average accuracy of 0. 40 and 0. 50 for Wab and PSI, while their corresponding single trait accuracy was 0. 26 and 0. 27, respectively. FlrYld and b* also positively responded to including more NIR data, where in S3 an accuracy of 0. 41 (Flour Yield) and 0. 64 (b*) were observed compared to the single trait model (0. 35, 0. 56, respectively). The scale of the genetic correlation between end-product and NIR predicted traits was re ected in the level of genomic prediction accuracy. For example, genetic correlations and prediction accuracies were high for Wab and PSI. There was a high correlation between the magnitude of prediction accuracy in multi-trait scheme and genetic correlation of end-product and NIR derived data (0.
2014; Rutkoski et al. 2016 ) and this increases the potential uses of NIR derived data to predict wheat end-product quality traits  (Dowell et al. 2006; Hayes et al. 2017a ). Increasing the size of the reference set through the addition of both end-product quality and NIR phenotypes is recommended to improve end-product predictions, especially for traits with moderate and low correlations (e. g. , FlrYld, Protein, and FSV).
Multi-trait prediction Multi-trait models combine information of correlated traits to deliver higher prediction accuracy thus are useful to enhance prediction accuracy of favourable traits which are di cult to be assessed in early cycles of the programme. We showed higher prediction accuracy across quality traits in multi-trait models (S1, S2 and S3), which is consistent with Lado et al. (  2018 ) who reported increased prediction ability of quality traits using information of correlated attributes compared to single trait prediction model. Using NIR predicted data in multi-trait scheme, prediction accuracies showed on average a 0. 05 to 0. 2 (8-73%) rise across all scenarios and traits (excluding Protein). This is comparable to Lado et al. (2018) who reported a 60-100% increase in prediction accuracy of target quality traits using correlated traits in different multi-trait models.
Hayes et al. (2017a) also reported an increase of 0. 03 to 0. 45 (6 to 300%) in soft and hard wheat for grain hardness, grain protein, b* and water absorption in different multi-trait scenarios, although the strategy used to predict performance was different. Overall, the addition of NIR predicted data substantially improved trait prediction accuracy and it is worthwhile perusing as strategy in breeding programmes. Higher accuracies of different multi-trait scenarios also suggested that an expanded reference set of predictors would improve prediction accuracy of correlated traits (S2 and S3). While using NIR data of the reference population (S1) showed an 11% increase in prediction accuracy over the single trait model, increasing NIR measures as a correlated trait in the second scenario (S2) had a 33% increase over the single model.
Conclusion We investigated the potential bene t of including NIR predicted data into multi-trait models for predicting six end-product quality traits in a wheat breeding program. In general, the multi-trait models combining direct measures of end-product quality traits with their NIR predictions had higher predictive accuracy than their respective single-trait models. The increased prediction accuracy was likely driven by the increased reference set size that arose from the addition of NIR predictions. The prediction accuracies we achieved using the combined data were at a level that breeding selections could be con dently applied earlier in the breeding process to increase breeding e ciency and genetic gain. Declarations Figures.
Prediction of peptide and protein isoelectric points The pI values of amino acid sequences were estimated (Supplementary Fig. S1 , Table  1 ) by online services. Unless otherwise stated, we used IPC 2. 0 (www. ipc2-isoelectric-point. org), which makes use of a combination of deep learning and support vector regression models  [47]   As an example, this diagram shows part of Fig. 7a , indicating how the bands in the 50 µg/ml AGP19p1 lane were semi-quantified. In Adobe Photoshop 5. 5, the scan was converted to greyscale, then a standard rectangle was placed on each RG-II band, and the histogram facility was applied, giving a mean luminosity (which is expressed by the software per unit area; in this case, 217. 41 and 158. 02 for the monomer and dimer, respectively). The rectangle always ha d the same area for every RG-II band on a given gel, and was always large enough to accommodate the whole band.
Acknowledgements We thank the numerous botanical colleagues in New Caledonia and elsewhere for their frequent assistance in our ongoing studies of Myrtaceae. We thank  Rémy Amice ,  Irène  and  Daniel Létocart ,  Benoît Henry , and  Hervé Vandrot  for allowing use of their digital images of living material. Thanks to  Gordon McPherson  ( MO ) for checking for type material and  Peter Wilson  ( NSW  ) for assisting with nomenclatural questions. NS thanks the  Franklinia Foundation  for its support of research through the  Muséum national d'Histoire naturelle and Odile Poncy (P) ,  Pete Lowry (MO) , and  Mathieu Donnat  ( NOU ) for their assistance. Finally, the authors thank  Grégoire Flament  (P) for his assistance in P and we gratefully acknowledge the careful reviews of  Gildas Gâteblé ,  Guillaume Lannuzel , and  Peter Wilson , which substantially improved the manuscript.
However, a recent study presented an alternative polygeneration system for bioenergy and biohydrogen production; no external energy is required with their cogeneration unit design  [18]. Seaweeds used in terms of the production of bioenergy represent an idea that has received increasing attention in recent years. An analysis of published research results was conducted regarding selected keywords in these research areas until August 2022 based on Web of Science databases. The advanced research was used to cluster documents including the topic ("macroalgae" or "seaweeds") and ("biofuel" or "biogas" or "anaerobic digestion" or "biohydrogen"). A total of 1686 scientific records could be found since 1980. 97% (1639) of them have been published since 2010. The research field is mainly "energy fuels", "biotechnology applied microbiology", "environmental sciences ecology", "engineering" and "agriculture".
For wet and residual biomass processing, drying is typically required to obtain a desire range of moisture content appropriate for the process or to stabilize this biomass before its valorization, which could be energy-intensive. However, a recent study presented an alternative polygeneration system for bioenergy and biohydrogen production; no external energy is required with their cogeneration unit design  [18]. Seaweeds used in terms of the production of bioenergy represent an idea that has received increasing attention in recent years. An analysis of published research results was conducted regarding selected keywords in these research areas until August 2022 based on Web of Science databases. The advanced research was used to cluster documents including the topic ("macroalgae" or "seaweeds") and ("biofuel" or "biogas" or "anaerobic digestion" or "biohydrogen").