dataesr/scientific-paragraphs-categorization huggingface dataset:
Roots are then mounted lengthwise on microscope slides with polyvinyl alcohol (PVA). A cover slip is gently laid down on the root from one side to another to avoid air bubbles without pressing the root which would lead to deformations. The slides with the coverslips on top are left to rest overnight to solidify the mounting medium. c. Measurement procedure For the first method, see section XIII. 4. Root cortex thickness and density measurement procedure. For the second method, lateral views of the stained roots allow measuring the thickness of the stele and the cortex without having to incise the root. These measurements can be done with a stereomicroscope and micrometre, or from microscope images with image analysis software (e. g. IMAGEJ). Similar to root cortex thickness measurements, the cortex area is typically approximated as all ToS (sensu  Kong et al.
Forum Community resources New Phytologist than thinner roots with low tissue density (B. Liu et al. , 2016; Liese et al. , 2019). Outside of the plant, additional factors such as climate  (Eissenstat et al. , 2013) , and the local availability of soil resources  (Adams et al. , 2013)  are also likely to be important factors mediating fine-root lifespan. However, datasets reporting lifespan across species and environments and studies that include experimental manipulations to directly test environmental effects on fineroot lifespan are still relatively few in number which limits our ability to make meaningful inferences at broad scales  (McCormack et al. , 2013; McCormack & Guo, 2014). The importance of fineroot lifespan to plant resource allocation and acquisition coupled with our limited understanding of variation in lifespan among species and across environments make this a particularly important process to target for future study.
For gas-phase measurements, linear regressions often underestimate the slope; for aqueousphase O 2 measurements, a quasilinear area of the curve is frequently visually selected for slope calculations. (3) The absolute gas concentration (mol m -3 ) is calculated by using either the ideal gas law (gas-phase measurements; in retrospect) or Henry's law (for dissolved gas; often integrated in O 2 electrode software) together with information on chamber/solute volume (m 3 ), air pressure (Pa) and temperature (K). (4) Absolute flux rates (mol s -1 ) are divided by root dry mass (g), or other size parameters, to calculate RR S (e. g. mol g -1 s -1 ). See text for details. New Phytologist (2021) 232: 973-1122 www. newphytologist. com Ó 2021 The Authors New Phytologist Ó 2021 New Phytologist Foundation chamber settings are used: in an open system, respiration rates are determined by the difference in the amounts of CO 2 entering and leaving the gas-phase cuvette, while in a closed system δCO 2 is monitored over a certain time (e.
Using either the ideal gas law (gas phase) or Henry's law (dissolved gas in solutions), together with information on temperature (K) and air pressure (Pa), the chamber/solute volume (m 3 ) is key to calculate the amount of CO 2 or O 2 molecules (mol; Fig. 21 ). For gas-phase, closed/static chamber approaches, the choice of a linear or exponential regression function (Fig. 21 ) for calculating slopes of gas concentration curves can significantly influence the results, with underestimations by using linear regressions, as frequently shown for soil gas efflux measurements  (Kutzbach et al. , 2007; Kroon et al. , 2008; Forbrich et al. , 2010). For O 2 -depletion measurements, a quasilinear section of the curve is often visually selected for slope calculation (in the instruments' software). The methodology of (slope) calculation should be reported to increase the reproducibility of RR S measurements.
, 1998) , and between P uptake with root mass, root surface phosphatase activity and total root length  (Fujita et al. , 2010)  the generality of such trait co-variations between resource uptake rates and other traits is still unclear, because there are few reports in the literature. This might also be due to the fact that uptake rates are not so simple to measure (see below), and therefore are rather rare in comparison to other trait data: for example, in the global TRY database there are less than 300 observations of root N uptake, less than 80 for P uptake, and less than 50 for cation uptake  (Kattge et al. , 2011) , but more than 80 000 observations for leaf N, more than 56 500 for leaf P, and more than 11 500 for leaf K content per dry mass. a. Sampling, storage and measurement procedure The quantification of short-term net uptake rates of soil resources by root systems is based on the detection of tracers in plant biomass which had been added to the soil.
Forum Community resources New Phytologist form two or even more types of mycorrhiza  (Wang & Qiu, 2006; Akhmetzhanova et al. , 2012; Brundrett & Tedersoo, 2018). Currently, several large databases describe plant-species mycorrhizal association types  (Wang & Qiu, 2006; Akhmetzhanova et al. , 2012; Hempel et al. , 2013)  that can be consulted for the expected type of mycorrhizal association of a plant species. However, recent assessments have discovered some errors in these listings, mostly due to inclusion of older reports, based on (at that time) poorly developed techniques of mycorrhizal assessments  (Brundrett & Tedersoo, 2019). This is particularly true for plants for which double colonisation is expected, and for particular groups of Ericales  (Brundrett & Tedersoo, 2018 , 2019). The recent work of  Tedersoo and Brundrett (Tedersoo & Brundrett, 2017; Tedersoo, 2017a,b)  highlights plant genera for which it is especially useful to check the mycorrhizal type(s) directly on roots instead of relying on previous assessments.
Many of these potential errors are acknowledged and highlighted in the recently published new FungalRoot database  (Soudzilovskaia et 2020). However, formal inspection of a mycorrhizal type is generally recommended. Finally, it is important to consider that only c. 14 000 plant species have been properly examined for mycorrhizal types  (Brundrett, 2009; Soudzilovskaia et al. , 2020). a. Sampling recommendations See section VI. Experimentation and sampling in laboratory and field. b. Storage and processing To preserve root material for microscopic mycorrhizal identification, the roots should be dried completely (60°C for 48 h) or immersed into 60-95% (v/v) ethanol or methanol. In the latter case, the roots in ethanol/methanol should be stored at 4°C. Formalin-based preservations have been commonly used, but is not recommended because of its toxicity.
, 2019) , and ITS2, SSU4, SSU9 and LSU3 for AM  (Krüger et al. , 2009). Given the rapid development of this field, we advise the reader to consult the most up-to-date literature to select a suitable primer set. Sequences of mycorrhizal fungi vs those of other root-associated fungi should next be separated using reference databases. More details on the fungal community analysis by high-throughput sequencing and on the available platforms can be found in reviews by  Nilsson et al. (2019)  and  Castaño et al. (2020). d. Future research directions We still need more precise understanding of how the identity of mycorrhizal fungi in associations within plant roots link to the overall soil microbial composition, plant community composition and soil processes. Fast development of new molecular techniques allows more and more accurate disentangling of the microbial community composition and linking community structure to function, via profiling of microbial traits such as, for instance, enzymatic activities.
New Phytologist The traits that we propose can be used either directly to analyse variations among plant genotypes or environmental conditions and to establish relevant relationships with local soil conditions (e. g. Bécel et al. , 2012; Schmidt et al. , 2013)  or they can be used to estimate the values of parameters included in models dedicated to the simulation of the dynamics of the RSA  (Leitner et al. , 2010; Schnepf et al. , 2012; Pagès et al. , 2014; Postma et al. , 2017). The traits are conceived and measured at the individual root level. However, for various objectives, it is possible to aggregate these data at larger scales or to study the distributions of traits. Simulation models can be used to make such integration and upscaling of analytical data  (Pagès & Picon-Cochard, 2014).
Quantitative observations can be undertaken with various pieces of software. For example, IMAGEJ (http://rsbweb. nih. gov/ij/) is a free and open-source software that is very convenient to make a large panel of measurements (e. g. diameter, length, surface and colour). With ImageJ, the 'straight line' tool can be used to measure root tip diameter. Selecting the healthy root tips. It is easy to visually evaluate the quality of root tip images under a binocular microscope or on a computer monitor, and to evaluate the state of the sampled root tips, to select healthy root samples, that is those which were growing or just recently stopped. At this stage, it is easy to identify and discard those which are broken and those which are degraded by the extraction procedure or by the normal processes of ageing and necrosis into the soil.
This way, root hair growth and organelle dynamics can be quantified while preventing drifting of the cell along the z-axis  (Aufrecht et al. , 2017). b. Measurement procedure There are many types of root hair measurements that can be made on living plants. From the setup described above (Fig. 31 ), one can obtain data on root hair growth rates (μm min -1 ) or growth rate kinetics. To obtain detailed growth rate kinetics, imaging root hairs at ×100-200 with an inverted compound microscope works best (Fig. 31e ). Various systems that enable time-lapse acquisition of root hair images are available. Root hair growth rates can be obtained from time-lapse movies by measuring the displacement of the root hair tip with IMAGEJ at each time point. The interval selected depends on the biological question of interest.
Acknowledgements We thank the many anonymous reviewers on this manuscript, as well as  Philipp Grande ,  Richard van Logtestijn  and  Joana Bergmann  for commenting on specific aspects of this manuscript. This work was supported by the  New Phytologist Foundation  via financial support to the 25 th New Phytologist Workshop 'Root traits as predictors of plant and soil functions: Aggregating current New Phytologist (2021) 232:  973-1122  www. newphytologist. comÓ 2021 The Authors New Phytologist Ó 2021  New Phytologist Foundation.
Potential proteins from three novel food sources (Chlorella variabilis, Galdieria sulphuraria, and Fusarium strain flavolapis) were predicted from genomic sequences and were evaluated for potential risks of allergic crossreactivity by comparing the predicted amino acid sequences against the allergens in the www. AllergenOnline. org (AOL) database. The preliminary analysis used CODEX Alimentarius limits of >35% identity over 80 amino acids to evaluate the predicted proteins which include many evolutionarily conserved proteins. Regulators might expect clinical serum IgE tests based on identity matches above the criteria if the proteins were introduced in genetically engineered crops. Some regulators have the same expectations for proteins in novel foods. To address the inequality of extensively conserved sequences, we compared the predicted proteins from curated genomes of 23 highly diverse allergenic species from animals, plants and arthropods as well as humans to AOL sequences and compiled identities.
Identity matches greater than CODEX limits (>35% ID over 80 AA) are common for many proteins that are conserved through extensive evolution but are not predictive of published allergy risks based on observed taxonomic cross-reactivity. Therefore, we recommend changes in the allergen databases or methods of identifying matches for risk evaluation of new food sources. Our results provide critical data for redefining allergens in AOL or for providing guidance on more predictive sequence identity matches for risk assessment of possible risks of food allergy.
, 2014). Abbreviations: AA, amino acid; AOL, AllergenOnline; BLAST, FASTA sequence search tool; NCBI, National Center for Biotechnology Information. ☆ Grant support: This research was funded primarily by the Goodman laboratory at the Food Allergy Research and Resource Program (FARRP) at the University of The history of human exposure to the relevant food source and records related to the developed food (novel food source, or gene/protein donor) including the published history of allergy is important in judging potential hazards and risks. Understanding food allergy risks requires knowledge of the proteins in various foods that commonly or less-commonly cause food allergy as well as the mechanisms of the allergic response. For example, peanut (Arachis hypogaea) is a source of severe allergic reactions in many countries.
In 1996 only a few hundred allergenic protein amino acid (AA) sequences were known in publications. The AA sequences of the new protein in the GE crops were compared to allergens in private databases of the developer, or in the NCBI Protein non-redundant (nr) database using keyword search limits. Searches were accomplished by FASTA in small databases or BLASTP in NCBI  (Pearson, WR, 2000; Pearson WR, 2014). Alignments that might represent an allergen were searched for identity matches of eight contiguous amino acids to any segment of any allergen. If matched, serum IgE binding tests would be conducted focusing on those with allergies to the source of the new protein. However, in practical terms, developers often abandon those as potential products. Evaluation of the short segment amino acid comparisons (6-8 amino acid matches) were later shown to be non-predictive  (Hileman et al.
Other aspects of protein structure and IgE binding are important to consider cross-reactivity  (Aalberse et al. , 2001). The AllergenOnline. org (AOL) database at the Food Allergy Research and Resource Program (FARRP) at the University of Nebraska was started in 2004-2005. It is a public, peer-reviewed database of allergens based on protein AA sequences in the NCBI Protein database following evaluation of published evidence in peer-reviewed literature  (Goodman et al. , 2005 (Goodman et al. , , 2016)). The AOL database includes proteins from studies of airway, contact, food, venom and salivary allergen sources with IgE binding. When provided in publications, evidence of histamine release and clinical reactivity adds confidence to calling the proteins an allergen. The AllergenOnline. org database has been updated annually by adding newly published allergens every year from 2006 through 2020 by a review process with a panel of allergen experts that include researchers and clinicians  (Goodman et al.
Therefore, evaluation of potential risks of food allergy from an organism such as an alga, fungus or new plant that does not have a history of human consumption requires new evaluation steps. Some regulators and scientific advisors have recommended using predicted proteins from the whole organism's genome or transcriptome for comparison to allergen databases using the CODEX guidelines to predict risks of food allergy. Importantly, the CODEX guideline was not intended to evaluate the full-proteome or predicted protein dataset of a whole organism as the criteria of >35% identity over 80 has not been validated for whole proteome comparisons. The end-result of the bioinformatics comparison of proteins with allergens is a decision about the need for specific serum testing and if so, the specific allergic population that should be used to collect serum samples  (Goodman et al.
There are three objectives in this study First, to evaluate identities of all possible proteins from the genomes of three species intended for food use based on comparison of the predicted proteins against allergens in the AllergenOnline. org database using the CODEX guidelines of >35% identity over 80 AA. Second, to address the inequality of extensively conserved sequences, we compared the predicted proteins from the genomes of 23 highly diverse allergenic and non-allergenic species; including human, animals, plants and arthropods to all AOL sequences, compiled identities to understand how common high identity matches are, and evaluated patterns of identity across protein types. Third, to critically evaluate the limits of the CODEX guidelines when used as a whole genome analysis, using all types of proteins.
Preparation of protein sequences of the three targeted genomes The predicted proteins for the genome of Chlorella variabilis NC64A were downloaded from the NCBI genome library (https://www. ncbi. nlm. nih. gov/genome/?term=Chlorella+variabilis+ %5Borgn%5D). For Galdieria sulphuraria, the company Fermentalg provided the DNA sequences which were identified using Illumina sequencing (2x150 bp reads). The sequencing quality was checked using FastQC  (Andrews 2010)  and cleaned using PRINSEQ (prinseq. sourceforge. net) by trimming off low quality bases. Two assemblers were used, SPAdes with 21, 33, 55 and 77 k-mer values  (Bankevich et al. , 2012) , and Trinity using 25 k-mer (https://github. com/trinityrnaseq/trinityrnaseq/wiki). Post assembly polishing was performed using Pilon  (Walker et al.
, 2014). The quality of assembly was checked using Quast  (Gurevich et al. , 2013). The percentage of mapping was evaluated using BWA mapper  (Li and Durbin, 2009). Genes were predicted using the Galdieria model from AUGUSTUS  (Stanke and Morgenstern, 2005). Sequences to exclude included tRNA sequences which were predicted using tRNAscan-SE  (Lowe and Eddy, 1997)  and rRNA which were predicted using barrnap (https://github. com/tseemann/barrnap). Functional annotation was conducted by a combination of AUGUSTUS software and BLASTP comparison for the predicted proteins against the published Galdieria sulphuraria genome (https://www. ncbi. nlm. nih. -gov/genome/?ter m=Galdieria+sulphuraria) from the NCBI library. Sequences were compiled into FASTA format files for comparison to the AllergenOnline.
org database. The compiled sequences were also compared to the published Galdieria sulphuraria genomic sequences filed by Schonknecht et al. , as described in 2013 as ASM34128v1 using alignment tools in order to check for potential sources of inaccuracy. Nature's Fynd provided the genomic sequences for Fusarium strain flavolapis, which they are developing for use as a food ingredient. They performed genomic sequencing using Pacbio (for long-reads) and Illumina (2x250 bp reads) for short, high quality reads of this cultured species. These sequences were compiled and evaluated for accuracy and completeness using FASTQC. Sequences were compiled using assemblers MaSuRCA with 22 k-mer value  (Zimin et al. , 2013)  and SPAdes  (Bankevich et al. , 2012) used K-mers of 21, 33, 55, 77, 99 and 127. Post assembly polishing used Pilon  (Walker et al.
, 2014). Pacbio reads were mapped using Minimap2  (Li 2016) , and Illumina reads were mapped using Bowtie2  (Langmead and Salzberg, 2012). Genes were predicted using the Fusarium model from AUGUSTUS  (King et al. , 2015) , mitochondrial genes were predicted using Prodigal  (Hyatt et al. , 2010) , tRNA were predicted using tRNA scan-SE  (Lowe and Chan, 2016)  and rRNA were predicted using Barrnap software (https://gith ub. com/tseeman/barrnap/). Functional annotation was done using the ERGO software package of IgenBio  (Wilder et al. , 2016). The overall sequence completeness was further evaluated by comparison to the genomes of strains of Fusarium sp. which had been previously characterized to provide a framework for understanding completeness  (Niehaus et al. , 2016). To provide reasonable comparisons, the predicted proteins for the genomes of 23 species representing foods of diverse allergenic risks and included those of human, other animals and plants.
The sequences were downloaded from public databases including the NCBI genome library (https://www. ncbi. nlm. nih. gov/genome), EnsemblPlants (http://plants. ensembl. org/index. html), and Phytozome V. 12, the Plant Genomics Resource (https://phytozome. jgi. doe. gov/pz/portal. html#) as summarized in Table  1. For species without published genomes as of October 2018, we downloaded the predicted protein sequences from the NCBI protein library. All protein sequences were downloaded on October 2018. The bioinformatics pipeline was completed using our lab cluster on the Holland Computer Center server at the University of Nebraska.
FASTA comparison for the predicted protein sequences of the genomes were compared to Allergenonline. org version 16 and 18B Predicted protein sequences from the proposed three novel food species and 23 diverse species were compared to allergens in versions 16 and 18B of www. AllergenOnline. org by overall FASTA 35. FASTA version 35 was installed on the Holland Computing Center server to allow batch searches that mimic the individual protein searches available on our AllergenOnline. org website, however based on the best identity matches over 80 AA long. Different E-score thresholds (10, 1, 0. 001, 1e-7, 1e-30, 1e-50, 1e-75, 1e-100) were used to check the significance of matches on the private HCC searches. The same scoring matrix was used (BLOSUM 50) as on the public AllergenOnline. org database.
BLASTP comparison of predicted protein sequences within the NCBI non-redundant protein sequences database that includes annotated protein sequences from GenBank, RefSeq and TPA as well as SwissProt, PIR, PRF and PDB Predicted protein sequences of Chlorella variabilis, Galdieria sp. and Fusarium strain flavolapis. as well as the 23 other species used in this study were used to search the general protein database using the current version of BLASTP in 2018 and early in 2019. The website is https://bla st. ncbi. nlm. nih. gov/BLAST. cgi. The current version of BLASTP outputs changed markedly in July 2019, removing the ability to use keyword limits in BLASTP searches to restrict matches to particular categories of sequences based on keywords. In addition, the output of BLASTP has changed and we used the Traditional Results for historical comparisons.
For example, BLASTP comparison of triosephosphate isomerase (EFN53775. 1) in Chlorella variabilis to non-redundant protein database had the top 100 matches to triosephosphate isomerase in diverse species with sequence identity ranged from 69 to 100%. Similarly, Chlorella heat shock protein 70 (EFN57963. 1) had matches to heat chock proteins in different species with sequence identities of 77. 5-100%. This shows the conservation of these proteins among diverse allergenic and non-allergenic species. Similarly, Table  5  shows that Galdieria sulphuraria had matches to 59 weak or putative allergens and 6 very weak matches to food allergens (tropomyosin, vicilin, and convicilin) with E-scores >0. 02. Due to high sequence identity of evolutionary homologues, these identity matches were over predictive for possible risks of allergic cross-reactivity.
The searches were rerun using an E score of 1e-7 that removed proteins that are clearly unlikely to cause cross reactive. The results are shown in Table  5. The identified food allergens represent important protein classes of allergens, yet the identity matches shown in this study show very low identities of proteins as with those from Chlorella, meaning they are unlikely to be significant risks for cross-reactivity. That can be demonstrated by comparing the matched allergens to the NCBI Protein database using BLASTP. The results for FASTA comparison of predicted proteins of the Quorn fungal genome-predicted proteome, another species of Fusarium, was tested for background evaluation. Quorn has been used as a food source in the United Kingdom for >30 years. The results are shown in Supplementary Table  1 , that identified 181 matches to weak or putative allergens and 12 very low identity matches to food allergens with very low sequence identity over short AA segments.