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Ficus

Ficus is a genus of woody trees, shrubs, and vines in the family Moraceae, commonly known as figs.
These plants are native to the tropics and subtropics around the world, and are known for their diverse foliage, edible fruits, and unique growth habits.
Ficus species range from large, towering fig trees to small, climbing vines, and are cultivated for their ornamental, edible, and medicinal properties.
The genus includes some of the world's most iconic and ecologically important plant species, such as the banyan tree and the common fig.
Reserchers studying Ficus can utilize PubCompare.ai to effortlessly locate the best protocols from literature, preprints, and patents, enabling them to identify the optimal protocols and products to elevat their Ficus studies.

Most cited protocols related to «Ficus»

1
Simulating and Reconstructing Cyanobacterial Gene Histories
To validate our approach we simulated sequences using tree topologies, branch lengths, and alignment sizes based on 1099 gene families from 36 cyanobacterial genomes available in the HOGENOM database (Penel et al. 2009 (link)). As described in detail in Appendix 1 and illustrated in Figure 2a, to generate the set of simulated alignments we first reconstructed reconciled gene trees that maximize the joint likelihood and subsequently used the reconstructed gene trees to simulate amino acid sequences. To emulate the relative complexity of real data compared with available models of sequence evolution, we used a complex model of sequence evolution to simulate sequences—an LG model (Le and Gascuel 2008 (link)) with across-site rate variation and invariant sites, and attempted to reconstruct their history with a simple model—a Poisson model (Felsenstein 1981 (link)) with no rate variation.
Data.—To construct a simulated dataset, we first reconstructed gene trees for 1099 cyanobacterial gene families with 10 or more genes in any of the 36 cyanobacteria present in version 5 of the HOGENOM database (Penel et al. 2009 (link)). Families with more than 150 genes were not considered. For each family, amino acid sequences were extracted from the database and aligned using MUSCLE (v3.8.31) (Edgar 2004 (link)) with default parameters. The multiple alignment was subsequently cleaned using GBLOCKS (v0.91b) (Talavera and Castresana 2007 (link)) with the options:

Cleaned alignments are available from the Dryad data repository at http://datadryad.org, doi:10.5061/dryad.pv6df.
Reconstructing “real” trees.—For each cleaned alignment, an MCMC sample was obtained using PhyloBayes (v3.2e) (Lartillot et al. 2009 (link)) using an LG+Γ4+I substitution model (Le and Gascuel 2008 (link)) with a burn-in of 1000 samples followed by at least 3000 samples. Following this step, gene families were separated into two datasets: (i) dataset I, composed of 342 universal single-copy families with exactly one copy in each of the 36 cyanobacteria and, (ii) dataset II, which includes dataset I, and is composed of 1099 families, each with at least 10 genes in any of the 36 cyanobacterial genomes considered. For the 342 single-copy universal gene families of dataset I 10 000 trees were sampled.
For each family, we used the species tree shown in Figure A.4, sampled reconciled gene trees using ALEsample (sampling at least 5000 reconciled trees) to sample DTL rates and reconciled gene trees, and ALEml to find the ML DTL rates and the corresponding ML reconciled gene tree.
For each ALEsample sample, we computed the majority consensus tree and fully resolved “real” trees for each gene family were calculated based on the ALEsample sample of trees by finding the tree that maximized CCPs based on the sample. For both real and simulated alignments, sequence-only trees were also inferred using PhyML (version 20110526) (Guindon and Gascuel 2003 (link)) using the LG+Γ4+I model with the options:

“Real” gene trees are available from the Dryad data repository at http://datadryad.org, doi:10.5061/dryad.pv6df.
Sequence simulation.—To simulate amino acid sequences, we used bppseqgen (v1.1.0) (Dutheil and Boussau 2008 (link)) keeping the branch lengths and alignment sizes and using the COMPLEX model corresponding to an LG model with site rate variation described by a gamma distribution with α = 0.1 and 10% invariant sites.
Simulated alignments are available from the Dryad data repository at http://datadryad.org, doi:10.5061/dryad.pv6df.
Inference for simulated data.—For each simulated alignment, an MCMC sample was obtained using PhyloBayes (v3.2e) using a SIMPLE model corresponding to a Poisson model (Felsenstein 1981 (link)) with no rate variation.
We sampled 10 000 trees after a burn-in of 1000 samples with a sample taken every 10 iterations. For the simulated sequence corresponding to the 342 single-copy universal gene families of dataset I, we also sampled trees using the COMPLEX model corresponding to an LG+Γ4+I substitution model, sampling 3000 trees after a burn-in of 1000 samples.
For each family, we sampled reconciled gene trees using ALEsample (sampling at least 5000 reconciled trees) to sample DTL rates and reconciled gene trees, and ALEml to find the ML DTL rates and the corresponding ML reconciled gene tree.
Distances to the “real” tree for gene trees of dataset I (Fig. 2b) were computed as the distance between majority consensus trees calculated from the sequence-only PhyloBayes samples for both the SIMPLE and the COMPLEX model as well as the joint ALEsample samples for both. The same procedure was used for the simulated sequence corresponding to dataset II (Fig. A.1a) for the SIMPLE model. For the COMPLEX model, joint trees were not computed and PhyML trees were used for the sequence-only trees.
Inference of numbers of DTL events.—The number of DTL events for joint trees was inferred using ALEml using a sample of trees obtained using the SIMPLE model. The number of DTL events for sequence trees was inferred using ALEml using fixed PhyML trees (based on LG+Γ4+I substitution model).
ML reconciled trees are available from the Dryad data repository at http://datadryad.org, doi:10.5061/dryad.pv6df.
Statistical support.—Statistical support of bipartitions was calculated from samples of gene trees obtained either using PhyloBayes, for the sequence-only case, or using ALEsample in the joint case. The support of each observed bipartition was estimated as the fraction of all trees in which it was present.
Szöllősi G.J., Rosikiewicz W., Boussau B., Tannier E, & Daubin V. (2013). Efficient Exploration of the Space of Reconciled Gene Trees. Systematic Biology, 62(6), 901-912.
Publication 2013
Amino Acid Sequence Biological Evolution Cyanobacteria Ficus Gamma Rays Genes Genome Joints Muscle Tissue Trees
2
Distinguishing Population Processes from Species Diversification
To distinguish population-level processes from species diversification we used the generalised mixed Yule coalescent (GMYC) likelihood test [85 (link)]. The mixed Yule coalescent analysis approach has been shown to be relatively robust to sampling effects, marker use, the numbers of markers used [86 ]. The test uses models that describe ultrametric phylogenetic trees that have either within-population coalescent branching or species branching signatures. The GMYC test assumes between-species branching according to a Yule pure-birth model [87 (link)] and within-species neutral coalescent model [88 (link)]. A likelihood test of the mixed model estimates the shift from speciation to within-population branching. Although the threshold at which this split occurs might not reflect true diversification processes across all lineages in large trees [89 (link)], the approach has been used with high rates of success on preliminary species delimitation of diverse groups including insects [85 (link)]. The test is intended to be complementary to traditional morpho-taxonomic approaches, but useful for detecting the presence of within-species diversification. The GMYC test was implemented using the 'R' [90 ] package SPLITS (available from: http://R-Forge.R-project.org). An ultrametric tree reconstruction was generated using a strict molecular clock (the GMYC test is based on the strict clock assumption) implemented in BEAST v.1.4.8 [91 ] with gamma distributed invariant sites, GTR substitution prior, empirically estimated base pair frequencies, and unlinked 1st plus 2nd positions, and 3rd codon position. We also used an ultrametric tree based on a relaxed clock using the same priors as above. To compare branching time estimates between fig wasps and figs, we reconstructed a molecular clock Ficus tree under the same priors, but with linked codon positions. We set topology priors that constrained the clade of each fig wasp genera and the monophyletic subsections as inferred by our Bayesian consensus. Sequence data comprising approximately 680 bp's of a COI mtDNA fragment was used in GMYC tests of the fig wasps and 1345 bp's of the ITS and ETS regions for the Ficus analysis. All available sequence data for Elisabethiella and Alfonsiella were used in the analysis as GMYC test performance is optimised using larger sample numbers comprising non-zero branch length information [92 (link)]. We generated a log-lineages through time plot to visualize the 'coalescent region' or shift between species and population branching patterns. Outgroup constraints were the same as for the phylogenetic inferences above. We also constrained the topology of fig wasp genera in the BEAST analysis and verified the generic relationships with the abridged multiple gene fragment phylogenetic inference. A tanglegram between the two BEAST molecular clock inferences was constructed by hand to illustrate fig wasp associations with Ficus species.
McLeish M.J, & van Noort S. (2012). Codivergence and multiple host species use by fig wasp populations of the Ficus pollination mutualism. BMC Evolutionary Biology, 12, 1.
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Publication 2012
Base Pairing Codon DNA, Mitochondrial Ficus Gamma Rays Generic Drugs Insecta Multiple Birth Offspring Reconstructive Surgical Procedures Trees Wasps
3
Inferring Ancestral Pollination in Figs
We inferred the evolution of pollination mode and the ancestral areas for figs and their pollinators using both ML and parsimony approaches implemented in Mesquite 2.73 (Maddison and Maddison 2008 ). Pollination modes and ancestral areas were inferred on the ML topologies. For ML optimization, we used a stochastic Markov model of evolution (Mk1). The Likelihood Decision Threshold was set to 2 log-likelihood units. Character data for Ficus and Agaonidae were obtained both from the literature (Kjellberg et al. 2001 ; Berg and Corner 2005 ) and from our examination of flowers, pollen pockets, and coxal combs. Following Lopez-Vaamonde et al. (2009) (link), current species distributions were categorized into 4 character states: (0) Afrotropics, (1) Australasia, (2) Neotropics, (3) Eurasia. However, because several taxa occur in both Eurasian and Australasian regions and a couple of taxa occur in both Eurasian and Afrotropical regions, and Mesquite requires unique character states, we also defined 2 other states: (4) Australasia + Eurasia and (5) Afrotropics + Eurasia. We took into account all published geographic localities for Ficus and agaonids, museum specimens and about 3000 samples of fig wasp communities that we collected over the last 15 years. We also used the dispersal-extinction-cladogenesis model implemented in Lagrange (Ree and Smith 2008 (link)), using the same raw data and 4 character states. Dispersal rate between all areas was set to 1 during the whole period considered (data available upon request).
Cruaud A., Rønsted N., Chantarasuwan B., Chou L.S., Clement W.L., Couloux A., Cousins B., Genson G., Harrison R.D., Hanson P.E., Hossaert-Mckey M., Jabbour-Zahab R., Jousselin E., Kerdelhué C., Kjellberg F., Lopez-Vaamonde C., Peebles J., Peng Y.Q., Pereira R.A., Schramm T., Ubaidillah R., van Noort S., Weiblen G.D., Yang D.R., Yodpinyanee A., Libeskind-Hadas R., Cook J.M., Rasplus J.Y, & Savolainen V. (2012). An Extreme Case of Plant–Insect Codiversification: Figs and Fig-Pollinating Wasps. Systematic Biology, 61(6), 1029-1047.
Publication 2012
Biological Evolution Character Comb Coxa Extinction, Psychological Ficus Flowers Genetic Speciation Pollen Pollination Prosopis Wasps
4
Optimizing Target Enrichment Efficiency in Phylogenomic Studies
Target enrichment is typically conducted on multiple samples that have been pooled during bait hybridization and sequencing. HybPiper maps reads against the target genes for each sample separately. This is a different procedure than several other target enrichment analysis pipelines (Straub et al., 2011 (link); Bi et al., 2012 (link); Faircloth, 2015 ), which typically begin with de novo assembly for each sample, and then attempt to match contigs to target loci. In HybPiper, reads are first sorted based on whether they map to a target locus. We explored two methods for aligning reads to the targets: (1) BLASTX (Camacho et al., 2009 (link)), which uses peptide sequences as a reference, and (2) BWA (Li and Durbin, 2009 ), which uses nucleotide sequences. In principle, the BLASTX approach should be more forgiving to substitutions between the target sequence and sample reads, because alignments are conducted at the peptide level and may detect similarity between more distant sequences than BWA. The BWA approach may result in fewer overall reads mapping to a distantly related target sequence, but is several times faster than the BLASTX method.
HybPiper sorts reads into separate directories for each gene using Biopython (Cock et al., 2009 ) to efficiently parse the FASTA format. In our tests of the BLASTX method, we set an E-value threshold of 1 × 10−5 to accept alignments, but the user can change this. For the BWA method, all alignable reads are sorted into each gene directory using a Python wrapper around SAMtools (Li et al., 2009 ). We calculate the enrichment efficiency as the percentage of trimmed, filtered reads that were sorted into a gene directory.
For the Artocarpus reads, an average of 71.9% of reads were on target (range 64.4–79.9%), based on the BLASTX method. Enrichment efficiency was lower for some of the outgroup samples, which ranged from just 5.0% for Antiaropsis K. Schum. to 71.6% for Ficus L. To address whether the presence of duplicate reads affects our estimate of enrichment efficiency, we removed paired duplicate reads using SuperDeduper (http://dstreett.github.io/Super-Deduper/). Most samples had between 6% and 18% duplicate read pairs, and a similar percentage of the duplicate read pairs mapped to the target loci (Appendix S1). One outlier was Ficus, which had 34% duplicate reads, 42% of which mapped to targets. After adjusting for duplicate reads, our estimates of enrichment efficiency were reduced by about 4% on average (Table 1). Removing duplicate reads did not affect the extraction of exon sequences in HybPiper for this data set.
The phylogenetic distance to Artocarpus did not seem related to percent enrichment. However, the two outgroup samples that were pooled in a hybridization with Artocarpus in the first sequencing run had much lower enrichment efficiency than ingroup samples (Table 1). This suggests that multiplexing at the hybridization stage should be nonrandom, and only libraries of taxa that are relatively equidistant from the taxa used to design the bait sequences should be pooled. This strategy has been previously recommended in other studies (McGee et al., 2016 ).
Johnson M.G., Gardner E.M., Liu Y., Medina R., Goffinet B., Shaw A.J., Zerega N.J, & Wickett N.J. (2016). HybPiper: Extracting coding sequence and introns for phylogenetics from high-throughput sequencing reads using target enrichment. Applications in Plant Sciences, 4(7), apps.1600016.
Publication 2016
Artocarpus Base Sequence Crossbreeding Exons Ficus Genes Microtubule-Associated Proteins Peptides Python
5
Phylogenetic Inferences of Ficus and Pollinator Data
Phylogenetic inferences of pollinator and Ficus sequence data were used for relative divergence timing estimation and for tests of congruence between them. To generate phylogenetic inferences among the wasps, fragments of mtDNA cytochrome oxidase one (COI ~ 620 bp), cytochrome b (Cytb ~ 380 bp), and nuclear DNA (nDNA) elongation factor-one alpha F2 copy (EF-1α ~ 500 bp) were sequenced. A comprehensive explanation of DNA extraction, PCR, and alignment protocols is given in [81 (link)]. The species delimitation test requires an (mtDNA) ultrametric tree. Substitution rates of COI mtDNA tend to be higher than in nuclear protein coding genes and therefore more prone to saturation bias that impedes deep node resolution. In order to implement reasonable prior tree topology constraints for ultrametric pollinator tree inference and for co-phylogenetic analyses, we used species for which multiple gene fragments including nDNA were available to infer a phylogeny using Bayesian and parsimony approaches. Sequence data of up to 767 bp's of a ribosomal internal transcribed spacer (ITS) and up to 479 bp of external transcribed spacer (ETS) were used to infer Ficus species phylogenies under Bayesian and parsimony methods. Analyses presented in this work assume the Ficus species phylogeny is fully resolved and does not consider population-level genetic variation influence on host associations. Species-level appraisal of host lineages does provide evidence of comparative genetic distances for instances of departures from one-to-one species specificity. Sequences of ITS and ETS were amplified using the protocol outlined in previous work [80 (link)].
A Bayesian approach implemented in MrBayes 3.1.1 [82 (link)] was used to partition the COI, Cytb, and EF-1α data into gene fragments and also codon positions. The Ficus sequence data was partitioned into ITS and ETS for the Bayesian phylogenetic analyses. A general time reversible DNA substitution model was used with gamma distributed (+G) rates with a proportion of invariant sites (+I). Posterior probabilities and mean branch lengths were derived from 15000 post-burnin trees sampled every 1000 trees from generations 5 to 20 million. Four separate Bayesian reconstructions were run to verify consistency of phylogenetic outcomes. The consensus trees were derived from post-burnin generations of Markov chains that had reached apparent stationarity. Convergence was assessed using the MCMC Tracer Analysis Tool v.1.4.1 [83 ] by plotting the log likelihoods to assess the point in the chain where stable values were reached and with the standard deviation of split frequencies of all runs. Parsimony bootstrap analysis implemented using PAUP version 4.0b10 [84 ] was used to assess the robustness of the Bayesian consensus phylogeny. The parsimony bootstrap consensus trees were derived from a search consisting of 500 bootstrap replicates using a full heuristic search. To calculate bootstrap support, we used branch-swapping by stepwise addition on best trees only, 100 random additional sequences holding 10 trees at each step, and the TBR search algorithm.
McLeish M.J, & van Noort S. (2012). Codivergence and multiple host species use by fig wasp populations of the Ficus pollination mutualism. BMC Evolutionary Biology, 12, 1.
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Publication 2012
Codon Cytochromes b DNA, A-Form DNA, Mitochondrial Elongation Factor Ficus Gamma Rays Genes Genetic Diversity Host Specificity Multiple Birth Offspring Nuclear Protein One-Alpha Oxidase, Cytochrome-c Reconstructive Surgical Procedures Reproduction Ribosomes Species Specificity Trees Wasps

Most recents protocols related to «Ficus»

1
Diversity of Schizophyllum commune on Weathered Wood

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Publication 2023
Aleurites Anacardium occidentale Auricularia auricula-judae Bambusa Cacao Cassia Ficus Forests Fungi Koro Lannea Lentinus sajor-caju Mangifera Manihot esculenta Myrobalans Pinus Schizophyllum commune Tamarindus indica
2
Selective SGB Resistance Identification
522 SGBs were selected for inclusion in the tree of Fig. 4 that are: i) detected in 1% or more of the adult stool metagenome samples, (ii) had at least 10 genomes, (iii) have three or more relevant ARG families, or showed significant association with one resistotype (≥5-fold difference in mean relative abundance and p < 0.05 by Mann–Whitney tests after Benjamini–Hochberg adjustment).
Lee K., Raguideau S., Sirén K., Asnicar F., Cumbo F., Hildebrand F., Segata N., Cha C.J, & Quince C. (2023). Population-level impacts of antibiotic usage on the human gut microbiome. Nature Communications, 14, 1191.
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Publication 2023
Adult Feces Ficus Genome Metagenome Simpson-Golabi-Behmel Syndrome, Type 1
3
Fault Tree Analysis for Hazard Identification

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Publication 2023
Accidents Diagnosis Ficus Trees
4
Inheritance and Variation of Traits in Vegetation Simulation
The new features of the model are variation and inheritance of traits. In a previous version of the model LAVESI (Kruse et al., 2016 ), almost all traits were the same for every individual (only maturation height being randomly assigned) of the same species, which is referred to as the uniform type throughout this article. In this work, we created variation and adaptation for the seed weight and the drought resistance. Both use the same following principles in their trait value determination.
Trait variation gives every newly produced seed a random value from a uniform distribution with upper and lower limits. Therefore, every seed that is either introduced or created by a tree during the simulation has the same chance of having any trait value from the distribution. This allows for variability in the traits present in the model permitting plastic responses but no adaptation while not being computationally much more intensive.
The inheritance (adaptive traits) system (Fig. 1), on the other hand, calculates a new seed value for every new seed production that is a function of the trait value of the mother tree and the pollen source (Geber and Griffen 2003 ). To calculate the likely pollen source, it was necessary to create a level of abstraction as calling every tree for every seed production proved too computationally intensive (Kruse et al., 2018 ). For this reason, a pollen grid was introduced with grid cells of, in this case, 100 m². The trait information of the pollen-producing trees is averaged per cell and these mean grid-cell values used as the trait values for that pollen source. The new seed's trait value is calculated by either using a mixture distribution, created by combining the two parental normal distributions, or by a normal distribution around a weighted average of the pollen source and the seed-producing tree. For the normal distributions the Box-Muller transform is used (Box and Muller, 1958 ). The mixed distribution is achieved by creating normal distributions for both parents and randomly selecting one or the other. For the northern tree expansion both methods are used and two different weights are applied to the second method, with the weight applied being equal in one case and 75% for the seed-producing tree in the other case, to assess the different options. Since all three methods produce comparative results (not shown) only the mixed distribution is used for the drought experiment.

Scheme showing the inheritance system. The upper part shows a schematic path from the pollen to the seed-producing tree and from the seeds to the offspring. Red vertical lines in the lower part show the trait values of individuals within a range of possible trait values represented by the black bars along the x axis. The specific trait values are examples and could be anywhere within the range. The blue curve shows the distribution of likely trait values, around the individuals own trait value, for the offspring based on the pollen and the orange based on the seed-producing tree. The right shows the offspring uses a mixed distribution resulting from these two to determine their trait values. The green dotted lines for the offspring show three possible trait values as examples that could be created by these two parent trees.

Fig 1
Gloy J., Herzschuh U, & Kruse S. (2023). Evolutionary adaptation of trees and modelled future larch forest extent in Siberia. Ecological Modelling, 478, 110278.
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Publication 2023
Acclimatization Cells Drought Resistance Droughts Epistropheus Ficus Grid Cells Mothers Parent Pattern, Inheritance Pollen Trees
5
Constructing Parsimonious B-cell Lineage Trees
To find the most parsimonious B-cell lineage tree, we model the problem as a multi-objective optimisation problem. Thus, we have two objective functions: the first one minimises the sum of edge weights, while the second function maximises the genotype abundance. There are many methods to solve a multi-objective problem [28 (link)]; we used the hierarchical optimisation criteria [29 (link)], in which two or more objective functions are ranked from the most to the least important, and are optimised in this pre-established order. The first function can be modelled by some minimum spanning tree algorithm, such as Prim’s [26 (link)] or Kruskal’s [27 (link)]. Both are greedy approaches and present low time complexity. However, Prim’s algorithm runs faster than Kruskal’s in dense graphs [30 ]. Therefore, we modified Prim’s algorithm to incorporate the second objective function. We start at the root and add all its neighbours with minimum edge weight to a priority queue. We then iteratively extract from the priority queue the node with the lowest edge weight (the first objective function) and highest genotype abundance (the second objective function). If no cycle is formed, the node and the edge are added to the tree. For each added node, all its neighbours with minimum edge weights are included in the priority queue. We keep on adding nodes and edges until we cover all nodes. To decrease the time complexity of the algorithm, we add each node only once to the priority queue.
We highlight the fact that the original Prim’s algorithm has only one objective function, which minimises the sum of edge weights (cost). Here we included a second objective function to maximise genotype abundance. If a set of edges have the same weight, we will choose the one that connects nodes with high abundance. Prim’s algorithm has a time complexity of O(|V|2) in the worst case but can be improved up to O(|E|+log|V|) when using data structures based on Fibonacci heaps [31 ]. Figure 1 shows a simple example of the tree construction process.

ClonalTree construction example. We start with a connected weighted graph (a) where nodes represent BCR genotype sequences, edge weights their distances, and node weights their abundances. The graph can be fully connected, or one can disable edges whose weight is lower than a threshold \documentclass[12pt]{minimal}
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\begin{document}$$\delta$$\end{document}δ. Then, we first place the inferred ancestral sequence (the root) (b) and iteratively add nodes to the tree with the lowest edge weight and highest genotype abundance (c, d); when edges have the same weight (e), we choose that connected to the node with the highest abundance (f), we repeat until all nodes were added to the tree (g), the final tree is shown in (h)

Abdollahi N., Jeusset L., de Septenville A., Davi F, & Bernardes J.S. (2023). Reconstructing B cell lineage trees with minimum spanning tree and genotype abundances. BMC Bioinformatics, 24, 70.
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Publication 2023
B-Lymphocytes Ficus Genotype oligomycin sensitivity-conferring protein Plant Roots Trees

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More about "Ficus"

Ficus, the genus of woody trees, shrubs, and vines in the Moraceae family, are renowned for their diverse foliage, edible fruits, and unique growth habits.
These iconic and ecologically important plants are native to the tropics and subtropics worldwide, and are commonly known as figs.
The genus encompasses a wide range of species, from towering banyan trees to small, climbing vines, and is cultivated for its ornamental, edible, and medicinal properties.
When conducting research on Ficus, researchers can utilize PubCompare.ai, an AI-powered platform that enables effortless identification of the best protocols from literature, preprints, and patents.
By leveraging the cutting-edge comparisons offered by PubCompare.ai, researchers can optimize their Ficus studies by finding the most suitable protocols and products.
In addition to the Ficus genus, researchers may also encounter related terms and techniques, such as Whatman No. 1 filter paper, Whatman No. 1, Dulbecco's modified Eagle's medium, Folin-Ciocalteu reagent, Ficin, EditSeq, Ultrasensitive Rat Insulin ELISA kit, No. 1 filter paper, Mini-PROTEAN Tetra cell electrophoresis unit, and BCA protein assay kit.
These tools and reagents can be utilized to support various aspects of Ficus research, from sample preparation to analysis and quantification.
By incorporating these insights and related terms, researchers can enhance their understanding and effectively navigate the complexities of Ficus studies, ultimately elevating the quality and impact of their research.