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   https://doi.org/10.1002/cbic.201800336  

   Blei, Felix; Fricke, Janis; Wick, Jonas; Slot, Jason C.; Hoffmeister, Dirk (October 2018, ChemBioChem)
2. Nucleotide-level characterization and improvement of l -arabinose- and l -rhamnose-inducible systems in E. coli using a high-throughput approach

   https://doi.org/10.1093/nar/gkaf224  

   Kim, Nancy M.; Peng, Danqia; Sandoval, Nicholas R. (April 2025, Nucleic Acids Research)

   Abstract The commonly used arabinose- and rhamnose-inducible Escherichia coli promoters, PBAD and PRha, exhibit tight regulation through activation via their respective transcription factors, AraC and RhaS, alongside the cyclic AMP receptor protein. The mechanisms of these promoters have been characterized on a parts level, but nucleotide-level analysis has yet to be elucidated. Therefore, we describe here a massively parallel reporter assay that maps regulatory sites at the nucleotide level. The relative importance of nucleotides in each binding site is revealed, including loci not included in previous annotations. For PBAD, we confirm known sites and reveal novel binding sites involved in modulating gene expression. In PRha, we refine the length and sequence specificity of rhaI half-sites, updating previous annotations and providing nucleotide level insights into RhaS-mediated regulation. Mutations that lead to increased promoter strength, wider dynamic range, and altered basal expression are identified for both promoters. Engineered versions of PBAD and PRha promoters based on this data show improvements in dynamic range alongside a seven- and three-fold increase in promoter strength, respectively, with a slight increase in basal expression for the PBAD promoters and no significant increase for PRha. This work expands the genetic parts “toolkit” and increases the understanding of these important commonly used promoters. 

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3. Parental origins of the cultivated tetraploid sour cherry ( Prunus cerasus L.)

   https://doi.org/10.1002/ppp3.10267  

   Bird, Kevin A.; Jacobs, MacKenzie; Sebolt, Audrey; Rhoades, Kathleen; Alger, Elizabeth I.; Colle, Marivi; Alekman, Mitchell L.; Bies, Paulina K.; Cario, Adare J.; Chigurupati, Ramya S.; et al (May 2022, PLANTS, PEOPLE, PLANET)
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   Morrison, Casey A; Deming, Timothy J (September 2025, ACS Macro Letters)
5. Comparison between p‐distance and single‐locus species delimitation models for delineating reproductively tested strains of pennate diatoms (Bacillariophyceae) using cox 1, rbcL and ITS

   https://doi.org/10.1111/jeu.12986  

   Franco, Andréa de; Ashworth, Matt P.; Odebrecht, Clarisse (September 2023, Journal of Eukaryotic Microbiology)

   Abstract Several automated molecular methods have emerged for distinguishing eukaryote species based on DNA sequence data. However, there are knowledge gaps around which of these single‐locus methods is more accurate for the identification of microalgal species, such as the highly diverse and ecologically relevant diatoms. We applied genetic divergence, Automatic Barcode Gap Discovery for primary species delimitation (ABGD), Assemble Species by Automatic Partitioning (ASAP), Statistical Parsimony Network Analysis (SPNA), Generalized Mixed Yule Coalescent (GMYC) and Poisson Tree Processes (PTP) using partialcox1,rbcL,5.8S + ITS2,ITS1 + 5.8S + ITS2 markers to delineate species and compare to published polyphasic identification data (morphological features, phylogeny and sexual reproductive isolation) to test the resolution of these methods. ASAP, ABGD, SPNA and PTP models resolved species ofEunotia,Seminavis, Nitzschia, SellaphoraandPseudo‐nitzschiacorresponding to previous polyphasic identification, including reproductive isolation studies. In most cases, these models identified diatom species in similar ways, regardless of sequence fragment length. GMYC model presented smallest number of results that agreed with previous published identification. Following the recommendations for proper use of each model presented in the present study, these models can be useful tools to identify cryptic or closely related species of diatoms, even when the datasets have relatively few sequences. 

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