Genomic insight into the developmental history of southern highbush blueberry populations

ハイブッシュブルーベリーに暖地適応性をもたらした遺伝要因を解明 --ゲノムに刻まれたブルーベリーの育種履歴--. 京都大学プレスリリース. 2020-09-07.Interspecific hybridization is a common breeding approach for introducing novel traits and genetic diversity to breeding populations. Southern highbush blueberry (SHB) is a blueberry cultivar group that has been intensively bred over the last 60 years. Specifically, it was developed by multiple interspecific crosses between northern highbush blueberry [NHB, Vaccinium corymbosum L. (2n = 4x = 48)] and low-chill Vaccinium species to expand the geographic limits of highbush blueberry production. In this study, we genotyped polyploid blueberries, including 105 SHB, 17 NHB, and 10 rabbiteye blueberry (RE) (Vaccinium virgatum Aiton), from the accessions planted at Poplarville, Mississippi, and accessions distributed in Japan, based on the double-digest restriction site-associated DNA sequencing. The genome-wide SNP data clearly indicated that RE cultivars were genetically distinct from SHB and NHB cultivars, whereas NHB and SHB were genetically indistinguishable. The population structure results appeared to reflect the differences in the allele selection strategies that breeders used for developing germplasm adapted to local climates. The genotype data implied that there are no or very few genomic segments that were commonly introgressed from low-chill Vaccinium species to the SHB genome. Principal component analysis-based outlier detection analysis found a few loci associated with a variable that could partially differentiate NHB and SHB. These SNP loci were detected in Mb-scale haplotype blocks and may be close to the functional genes related to SHB development. Collectively, the data generated in this study suggest a polygenic adaptation of SHB to the southern climate, and may be relevant for future population-scale genome-wide analyses of blueberry

Deep learning models for predicting RNA degradation via dual crowdsourcing

Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition (‘Stanford OpenVaccine’) on Kaggle, involving single-nucleotide resolution measurements on 6,043 diverse 102–130-nucleotide RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504–1,588 nucleotides) with improved accuracy compared with previously published models. These results indicate that such models can represent in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for dataset creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales

Deep learning models for predicting RNA degradation via dual crowdsourcing

Messenger RNA-based medicines hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition ("Stanford OpenVaccine") on Kaggle, involving single-nucleotide resolution measurements on 6043 102-130-nucleotide diverse RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1588 nucleotides) with improved accuracy compared to previously published models. Top teams integrated natural language processing architectures and data augmentation techniques with predictions from previous dynamic programming models for RNA secondary structure. These results indicate that such models are capable of representing in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for data set creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales

Belle II Technical Design Report

The Belle detector at the KEKB electron-positron collider has collected almost 1 billion Y(4S) events in its decade of operation. Super-KEKB, an upgrade of KEKB is under construction, to increase the luminosity by two orders of magnitude during a three-year shutdown, with an ultimate goal of 8E35 /cm^2 /s luminosity. To exploit the increased luminosity, an upgrade of the Belle detector has been proposed. A new international collaboration Belle-II, is being formed. The Technical Design Report presents physics motivation, basic methods of the accelerator upgrade, as well as key improvements of the detector.Comment: Edited by: Z. Dole\v{z}al and S. Un

The Physics of the B Factories

This work is on the Physics of the B Factories. Part A of this book contains a brief description of the SLAC and KEK B Factories as well as their detectors, BaBar and Belle, and data taking related issues. Part B discusses tools and methods used by the experiments in order to obtain results. The results themselves can be found in Part C