Stock species of Brassica rapa and Brassica nigra were crossed with the intention of synthesizing the amphidiploid species Brassica juncea. Of the stock species, a diploid and a tetraploid were given of both the B. rapa (Aaa and Aaaaa) and the B. nigra (Bbb and Bbbbb). However the identity of these was concealed, and they were presented as 1A 1, 1A2, 1A3, and 1A4. The objective of this experiment was not only to synthesize the species B. juncea (Abaabbb), but to determine the identity of 1A1, 1A2, 1A3, and 1A4 as to which of these were the diploid and tetraploid species, and to test the synthesized B. juncea by crossing it with the natural, male sterile B. juncea.
<div>A variety of fields would benefit from accurate pK<sub>a</sub> predictions, especially drug design due to the affect a change in ionization state can have on a molecules physiochemical properties.</div><div>Participants in the recent SAMPL6 blind challenge were asked to submit predictions for microscopic and macroscopic pK<sub>a</sub>s of 24 drug like small molecules.</div><div>We recently built a general model for predicting pK<sub>a</sub>s using a Gaussian process regression trained using physical and chemical features of each ionizable group.</div><div>Our pipeline takes a molecular graph and uses the OpenEye Toolkits to calculate features describing the removal of a proton.</div><div>These features are fed into a Scikit-learn Gaussian process to predict microscopic pK<sub>a</sub>s which are then used to analytically determine macroscopic pK<sub>a</sub>s.</div><div>Our Gaussian process is trained on a set of 2,700 macroscopic pK<sub>a</sub>s from monoprotic and select diprotic molecules.</div><div>Here, we share our results for microscopic and macroscopic predictions in the SAMPL6 challenge.</div><div>Overall, we ranked in the middle of the pack compared to other participants, but our fairly good agreement with experiment is still promising considering the challenge molecules are chemically diverse and often polyprotic while our training set is predominately monoprotic.</div><div>Of particular importance to us when building this model was to include an uncertainty estimate based on the chemistry of the molecule that would reflect the likely accuracy of our prediction. </div><div>Our model reports large uncertainties for the molecules that appear to have chemistry outside our domain of applicability, along with good agreement in quantile-quantile plots, indicating it can predict its own accuracy.</div><div>The challenge highlighted a variety of means to improve our model, including adding more polyprotic molecules to our training set and more carefully considering what functional groups we do or do not identify as ionizable. </div>
В течение трех лет изучали 34 реципрокных гибрида, полученные при гибридизации 17 родительских пар между подвидами вида Brassica rapa. Анализировали содержание сухих веществ, белка, сахаров, аскорбиновой кислоты, общей кислотности, пигментов (хлорофиллы A и B, каротиноиды, каротины, β-каротин), нитратов. Показано, что все изученные гибриды пекинской, китайской, розеточной и японской капуст могут быть источниками высокого содержания аскорбиновой кислоты. В свою очередь, источниками высокого содержания хлорофиллов и β-каротина являются темно-зеленые образцы розеточной и японской капуст.
The high-throughput sequencing of small RNAs profiling in wide hybridisation and allopolyploidisation between Brassica rapa and Brassica nigra
