Machine Learning for RNA Design: LEARNA.

Machine learning algorithms, and in particular deep learning approaches, have recently garnered attention in the field of molecular biology due to remarkable results. In this chapter, we describe machine learning approaches specifically developed for the design of RNAs, with a focus on the learna_tools Python package, a collection of automated deep reinforcement learning algorithms for secondary structure-based RNA design. We explain the basic concepts of reinforcement learning and its extension, automated reinforcement learning, and outline how these concepts can be successfully applied to the design of RNAs.

Partial RNA design.

Motivation: RNA design is a key technique to achieve new functionality in fields like synthetic biology or biotechnology. Computational tools could help to find such RNA sequences but they are often limited in their formulation of the search space.

Results: In this work, we propose partial RNA design, a novel RNA design paradigm that addresses the limitations of current RNA design formulations.

Distribution, frequency and molecular basis of clethodim and quizalofop resistance in brome grass (Bromus diandrus).

Background: Brome grass (Bromus diandrus Roth) is prevalent in the southern and western cropping regions of Australia, where it causes significant economic damage. A targeted herbicide resistance survey was conducted in 2020 by collecting brome grass populations from 40 farms in Western Australia and subjecting these samples to comprehensive herbicide screening. One sample (population 172-20), from a field that had received 12 applications of clethodim over 20 years of continuous cropping, was found to be highly resistant to the acetyl-CoA carboxylase (ACCase)-inhibiting herbicides clethodim and quizalofop, and so the molecular basis of resistance was investigated.

"Jumping Jack": Genomic Microsatellites Underscore the Distinctiveness of Closely Related and and Provide New Insights Into Their Evolutionary Past.

Downy mildews caused by obligate biotrophic oomycetes result in severe crop losses worldwide. Among these pathogens, and , two closely related oomycetes, adversely affect cucurbits and hop, respectively. Discordant hypotheses concerning their taxonomic relationships have been proposed based on host-pathogen interactions and specificity evidence and gene sequences of a few individuals, but population genetics evidence supporting these scenarios is missing.

Acoustic Ejection Mass Spectrometry: A Fully Automatable Technology for High-Throughput Screening in Drug Discovery.

Acoustic droplet ejection (ADE)-open port interface (OPI)-mass spectrometry (MS) has recently been introduced as a versatile analytical method that combines fast and contactless acoustic sampling with sensitive and accurate electrospray ionization (ESI)-MS-based analyte detection. The potential of the technology to provide label-free measurements in subsecond analytical cycle times makes it an attractive option for high-throughput screening (HTS). Here, we report the first implementation of ADE-OPI-MS in a fully automated HTS environment, based on the example of a biochemical assay aiming at the identification of small-molecule inhibitors of the cyclic guanosine monophosphate-adenosine monophosphate (GMP-AMP) synthase (cGAS).