Spontaneous mutation rate in the smallest photosynthetic eukaryotes

Mutation is the ultimate source of genetic variation, and knowledge of mutation rates is fundamental for our understanding of all evolutionary processes. High throughput sequencing of mutation accumulation lines has provided genome wide spontaneous mutation rates in a dozen model species, but estimates from nonmodel organisms from much of the diversity of life are very limited. Here, we report mutation rates in four haploid marine bacterial-sized photosynthetic eukaryotic algae; Bathycoccus prasinos, Ostreococcus tauri, Ostreococcus mediterraneus, and Micromonas pusilla. The spontaneous mutation rate between species varies from μ = 4.4 × 10−10 to 9.8 × 10−10 mutations per nucleotide per generation. Within genomes, there is a two-fold increase of the mutation rate in intergenic regions, consistent with an optimization of mismatch and transcription-coupled DNA repair in coding sequences. Additionally, we show that deviation from the equilibrium GC content increases the mutation rate by ∼2% to ∼12% because of a GC bias in coding sequences. More generally, the difference between the observed and equilibrium GC content of genomes explains some of the inter-specific variation in mutation rates

Machine Learning Education for Artists, Musicians, and Other Creative Practitioners

This article aims to lay a foundation for the research and practice of machine learning education for creative practitioners. It begins by arguing that it is important to teach machine learning to creative practitioners and to conduct research about this teaching, drawing on related work in creative machine learning, creative computing education, and machine learning education. It then draws on research about design processes in engineering and creative practice to motivate a set of learning objectives for students who wish to design new creative artifacts with machine learning. The article then draws on education research and knowledge of creative computing practices to propose a set of teaching strategies that can be used to support creative computing students in achieving these objectives. Explanations of these strategies are accompanied by concrete descriptions of how they have been employed to develop new lectures and activities, and to design new experiential learning and scaffolding technologies, for teaching some of the first courses in the world focused on teaching machine learning to creative practitioners. The article subsequently draws on data collected from these courses—an online course as well as undergraduate and masters-level courses taught at a university—to begin to understand how this curriculum supported student learning, to understand learners’ challenges and mistakes, and to inform future teaching and research

The nightmare unfolds Spring 1993 IT skills report

SIGLEAvailable from British Library Document Supply Centre- DSC:q93/14925 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations

Data in many applications follows systems of Ordinary Differential Equations (ODEs). This paper presents a novel algorithmic and symbolic construction for covariance functions of Gaussian Processes (GPs) with realizations strictly following a system of linear homogeneous ODEs with constant coefficients, which we call LODE-GPs. Introducing this strong inductive bias into a GP improves modelling of such data. Using smith normal form algorithms, a symbolic technique, we overcome two current restrictions in the state of the art: (1) the need for certain uniqueness conditions in the set of solutions, typically assumed in classical ODE solvers and their probabilistic counterparts, and (2) the restriction to controllable systems, typically assumed when encoding differential equations in covariance functions. We show the effectiveness of LODE-GPs in a number of experiments, for example learning physically interpretable parameters by maximizing the likelihood