In industrial CT, objects are often reconstructed from a circular scan trajectory. This reconstruction is possible even if only half of the object is visible in the detector at any given time, but at the cost of redundancies that can be used for the correction of miscalibrations. We propose a scheme for recalibration using only a sinogram of projections truncated in such a manner. The sinogram does not need to be of any particular phantom and can depict any object. We show that the scheme is effective and produces calibrations that are similarly accurate as their untruncated counterparts.

The comparison of genomes using models of molecular evolution is a powerful approach for finding or towards understanding functional elements. In particular, comparative genomics is a fundamental building brick in building high-quality, complete and consistent annotations of ever larger sets of alignable genomes. We here present our new program ClaMSA that classifies multiple sequence alignments using a phylogenetic model. It uses a novel continuous-time Markov chain machine learning layer, named CTMC, that is learned end-to-end together with (recurrent) neural networks for a learning task. We trained ClaMSA discriminately to classify aligned codon sequences that are candidates of coding regions into coding or non-coding and obtained six times fewer false positives for this task on vertebrate and fly alignments than existing methods at the same true positive rate.ClaMSA and the CTMC layer are general tools that could be used for other machine learning tasks on tree-related sequence data.

Motivation The comparison of genomes using models of molecular evolution is a powerful approach for finding, or towards understanding, functional elements. In particular, comparative genomics is a fundamental building brick in annotating ever larger sets of alignable genomes completely, accurately and consistently. Results We here present our new program ClaMSA that classifies multiple sequence alignments using a phylogenetic model. It uses a novel continuous-time Markov chain machine learning layer, named CTMC, whose parameters are learned end-to-end and together with (recurrent) neural networks for a learning task. We trained ClaMSA discriminatively to classify aligned codon sequences that are candidates of coding regions into coding or non-coding and obtained four times fewer false positives for this task on vertebrate and fly alignments than existing methods at the same true positive rate. ClaMSA and the CTMC layer are general tools that could be used for other machine learning tasks on tree-related sequence data. Availability Freely from https://github.com/Gaius-Augustus/clamsa. Supplementary information Supplementary data are available online.