An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval

Machine learning is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the potential of using machine learning algorithms for atmospheric retrieval by implementing a random forest to perform retrievals in seconds that are consistent with the traditional, computationally-expensive nested-sampling retrieval method. We expand upon their approach by presenting a new machine learning model, \texttt{plan-net}, based on an ensemble of Bayesian neural networks that yields more accurate inferences than the random forest for the same data set of synthetic transmission spectra. We demonstrate that an ensemble provides greater accuracy and more robust uncertainties than a single model. In addition to being the first to use Bayesian neural networks for atmospheric retrieval, we also introduce a new loss function for Bayesian neural networks that learns correlations between the model outputs. Importantly, we show that designing machine learning models to explicitly incorporate domain-specific knowledge both improves performance and provides additional insight by inferring the covariance of the retrieved atmospheric parameters. We apply \texttt{plan-net} to the Hubble Space Telescope Wide Field Camera 3 transmission spectrum for WASP-12b and retrieve an isothermal temperature and water abundance consistent with the literature. We highlight that our method is flexible and can be expanded to higher-resolution spectra and a larger number of atmospheric parameters

Photosynthetic antenna size in higher plants is controlled by the plastoquinone redox state at the post-transcriptional rather than transcriptional level.

We analyze the effect of the plastoquinone redox state on the regulation of the light-harvesting antenna size at transcriptional and post-transcriptional levels. This was approached by studying transcription and accumulation of light-harvesting complexes in wild type versus the barley mutant viridis zb63, which is depleted in photosystem I and where plastoquinone is constitutively reduced. We show that the mRNA level of genes encoding antenna proteins is almost unaffected in the mutant; this stability of messenger level is not a peculiarity of antenna-encoding genes, but it extends to all photosynthesis-related genes. In contrast, analysis of protein accumulation by two-dimensional PAGE shows that the mutant undergoes strong reduction of its antenna size, with individual gene products having different levels of accumulation. We conclude that the plastoquinone redox state plays an important role in the long term regulation of chloroplast protein expression. However, its modulation is active at the post-transcriptional rather than transcriptional level

Accurate Machine Learning Atmospheric Retrieval via a Neural Network Surrogate Model for Radiative Transfer

Atmospheric retrieval determines the properties of an atmosphere based on its measured spectrum. The low signal-to-noise ratio of exoplanet observations require a Bayesian approach to determine posterior probability distributions of each model parameter, given observed spectra. This inference is computationally expensive, as it requires many executions of a costly radiative transfer (RT) simulation for each set of sampled model parameters. Machine learning (ML) has recently been shown to provide a significant reduction in runtime for retrievals, mainly by training inverse ML models that predict parameter distributions, given observed spectra, albeit with reduced posterior accuracy. Here we present a novel approach to retrieval by training a forward ML surrogate model that predicts spectra given model parameters, providing a fast approximate RT simulation that can be used in a conventional Bayesian retrieval framework without significant loss of accuracy. We demonstrate our method on the emission spectrum of HD 189733 b and find good agreement with a traditional retrieval from the Bayesian Atmospheric Radiative Transfer (BART) code (Bhattacharyya coefficients of 0.9843--0.9972, with a mean of 0.9925, between 1D marginalized posteriors). This accuracy comes while still offering significant speed enhancements over traditional RT, albeit not as much as ML methods with lower posterior accuracy. Our method is ~9x faster per parallel chain than BART when run on an AMD EPYC 7402P central processing unit (CPU). Neural-network computation using an NVIDIA Titan Xp graphics processing unit is 90--180x faster per chain than BART on that CPU.Comment: 16 pages, 4 figures, submitted to PSJ 3/4/2020, revised 1/22/2021. Text restructured and updated for clarity, model updated and expanded to work for range of hot Jupiters, results/plots updated, two new appendices to further justify model selection and methodolog

Machine Learning to Support the Presentation of Complex Pathway Graphs.

Visualization of biological mechanisms by means of pathway graphs is necessary to better understand the often complex underlying system. Manual layout of such pathways or maps of knowledge is a difficult and time consuming process. Node duplication is a technique that makes layouts with improved readability possible by reducing edge crossings and shortening edge lengths in drawn diagrams. In this article we propose an approach using Machine Learning (ML) to facilitate parts of this task by training a Support Vector Machine (SVM) with actions taken during manual biocuration. Our training input is a series of incremental snapshots of a diagram describing mechanisms of a disease, progressively curated by a human expert employing node duplication in the process. As a test of the trained SVM models, they are applied to a single large instance and 25 medium-sized instances of hand-curated biological pathways. Finally, in a user validation study, we compare the model predictions to the outcome of a node duplication questionnaire answered by users of biological pathways with varying experience. We successfully predicted nodes for duplication and emulated human choices, demonstrating that our approach can effectively learn human-like node duplication preferences to support curation of pathway diagrams in various contexts

Light-induced Dissociation of an Antenna Hetero-oligomer Is Needed for Non-photochemical Quenching InductionS⃞

PsbS plays a major role in activating the photoprotection mechanism known as “non-photochemical quenching,” which dissipates chlorophyll excited states exceeding the capacity for photosynthetic electron transport. PsbS activity is known to be triggered by low lumenal pH. However, the molecular mechanism by which this subunit regulates light harvesting efficiency is still unknown. Here we show that PsbS controls the association/dissociation of a five-subunit membrane complex, composed of two monomeric Lhcb proteins (CP29 and CP24) and the trimeric LHCII-M. Dissociation of this supercomplex is indispensable for the onset of non-photochemical fluorescence quenching in high light, strongly suggesting that protein subunits catalyzing the reaction of heat dissipation are buried into the complex and thus not available for interaction with PsbS. Consistently, we showed that knock-out mutants on two subunits participating to the B4C complex were strongly affected in heat dissipation. Direct observation by electron microscopy and image analysis showed that B4C dissociation leads to the redistribution of PSII within grana membranes. We interpreted these results to mean that the dissociation of B4C makes quenching sites, possibly CP29 and CP24, available for the switch to an energy-quenching conformation. These changes are reversible and do not require protein synthesis/degradation, thus allowing for changes in PSII antenna size and adaptation to rapidly changing environmental conditions

Transcriptome profiling of hemp bast fibres at different developmental stages

Bast fibres are long extraxylary cells which mechanically support the phloem and they are divided into xylan-and gelatinous-type, depending on the composition of their secondary cell walls.The former, typical of jute/ kenaf bast fibres, are characterized by the presence of xylan and a high degree of lignification, while the latter, found in tension wood, as well as flax, ramie and hemp bast fibres, have a high abundance of crystalline cellulose. During their differentiation, bast fibres undergo specific developmental stages: the cells initially elongate rapidly by intrusive growth, subsequently they cease elongation and start to thicken. The goal of the present study is to provide a transcriptomic close-up of the key events accompanying bast fibre development in textile hemp (Cannabis sativa L.), a fibre crop of great importance. Bast fibres have been sampled from different stem regions. The developmental stages corresponding to active elongation and cell wall thickening have been studied using RNASeq.The results show that the fibres sampled at each stem region are characterized by a specific transcriptomic signature and that the major changes in cell wall-related processes take place at the internode containing the snap point. The data generated also identify several interesting candidates for future functional analysis

MINERVA—a platform for visualization and curation of molecular interaction networks

Our growing knowledge about various molecular mechanisms is becoming increasingly more structured and accessible. Different repositories of molecular interactions and available literature enable construction of focused and high-quality molecular interaction networks. Novel tools for curation and exploration of such networks are needed, in order to foster the development of a systems biology environment. In particular, solutions for visualization, annotation and data cross-linking will facilitate usage of network-encoded knowledge in biomedical research. To this end we developed the MINERVA (Molecular Interaction NEtwoRks VisuAlization) platform, a standalone webservice supporting curation, annotation and visualization of molecular interaction networks in Systems Biology Graphical Notation (SBGN)-compliant format. MINERVA provides automated content annotation and verification for improved quality control. The end users can explore and interact with hosted networks, and provide direct feedback to content curators. MINERVA enables mapping drug targets or overlaying experimental data on the visualized networks. Extensive export functions enable downloading areas of the visualized networks as SBGN-compliant models for efficient reuse of hosted networks. The software is available under Affero GPL 3.0 as a Virtual Machine snapshot, Debian package and Docker instance at http://r3lab.uni.lu/web/minerva-website/. We believe that MINERVA is an important contribution to systems biology community, as its architecture enables set-up of locally or globally accessible SBGN-oriented repositories of molecular interaction networks. Its functionalities allow overlay of multiple information layers, facilitating exploration of content and interpretation of data. Moreover, annotation and verification workflows of MINERVA improve the efficiency of curation of networks, allowing life-science researchers to better engage in development and use of biomedical knowledge repositories