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- Aphid host (2)
- Aphidius ervi (2)
- Chemosensory genes (2)
- DNA methylation loss (2)
- GC content (2)
- Lysiphlebus fabarum (2)
- Markov cluster algorithm (2)
- Parasitoid wasp (2)
- Toll and Imd pathways (2)
- Venom proteins (2)
- XMRV (2)
- Xpr1 (2)
- de novo genome assembly (2)
- Endogenous retrovirus (1)
- Genomic evolution (1)
- Murine leukemia virus (1)
- carbon dioxide removal (CDR) (1)
- echno-economic assessment (1)
- endogenous retrovirus (1)
- genomic evolution (1)
- murine leukemia virus (1)
- ocean alkalinity enhancement (OAE) (1)
- ocean liming (OL) (1)
- uptake efficiency (1)
Background
Parasitoid wasps have fascinating life cycles and play an important role in trophic networks, yet little is known about their genome content and function. Parasitoids that infect aphids are an important group with the potential for biological control. Their success depends on adapting to develop inside aphids and overcoming both host aphid defenses and their protective endosymbionts.
Results
We present the de novo genome assemblies, detailed annotation, and comparative analysis of two closely related parasitoid wasps that target pest aphids: Aphidius ervi and Lysiphlebus fabarum (Hymenoptera: Braconidae: Aphidiinae). The genomes are small (139 and 141 Mbp) and the most AT-rich reported thus far for any arthropod (GC content: 25.8 and 23.8%). This nucleotide bias is accompanied by skewed codon usage and is stronger in genes with adult-biased expression. AT-richness may be the consequence of reduced genome size, a near absence of DNA methylation, and energy efficiency. We identify missing desaturase genes, whose absence may underlie mimicry in the cuticular hydrocarbon profile of L. fabarum. We highlight key gene groups including those underlying venom composition, chemosensory perception, and sex determination, as well as potential losses in immune pathway genes.
Conclusions
These findings are of fundamental interest for insect evolution and biological control applications. They provide a strong foundation for further functional studies into coevolution between parasitoids and their hosts. Both genomes are available at https://bipaa.genouest.org.
Background
Parasitoid wasps have fascinating life cycles and play an important role in trophic networks, yet little is known about their genome content and function. Parasitoids that infect aphids are an important group with the potential for biological control. Their success depends on adapting to develop inside aphids and overcoming both host aphid defenses and their protective endosymbionts.
Results
We present the de novo genome assemblies, detailed annotation, and comparative analysis of two closely related parasitoid wasps that target pest aphids: Aphidius ervi and Lysiphlebus fabarum (Hymenoptera: Braconidae: Aphidiinae). The genomes are small (139 and 141 Mbp) and the most AT-rich reported thus far for any arthropod (GC content: 25.8 and 23.8%). This nucleotide bias is accompanied by skewed codon usage and is stronger in genes with adult-biased expression. AT-richness may be the consequence of reduced genome size, a near absence of DNA methylation, and energy efficiency. We identify missing desaturase genes, whose absence may underlie mimicry in the cuticular hydrocarbon profile of L. fabarum. We highlight key gene groups including those underlying venom composition, chemosensory perception, and sex determination, as well as potential losses in immune pathway genes.
Conclusions
These findings are of fundamental interest for insect evolution and biological control applications. They provide a strong foundation for further functional studies into coevolution between parasitoids and their hosts. Both genomes are available at https://bipaa.genouest.org.
We introduce a class of variational states to describe quantum many-body systems. This class generalizes matrix product states which underlie the density-matrix renormalization-group approach by combining them with weighted graph states. States within this class may (i) possess arbitrarily long-ranged two-point correlations, (ii) exhibit an arbitrary degree of block entanglement entropy up to a volume law, (iii) be taken translationally invariant, while at the same time (iv) local properties and two-point correlations can be computed efficiently. This variational class of states can be thought of as being prepared from matrix product states, followed by commuting unitaries on arbitrary constituents, hence truly generalizing both matrix product and weighted graph states. We use this class of states to formulate a renormalization algorithm with graph enhancement and present numerical examples, demonstrating that improvements over density-matrix renormalization-group simulations can be achieved in the simulation of ground states and quantum algorithms. Further generalizations, e.g., to higher spatial dimensions, are outlined.
We present applications of the renormalization algorithm with graph enhancement (RAGE). This analysis extends the algorithms and applications given for approaches based on matrix product states introduced in [Phys. Rev. A 79, 022317 (2009)] to other tensor-network states such as the tensor tree states (TTS) and projected entangled pair states. We investigate the suitability of the bare TTS to describe ground states, showing that the description of certain graph states and condensed-matter models improves. We investigate graph-enhanced tensor-network states, demonstrating that in some cases (disturbed graph states and for certain quantum circuits) the combination of weighted graph states with TTS can greatly improve the accuracy of the description of ground states and time-evolved states. We comment on delineating the boundary of the classically efficiently simulatable states of quantum many-body systems.
Background: Endogenous murine leukemia retroviruses (MLVs) are high copy number proviral elements difficult to comprehensively characterize using standard low throughput sequencing approaches. However, high throughput approaches generate data that is challenging to process, interpret and present.
Results: Next generation sequencing (NGS) data was generated for MLVs from two wild caught Mus musculus domesticus (from mainland France and Corsica) and for inbred laboratory mouse strains C3H, LP/J and SJL. Sequence reads were grouped using a novel sequence clustering approach as applied to retroviral sequences. A Markov cluster algorithm was employed, and the sequence reads were queried for matches to specific xenotropic (Xmv), polytropic (Pmv) and modified polytropic (Mpmv) viral reference sequences.
Conclusions: Various MLV subtypes were more widespread than expected among the mice, which may be due to the higher coverage of NGS, or to the presence of similar sequence across many different proviral loci. The results did not correlate with variation in the major MLV receptor Xpr1, which can restrict exogenous MLVs, suggesting that endogenous MLV distribution may reflect gene flow more than past resistance to infection.
To achieve the Paris climate target, deep emissions reductions have to be complemented with carbon dioxide removal (CDR). However, a portfolio of CDR options is necessary to reduce risks and potential negative side effects. Despite a large theoretical potential, ocean-based CDR such as ocean alkalinity enhancement (OAE) has been omitted in climate change mitigation scenarios so far. In this study, we provide a techno-economic assessment of large-scale OAE using hydrated lime ('ocean liming'). We address key uncertainties that determine the overall cost of ocean liming (OL) such as the CO2 uptake efficiency per unit of material, distribution strategies avoiding carbonate precipitation which would compromise efficiency, and technology availability (e.g., solar calciners). We find that at economic costs of 130–295 $/tCO2 net-removed, ocean liming could be a competitive CDR option which could make a significant contribution towards the Paris climate target. As the techno-economic assessment identified no showstoppers, we argue for more research on ecosystem impacts, governance, monitoring, reporting, and verification, and technology development and assessment to determine whether ocean liming and other OAE should be considered as part of a broader CDR portfolio.
Background: Endogenous murine leukemia retroviruses (MLVs) are high copy number proviral elements difficult to comprehensively characterize using standard low throughput sequencing approaches. However, high throughput approaches generate data that is challenging to process, interpret and present.
Results: Next generation sequencing (NGS) data was generated for MLVs from two wild caught Mus musculus domesticus (from mainland France and Corsica) and for inbred laboratory mouse strains C3H, LP/J and SJL. Sequence reads were grouped using a novel sequence clustering approach as applied to retroviral sequences. A Markov cluster algorithm was employed, and the sequence reads were queried for matches to specific xenotropic (Xmv), polytropic (Pmv) and modified polytropic (Mpmv) viral reference sequences.
Conclusions: Various MLV subtypes were more widespread than expected among the mice, which may be due to the higher coverage of NGS, or to the presence of similar sequence across many different proviral loci. The results did not correlate with variation in the major MLV receptor Xpr1, which can restrict exogenous MLVs, suggesting that endogenous MLV distribution may reflect gene flow more than past resistance to infection.
We present real-world data processing on measured electron time-of-flight data via neural networks. Specifically, the use of disentangled variational autoencoders on data from a diagnostic instrument for online wavelength monitoring at the free electron laser FLASH in Hamburg. Without a-priori knowledge the network is able to find representations of single-shot FEL spectra, which have a low signal-to-noise ratio. This reveals, in a directly human-interpretable way, crucial information about the photon properties. The central photon energy and the intensity as well as very detector-specific features are identified. The network is also capable of data cleaning, i.e. denoising, as well as the removal of artefacts. In the reconstruction, this allows for identification of signatures with very low intensity which are hardly recognisable in the raw data. In this particular case, the network enhances the quality of the diagnostic analysis at FLASH. However, this unsupervised method also has the potential to improve the analysis of other similar types of spectroscopy data.