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- Glasbey, JC, et al.
(författare)
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- 2021
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swepub:Mat__t
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- Douchkov, D., et al.
(författare)
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The barley (Hordeum vulgare) cellulose synthase-like D2 gene (HvCslD2) mediates penetration resistance to host-adapted and nonhost isolates of the powdery mildew fungus
- 2016
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Ingår i: New Phytologist. - : Blackwell Publishing. - 0028-646X .- 1469-8137. ; 212:2, s. 421-433
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Tidskriftsartikel (refereegranskat)abstract
- Cell walls and cellular turgor pressure shape and suspend the bodies of all vascular plants. In response to attack by fungal and oomycete pathogens, which usually breach their host's cell walls by mechanical force or by secreting lytic enzymes, plants often form local cell wall appositions (papillae) as an important first line of defence. The involvement of cell wall biosynthetic enzymes in the formation of these papillae is still poorly understood, especially in cereal crops. To investigate the role in plant defence of a candidate gene from barley (Hordeum vulgare) encoding cellulose synthase-like D2 (HvCslD2), we generated transgenic barley plants in which HvCslD2 was silenced through RNA interference (RNAi). The transgenic plants showed no growth defects but their papillae were more successfully penetrated by host-adapted, virulent as well as avirulent nonhost isolates of the powdery mildew fungus Blumeria graminis. Papilla penetration was associated with lower contents of cellulose in epidermal cell walls and increased digestion by fungal cell wall degrading enzymes. The results suggest that HvCslD2-mediated cell wall changes in the epidermal layer represent an important defence reaction both for nonhost and for quantitative host resistance against nonadapted wheat and host-adapted barley powdery mildew pathogens, respectively.
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- van der Heide, T., et al.
(författare)
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Fortnet, a software package for training Behler-Parrinello neural networks
- 2023
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Ingår i: Computer Physics Communications. - : Elsevier. - 0010-4655 .- 1879-2944. ; 284
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Tidskriftsartikel (refereegranskat)abstract
- A new, open source, parallel, stand-alone software package (Fortnet) has been developed, which implements Behler-Parrinello neural networks. It covers the entire workflow from feature generation to the evaluation of generated potentials, coupled with higher-level analysis such as the analytic calculation of atomic forces. The functionality of the software package is demonstrated by driving the training for the fitted correction functions of the density functional tight binding (DFTB) method, which are commonly used to compensate the inaccuracies resulting from the DFTB approximations to the Kohn -Sham Hamiltonian. The usual two-body form of those correction functions limits the transferability of the parametrizations between very different structural environments. The recently introduced DFTB+ANN approach strives to lift these limitations by combining DFTB with a near-sighted artificial neural network (ANN). After investigating various approaches, we have found the combination of DFTB with an ANN acting on-top of some baseline correction functions (delta learning) the most accurate one. It allowed to introduce many-body corrections on top of two-body parametrizations, while excellent transferability to chemical environments with deviating energetics could be demonstrated.
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