@article{deAbreueLimaLiWenetal.2018,
  author    = {de Abreu e Lima, Francisco Anastacio and Li, Kun and Wen, Weiwei and Yan, Jianbing and Nikoloski, Zoran and Willmitzer, Lothar and Brotman, Yariv},
  title     = {Unraveling lipid metabolism in maize with time-resolved multi-omics data},
  series = {The plant journal},
  volume    = {93},
  journal   = {The plant journal},
  number    = {6},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0960-7412},
  doi       = {10.1111/tpj.13833},
  pages     = {1102 -- 1115},
  year      = {2018},
  abstract  = {Maize is the cereal crop with the highest production worldwide, and its oil is a key energy resource. Improving the quantity and quality of maize oil requires a better understanding of lipid metabolism. To predict the function of maize genes involved in lipid biosynthesis, we assembled transcriptomic and lipidomic data sets from leaves of B73 and the high-oil line By804 in two distinct time-series experiments. The integrative analysis based on high-dimensional regularized regression yielded lipid-transcript associations indirectly validated by Gene Ontology and promoter motif enrichment analyses. The co-localization of lipid-transcript associations using the genetic mapping of lipid traits in leaves and seedlings of a B73 x By804 recombinant inbred line population uncovered 323 genes involved in the metabolism of phospholipids, galactolipids, sulfolipids and glycerolipids. The resulting association network further supported the involvement of 50 gene candidates in modulating levels of representatives from multiple acyl-lipid classes. Therefore, the proposed approach provides high-confidence candidates for experimental testing in maize and model plant species.},
  language  = {en}
}
@article{RosinLaiMouldetal.2022,
  author    = {Rosin, Paul L. and Lai, Yu-Kun and Mould, David and Yi, Ran and Berger, Itamar and Doyle, Lars and Lee, Seungyong and Li, Chuan and Liu, Yong-Jin and Semmo, Amir and Shamir, Ariel and Son, Minjung and Winnem{\"o}ller, Holger},
  title     = {NPRportrait 1.0: A three-level benchmark for non-photorealistic rendering of portraits},
  series = {Computational visual media},
  volume    = {8},
  journal   = {Computational visual media},
  number    = {3},
  publisher = {Springer Nature},
  address   = {London},
  issn      = {2096-0433},
  doi       = {10.1007/s41095-021-0255-3},
  pages     = {445 -- 465},
  year      = {2022},
  abstract  = {Recently, there has been an upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer (NST). However, the state of performance evaluation in this field is poor, especially compared to the norms in the computer vision and machine learning communities. Unfortunately, the task of evaluating image stylisation is thus far not well defined, since it involves subjective, perceptual, and aesthetic aspects. To make progress towards a solution, this paper proposes a new structured, three-level, benchmark dataset for the evaluation of stylised portrait images. Rigorous criteria were used for its construction, and its consistency was validated by user studies. Moreover, a new methodology has been developed for evaluating portrait stylisation algorithms, which makes use of the different benchmark levels as well as annotations provided by user studies regarding the characteristics of the faces. We perform evaluation for a wide variety of image stylisation methods (both portrait-specific and general purpose, and also both traditional NPR approaches and NST) using the new benchmark dataset.},
  language  = {en}
}