mRNA export: threading the needle

After mRNA biogenesis, several proteins interact with the messenger to ensure its proper export to the cytoplasm. Some of these proteins will bind RNA early on, at the onset of transcription by RNA polymerase II holoenzyme, while others will join later for downstream processing steps, such as poly-adenylation or splicing, or may direct mRNA ribonucleoprotein particle migration to the nucleopore. We recently discovered that Arabidopsis plant knockout for the protein MOS11 (MODIFIER OF SNC1, 11) partially suppresses autoimmune responses observed in the TNL-type [TIR/NBS/LRR (Toll-interleukin-like receptor/nucleotide-binding site/C-terminal leucine-rich repeat)] R gene gain-of-function variant snc1 (suppressor of npr1-1, constitutive 1). This suppression of resistance to pathogens appears to be caused by a decrease in nuclear mRNA export in mos11-1 snc1 plants. In humans, the putative ortholog of MOS11, CIP29 (29-kDa cytokine-induced protein), interacts with three proteins that are also involved in mRNA export: DDX39 (DEAD-box RNA helicase), TAF15 of the FUS family (FUSED IN SARCOMA), and ALY (ALWAYS EARLY), a protein implicated in mRNA export in mammalian systems. These proteins have received very little attention in plants. Here, we will discuss their particularities and role in mRNA export and biotic stress

Early topping: an alternative to standard topping increases yield in cannabis production

In commercial settings, cannabis is generally propagated through cuttings, a process referred in the industry as cloning. Some producers perform either topping or fimming to trigger the production of axillary shoots, which will enhance the number of flowers per plants and thus increase the yield of the cannabis plants. Topping or fimming is generally performed after the cuttings have been transferred to rooting media for two weeks. We have tested a new method to increase the shoot number per plant. The modification of the standard topping method consist of performing the topping on mother plants, prior to taking the cuttings for cloning, and the cuttings are taken one week after the topping is performed. The resulting plantlets develop axillary shoots much faster and the time of production from cuttings to harvesting is decreased by 7-10 days. The method proposed herein requires minimal adjustment to the existing workflow and the plants produce as much as when standard topping is performed. Moreover, this method cuts backs on the production time and nearly two weeks are saved compared to the standard topping procedure since the plantlets do not need to recover after topping. Application of this new procedure results in faster production time and ultimately enhanced productivity

Génomique fonctionnelle de la transduction de signal, isolement et caractérisation de récepteurs kinases chez Solanum chacoense

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

Domain-Adversarial Training of Neural Networks

We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains. The approach implements this idea in the context of neural network architectures that are trained on labeled data from the source domain and unlabeled data from the target domain (no labeled target-domain data is necessary). As the training progresses, the approach promotes the emergence of features that are (i) discriminative for the main learning task on the source domain and (ii) indiscriminate with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation and stochastic gradient descent, and can thus be implemented with little effort using any of the deep learning packages. We demonstrate the success of our approach for two distinct classification problems (document sentiment analysis and image classification), where state-of-the-art domain adaptation performance on standard benchmarks is achieved. We also validate the approach for descriptor learning task in the context of person re-identification application.Comment: Published in JMLR: http://jmlr.org/papers/v17/15-239.htm