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We developed a transcript profiling methodology to elucidate expression patterns of the cyanobacterium Synechocystis sp. strain PCC 6803 and used the technology to investigate changes in gene expression caused by irradiation with either intermediate-wavelength UV light (UV-B) or high-intensity white light. Several families of transcripts were altered by UV-B treatment, including mRNAs specifying proteins involved in light harvesting, photosynthesis, photoprotection, and the heat shock response. In addition, UV-B light induced the stringent response in Synechocystis, as indicated by the repression of ribosomal protein transcripts and other mRNAs involved in translation. High-intensity white light-and UV-B-mediated expression profiles overlapped in the down-regulation of photosynthesis genes and induction of heat shock response but differed in several other transcriptional processes including those specifying carbon dioxide uptake and fixation, the stringent response, and the induction profile of the high-light-inducible proteins. These two profile comparisons not only corroborated known physiological changes but also suggested coordinated regulation of many pathways, including synchronized induction of D1 protein recycling and a coupling between decreased phycobilisome biosynthesis and increased phycobilisome degradation. Overall, the gene expression profile analysis generated new insights into the integrated network of genes that adapts rapidly to different wavelengths and intensities of light.While sunlight provides the energy for life, intermediatewavelength UV light (UV-B) and long-wavelength UV light (UV-A) injure many organisms. UV-B (280-to 320-nm-wavelength) light generates radicals that damage proteins, nucleic acids, lipids, and the photosynthetic apparatus. The latter damage appears in the form of impaired photosystems I and II, decreased oxygen evolution and CO 2 fixation, inactivation of ATPase activity, reduction in chlorophyll content, and decreased biomass (33). Notably, the D1 protein of the light energy converting complex photosystem II is quite sensitive to UV-B light (10). Although less harmful than UV-B irradiation, UV-A irradiation (320-to 400-nm-wavelength light) also damages proteins, nucleic acids, lipids, and photosystems (11). UV-A irradiation leads to the intramolecular cross-linking of several tRNA species (2), which results in poor aminoacylation and triggering of the stringent response-a phenomenon that reduces the rate of stable RNA, ribosome, and translation factor synthesis, thus arresting growth (32). Higher fluxes of white light also cause a loss of photosynthetic productivity (photoinhibition) (30).In cyanobacteria, more than 99% of UV-B is absorbed by chlorophyll-binding proteins and the light-harvesting complexes (phycobilisomes) (20). Carotenoids protect cells against photooxidative damage by absorbing triplet state energy from chlorophyll and quenching singlet state oxygen (15). In response to changes in light quantity or quality, cyanobacteria modulate the abundance of ch...

Fungal pathogens secrete effector proteins to suppress plant basal defense for successful colonization. Resistant plants, however, can recognize effectors by cognate R proteins to induce effector-triggered immunity (ETI). By analyzing secretomes of the vascular fungal pathogen Verticillium dahliae, we identified a novel secreted protein VdSCP7 that targets the plant nucleus. The green fluorescent protein (GFP)-tagged VdSCP7 gene with either a mutated nuclear localization signal motif or with additional nuclear export signal was transiently expressed in Nicotiana benthamiana, and investigated for induction of plant immunity. The role of VdSCP7 in V. dahliae pathogenicity was characterized by gene knockout and complementation, and GFP labeling. Expression of the VdSCP7 gene in N. benthamiana activated both salicylic acid and jasmonate signaling, and altered the plant's susceptibility to the pathogens Botrytis cinerea and Phytophthora capsici. The immune response activated by VdSCP7 was highly dependent on its initial extracellular secretion and subsequent nuclear localization in plants. Knockout of the VdSCP7 gene significantly enhanced V. dahliae aggressiveness on cotton. GFP-labeled VdSCP7 is secreted by V. dahliae and accumulates in the plant nucleus. We conclude that VdSCP7 is a novel effector protein that targets the host nucleus to modulate plant immunity, and suggest that plants can recognize VdSCP7 to activate ETI during fungal infection.

Rough path theory is a mathematical toolbox providing for the deterministic modelling of interactions between highly oscillatory systems (rough paths). The theory is rich enough to capture and extend classical Itô stochastic calculus but has far wider significance. Fundamental to this approach is the realisation that the evolving state of the system is best described or measured over short time intervals by considering the realised effect of the system on certain controlled systems (the measurement instruments). The approach is crucially more efficient than an approach based on sampling. Even in the limit, the sequence of time series obtained through finer sampling need not provide a sufficient statistic that could predict the effects of a Brownian motion in an interaction ([13])! This divergence in the analysis arising from the choice of feature set is relevant in any context where the data streams are highly oscillatory on normal time scales.We bring the theory of rough paths to the study of non-parametric statistics on streamed data and particularly to the problem of regression where the input variable is a stream of information, and the dependent response is also (potentially) a path or a stream. We explain how a certain graded feature set of a stream, known in the rough path literature as the signature of the path, has a universality that allows one to characterise the functional relationship summarising the conditional distribution of the dependent response. At the same time this feature set allows explicit computational approaches through linear regression.Truncating the signature of a stream leads to an efficient local description for a rough path. The intrinsic non-linearity provides a provable efficiency gain for the signature over linear descriptions of the path

Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3DLGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0.27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF.

Online handwritten Chinese text recognition (OHCTR) is a challenging problem as it involves a large-scale character set, ambiguous segmentation, and variable-length input sequences. In this paper, we exploit the outstanding capability of path signature to translate online pen-tip trajectories into informative signature feature maps, successfully capturing the analytic and geometric properties of pen strokes with strong local invariance and robustness. A multi-spatial-context fully convolutional recurrent network (MC-FCRN) is proposed to exploit the multiple spatial contexts from the signature feature maps and generate a prediction sequence while completely avoiding the difficult segmentation problem. Furthermore, an implicit language model is developed to make predictions based on semantic context within a predicting feature sequence, providing a new perspective for incorporating lexicon constraints and prior knowledge about a certain language in the recognition procedure. Experiments on two standard benchmarks, Dataset-CASIA and Dataset-ICDAR, yielded outstanding results, with correct rates of 97.50 and 96.58 percent, respectively, which are significantly better than the best result reported thus far in the literature.

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The signature of a path provides a top down description of the path in terms of its effects as a control [Differential Equations Driven by Rough Paths (2007) Springer]. The signature transforms a path into a group-like element in the tensor algebra and is an essential object in rough path theory. The expected signature of a stochastic process plays a similar role to that played by the characteristic function of a random variable. In [Chevyrev (2013)], it is proved that under certain boundedness conditions, the expected value of a random signature already determines the law of this random signature. It becomes of great interest to be able to compute examples of expected signatures and obtain the upper bounds for the decay rates of expected signatures. For instance, the computation for Brownian motion on [0, 1] leads to the "cubature on Wiener space" methodology [Lyons and Victoir, Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 460 (2004) 169-198]. In this paper we fix a bounded domain Γ in a Euclidean space E and study the expected signature of a Brownian path starting at z ∈ Γ and stopped at the first exit time from Γ. We denote this tensor series valued function by ΦΓ(z) and focus on the case E = R d . We show that ΦΓ(z) satisfies an elliptic PDE system and a boundary condition. The equations determining ΦΓ can be recursively solved; by an iterative application of Sobolev estimates we are able, under certain smoothness and boundedness condition of the domain Γ, to prove geometric bounds for the terms in ΦΓ(z). However, there is still a gap and we have not shown that ΦΓ(z) determines the law of the signature of this stopped Brownian motion even if Γ is a unit ball.