Functional analysis of MPK3 and MPK6, two mitogen-activated protine kinases in Arabidopsis thaliana

Abstract only availableMitogen-activated protein kinase (MAPK) cascades are major pathways involved in the transduction of extracellular signals into intracellular responses. A MAPK cascade consists of three kinases; MAPK, MAPK kinase (MAPKK or MEK) and MAPKK kinase (MAPKKK or MEKK). MAPKKK is at the top of this three-tier cascade. Upon its activation by a receptor/sensor, MAPKKK phosphorylates MAPKK, which in turn phosphorylates MAPK and activates it. The activated MAPK can then phosphorylate other protein kinases or be translocated to the nucleus where it can phosphorylate transcription factors and activate gene expression. About 20 MAPKs were identified in the fully sequenced Arabidopsis genome. To study the function of MPK3 and MPK6, the two most closely related MAPKs in Arabidopsis, we isolated the corresponding T-DNA mutants from mutant libraries generated at Wisconsin Arabidopsis Knockout Facility and Salk Institute Genomic Analysis Laboratory. No morphological or developmental phenotypes were observed in the MPK3-/- and MPK6-/- single mutants. In order to determine if MPK3 and MPK6 have overlapping functions, we crossed the two single mutants (MPK3-/- and MPK6-/-) to generate double mutants. Among the 172 F2 plants that we genotyped, no double homozygous (MPK3-/-/MPK6-/-) mutant plants was identified, indicating that this genotype is lethal. We further observed that plants with the MPK3+/-/MPK6-/- genotype are a little smaller and sterile. Reciprocal back cross to wild type plants demonstrated that MPK3+/-/MPK6-/- plants are female sterile. The resilience of the pollens from such plants is still under investigation. In contrast to MPK3+/-/MPK6-/- plants, MPK3-/-/MPK6+/- plants are fertile and apparently normal. Together with the normal phenotype of MPK3-/- and MPK6-/- single mutants, we conclude that MPK3 and MPK6 perform overlapping but not identical roles in the reproduction and development of Arabidopsis thaliana.EXPRESS Progra

Functional analysis of MAP kinases in Arabidopsis thaliana: Fully rescuing the mpk3/mpk6 mutant phenotypes

Abstract only availableMitogen Activated Protein Kinase (MAPK) cascades are three-stage modules involved in signal transduction. MAPKs function at the lower tier of these cascades and they phosphorylate transcription factors and other protein kinases upon activation, ultimately leading to cellular responses. Twenty genes coding for MAPKs were identified in the fully sequenced Arabidopsis genome. MPK3 and MPK6 are the most closely related. Analysis of T-DNA insertional lines revealed no phenotype in the mpk3 and mpk6 single mutants; however, female sterility is observed in MPK3+/-/MPK6-/- plants and embryo lethality results from knocking out both genes. This indicates overlapping function of MPK3 and MPK6. To better understand the function of these two kinases, an attempt was made to rescue these phenotypes by introducing a Dexamethasone (DEX) inducible: MPK6 transgene. This construct led to only partial rescue of the lethal double mutants, and no signs of fertility were evident in MPK3+/-/MPK6-/- plants. In an attempt to attain complete rescue of these phenotypes, new MPK3 and MPK6 constructs were engineered with the following features: • Transgenes regulated by endogenous promoters were used in order to maintain normal cell/tissue specific expression of the protein, which may be essential for normal plant function. • The transgene products were tagged with Yellow Florescent Protein and Green Florescent Protein in order to ascertain their expression patterns. • Genomic DNA, as opposed to complementary DNA, was used as the coding regions in order to ensure the presence of introns, which may be significant for gene function. Currently, T1 generation transgenic plants have been isolated and transgenic lines with good expression of the transgene proteins, in vivo, will be identified by Western Blot analysis. Indication of a full rescue will be verified in the T2 generation. Failure to observe completely rescued lines may indicate protein tag interference, and untagged constructs will then be attempted.MU Monsanto Undergraduate Research Fellowshi

Pedestrian Walking Behavior Revealed through a Random Walk Model

This paper applies method of continuous-time random walks for pedestrian flow simulation. In the model, pedestrians can walk forward or backward and turn left or right if there is no block. Velocities of pedestrian flow moving forward or diffusing are dominated by coefficients. The waiting time preceding each jump is assumed to follow an exponential distribution. To solve the model, a second-order two-dimensional partial differential equation, a high-order compact scheme with the alternating direction implicit method, is employed. In the numerical experiments, the walking domain of the first one is two-dimensional with two entrances and one exit, and that of the second one is two-dimensional with one entrance and one exit. The flows in both scenarios are one way. Numerical results show that the model can be used for pedestrian flow simulation

Traffic Speed Data Imputation Method Based on Tensor Completion

Traffic speed data plays a key role in Intelligent Transportation Systems (ITS); however, missing traffic data would affect the performance of ITS as well as Advanced Traveler Information Systems (ATIS). In this paper, we handle this issue by a novel tensor-based imputation approach. Specifically, tensor pattern is adopted for modeling traffic speed data and then High accurate Low Rank Tensor Completion (HaLRTC), an efficient tensor completion method, is employed to estimate the missing traffic speed data. This proposed method is able to recover missing entries from given entries, which may be noisy, considering severe fluctuation of traffic speed data compared with traffic volume. The proposed method is evaluated on Performance Measurement System (PeMS) database, and the experimental results show the superiority of the proposed approach over state-of-the-art baseline approaches

Robust Missing Traffic Flow Imputation Considering Nonnegativity and Road Capacity

There are increasing concerns about missing traffic data in recent years. In this paper, a robust missing traffic flow data imputation approach based on matrix completion is proposed. In the proposed method, the similarity of traffic flow from day to day is exploited to impute missing data by the low-rank hypothesis of constructed traffic flow matrix. And the physical limitation of road capacity and nonnegativity is also considered through the optimization process, which avoids the possibility of producing negative and overcapacity values. Moreover, the proposed algorithm can impute missing data and recover outlier in a unify framework. The experiment results show that the proposed method is more accurate, stable, and reasonable

Using protection motivation theory to explain the intention to initiate human papillomavirus vaccination among men who have sex with men in China

Human papillomavirus (HPV) infection and related diseases are common among men who have sex with men (MSM). The most effective prevention is HPV vaccination. In China, however, men are not included in the HPV vaccination plan. We investigated the intention to initiate HPV vaccination and associated factors among MSM in China. Methods We surveyed 563 unvaccinated MSM aged 18 or older from six cities in China. Participants completed an electronic questionnaire about demographics, knowledge of and attitude towards HPV and HPV vaccine, intention to initiate HPV vaccination, willingness to recommend HPV vaccine to peers, feeling about government policy about HPV vaccination. We used the structural equation modeling (SEM) to analyze factors associated with HPV vaccine intention. Results The knowledge of HPV and HPV vaccine among participants was low. The mean score of knowledge about HPV and HPV vaccine was only 1.59 (range 0–11). The intention to initiate HPV vaccination within 6 months among participants was moderate (43.3% in total, 18.1% for ‘very high' and 25.2% for ‘above average')

Mixture Augmented Lagrange Multiplier Method for Tensor Recovery and Its Applications

The problem of data recovery in multiway arrays (i.e., tensors) arises in many fields such as computer vision, image processing, and traffic data analysis. In this paper, we propose a scalable and fast algorithm for recovering a low-n-rank tensor with an unknown fraction of its entries being arbitrarily corrupted. In the new algorithm, the tensor recovery problem is formulated as a mixture convex multilinear Robust Principal Component Analysis (RPCA) optimization problem by minimizing a sum of the nuclear norm and the ℓ1-norm. The problem is well structured in both the objective function and constraints. We apply augmented Lagrange multiplier method which can make use of the good structure for efficiently solving this problem. In the experiments, the algorithm is compared with the state-of-art algorithm both on synthetic data and real data including traffic data, image data, and video data

Robust Missing Traffic Flow Imputation Considering Nonnegativity and Road Capacity

There are increasing concerns about missing traffic data in recent years. In this paper, a robust missing traffic flow data imputation approach based on matrix completion is proposed. In the proposed method, the similarity of traffic flow from day to day is exploited to impute missing data by the low-rank hypothesis of constructed traffic flow matrix. And the physical limitation of road capacity and nonnegativity is also considered through the optimization process, which avoids the possibility of producing negative and overcapacity values. Moreover, the proposed algorithm can impute missing data and recover outlier in a unify framework. The experiment results show that the proposed method is more accurate, stable, and reasonable