The GOLVEN (GLV)/ROOT GROWTH FACTORS/CLE-Like small signaling peptide family is encoded by 11 genes in Arabidopsis (Arabidopsis thaliana). Some of them have already been shown to control root meristem maintenance, auxin fluxes, and gravitropic responses. As a basis for the detailed analysis of their function, we determined the expression domains for each of the 11 GLV genes with promoter-reporter lines. Although they are collectively active in all examined plant parts, GLV genes have highly specific transcription patterns, generally restricted to very few cells or cell types in the root and shoot and in vegetative and reproductive tissues. GLV functions were further investigated with the comparative analysis of root phenotypes induced by gain-and loss-of-function mutants or in treatments with GLV-derived synthetic peptides. We identified functional classes that relate to the gene expression domains in the primary root and suggest that different GLV signals trigger distinct downstream pathways. Interestingly, GLV genes transcribed at the early stages of lateral root development strongly inhibited root branching when overexpressed. Furthermore, transcription patterns together with mutant phenotypes pointed to the involvement of GLV4 and GLV8 in root hair formation. Overall, our data suggest that nine GLV genes form three subgroups according to their expression and function within the root and offer a comprehensive framework to study the role of the GLV signaling peptides in plant development.

mRNA degradation plays an important role in the rapid and dynamic alteration of gene expression in response to environmental stimuli. Arabidopsis 5'-3' exoribonuclease (AtXRN4), a homolog of yeast Xrn1p, functions after a de-capping step in the degradation of uncapped RNAs. While Xrn1p-dependent degradation of mRNA is the main process of mRNA decay in yeast, information pertaining to the targets of XRN4-based degradation in plants is limited. In order to better understand the biological function of AtXRN4, the current study examined the survivability of atxrn4 mutants subjected to heat stress. The results indicated that atxrn4 mutants, compared with wild-type plants, exhibited an increased survival rate when subjected to a short-term severe heat stress. A microarray and mRNA decay assay showed that loss of AtXRN4 function caused a reduction in the degradation of heat shock factor A2 (HSFA2) and ethylene response factor 1 (ERF1) mRNA. The heat stress tolerance phenotype of atxrn4 mutants was significantly reduced or lost by mutation of HSFA2, a known key regulator of heat acclimation, thus indicating that HSFA2 is a target gene of AtXRN4-mediated mRNA degradation both under non-stress conditions and during heat acclimation. These results demonstrate that AtXRN4-mediated mRNA degradation is linked to the suppression of heat acclimation.

In our efforts to develop novel hydrophilic monolithic porous materials for use as supports in liquid chromatographic separation of proteins, polymers based on epoxy monomers and diamines as curing agents were synthesized. The epoxy dispersed phase was emulsified in an aqueous phase containing the amine with the aid of a nonionic polymeric surfactant, and the resulting emulsions were thermally polymerized. Various factors, namely, the type of epoxy component, levels of reactants, type and concentration of diluents, and curing procedures, were studied to obtain suitable morphology and adequate mechanical properties for their intended use. Characterization of their morphologies and porous properties was done using scanning electron microscopy, nitrogen adsorption/desorption measurement (BET method), mercury intrusion porosimetry, and X-ray photoelectron spectroscopy.

Abstract. In this paper, we propose a novel gait authentication mechanism by mining sensor resources on mobile phone. Unlike previous works, both built-in accelerometer and magnetometer are used to handle mobile installation issues, including but not limited to disorientation, and misplacement errors. The authentication performance is improved by executing deep examination at pre-processing steps. A novel and effective segmentation algorithm is also provided to segment signal into separate gait cycles with perfect accuracy. Subsequently, features are then extracted on both time and frequency domains. We aim to construct a lightweight but high reliable model; hence feature subsets selection algorithms are applied to optimize the dimension of the feature vectors as well as the processing time of classification tasks. Afterward, the optimal feature vector is classified using SVM with RBF kernel. Since there is no public dataset in this field to evaluate fairly the effectiveness of our mechanism, a realistic dataset containing the influence of mobile installation errors and footgear is also constructed with the participation of 38 volunteers (28 males, 10 females). We achieved the accuracy approximately 94.93% under identification mode, the FMR, FNMR of 0%, 3.89% and processing time of less than 4 seconds under authentication mode.