The maize root stem cell niche: a partnership between two sister cell populations

Using transcript profile analysis, we explored the nature of the stem cell niche in roots of maize (Zea mays). Toward assessing a role for specific genes in the establishment and maintenance of the niche, we perturbed the niche and simultaneously monitored the spatial expression patterns of genes hypothesized as essential. Our results allow us to quantify and localize gene activities to specific portions of the niche: to the quiescent center (QC) or the proximal meristem (PM), or to both. The data point to molecular, biochemical and physiological processes associated with the specification and maintenance of the niche, and include reduced expression of metabolism-, redox- and certain cell cycle-associated transcripts in the QC, enrichment of auxin-associated transcripts within the entire niche, controls for the state of differentiation of QC cells, a role for cytokinins specifically in the PM portion of the niche, processes (repair machinery) for maintaining DNA integrity and a role for gene silencing in niche stabilization. To provide additional support for the hypothesized roles of the above-mentioned and other transcripts in niche specification, we overexpressed, in Arabidopsis, homologs of representative genes (eight) identified as highly enriched or reduced in the maize root QC. We conclude that the coordinated changes in expression of auxin-, redox-, cell cycle- and metabolism-associated genes suggest the linkage of gene networks at the level of transcription, thereby providing additional insights into events likely associated with root stem cell niche establishment and maintenance

Sign Language Gesture Recognition and Classification Based on Event Camera with Spiking Neural Networks

Sign language recognition has been utilized in human–machine interactions, improving the lives of people with speech impairments or who rely on nonverbal instructions. Thanks to its higher temporal resolution, less visual redundancy information and lower energy consumption, the use of an event camera with a new dynamic vision sensor (DVS) shows promise with regard to sign language recognition with robot perception and intelligent control. Although previous work has focused on event camera-based, simple gesture datasets, such as DVS128Gesture, event camera gesture datasets inspired by sign language are critical, which poses a great impediment to the development of event camera-based sign language recognition. An effective method to extract spatio-temporal features from event data is significantly desired. Firstly, the event-based sign language gesture datasets are proposed and the data have two sources: traditional sign language videos to event stream (DVS_Sign_v2e) and DAVIS346 (DVS_Sign). In the present dataset, data are divided into five classification, verbs, quantifiers, position, things and people, adapting to actual scenarios where robots provide instruction or assistance. Sign language classification is demonstrated in spike neuron networks with a spatio-temporal back-propagation training method, leading to the best recognition accuracy of 77%. This work paves the way for the combination of event camera-based sign language gesture recognition and robotic perception for the future intelligent systems

Measuring similarities between gene expression profiles through new data transformations

Background: Clustering methods have been widely applied to gene expression data in order to group genes sharing common or similar expression profiles into discrete functional groups. In such analyses, designing an appropriate (dis)similarity measure is critical. In this study, we aim to develop a new distance measure for gene expression profiles. The new measure is expected to be especially efficient when the shape of expression profile is vital in determining the gene relationship, yet the expression magnitude should also be accounted for to some extent. Results: The new measure, named TransChisq, was developed by separately modeling the shape and magnitude information and then using the estimated shape and magnitude parameters to define a distance measure in a new feature space. The feature space was constructed based on the specific clustering purpose of grouping genes with similar shape of expression curves, while the magnitude information should also be considered when determining the shape similarity. The new measure was employed into a k-means clustering procedure for performing clustering analyses. Results from applications to a simulation dataset, a developing mouse retina SAGE dataset, a small yeast sporulation cDNA datase

Expression and Characterization of a Redox-Sensing Green Fluorescent Protein (Reduction-Oxidation-Sensitive Green Fluorescent Protein) in Arabidopsis

Arabidopsis (Arabidopsis thaliana) was transformed with a redox-sensing green fluorescent protein (reduction-oxidation-sensitive green fluorescent protein [roGFP]), with expression targeted to either the cytoplasm or to the mitochondria. Both the mitochondrial and cytosolic forms are oxidation-reduction sensitive, as indicated by a change in the ratio of 510 nm light (green light) emitted following alternating illumination with 410 and 474 nm light. The 410/474 fluorescence ratio is related to the redox potential (in millivolts) of the organelle, cell, or tissue. Both forms of roGFP can be reduced with dithiothreitol and oxidized with hydrogen peroxide. The average resting redox potentials for roots are −318 mV for the cytoplasm and −362 mV for the mitochondria. The elongation zone of the Arabidopsis root has a more oxidized redox status than either the root cap or meristem. Mitochondria are much better than the cytoplasm, as a whole, at buffering changes in redox. The data show that roGFP is redox sensitive in plant cells and that this sensor makes it possible to monitor, in real time, dynamic changes in redox in vivo