Transcriptomic Profiling of Arabidopsis Abscisic Acid Sensitivity and Receptor Function

Abscisic acid (ABA) is a plant hormone that regulates various responses to environmental stress and plant development. In the study, the transcriptional responses of Arabidopsis seedlings to ABA at concentrations ranging from 0.001 to 100 micromolar were analyzed. A total of 5749 ABA-responsive genes were identified, which can be grouped into nine clusters based on their dose-dependent response profiles. Approximately 20% of these genes showed atypical biphasic (bell-shaped or U-shaped) dose-response trends and were found to participate in specific biological processes including water transport. The sensitivity of ABA-responsive genes to ABA was also determined using effective dose (ED50) and benchmark dose (BMD) values. ABA receptors, which play a key role in ABA signaling, are encoded by 14 genes in three subfamilies with differing oligomeric states and ABA affinities. The transcriptional responses of mutant strains lacking each subfamily of ABA receptors were compared to the wild type using RNA sequencing. The results showed that the removal of subfamily III receptors had the largest effect on ABA-regulated gene expression, indicating the importance of these receptors in Arabidopsis seedlings. Additionally, the role of ABA signaling in osmotic stress was examined using an ABA receptor antagonist, antabactin (ANT), during osmotic stress caused by PEG treatment. The ABA dependency of genes under osmotic stress was quantitatively defined using a ?-value, which has a higher value when the effect of ANT increases. Motif analyses of cis-elements in the promoter regions of genes revealed a correlation between ABA dependency and the enrichment of ABA-related motifs. This study also reveals that the majority of osmotic-stress-responsive genes formerly classified as ABA-independent require ABA signaling for proper osmotic stress. Overall, this study provides insight into the complexity of the ABA signaling network and identifies a set of transcriptional markers for characterizing ABA sensitivity. It also highlights the importance of defining ABA responses using pharmacological methods and the role of ABA receptor affinity in the wide range of effects exerted by ABA

Detection and Analysis of Multiple Events Based on High-Dimensional Factor Models in Power Grid

Multiple event detection and analysis in real time is a challenge for a modern grid as its features are usually non-identifiable. This paper, based on high-dimensional factor models, proposes a data-driven approach to gain insight into the constituent components of a multiple event via the high-resolution phasor measurement unit (PMU) data, such that proper actions can be taken before any sporadic fault escalates to cascading blackouts. Under the framework of random matrix theory, the proposed approach maps the raw data into a high-dimensional space with two parts: (1) factors (spikes, mapping faults); (2) residuals (a bulk, mapping white/non-Gaussian noises or normal fluctuations). As for the factors, we employ their number as a spatial indicator to estimate the number of constituent components in a multiple event. Simultaneously, the autoregressive rate of the noises is utilized to measure the variation of the temporal correlation of the residuals for tracking the system movement. Taking the spatial-temporal correlation into account, this approach allows for detection, decomposition and temporal localization of multiple events. Case studies based on simulated data and real 34-PMU data verify the effectiveness of the proposed approach