ELONGATED HYPOCOTYL5 Negatively Regulates DECREASE WAX BIOSYNTHESIS to Increase Survival during UV-B Stress.

Understanding how the distinct cell types of the shoot apical meristem (SAM) withstand ultraviolet radiation (UVR) stress can improve cultivation of plants in high-UVR environments. Here, we show that UV-B irradiation selectively kills epidermal and niche cells in the shoot apex. Plants harboring a mutation in DECREASE WAX BIOSYNTHESIS (DEWAX) are tolerant to UV-B. Our data show that DEWAX negatively regulates genes involved in anthocyanin biosynthesis. ELONGATED HYPOCOTYL5 (HY5) binds to the DEWAX promoter elements and represses its expression to promote the anthocyanin biosynthesis. The HY5-DEWAX regulatory network regulates anthocyanin content in Arabidopsis (Arabidopsis thaliana) and influences the survivability of plants under UV-B irradiation stress. Our cell sorting-based study of the epidermal cell layer transcriptome confirms that core UV-B stress signaling pathway genes are conserved and upregulated in response to UV-B irradiation of the SAM. Furthermore, we show that UV-B induces genes involved in shoot development and organ patterning. We propose that the HY5-DEWAX regulatory relationship is conserved; however, changes in the expression levels of these genes can determine anthocyanin content in planta and, hence, fitness under UV-B irradiation stress.

Internal short circuit detection in Li-ion batteries using supervised machine learning.

With the proliferation of Li-ion batteries in smart phones, safety is the main concern and an on-line detection of battery faults is much wanting. Internal short circuit is a very critical issue that is often ascribed to be a cause of many accidents involving Li-ion batteries. A novel method that can detect the Internal short circuit in real time based on an advanced machine leaning approach, is proposed. Based on an equivalent electric circuit model, a set of features encompassing the physics of Li-ion cell with short circuit fault are identified and extracted from each charge-discharge cycle. The training feature set is generated with and without an external short-circuit resistance across the battery terminals. To emulate a real user scenario, internal short is induced by mechanical abuse. The testing feature set is generated from the battery charge-discharge data before and after the abuse. A random forest classifier is trained with the training feature set. The fault detection accuracy for the testing dataset is found to be more than 97%. The proposed algorithm does not interfere with the normal usage of the device, and the trained model can be implemented in any device for online fault detection.