CLAVATA signaling in plant-environment interactions.

Plants must rapidly and dynamically adapt to changes in their environment. Upon sensing environmental signals, plants convert them into cellular signals, which elicit physiological or developmental changes that allow them to respond to various abiotic and biotic cues. Because plants can be simultaneously exposed to multiple environmental cues, signal integration between plant cells, tissues, and organs is necessary to induce specific responses. Recently, CLAVATA3/EMBRYO SURROUNDING REGION-related (CLE) peptides and their cognate CLAVATA-type receptors received increased attention for their roles in plant-environment interactions. CLE peptides are mobile signaling molecules, many of which are induced by a variety of biotic and abiotic stimuli. Secreted CLE peptides are perceived by receptor complexes on the surface of their target cells, which often include the leucine-rich repeat receptor-like kinase CLAVATA1. Receptor activation then results in cell-type and/or environment-specific responses. This review summarizes our current understanding of the diverse roles of environment-regulated CLE peptides in modulating plant responses to environmental cues. We highlight how CLE signals regulate plant physiology by fine-tuning plant-microbe interactions, nutrient homeostasis, and carbon allocation. Finally, we describe the role of CLAVATA receptors in the perception of environment-induced CLE signals and discuss how diverse CLE-CLAVATA signaling modules may integrate environmental signals with plant physiology and development.

Tf-GCZSL: Task-free generalized continual zero-shot learning.

Learning continually from a stream of training data or tasks with an ability to learn the unseen classes using a zero-shot learning framework is gaining attention in the literature. It is referred to as continual zero-shot learning (CZSL). Existing CZSL requires clear task-boundary information during training which is not practically feasible. This paper proposes a task-free generalized CZSL (Tf-GCZSL) method with short-term/long-term memory to overcome the requirement of task-boundary in training. A variational autoencoder (VAE) handles the fundamental ZSL tasks. The short-term and long-term memory help to overcome the condition of the task boundary in the CZSL framework. Further, the proposed Tf-GCZSL method combines the concept of experience replay with dark knowledge distillation and regularization to overcome the catastrophic forgetting issues in a continual learning framework. Finally, the Tf-GCZSL uses a fully connected classifier developed using the synthetic features generated at the latent space of the VAE. The performance of the proposed Tf-GCZSL is evaluated in the existing task-agnostic prediction setting and the proposed task-free setting for the generalized CZSL over the five ZSL benchmark datasets. The results clearly indicate that the proposed Tf-GCZSL improves the prediction at least by 12%, 1%, 3%, 4%, and 3% over existing state-of-the-art and baseline methods for CUB, aPY, AWA1, AWA2, and SUN datasets, respectively in both settings (task-agnostic prediction and task-free learning). The source code is available at https://github.com/Chandan-IITI/Tf-GCZSL.

A Quick Method to Quantify Iron in Arabidopsis Seedlings.

Iron (Fe) is an indispensable micronutrient for plant growth and development. Since both deficiency, as well as a surplus of Fe, can be detrimental to plant health, plants need to constantly tune uptake rates to maintain an optimum level of Fe. Quantification of Fe serves as an important parameter for analyzing the fitness of plants from different accessions, or mutants and transgenic lines with altered expression of specific genes. To quantify metals in plant samples, methods based on inductively coupled plasma-optical emission spectrometry (ICP-OES) or inductively coupled plasma-mass spectrometry (ICP-MS) have been widely employed. Although these methods are highly accurate, these methodologies rely on sophisticated equipment which is not always available. Moreover, ICP-OES and ICP-MS allow for surveying several metals in the same sample, which may not be necessary if only the Fe status is to be determined. Here, we outline a simple and cost-efficient protocol to quantify Fe concentrations in roots and shoots of Arabidopsis seedlings, by using a spectroscopy-based assay to quantify Fe2+-BPDS3 complexes against a set of standards. This protocol provides a fast and reproducible method to determine Fe levels in plant samples with high precision and low costs, which does not depend on expensive equipment and expertise to operate such equipment.

IRONMAN tunes responses to iron deficiency in concert with environmental pH.

Iron (Fe) is an essential mineral element that governs the composition of natural plant communities and limits crop yield in agricultural ecosystems due to its extremely low availability in most soils, particularly at alkaline pH. To extract sufficient Fe from the soil under such conditions, some plants, including Arabidopsis (Arabidopsis thaliana), secrete Fe-mobilizing phenylpropanoids, which mobilize sparingly soluble Fe hydroxides by reduction and chelation. We show here that ectopic expression of the peptides IRONMAN (IMA1) and IMA2 improves growth on calcareous soil by inducing biosynthesis and secretion of the catecholic coumarin 7,8-dihydroxy-6-methoxycoumarin (fraxetin) via increased expression of MYB72 and SCOPOLETIN 8-HYDROXYLASE, a response that is strictly dependent on elevated environmental pH (pHe). By contrast, transcription of the cytochrome P450 family protein CYP82C4, catalyzing the subsequent hydroxylation of fraxetin to sideretin, which forms less stable complexes with iron, was strongly repressed under such conditions. We concluded that IMA peptides regulate processes supporting Fe uptake at both acidic and elevated pH by controlling gene expression upstream of or in concert with a putative pHe signal, adapting the plant to prevailing edaphic conditions. This regulatory pattern confers tolerance to calcareous soils by extending the pH range in which Fe can be efficiently absorbed from the soil. Our results further suggest that pHe calibrates the activities of components of the Fe deficiency response, accentuating processes that are most efficient under the prevailing conditions. Altering the expression of IMA peptides provides a route for generating plants adapted to calcareous soils.

Minimum variance-embedded deep kernel regularized least squares method for one-class classification and its applications to biomedical data.

Deep kernel learning has been well explored for multi-class classification tasks; however, relatively less work is done for one-class classification (OCC). OCC needs samples from only one class to train the model. Most recently, kernel regularized least squares (KRL) method-based deep architecture is developed for the OCC task. This paper introduces a novel extension of this method by embedding minimum variance information within this architecture. This embedding improves the generalization capability of the classifier by reducing the intra-class variance. In contrast to traditional deep learning methods, this method can effectively work with small-size datasets. We conduct a comprehensive set of experiments on 18 benchmark datasets (13 biomedical and 5 other datasets) to demonstrate the performance of the proposed classifier. We compare the results with 16 state-of-the-art one-class classifiers. Further, we also test our method for 2 real-world biomedical datasets viz.; detection of Alzheimer's disease from structural magnetic resonance imaging data and detection of breast cancer from histopathological images. Proposed method exhibits more than 5% F1 score compared to existing state-of-the-art methods for various biomedical benchmark datasets. This makes it viable for application in biomedical fields where relatively less amount of data is available.

VIT-CMJ2: Endophyte of Agaricus bisporus in Production of Bioactive Compounds.

BACKGROUND: Agaricus bisporus is an edible basidiomycete fungus. Both the body and the mycelium contain compounds comprising a wide range of antimicrobial molecules, contributing in improvement of immunity and tumor-retardation. OBJECTIVES: The presence of endophytes capable of producing bioactive compounds was investigated in Agaricus bisporus. MATERIALS AND METHODS: Endophytes from Agaricus bisporus was isolated on LB agar. The obtained isolates were characterized morphologically and biochemically. Further 16S rRNA sequencing was implemented for molecular analysis of isolates. The isolate was mass produced and the bioactive compounds were extracted using ethyl acetate, chloroform and hexane. Agar well diffusion method was carried out to seek the potential of any antimicrobial activity of the crude bioactive compounds against known pathogens. GC-MS and FT-IR analysis were performed for the identification of bioactive compounds. RESULTS: VIT-CMJ2 was identified as Enterobacter sp. as revealed by 16S rRNA sequencing. Chloroform extract of VIT-CMJ2 showed a maximum zone of inhibition of 19 mm against Salmonella typhi followed by hexane and ethyl acetate extracts. The GC-MS analysis revealed the presence of several bioactive compounds having effective antimicrobial activity like butyl ester, Behenicalcohol, S , S-dioxide derivatives and some others which were later confirmed by FT-IR spectral stretches. CONCLUSIONS: The present study shows the insight on the way endophytes interact with Agaricus bisporus; thereby improving the nutritional profile.