Differential CLE peptide perception by plant receptors implicated from structural and functional analyses of TDIF-TDR interactions

<div><p>Tracheary Element Differentiation Inhibitory Factor (TDIF) belongs to the family of post-translationally modified CLE (CLAVATA3/embryo surrounding region (ESR)-related) peptide hormones that control root growth and define the delicate balance between stem cell proliferation and differentiation in SAM (shoot apical meristem) or RAM (root apical meristem). In <i>Arabidopsis</i>, Tracheary Element Differentiation Inhibitory Factor Receptor (TDR) and its ligand TDIF signaling pathway is involved in the regulation of procambial cell proliferation and inhibiting its differentiation into xylem cells. Here we present the crystal structures of the extracellular domains (ECD) of TDR alone and in complex with its ligand TDIF resolved at 2.65 Ǻ and 2.75 Ǻ respectively. These structures provide insights about the ligand perception and specific interactions between the CLE peptides and their cognate receptors. Our <i>in vitro</i> biochemical studies indicate that the interactions between the ligands and the receptors at the C-terminal anchoring site provide conserved binding. While the binding interactions occurring at the N-terminal anchoring site dictate differential binding specificities between different ligands and receptors. Our studies will open different unknown avenues of TDR-TDIF signaling pathways that will enhance our knowledge in this field highlighting the receptor ligand interaction, receptor activation, signaling network, modes of action and will serve as a structure function relationship model between the ligand and the receptor for various similar leucine-rich repeat receptor-like kinases (LRR-RLKs).</p></div

Sequences of the overlapping polypeptides from TMUV NS1 (SDSG strain, Accession number: KJ740747.1).

<p>Sequences of the overlapping polypeptides from TMUV NS1 (SDSG strain, Accession number: KJ740747.1).</p

Primary screening of epitope with mAb 3G2.

<p>One 16-AA polypeptide of TMUV NS1 protein (NS1-27) was screened with mAb 3G2 by indirect ELISA. Mouse serum against TMUV NS1 protein and normal mouse serum were used as positive and negative controls, respectively. Each sample was detected in triplicate. Error bars were expressed as standard deviation of the means (n = 3). The mean value was statistically significant, calculated by the two-tailed Student’s unpaired t-test (*P < 0.05).</p

Suppression and Revival of Superconducting Phase Coherence in Monolayer FeSe/SrTiO₃

Monolayer FeSe grown on SrTiO3 (FeSe/STO) is an interfacial high-temperature superconductor distinctively different from bulk FeSe. However, the superconducting phase coherence of the interface is challenging to probe due to its fragility in the atmosphere. Here, we perform in situ mutual inductance under ultrahigh vacuum on FeSe/STO in combination with band mapping by angle-resolved photoemission spectroscopy. We find that even though the monolayer shows a gap-closing temperature above 50 K, no diamagnetism is visible down to 5 K. This is the case for few-layer FeSe/STO until it exceeds a critical number of five layers, where diamagnetism suddenly appears. The suppression of diamagnetism in the monolayer is also lifted by depositing a top FeTe layer. However, Tc and superfluid density both decrease with thicker FeTe, suggesting unconventional electron pairing and phase coherence competition. Our observation may be understood by a scenario in which the interfacial superconducting phase coherence is highly anisotropic

Manufacturing process impacts on occupational health: a machine learning framework

The Operator 4.0 generation denotes a smart and skilled operator accomplishing ‘cooperative work’ with robots, machines and cyber-physical systems. In this taxonomy, a healthy operator is an operator equipped with wearable technology to monitor biometrics in a workplace to monitor and ideally prevent urgent threats to safety, stress in manufacturing and production quality. In a digitalized context, a cloud manufacturing platform for occupational health assessment, capable of collecting physiological, environmental and manufacturing process data can potentially enable prompt action to prevent fatalities. This paper proposes a novel machine learning-based framework and associated methods to classify physiological data acquired using wearable sensors during manufacturing work, to be utilized in a fuzzy-based expert system to determine the level and type of health risk for Operator 4.0. Classification algorithms are presented and a manufacturing case study is illustrated to exemplify the proposed methodology and to evaluate the industrial suitability.</p