Genome-wide characterization of the PP2C gene family in peanut (Arachis hypogaea L.) and the identification of candidate genes involved in salinity-stress response

Plant protein phosphatase 2C (PP2C) play important roles in response to salt stress by influencing metabolic processes, hormone levels, growth factors, etc. Members of the PP2C family have been identified in many plant species. However, they are rarely reported in peanut. In this study, 178 PP2C genes were identified in peanut, which were unevenly distributed across the 20 chromosomes, with segmental duplication in 78 gene pairs. AhPP2Cs could be divided into 10 clades (A-J) by phylogenetic analysis. AhPP2Cs had experienced segmental duplications and strong purifying selection pressure. 22 miRNAs from 14 different families were identified, targeting 57 AhPP2C genes. Gene structures and motifs analysis exhibited PP2Cs in subclades AI and AII had high structural and functional similarities. Phosphorylation sites of AhPP2C45/59/134/150/35/121 were predicted in motifs 2 and 4, which located within the catalytic site at the C-terminus. We discovered multiple MYB binding factors and ABA response elements in the promoter regions of the six genes (AhPP2C45/59/134/150/35/121) by cis-elements analysis. GO and KEGG enrichment analysis confirmed AhPP2C-A genes in protein binding, signal transduction, protein modification process response to abiotic stimulus through environmental information processing. Based on RNA-Seq data of 22 peanut tissues, clade A AhPP2Cs showed a varying degree of tissue specificity, of which, AhPP2C35 and AhPP2C121 specifically expressed in seeds, while AhPP2C45/59/134/150 expressed in leaves and roots. qRT-PCR indicated that AhPP2C45 and AhPP2C134 displayed significantly up-regulated expression in response to salt stress. These results indicated that AhPP2C45 and AhPP2C134 could be candidate PP2Cs conferring salt tolerance. These results provide further insights into the peanut PP2C gene family and indicate PP2Cs potentially involved in the response to salt stress, which can now be further investigated in peanut breeding efforts to obtain cultivars with improved salt tolerance

"A continuous awaking movement". Note sul choreocinema di Maya Deren

Maya Deren ha prodotto tra gli anni quaranta e la metà degli anni cinquanta del secolo scorso un cinema visionario in cui la danza e i danzatori diventano i protagonisti della trasfigurazione del reale in arte, del viaggio dal mondo visibile a quello invisibile delle forme sottili dell’inconscio e della mente. Il saggio si incentra sul ciclo di film di Maya Deren che John Martin, critico di danza del New York Times e teorico di punta della modern dance, definì come choreocinema: >, soffermandosi principalmente su A Study in Choreography for Camera del 1945 che Deren creò collaborando con il danzatore afro-americano Talley Beatty

Optical neural network architecture for deep learning with the temporal synthetic dimension

The physical concept of synthetic dimensions has recently been introduced into optics. The fundamental physics and applications are not yet fully understood, and this report explores an approach to optical neural networks using synthetic dimension in time domain, by theoretically proposing to utilize a single resonator network, where the arrival times of optical pulses are interconnected to construct a temporal synthetic dimension. The set of pulses in each roundtrip therefore provides the sites in each layer in the optical neural network, and can be linearly transformed with splitters and delay lines, including the phase modulators, when pulses circulate inside the network. Such linear transformation can be arbitrarily controlled by applied modulation phases, which serve as the building block of the neural network together with a nonlinear component for pulses. We validate the functionality of the proposed optical neural network for the deep learning purpose with examples handwritten digit recognition and optical pulse train distribution classification problems. This proof of principle computational work explores the new concept of developing a photonics-based machine learning in a single ring network using synthetic dimensions, which allows flexibility and easiness of reconfiguration with complex functionality in achieving desired optical tasks