High-Throughput Sequencing of Arabidopsis microRNAs: Evidence for Frequent Birth and Death of MIRNA Genes

In plants, microRNAs (miRNAs) comprise one of two classes of small RNAs that function primarily as negative regulators at the posttranscriptional level. Several MIRNA genes in the plant kingdom are ancient, with conservation extending between angiosperms and the mosses, whereas many others are more recently evolved. Here, we use deep sequencing and computational methods to identify, profile and analyze non-conserved MIRNA genes in Arabidopsis thaliana. 48 non-conserved MIRNA families, nearly all of which were represented by single genes, were identified. Sequence similarity analyses of miRNA precursor foldback arms revealed evidence for recent evolutionary origin of 16 MIRNA loci through inverted duplication events from protein-coding gene sequences. Interestingly, these recently evolved MIRNA genes have taken distinct paths. Whereas some non-conserved miRNAs interact with and regulate target transcripts from gene families that donated parental sequences, others have drifted to the point of non-interaction with parental gene family transcripts. Some young MIRNA loci clearly originated from one gene family but form miRNAs that target transcripts in another family. We suggest that MIRNA genes are undergoing relatively frequent birth and death, with only a subset being stabilized by integration into regulatory networks

A Genome-Wide Characterization of MicroRNA Genes in Maize

MicroRNAs (miRNAs) are small, non-coding RNAs that play essential roles in plant growth, development, and stress response. We conducted a genome-wide survey of maize miRNA genes, characterizing their structure, expression, and evolution. Computational approaches based on homology and secondary structure modeling identified 150 high-confidence genes within 26 miRNA families. For 25 families, expression was verified by deep-sequencing of small RNA libraries that were prepared from an assortment of maize tissues. PCR–RACE amplification of 68 miRNA transcript precursors, representing 18 families conserved across several plant species, showed that splice variation and the use of alternative transcriptional start and stop sites is common within this class of genes. Comparison of sequence variation data from diverse maize inbred lines versus teosinte accessions suggest that the mature miRNAs are under strong purifying selection while the flanking sequences evolve equivalently to other genes. Since maize is derived from an ancient tetraploid, the effect of whole-genome duplication on miRNA evolution was examined. We found that, like protein-coding genes, duplicated miRNA genes underwent extensive gene-loss, with ∼35% of ancestral sites retained as duplicate homoeologous miRNA genes. This number is higher than that observed with protein-coding genes. A search for putative miRNA targets indicated bias towards genes in regulatory and metabolic pathways. As maize is one of the principal models for plant growth and development, this study will serve as a foundation for future research into the functional roles of miRNA genes

Discovery of barley miRNAs through deep sequencing of short reads

Background: MicroRNAs are important components of the regulatory network of biological systems and thousands have been discovered in both animals and plants. Systematic investigations performed in species with sequenced genomes such as Arabidopsis, rice, poplar and Brachypodium have provided insights into the evolutionary relationships of this class of small RNAs among plants. However, miRNAs from barley, one of the most important cereal crops, remain unknown. Results: We performed a large scale study of barley miRNAs through deep sequencing of small RNAs extracted from leaves of two barley cultivars. By using the presence of miRNA precursor sequences in related genomes as one of a number of supporting criteria, we identified up to 100 miRNAs in barley. Of these only 56 have orthologs in wheat, rice or Brachypodium that are known to be expressed, while up to 44 appear to be specifically expressed in barley. Conclusions: Our study, the first large scale investigation of small RNAs in barley, has identified up to 100 miRNAs. We demonstrate that reliable identification of miRNAs via deep sequencing in a species whose genome has not been sequenced requires a more careful analysis of sequencing errors than is commonly performed. We devised a read filtering procedure for dealing with errors. In addition, we found that the use of a large dataset of almost 35 million reads permits the use of read abundance distributions along putative precursor sequences as a practical tool for isolating miRNAs in a large background of reads originating from other non-coding and coding RNAs. This study therefore provides a generic approach for discovering novel miRNAs where no genome sequence is available.Andreas W Schreiber, Bu-Jun Shi, Chun-Yuan Huang, Peter Langridge, Ute Bauman

Engineering the tilt angle in quasi-perpendicularly magnetized Ta/Pt/CoFeB/Pt thin films

Kerr microscopy measurements of Ta/Pt/CoFeB/Pt thin films, with ferromagnetic (FM) CoFeB layer grown by oblique-angle sputter deposition, indicated a quasi-perpendicular magnetic anisotropy. In such a system, the anisotropy is slightly tilted from the normal to substrate plane. The effect of tilt was observed as shift in out-of-plane (OP) room-temperature hysteresis, in presence of in-plane (IP) bias field. Tilt angles were obtained by measuring the shifts in OP hysteresis for several magnitudes and relative azimuthal orientations of bias field with respect to IP projection of tilted anisotropy direction. We studied variation of tilt angle as function of FM layer thickness which was increased near to Spin Reorientation Transition (SRT) value for FM layer in corresponding system. We also studied this variation for different bottom Pt layer thicknesses. The results of our experiment show the variation to follow two different profiles - one monotonic and the other non-monotonic

DroneScale: Drone load estimation via remote passive RF sensing

Drones have carried weapons, drugs, explosives and illegal packages in the recent past, raising strong concerns from public authorities. While existing drone monitoring systems only focus on detecting drone presence, localizing or fingerprinting the drone, there is a lack of a solution for estimating the additional load carried by a drone. In this paper, we present a novel passive RF system, namely DroneScale, to monitor the wireless signals transmitted by commercial drones and then confirm their models and loads. Our key technical contribution is a proposed technique to passively capture vibration at high resolution (i.e., 1Hz vibration) from afar, which was not possible before. We prototype DroneScale using COTS RF components and illustrate that it can monitor the body vibration of a drone at the targeted resolution. In addition, we develop learning algorithms to extract the physical vibration of the drone from the transmitted signal to infer the model of a drone and the load carried by it. We evaluate the DroneScale system using 5 different drone models, which carry external loads of up to 400g. The experimental results show that the system is able to estimate the external load of a drone with an average accuracy of 96.27%. We also analyze the sensitivity of the system with different load placements with respect to the drone's body, flight modes, and distances up to 200 meters

DroneScale: Drone load estimation via remote passive RF sensing

Drones have carried weapons, drugs, explosives and illegal packages in the recent past, raising strong concerns from public authorities. While existing drone monitoring systems only focus on detecting drone presence, localizing or fingerprinting the drone, there is a lack of a solution for estimating the additional load carried by a drone. In this paper, we present a novel passive RF system, namely DroneScale, to monitor the wireless signals transmitted by commercial drones and then confirm their models and loads. Our key technical contribution is a proposed technique to passively capture vibration at high resolution (i.e., 1Hz vibration) from afar, which was not possible before. We prototype DroneScale using COTS RF components and illustrate that it can monitor the body vibration of a drone at the targeted resolution. In addition, we develop learning algorithms to extract the physical vibration of the drone from the transmitted signal to infer the model of a drone and the load carried by it. We evaluate the DroneScale system using 5 different drone models, which carry external loads of up to 400g. The experimental results show that the system is able to estimate the external load of a drone with an average accuracy of 96.27%. We also analyze the sensitivity of the system with different load placements with respect to the drone's body, flight modes, and distances up to 200 meters