RegRNA: an integrated web server for identifying regulatory RNA motifs and elements

Numerous regulatory structural motifs have been identified as playing essential roles in transcriptional and post-transcriptional regulation of gene expression. RegRNA is an integrated web server for identifying the homologs of regulatory RNA motifs and elements against an input mRNA sequence. Both sequence homologs and structural homologs of regulatory RNA motifs can be recognized. The regulatory RNA motifs supported in RegRNA are categorized into several classes: (i) motifs in mRNA 5′-untranslated region (5′-UTR) and 3′-UTR; (ii) motifs involved in mRNA splicing; (iii) motifs involved in transcriptional regulation; (iv) riboswitches; (v) splicing donor/acceptor sites; (vi) inverted repeats; and (vii) miRNA target sites. The experimentally validated regulatory RNA motifs are extracted from literature survey and several regulatory RNA motif databases, such as UTRdb, TRANSFAC, alternative splicing database (ASD) and miRBase. A variety of computational programs are integrated for identifying the homologs of the regulatory RNA motifs. An intuitive user interface is designed to facilitate the comprehensive annotation of user-submitted mRNA sequences. The RegRNA web server is now available at

Effects of Light Quality on the Chlorophyll Degradation Pathway in Rice Seedling Leaves

The objective of this study was to investigate the dynamics of chlorophyll (Chl), biosynthetic intermediates (protoporphyrin IX, magnesium protoporphyrin IX, and protochlorophyllide), degradation intermediates [chlorophyllide (Chlide), pheophytin (Phe), and pheophorbide (Pho)], and carotenoids (Car) in leaves of rice seedlings. Two rice varieties, 'Taichung Shen 10' ('TCS10') and 'IR1552', were grown under different light quality conditions controlled by light emitting diodes (LED). Lighting treatments for rice seedlings were included by red (R), blue (B), green (G), and red + blue (RB), with fluorescent lighting (FL) as the control and photosynthetic photon flux density being set at 105 µmol m-2 s-1. The results show that lower levels of Chl and Car in leaves were detected under G lighting, and light quality did not mediate porphyrins in biosynthetic pathways. Rice seedling leaves took Chl→Phe→Pho and Chl→Chlide→Pho as the major and minor degradation routes, respectively. Furthermore, higher Phe/Chlide ratios were observed under G and FL lighting conditions, indicating that green-enriched environments can up-regulate the minor degradation route in leaves

2013-2014 Master Class - Elmar Oliveira (Violin)

https://spiral.lynn.edu/conservatory_masterclasses/1045/thumbnail.jp

Optimal Computational Power Allocation in Multi-Access Mobile Edge Computing for Blockchain

Blockchain has emerged as a decentralized and trustable ledger for recording and storing digital transactions. The mining process of Blockchain, however, incurs a heavy computational workload for miners to solve the proof-of-work puzzle (i.e., a series of the hashing computation), which is prohibitive from the perspective of the mobile terminals (MTs). The advanced multi-access mobile edge computing (MEC), which enables the MTs to offload part of the computational workloads (for solving the proof-of-work) to the nearby edge-servers (ESs), provides a promising approach to address this issue. By offloading the computational workloads via multi-access MEC, the MTs can effectively increase their successful probabilities when participating in the mining game and gain the consequent reward (i.e., winning the bitcoin). However, as a compensation to the ESs which provide the computational resources to the MTs, the MTs need to pay the ESs for the corresponding resource-acquisition costs. Thus, to investigate the trade-off between obtaining the computational resources from the ESs (for solving the proof-of-work) and paying for the consequent cost, we formulate an optimization problem in which the MTs determine their acquired computational resources from different ESs, with the objective of maximizing the MTs’ social net-reward in the mining process while keeping the fairness among the MTs. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose efficient distributed algorithms for the MTs to individually determine their optimal computational resources acquired from different ESs. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our proposed multi-access MEC for Blockchain

Optimal Computational Power Allocation in Multi-Access Mobile Edge Computing for Blockchain

Blockchain has emerged as a decentralized and trustable ledger for recording and storing digital transactions. The mining process of Blockchain, however, incurs a heavy computational workload for miners to solve the proof-of-work puzzle (i.e., a series of the hashing computation), which is prohibitive from the perspective of the mobile terminals (MTs). The advanced multi-access mobile edge computing (MEC), which enables the MTs to offload part of the computational workloads (for solving the proof-of-work) to the nearby edge-servers (ESs), provides a promising approach to address this issue. By offloading the computational workloads via multi-access MEC, the MTs can effectively increase their successful probabilities when participating in the mining game and gain the consequent reward (i.e., winning the bitcoin). However, as a compensation to the ESs which provide the computational resources to the MTs, the MTs need to pay the ESs for the corresponding resource-acquisition costs. Thus, to investigate the trade-off between obtaining the computational resources from the ESs (for solving the proof-of-work) and paying for the consequent cost, we formulate an optimization problem in which the MTs determine their acquired computational resources from different ESs, with the objective of maximizing the MTs’ social net-reward in the mining process while keeping the fairness among the MTs. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose efficient distributed algorithms for the MTs to individually determine their optimal computational resources acquired from different ESs. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our proposed multi-access MEC for Blockchain