A general approach to high-yield biosynthesis of chimeric RNAs bearing various types of functional small RNAs for broad applications.

RNA research and therapy relies primarily on synthetic RNAs. We employed recombinant RNA technology toward large-scale production of pre-miRNA agents in bacteria, but found the majority of target RNAs were not or negligibly expressed. We thus developed a novel strategy to achieve consistent high-yield biosynthesis of chimeric RNAs carrying various small RNAs (e.g. miRNAs, siRNAs and RNA aptamers), which was based upon an optimal noncoding RNA scaffold (OnRS) derived from tRNA fusion pre-miR-34a (tRNA/mir-34a). Multi-milligrams of chimeric RNAs (e.g. OnRS/miR-124, OnRS/GFP-siRNA, OnRS/Neg (scrambled RNA) and OnRS/MGA (malachite green aptamer)) were readily obtained from 1 l bacterial culture. Deep sequencing analyses revealed that mature miR-124 and target GFP-siRNA were selectively released from chimeric RNAs in human cells. Consequently, OnRS/miR-124 was active in suppressing miR-124 target gene expression and controlling cellular processes, and OnRS/GFP-siRNA was effective in knocking down GFP mRNA levels and fluorescent intensity in ES-2/GFP cells and GFP-transgenic mice. Furthermore, the OnRS/MGA sensor offered a specific strong fluorescence upon binding MG, which was utilized as label-free substrate to accurately determine serum RNase activities in pancreatic cancer patients. These results demonstrate that OnRS-based bioengineering is a common, robust and versatile strategy to assemble various types of small RNAs for broad applications

Echocardiographic parameters versus CHA2DS2-VASc score in prediction of overall cardiac events, heart failure, and stroke in non-valvular atrial fibrillation

     Background: Apart from stroke, atrial fibrillation (AF) is associated with higher mortality and heart failure (HF), in which risk stratification scheme is lacking. Therefore this investigation examined the prognostic value of echocardiographic predictors against CHA2DS2-VASc score in permanent non- -valvular AF (NVAF). Methods: In 252 asymptomatic or mildly symptomatic consecutive patients with NVAF, comprehensive echocardiography was performed. Left atrial deformation parameters were also obtained by two-dimen­sional speckle tracking echocardiography. End-points pertaining to HF deterioration, ischemic stroke and cardiac death were recorded. Results: There were 74 cardiovascular events, including 44 deterioration of HF, 22 ischemic strokes and 8 cardiovascular deaths during an average follow-up period of 20.8 ± 13.5 months (interquartile range, 8–31 months). For prediction of overall prognosis and HF, left ventricular mass index, peak early filling velocity (E), and E to tissue Doppler mitral annular early diastolic velocity ratio (E/e’) outper­formed CHA2DS2-VASc score in multivariate analysis, area under curve, and stepwise nested regression models. Left ventricular hypertrophy and E/e’ > 8 showed worse overall and heart-failure free survival in Kaplan-Meier curves. For prediction of ischemic stroke, the addition of E or E/e’ to CHA2DS2-VASc score provides extra prognostic value. Conclusions: Echocardiographic parameters offer incremental value over CHA2DS2-VASc score for prediction of future cardiac events in NVAF. (Cardiol J 2018; 25, 1: 60–71

Tie-dye technique and pattern features

Relationship between tie-dye technique and image pattern has been studied. Based on digital image processing, average value of HSV (hue, saturation, value) tri-component of valid tie-dye area, proportion of incompletely dyed area in HSV color space and the Tamura first three texture features of digital tie-dye image have been extracted. Then, the repeated tests and analysis of variance (ANOVA) of rotation speed and concentration have been done. The results show the obvious impact of concentration and rotation speed on the color and texture feature of tie-dye pattern. Furthermore, back propagation neutral network is developed and partial pattern features with low correlation is used to forecast the tie-dye concentration and rotation speed. The experiment results show the 100% rate of forecast, which proves that pattern features can effectively achieve the technique forecast

Data preprocessing for artificial neural network applications in prioritizing railroad projects â a practical experience in Taiwan

[[abstract]]Financial constraints necessitate the tradeoff among proposed railroad projects, so that the project priorities for implementation and budget allocation need to be determined by the ranking mechanisms in the government. At present, the Taiwan central government prioritizes funding allocations primarily using the analytic hierarchy process (AHP), a methodology that permits the synthesizing of subjective judgments systematically and logically into objective consensus. However, due to the coopetition and heterogeneity of railway projects, the proper priorities of railroad projects could not be always evaluated by the AHP. The decision makers prefer subjective judgments to referring to the AHP evaluation re- sults. This circumstance not only decreased the AHP advantages, but also raised the risk of the policies. A method to con- sider both objective measures and subjective judgments of project attributes can help reduce this problem. Accordingly, combining the AHP with the artificial neural network (ANN) methodologies would theoretically be a proper solution to bring a ranking predication model by creating the obscure relations between objective measures by the AHP and subjec- tive judgments. However, the inconsistency between the AHP evaluation and subjective judgments resulted in the inferior soundness of the AHP/ANN ranking forecast model. To overcome this problem, this study proposes the data prepro- cessing method (DPM) to calculate the correlation coefficient value using the subjective and objective ranking incidence matrixes; according to the correlation coefficient value, the consistency between the AHP rankings and subjective judg- ments of railroad projects can be evaluated and improved, so that the forecast accuracy of the AHP/ANN ranking forecast model can also be enhanced. Based on this concept, a practical railroad project ranking experience derived from the Insti- tute of Transportation of Taiwan is illustrated in this paper to reveal the feasibility of applying the DPM to the AHP/ANN ranking prediction model.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]電子版[[countrycodes]]LT

DNMT3a in the hippocampal CA1 is crucial in the acquisition of morphine self‐administration in rats

Drug‐reinforced excessive operant responding is one fundamental feature of long-lasting addiction‐like behaviors and relapse in animals. However, the transcriptional regulatory mechanisms responsible for the persistent drug‐specific (not natural rewards) operant behavior are not entirely clear. In this study, we demonstrate a key role for one of the de novo DNA methyltransferase, DNMT3a, in the acquisition of morphine self‐administration (SA) in rats. The expression of DNMT3a in the hippocampal CA1 region but not in the nucleus accumbens shell was significantly up‐regulated after 1‐ and 7‐day morphine SA (0.3 mg/kg/infusion) but not after the yoked morphine injection. On the other hand, saccharin SA did not affect the expression of DNMT3a or DNMT3b. DNMT inhibitor 5‐aza‐2‐deoxycytidine (5‐aza) microinjected into the hippocampal CA1 significantly attenuated the acquisition of morphine SA. Knockdown of DNMT3a also impaired the ability to acquire the morphine SA. Overall, these findings suggest that DNMT3a in the hippocampus plays an important role in the acquisition of morphine SA and may be a valid target to prevent the development of morphine addiction. Includes Supplemental informatio