Development and Applications of Fluorogen/Light-Up RNA Aptamer Pairs for RNA Detection and More.

The central role of RNA in living systems made it highly desirable to have noninvasive and sensitive technologies allowing for imaging the synthesis and the location of these molecules in living cells. This need motivated the development of small pro-fluorescent molecules called "fluorogens" that become fluorescent upon binding to genetically encodable RNAs called "light-up aptamers." Yet, the development of these fluorogen/light-up RNA pairs is a long and thorough process starting with the careful design of the fluorogen and pursued by the selection of a specific and efficient synthetic aptamer. This chapter summarizes the main design and the selection strategies used up to now prior to introducing the main pairs. Then, the vast application potential of these molecules for live-cell RNA imaging and other applications is presented and discussed.journal article2020importe

Women’s Public Office Positions in Islamic Jurisprudence: The Case of Malaysia

The disagreement opinions among Muslim scholars about permissibility for women to hold public office positions seem to be a never ending episode. The issue has been raised by Muslims all over the world, whereby the root cause is actually related to the misunderstanding of Muslim scholars about some controversial nuṣūṣ which related to the issue. Therefore, the objective of the study is to examine the reality of those arguments made by the Muslim scholars and the subsequent effects in the issue of fatwas by the scholars pertaining to the matter. The study also will include the finding data collected from a sample of 369 Malaysian Muslims respondents who met certain criteria set by the study conducted by the researchers; results of the study show several significant findings. The most important finding is the majority of respondents agreed that women can hold some public office positions, but not all the positions. The findings therefore will help to address the issue more appropriately and plan for better educational programs

Optimization of fluorogenic RNA-based biosensors using droplet-based microfluidic ultrahigh-throughput screening

International audienceBiosensors are biological molecules able to detect and report the presence of a target molecule by the emission of a signal. Nucleic acids are particularly appealing for the design of such molecule since their great structural plasticity makes them able to specifically interact with a wide range of ligands and their structure can rearrange upon recognition to trigger a reporting event. A biosensor is typically made of three main domains: a sensing domain that is connected to a reporting domain via a communication module in charge of transmitting the sensing event through the molecule. The communication module is therefore an instrumental element of the sensor. This module is usually empirically developed through a trial-and-error strategy with the testing of only a few combinations judged relevant by the experimenter. In this work, we introduce a novel method combining the use of droplet-based microfluidics and next generation sequencing. This method allows to functionally characterize up to a million of different sequences in a single set of experiments and, by doing so, to exhaustively test every possible sequence permutations of the communication module. Here, we demonstrate the efficiency of the approach by isolating a set of optimized RNA biosensors able to sense theophylline and to convert this recognition into fluorescence emission

Predicting the concentration of sulfate (So4 2– ) in drinking water using artificial neural networks: A case study: Médéa-algeria

International audienceThe aim of this work was to use artificial neural networks (ANN) and multiple linear regressions (MLR) models to predict the soluble sulfate content in drinking water. A set of 84 data points were used. For the ANN, 18 neurons were used in the input layer, 8 neurons at hidden layer, and 1 was used in the output layer. Levenberg Marquardt learning (LM) algorithm with hyperbolic tangent sigmoid transfer function logarithmic was used at the hidden and output layer. The comparison of the obtained results in term of root mean square error (RMSE) and correlation coefficient (R) using the ANN and MLR models revealed the superiority of the (ANN) model in predicting the soluble sulfate content in drinking water. Indeed, the statistical results showed a correlation coefficient R = 0.99973 with RMSE = 5.9755 for the ANN model and R = 0.941 with RMSE = 88.3068 for the MLR model. A nonlinear relationship between the soluble sulfate content and the physi-co-chemical characteristics of drinking water (conductivity, turbidity, potential hydrogen, hardness, calcium, magnesium, chlorides, total alkali metric titre, material organic, nitrogen dioxide, nitrates, sodium, bicarbonate, potassium, heavy metals (Mn2+, Fe3+, and Al+) and dry residues) was demonstrated, showing that the soluble sulfate content concentration can be predicted. © 2021 Desalination Publications. All rights reserved