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

Similarity from multi-dimensional scaling: solving the accuracy and diversity dilemma in information filtering

Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs and item pairs. In this paper, we employ the multidimensional scaling (MDS) method to measure the similarities between nodes in user-item bipartite networks. The MDS method can extract the essential similarity information from the networks by smoothing out noise, which provides a graphical display of the structure of the networks. With the similarity measured from MDS, we find that the item-based collaborative filtering algorithm can outperform the diffusion-based recommendation algorithms. Moreover, we show that this method tends to recommend unpopular items and increase the global diversification of the networks in long term

Mobility enhancement and highly efficient gating of monolayer MoS2 transistors with Polymer Electrolyte

We report electrical characterization of monolayer molybdenum disulfide (MoS2) devices using a thin layer of polymer electrolyte consisting of poly(ethylene oxide) (PEO) and lithium perchlorate (LiClO4) as both a contact-barrier reducer and channel mobility booster. We find that bare MoS2 devices (without polymer electrolyte) fabricated on Si/SiO2 have low channel mobility and large contact resistance, both of which severely limit the field-effect mobility of the devices. A thin layer of PEO/ LiClO4 deposited on top of the devices not only substantially reduces the contact resistance but also boost the channel mobility, leading up to three-orders-of-magnitude enhancement of the field-effect mobility of the device. When the polymer electrolyte is used as a gate medium, the MoS2 field-effect transistors exhibit excellent device characteristics such as a near ideal subthreshold swing and an on/off ratio of 106 as a result of the strong gate-channel coupling.Comment: 17 pages, 4 figures, accepted by J. Phys.

MixNet: Toward Accurate Detection of Challenging Scene Text in the Wild

Detecting small scene text instances in the wild is particularly challenging, where the influence of irregular positions and nonideal lighting often leads to detection errors. We present MixNet, a hybrid architecture that combines the strengths of CNNs and Transformers, capable of accurately detecting small text from challenging natural scenes, regardless of the orientations, styles, and lighting conditions. MixNet incorporates two key modules: (1) the Feature Shuffle Network (FSNet) to serve as the backbone and (2) the Central Transformer Block (CTBlock) to exploit the 1D manifold constraint of the scene text. We first introduce a novel feature shuffling strategy in FSNet to facilitate the exchange of features across multiple scales, generating high-resolution features superior to popular ResNet and HRNet. The FSNet backbone has achieved significant improvements over many existing text detection methods, including PAN, DB, and FAST. Then we design a complementary CTBlock to leverage center line based features similar to the medial axis of text regions and show that it can outperform contour-based approaches in challenging cases when small scene texts appear closely. Extensive experimental results show that MixNet, which mixes FSNet with CTBlock, achieves state-of-the-art results on multiple scene text detection datasets

Determination of ketamine in rabbit plasma by gradient elution liquid chromatography/electrospray mass spectrometry

A sensitive and simple liquid chromatography/electrospray mass spectrometry (LC-ESI-MS) method for determination of ketamine in rabbit plasma using one-step protein precipitation was developed and validated. After addition of methadone as internal standard (IS), protein precipitation by acetonitrile was used as sample preparation. Chromatographically separation was achieved on an SB-C18 (2.1 mm × 50 mm, 3.5 μm) column with methanol-0.1 % formic acid as the mobile phase with gradient elution. Electrospray ionization (ESI) source was applied and operated in positive ion mode; multiple reaction monitoring (MRM) mode was used to quantification using target fragment ions m/z 237.7 → 219.7 for ketamine and m/z 309.9 → 264.8 for the IS. Calibration plots were linear over the range of 5-1000 ng/mL for ketamine in rabbit plasma. Lower limit of quantification (LLOQ) for ketamine was 5 ng/mL. Mean recovery of ketamine from plasma was in the range of 97.5-100.1 %. RSD of intra-day and inter-day precision were both less than 11 %. This method is simple and sensitive enough to be used in pharmacokinetic research for determination of ketamine in rabbit plasma.Colegio de Farmacéuticos de la Provincia de Buenos Aire