Nonessential tRNA modifications by methyltransferases are evolutionarily conserved and have been reported to stabilize mature tRNA molecules and prevent rapid tRNA decay (RTD). The tRNA modifying enzymes, NSUN2 and METTL1, are mammalian orthologs of yeast Trm4 and Trm8, which are required for protecting tRNA against RTD. A simultaneous overexpression of NSUN2 and METTL1 is widely observed among human cancers suggesting that targeting of both proteins provides a novel powerful strategy for cancer chemotherapy. Here, we show that combined knockdown of NSUN2 and METTL1 in HeLa cells drastically potentiate sensitivity of cells to 5-fluorouracil (5-FU) whereas heat stress of cells revealed no effects. Since NSUN2 and METTL1 are phosphorylated by Aurora-B and Akt, respectively, and their tRNA modifying activities are suppressed by phosphorylation, overexpression of constitutively dephosphorylated forms of both methyltransferases is able to suppress 5-FU sensitivity. Thus, NSUN2 and METTL1 are implicated in 5-FU sensitivity in HeLa cells. Interfering with methylation of tRNAs might provide a promising rationale to improve 5-FU chemotherapy of cancer.

Bandwidth interleaving digital-to-analog converter (BI-DAC) is a new method for breaking through the bandwidth restriction of the DAC to generate a wideband signal. However, there are some errors in the BI-DAC system such as the aliasing errors caused by the non-ideal performance of the analog filters. To achieve the aliasing errors cancellation, this paper studied the minimax design of digital finite impulse response (FIR) filters. The design goal was to meet a given desired spurious free dynamic range (SFDR) of the BI-DAC system. The problem of designing the digital FIR filters was formulated as a linear programming (LP) problem which could be used to find the global optimal solution of the coefficients of the digital FIR filters. Additionally, this proposed design method performance analysis consist of the computational complexity was derived. Finally, all the proposed designs are verified by both theoretical analysis and numerical simulations, and satisfactory simulation results were achieved.

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The differences on DNA yield and purity of three different DNA extraction protocols were compared with regard to the use for PCR and other molecular analyses. Total DNA was extracted from compost by the three protocols, and then was purified by spin-bind cartridges after being precipitated by PEG8000. The detection performed on a nucleic acid and protein analyzer showed that all three methods produced high DNA yields. The agarose gel electrophoresis showed that the fragments of crude and purified DNA had a length of about 23 kb. A eubacterial 16S rRNA gene-targeted primer pair was used for PCR amplification, and full length 16S rDNAs were amplified from all the purified DNA samples. After being digested by restriction endonucleases, the restriction map of amplified rDNA showed identical genetic diversity. The products of PCR using primer pair GC341F and 907R were also used for denaturing gradient gel electrophoresis analysis. The results indicated that high-quality DNA was extracted from compost by the three protocols, and each of the protocols is adapted to extract microbial genome DNA from compost expediently and cheaply.

In this paper, a multichannel nonlinear adaptive noise canceller (ANC) based on the generalized functional link artificial neural network (FLANN, GFLANN) is proposed for fetal electrocardiogram (FECG) extraction. A FIR filter and a GFLANN are equipped in parallel in each reference channel to respectively approximate the linearity and nonlinearity between the maternal ECG (MECG) and the composite abdominal ECG (AECG). A fast scheme is also introduced to reduce the computational cost of the FLANN and the GFLANN. Two (2) sets of ECG time sequences, one synthetic and one real, are utilized to demonstrate the improved effectiveness of the proposed nonlinear ANC. The real dataset is derived from the Physionet non-invasive FECG database (PNIFECGDB) including 55 multichannel recordings taken from a pregnant woman. It contains two subdatasets that consist of 14 and 8 recordings, respectively, with each recording being 90 s long. Simulation results based on these two datasets reveal, on the whole, that the proposed ANC does enjoy higher capability to deal with nonlinearity between MECG and AECG as compared with previous ANCs in terms of fetal QRS (FQRS)-related statistics and morphology of the extracted FECG waveforms. In particular, for the second real subdataset, the F1-measure results produced by the PCA-based template subtraction (TSpca) technique and six (6) single-reference channel ANCs using LMS- and RLS-based FIR filters, Volterra filter, FLANN, GFLANN, and adaptive echo state neural network (ESNa) are 92.47%, 93.70%, 94.07%, 94.22%, 94.90%, 94.90%, and 95.46%, respectively. The same F1-measure statistical results from five (5) multi-reference channel ANCs (LMS- and RLS-based FIR filters, Volterra filter, FLANN, and GFLANN) for the second real subdataset turn out to be 94.08%, 94.29%, 94.68%, 94.91%, and 94.96%, respectively. These results indicate that the ESNa and GFLANN perform best, with the ESNa being slightly better than the GFLANN but about four times more computationally expensive than the GFLANN, which makes the GFLANN a good alternative for NI-FECG extraction.

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