Messenger RNA (mRNA) vaccines are being used to combat the spread of COVID-19 (refs. 1–3), but they still exhibit critical limitations caused by mRNA instability and degradation, which are major obstacles for the storage, distribution and efficacy of the vaccine products4. Increasing secondary structure lengthens mRNA half-life, which, together with optimal codons, improves protein expression5. Therefore, a principled mRNA design algorithm must optimize both structural stability and codon usage. However, owing to synonymous codons, the mRNA design space is prohibitively large—for example, there are around 2.4 × 10632 candidate mRNA sequences for the SARS-CoV-2 spike protein. This poses insurmountable computational challenges. Here we provide a simple and unexpected solution using the classical concept of lattice parsing in computational linguistics, where finding the optimal mRNA sequence is analogous to identifying the most likely sentence among similar-sounding alternatives6. Our algorithm LinearDesign finds an optimal mRNA design for the spike protein in just 11 minutes, and can concurrently optimize stability and codon usage. LinearDesign substantially improves mRNA half-life and protein expression, and profoundly increases antibody titre by up to 128 times in mice compared to the codon-optimization benchmark on mRNA vaccines for COVID-19 and varicella-zoster virus. This result reveals the great potential of principled mRNA design and enables the exploration of previously unreachable but highly stable and efficient designs. Our work is a timely tool for vaccines and other mRNA-based medicines encoding therapeutic proteins such as monoclonal antibodies and anti-cancer drugs7,8.

This paper considers the problem of polynomial phase signal (PPS) denoising. To prove that proper use of semantic information can further improve the denoising performance based on deep neural networks, we propose an architecture combining the segmentation network and the denoising network. The vision semantic information is extracted from the segmentation network first. Then, that information connecting the time-frequency representation of noisy signal are fed into the denoising network for reconstructing signal. To effectively apply the semantic information, three connection strategies and the corresponding lower bound are presented and compared. The proposed method does not require the pre-identification of signal noise conditions and is suitable for a wide range of Signal-to-Noise-Ratio (SNR) scenarios. Simulation results demonstrate that the F1 scores of the spectrum segmentation results are over 0.98 and the proposed method connecting vision semantics for PPS denoising tasks outperforms the baseline and state-of-the-art architectures, when the SNR is larger than -8dB.

COVID-19 has become a global pandemic not long after its inception in late 2019. SARS-CoV-2 genomes are being sequenced and shared on public repositories at a fast pace. To keep up with these updates, scientists need to frequently refresh and reclean datasets, which is ad hoc and labor-intensive. Further, scientists with limited bioinformatics or programming knowledge may find it difficult to analyze SARS-CoV-2 genomes.

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This paper presents integration challenges on M1 to CT connection in Ultra low-k Back-End-Of-Line interconnects for 40nm node and beyond. In advance IC fabrication, porous dielectric materials, such as BDII (k~2.5), are used as insulator in copper interconnects for RC delay reduction. But the materials of inter-dielectric layer are still high density SiO2-based. The difference of physical properties in materials of ILD and IMD would potentially induce connection deterioration, which would further impact product yield by Contact open fail or other issues. Cross section pictures of failing point were exhibited with a special spacer profile to illustrate the phenomenon. Solutions were proposed, through optimization of Etch, Wet clean and CMP to improve process window. Layout optimization is also suggested as OPC and DFM solution for related layers. Solutions were examined by experiments with 40nm BEOL test masks. Results of physical and electric characterization were presented and discussed.