Valorization of CO2 to ß-farnesene in Rhodobacter sphaeroides.

The valorization of CO2 into valuable products is a sustainable strategy to help overcome the climate crisis. In particular, biological conversion is attractive as it can produce long-chain hydrocarbons such as terpenoids. This study reports the high yield of ß-farnesene production from CO2 by expressing heterologous ß-farnesene synthase (FS) into Rhodobacter sphaeroides. To increase the expression of FS, a strong active promoter and a ribosome binding site (RBS) were engineered. Moreover, ß-farnesene production was improved further through the supply of exogenous antioxidants and additional nutrients. Finally, ß-farnesene was produced from CO2 at a titer of 44.53 mg/L and yield of 234.08 mg/g, values that were correspondingly 23 times and 46 times higher than those from the initial production of ß-farnesene. Altogether, the results here suggest that the autotrophic production of ß-farnesene can provide a starting point for achieving a circular carbon economy.

Efficient knowledge distillation for liver CT segmentation using growing assistant network.

Segmentation has been widely used in diagnosis, lesion detection, and surgery planning. Although deep learning (DL)-based segmentation methods currently outperform traditional methods, most DL-based segmentation models are computationally expensive and memory inefficient, which are not suitable for the intervention of liver surgery. To address this issue, a simple solution is to make a segmentation model very small for the fast inference time, however, there is a trade-off between the model size and performance. In this paper, we propose a DL-based real-time 3-D liver CT segmentation method, where knowledge distillation (KD) method, known as knowledge transfer from teacher to student models, is incorporated to compress the model while preserving the performance. Because it is well known that the knowledge transfer is inefficient when the disparity of teacher and student model sizes is large, we propose a growing teacher assistant network (GTAN) to gradually learn the knowledge without extra computational cost, which can efficiently transfer knowledge even with the large gap of teacher and student model sizes. In our results, dice similarity coefficient of the student model with KD improved 1.2% (85.9% to 87.1%) compared to the student model without KD, which is a similar performance of the teacher model using only 8% (100k) parameters. Furthermore, with a student model of 2% (30k) parameters, the proposed model using the GTAN improved the dice coefficient about 2% compared to the student model without KD, and the inference time is 13 ms per a 3-D image. Therefore, the proposed method has a great potential for intervention in liver surgery as well as in many real-time applications.