Synthetic biology for evolutionary engineering: from perturbation of genotype to acquisition of desired phenotype

With the increased attention on bio-based industry, demands for techniques that enable fast and effective strain improvement have been dramatically increased. Evolutionary engineering, which is less dependent on biological information, has been applied to strain improvement. Currently, synthetic biology has made great innovations in evolutionary engineering, particularly in the development of synthetic tools for phenotypic perturbation. Furthermore, discovering biological parts with regulatory roles and devising novel genetic circuits have promoted high-throughput screening and selection. In this review, we first briefly explain basics of synthetic biology tools for mutagenesis and screening of improved variants, and then describe how these strategies have been improved and applied to phenotypic engineering. Evolutionary engineering using advanced synthetic biology tools will enable further innovation in phenotypic engineering through the development of novel genetic parts and assembly into well-designed logic circuits that perform complex tasks.This work was supported by the Bio & Medical Technology Development Program (NRF-2018M3A9H3020459) and the C1 Gas Refnery Program (NRF2016M3D3A1A01913561) through the National Research Foundation (NRF) funded by Ministry of Science and ICT (MSIT). JY was partially supported by Basic Science Research Program (NRF-2018R1C1B6005764) through the National Research Foundation (NRF) funded by MSIT and SWS was partially supported by Creative-Pioneering Researchers Program through Seoul National University (SNU)

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts

Probabilistic TSDF Fusion Using Bayesian Deep Learning for Dense 3D Reconstruction with a Single RGB Camera

In this paper, we address a 3D reconstruction problem using depth prediction from a single RGB image. With the recent advances in deep learning, depth prediction shows high performance. However, due to the discrepancy between training environment and test environment, 3D reconstruction can be vulnerable to the uncertainty of depth prediction. To consider the uncertainty of depth prediction for robust 3D reconstruction, we adopt Bayesian deep learning framework. Conventional Bayesian deep learning requires a large amount of time and GPU memory to perform Monte Carlo sampling. To address this problem, we propose a lightweight Bayesian neural network consisting of U-net structure and summation-based skip connections, which is performed in real-time. Estimated uncertainty is utilized in probabilistic TSDF fusion for dense 3D reconstruction by maximizing the posterior of TSDF value per voxel. As a result, global TSDF robust to erroneous depth values can be obtained and then dense 3D reconstruction from the global TSDF is achievable more accurately. To evaluate the performance of depth prediction and 3D reconstruction using our method, we utilized two official datasets and demonstrated the outperformance of the proposed method over other conventional methods.N

RGB-to-TSDF: Direct TSDF Prediction from a Single RGB Image for Dense 3D Reconstruction

In this paper, we present a novel method to predict 3D TSDF voxels from a single image for dense 3D reconstruction. 3D reconstruction with RGB images has two inherent problems: scale ambiguity and sparse reconstruction. With the advent of deep learning, depth prediction from a single RGB image has addressed these problems. However, as the predicted depth is typically noisy, de-noising methods such as TSDF fusion should be adapted for the accurate scene reconstruction. To integrate the two-step processing of depth prediction and TSDF generation, we design an RGB-to-TSDF network to directly predict 3D TSDF voxels from a single RGB image. The TSDF using our network can be generated more efficiently in terms of time and accuracy than the TSDF converted from depth prediction. We also use the predicted TSDF for a more accurate and robust camera pose estimation to complete scene reconstruction. The global TSDF is updated from TSDF prediction and pose estimation, and thus dense isosurface can be extracted. In the experiments, we evaluate our TSDF prediction and camera pose estimation results against the conventional method.N

The Cut-off Value of Blood Mercury Concentration in Relation to Insulin Resistance

Background : Increased blood mercury concentration is associated with inflammation, and chronic inflammation can cause insulin resistance. We examined the cut-off value of blood mercury in relation to an increased score on the homeostasis model assessment for insulin resistance (HOMA-IR). Methods : We used data from the Korean National Health and Nutrition Examination Survey (2008–2010). Relevant data from 5,184 subjects (2,523 men and 2,661 women) were analyzed cross-sectionally. General linear analysis was performed to evaluate the relationship between HOMA-IR score and blood mercury concentration. In addition, we determined the cut-off value of blood mercury concentration in relation to increased HOMA-IR score (> 2.34) using an ROC curve. Results : The mean value of blood mercury concentration in men and women was 5.88 μg/L and 4.11 μg/L, respectively. In men, comparing to the first quartile, HOMA-IR score increased significantly in the third and fourth blood mercury quartiles. In women, however, the increase in HOMA-IR score was not significant. The cut-off value that best represented the association between increased HOMA-IR score and blood mercury concentration in men was found to be 4.71 μg/L. Conclusion : Blood mercury concentration was associated with increased HOMA-IR score in men, and the cut-off value of blood mercury concentration that was correlated with increased HOMA-IR score was around 4.71 μg/L

Dietary Calcium Intake May Contribute to the HOMA-IR Score in Korean Females with Vitamin D Deficiency (2008–2012 Korea National Health and Nutrition Examination Survey)

Background : Vitamin D and calcium are important factors involved in the regulation of blood glucose and insulin secretion. The Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) score is a useful variable for evaluating insulin resistance, and therefore we cross-sectionally compared HOMA-IR scores according to serum vitamin D levels and dietary calcium intake. Methods : We selected data from healthy males (n=5,163) and females (n=7,506) analyzed over 5 years (2008–2012) via the Korea National Health and Nutrition Examination Survey (KNHANES). We calculated HOMA-IR scores and compared them according to serum 25-hydroxyvitamin D (25(OH)D) concentration classification (30 ng/mL) and dietary calcium quintile after adjustment for relevant variables using complex sample analysis. Comparisons were done after data weighting. Results : The mean dietary calcium intake in males and females was 558.1 mg/day and 445.9 mg/day, respectively. The mean serum 25(OH)D concentration in males and females was 19.4 ng/mL and 16.8 ng/mL, respectively. After adjustment for relevant variables, HOMA-IR score was significantly correlated with serum 25(OH)D concentration and dietary calcium intake in females, whereas it was only correlated with serum 25(OH)D concentration in males. HOMA-IR was significantly lower in the top quintile of dietary calcium intake (mean, 866 mg/day) within females with vitamin D deficiency (P=0.047). Conclusion : Adequate dietary calcium intake may be important for normal HOMA-IR in females with vitamin D deficiency