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Polyhydroxyalkanoates (PHAs) are green and sustainable bioplastics that could replace petrochemical synthetic plastics without posing environmental threats to living organisms. In addition, sustainable PHA production could be achieved using marine photosynthetic purple nonsulfur bacteria (PNSBs) that utilize natural seawater, sunlight, carbon dioxide gas, and nitrogen gas for growth. However, PHA production using marine photosynthetic PNSBs has not been economically feasible yet due to its high cost and low productivity. In this work, strain improvement, using genome-wide mutagenesis coupled with high-throughput screening via fluorescence-activated cell sorting, we were able to create Rhodovulum sulfidophilum mutants with enhanced volumetric PHA productivity, with an up to 1.7-fold increase. The best selected mutants (E6 and E6M4) reached the stationary growth phase 1 day faster and accumulated the maximum PHA content 2 days faster than the wild type. Maximizing volumetric PHA productivity before the stationary growth phase is indeed an additional advantage for R. sulfidophilum as a growth-associated PHA producer.

Combining the strength of flow cytometry with fluorescence imaging and digital image analysis, imaging flow cytometry is a powerful tool in diverse fields including cancer biology, immunology, drug discovery, microbiology, and metabolic engineering. It enables measurements and statistical analyses of chemical, structural, and morphological phenotypes of numerous living cells to provide systematic insights into biological processes. However, its utility is constrained by its requirement of fluorescent labeling for phenotyping. Here we present label-free chemical imaging flow cytometry to overcome the issue. It builds on a pulse pair-resolved wavelength-switchable Stokes laser for the fastest-to-date multicolor stimulated Raman scattering (SRS) microscopy of fast-flowing cells on a 3D acoustic focusing microfluidic chip, enabling an unprecedented throughput of up to ∼140 cells/s. To show its broad utility, we use the SRS imaging flow cytometry with the aid of deep learning to study the metabolic heterogeneity of microalgal cells and perform marker-free cancer detection in blood., Competing Interests: Conflict of interest statement: Y.O., Y.S., Y.W., and K.G. are inventors of a patent application covering SRS imaging with a wavelength-switched laser. All other authors declare no conflict of interest., (Copyright © 2019 the Author(s). Published by PNAS.)

Drug-induced liver injury (DILI), one of the most common adverse effects, leads to drug development failure or withdrawal from the market in most cases, showing an emerging challenge that is to accurately predict DILI in the early stage. Recently, the vast amount of gene expression data provides us valuable information for distinguishing DILI on a genomic scale. Moreover, the deep learning algorithm is a powerful strategy to automatically learn important features from raw and noisy data and shows great success in the field of medical diagnosis. In this study, a gene expression data based deep learning model was developed to predict DILI in advance by using gene expression data associated with DILI collected from ArrayExpress and then optimized by feature gene selection and parameters optimization. In addition, the previous machine learning algorithm support vector machine (SVM) was also used to construct another prediction model based on the same data sets, comparing the model performance with the optimal DL model. Finally, the evaluation test using 198 randomly selected samples showed that the optimal DL model achieved 97.1% accuracy, 97.4% sensitivity, 96.8% specificity, 0.942 matthews correlation coefficient, and 0.989 area under the ROC curve, while the performance of SVM model only reached 88.9% accuracy, 78.8% sensitivity, 99.0% specificity, 0.794 matthews correlation coefficient, and 0.901 area under the ROC curve. Furthermore, external data sets verification and animal experiments were conducted to assess the optimal DL model performance. Finally, the predicted results of the optimal DL model were almost consistent with experiment results. These results indicated that our gene expression data based deep learning model could systematically and accurately predict DILI in advance. It could be a useful tool to provide safety information for drug discovery and clinical rational drug use in early stage and become an important part of drug safety assessment.

Yellow catfish has become one of the most important freshwater aquaculture species in China. The mono-sex male yellow catfish has important application value in aquaculture because the male grows generally faster than the sibling females under the same conditions. This study has screened YY super-male and YY physiological female yellow catfish by sex reversal, gynogenesis, and progeny testing, which can help to achieve the large-scale production of YY super-male and XY all-male. From 2008 to 2010, about 123,000 YY super-male were produced, and about 81 million XY all-male fry were produced with 100% male rate by random sampling. Therefore, these results indicate that YY super-male and YY physiological female yellow catfish can be viable and fertile. We conclude that the mono-sex breeding technique by YY super-male yellow catfish is stable and reliable, which has great potential for application in yellow catfish aquaculture.