Distinct species turnover patterns shaped the richness of antibiotic resistance genes on eight different microplastic polymers.

Elucidating the formation mechanism of plastisphere antibiotic resistance genes (ARGs) on different polymers is necessary to understand the ecological risks of plastisphere ARGs. Here, we explored the turnover and assembly mechanism of plastisphere ARGs on 8 different microplastic polymers (4 biodegradable (bMPs) and 4 non-biodegradable microplastics (nMPs)) by metagenomic sequencing. Our study revealed the presence of 479 ARGs with abundance ranging from 41.37 to 58.17 copies/16S rRNA gene in all plastispheres. These ARGs were predominantly multidrug resistance genes. The richness of plastisphere ARGs on different polymers had a significant correlation with the contribution of species turnover to plastisphere ARGs ß diversity. Furthermore, polymer type was the most critical factor affecting the composition of plastisphere ARGs. More opportunistic pathogens carrying diverse ARGs on BMPs (PBAT, PBS, and PHA) with higher horizontal gene transfer potential may further magnify the ecological risks and human health threats. For example, the opportunistic pathogens Riemerella anatipestifer, Vibrio campbellii, and Vibrio cholerae are closely related to human production and life, which were the important potential hosts of many plastisphere ARGs and mobile genetic elements on BMPs. Thus, we emphasize the urgency of developing the formation mechanism of plastisphere ARGs and the necessity of controlling BMPs and ARG pollution, especially BMPs, with ever-increasing usage in daily life.

ERK signaling expands mammalian cortical radial glial cells and extends the neurogenic period.

The molecular basis for cortical expansion during evolution remains largely unknown. Here, we report that fibroblast growth factor (FGF)-extracellular signal-regulated kinase (ERK) signaling promotes the self-renewal and expansion of cortical radial glial (RG) cells. Furthermore, FGF-ERK signaling induces bone morphogenic protein 7 (Bmp7) expression in cortical RG cells, which increases the length of the neurogenic period. We demonstrate that ERK signaling and Sonic Hedgehog (SHH) signaling mutually inhibit each other in cortical RG cells. We provide evidence that ERK signaling is elevated in cortical RG cells during development and evolution. We propose that the expansion of the mammalian cortex, notably in human, is driven by the ERK-BMP7-GLI3R signaling pathway in cortical RG cells, which participates in a positive feedback loop through antagonizing SHH signaling. We also propose that the relatively short cortical neurogenic period in mice is partly due to mouse cortical RG cells receiving higher SHH signaling that antagonizes ERK signaling.

Genetic algorithm-based semisupervised convolutional neural network for real-time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor.

As a non-destructive sensing technique, Raman spectroscopy is often combined with regression models for real-time detection of key components in microbial cultivation processes. However, achieving accurate model predictions often requires a large amount of offline measurement data for training, which is both time-consuming and labor-intensive. In order to overcome the limitations of traditional models that rely on large datasets and complex spectral preprocessing, in addition to the difficulty of training models with limited samples, we have explored a genetic algorithm-based semi-supervised convolutional neural network (GA-SCNN). GA-SCNN integrates unsupervised process spectral labeling, feature extraction, regression prediction, and transfer learning. Using only an extremely small number of offline samples of the target protein, this framework can accurately predict protein concentration, which represents a significant challenge for other models. The effectiveness of the framework has been validated in a system of Escherichia coli expressing recombinant ProA5M protein. By utilizing the labeling technique of this framework, the available dataset for glucose, lactate, ammonium ions, and optical density at 600 nm (OD600) has been expanded from 52 samples to 1302 samples. Furthermore, by introducing a small component of offline detection data for recombinant proteins into the OD600 model through transfer learning, a model for target protein detection has been retrained, providing a new direction for the development of associated models. Comparative analysis with traditional algorithms demonstrates that the GA-SCNN framework exhibits good adaptability when there is no complex spectral preprocessing. Cross-validation results confirm the robustness and high accuracy of the framework, with the predicted values of the model highly consistent with the offline measurement results.