Ammonia-oxidizing bacteria (AOB) have long been considered key to the removal of nitrogen in activated sludge bioreactors. Culture-independent molecular analyses have established that AOB lineages in bioreactors are dynamic, but the underlying operational or environmental factors are unclear. Furthermore, the contribution of ammonia-oxidizing archaea (AOA) to nitrogen removal in bioreactors has not been studied. To this end, we investigated the abundance of AOA and AOB as well as correlations between dynamics in AOB lineages and operational parameters at a municipal wastewater treatment plant sampled weekly over a 1 year period. Quantitative PCR measurements of bacterial and archaeal ammonia monooxygenase subunit A (amoA) genes revealed that the bacterial homologue predominated by at least three orders of magnitude in all samples. Archaeal amoA was only detectable in approximately 15% of these samples. Using terminal restriction fragment length polymorphism analysis, we monitored AOB lineages based on amoA genes. The Nitrosomonas europaea lineage and a novel Nitrosomonas-like cluster were the dominant AOB signatures, with a Nitrosospira lineage present at lower relative abundance. These lineages exhibited strong temporal oscillations, with one becoming sequentially dominant over the other. Using non-metric multidimensional scaling and redundancy analyses, we tested correlations between terminal restriction fragment length polymorphism profiles and 20 operational and environmental parameters. The redundancy analyses indicated that the dynamics of AOB lineages correlated most strongly with temperature, dissolved oxygen and influent nitrite and chromium. The Nitrosospira lineage signal had a strong negative correlation to dissolved oxygen and temperature, while the Nitrosomonas-like (negative correlations) and N. europaea lineages (positive correlations) were inversely linked (relative to one another) to influent nitrite and chromium. Overall, this study suggests that AOA may be minor contributors to ammonia oxidation in highly aerated activated sludge, and provides insight into parameters controlling the diversity and dominance of AOB lineages within bioreactors during periods of stable nitrification.

Transformation of carbon tetrachloride (CT) by Shewanella oneidensis MR-1 has been proposed to involve the anaerobic respiratory-chain component menaquinone. To investigate this hypothesis a series of menaquinone mutants were constructed. The menF mutant is blocked at the start of the menaquinone biosynthetic pathway. The menB, menA and menG mutants are all blocked towards the end of the pathway, being unable to produce 1,4-dihydroxy-2-naphthoic acid (DHNA), demethyl-menaquinone and menaquinone, respectively. Aerobically grown mutants unable to produce the menaquinone precursor DHNA (menF and menB mutants) showed a distinctly different CT transformation profile than mutants able to produce DHNA but unable to produce menaquinone (menA and menG mutants). While DHNA did not reduce CT in an abiotic assay, the addition of DHNA to the menF and menB mutants restored normal CT transformation activity. We conclude that a derivative of DHNA, that is distinct from menaquinone, is involved in the reduction of CT by aerobically grown S. oneidensis MR-1. When cells were grown anaerobically with trimethylamine-N-oxide as the terminal electron acceptor, all the menaquinone mutants showed wild-type levels of CT reduction. We conclude that S. oneidensis MR-1 produces two different factors capable of dehalogenating CT. The factor produced under anaerobic growth conditions is not a product of the menaquinone biosynthetic pathway.

The observation and detection of the microplastic pollutants generated by industrial manufacturing require the use of precise optical systems. Digital holography is well suited for this task because of its non-contact and non-invasive detection features and the ability to generate information-rich holograms. However, traditional digital holography usually requires post-processing steps, which is time-consuming and may not achieve the final object detection performance. In this work, we develop a deep learning-based holographic classification method, which computes directly on the raw holographic data to extract quantitative information of the microplastic pollutants so as to classify them according to the extent of the pollution. We further show that our method can generalize to the classification task of other micro-objects through cross-dataset validation. Without bulky optical devices, our system can be further developed into a portable microplastics detection system, with wide applicability in the monitoring of microplastic particle pollution in the ecological environment.

We devise an inline digital holographic imaging system equipped with a lightweight deep learning network, termed CompNet, and develop the transfer learning for classification and analysis. It has a compression block consisting of a concatenated rectified linear unit (CReLU) activation to reduce the channels, and a class-balanced cross-entropy loss for training. The method is particularly suitable for small and imbalanced datasets, and we apply it to the detection and classification of microplastics. Our results show good improvements both in feature extraction, and generalization and classification accuracy, effectively overcoming the problem of overfitting. This method could be attractive for future in situ microplastic particle detection and classification applications.

Microplastic pollution poses severe environmental problems. Developing effective imaging tools for the identification and analysis of microplastics is a critical step to curtail their proliferation. Digital holographic imaging can record the morphological and refractive index information of such small plastic fragments, yet due to the heterogeneous sampling environments and variations in the microplastic shapes, traditional supervised learning methods are of limited use. In this work, we pioneer a zero-shot learning method that combines the holographic images with their semantic attributes to identify the microplastics in heterogeneous samples, even if they have not appeared in the training dataset. It makes use of the attention mechanism for image feature extraction, and the Kullback-Leibler divergence both to alleviate the domain shift problem and to guide the training of the mapping function. Experimental results demonstrate the effectiveness of our approach, and the potential use in a wide variety of environmental pollution assessment.

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An inline digital holography with deep learning is developed to detect microplastics automatically from the raw holograms, without any additional image processing and analysis.