Cobalt Release from a Nanoscale Multiphase Lithiated Cobalt Phosphate Dominates Interaction with Shewanella oneidensis MR-1 and Bacillus subtilis SB491

Optimized Bags of Artificial Neural Networks Can Predict the Viability of Organisms Exposed to Nanoparticles

Prediction of organismal viability upon exposure to a nanoparticle in varying environments─as fully specified at the molecular scale─has emerged as a useful figure of merit in the design of engineered nanoparticles. We build on our earlier finding that a bag of artificial neural networks (ANNs) can provide such a prediction when such machines are trained with a relatively small data set (with ca. 200 examples). Therein, viabilities were predicted by consensus using the weighted means of the predictions from the bags. Here, we confirm the accuracy and precision of the prediction of nanoparticle viabilities using an optimized bag of ANNs over sets of data examples that had not previously been used in the training and validation process. We also introduce the viability strip, rather than a single value, as the prediction and construct it from the viability probability distribution of an ensemble of ANNs compatible with the data set. Specifically, the ensemble consists of the ANNs arising from subsets of the data set corresponding to different splittings between training and validation, and the different bags (k-folds). A k - 1 k machine uses a single partition (or bag) of k - 1 ANNs each trained on 1/k of the data to obtain a consensus prediction, and a k-bag machine quorum samples the k possible k - 1 k machines available for a given partition. We find that with increasing k in the k-bag or k - 1 k machines, the viability strips become more normally distributed and their predictions become more precise. Benchmark comparisons between ensembles of 4-bag machines and 3 4 fraction machines suggest that the 3 4 fraction machine has similar accuracy while overcoming some of the challenges arising from divergent ANNs in the 4-bag machines.

Nanotechnology for Targeted Detection and Removal of Bacteria: Opportunities and Challenges

The emergence of nanotechnology has created unprecedented hopes for addressing several unmet industrial and clinical issues, including the growing threat so-termed "antibiotic resistance" in medicine. Over the last decade, nanotechnologies have demonstrated promising applications in the identification, discrimination, and removal of a wide range of pathogens. Here, recent insights into the field of bacterial nanotechnology are examined that can substantially improve the fundamental understanding of nanoparticle and bacteria interactions. A wide range of developed nanotechnology-based approaches for bacterial detection and removal together with biofilm eradication are summarized. The challenging effects of nanotechnologies on beneficial bacteria in the human body and environment and the mechanisms of bacterial resistance to nanotherapeutics are also reviewed.

Nanoscale battery cathode materials induce DNA damage in bacteria

The increasing use of nanoscale lithium nickel manganese cobalt oxide (Li x Ni y Mn z Co1-y-z O2, NMC) as a cathode material in lithium-ion batteries poses risk to the environment. Learning toxicity mechanisms on molecular levels is critical to promote proactive risk assessment of these complex nanomaterials and inform their sustainable development. We focused on DNA damage as a toxicity mechanism and profiled in depth chemical and biological changes linked to DNA damage in two environmentally relevant bacteria upon nano-NMC exposure. DNA damage occurred in both bacteria, characterized by double-strand breakage and increased levels of many putative chemical modifications on bacterial DNA bases related to direct oxidative stress and lipid peroxidation, measured by cutting-edge DNA adductomic techniques. Chemical probes indicated elevated intracellular reactive oxygen species and transition metal ions, in agreement with DNA adductomics and gene expression analysis. By integrating multi-dimensional datasets from chemical and biological measurements, we present rich mechanistic insights on nano-NMC-induced DNA damage in bacteria, providing targets for biomarkers in the risk assessment of reactive materials that may be extrapolated to other nano-bio interactions.