Benchmarking Long-Read Assemblers for Genomic Analyses of Bacterial Pathogens Using Oxford Nanopore Sequencing

Oxford Nanopore sequencing can be used to achieve complete bacterial genomes. However, the error rates of Oxford Nanopore long reads are greater compared to Illumina short reads. Long-read assemblers using a variety of assembly algorithms have been developed to overcome this deficiency, which have not been benchmarked for genomic analyses of bacterial pathogens using Oxford Nanopore long reads. In this study, long-read assemblers, namely Canu, Flye, Miniasm/Racon, Raven, Redbean, and Shasta, were thus benchmarked using Oxford Nanopore long reads of bacterial pathogens. Ten species were tested for mediocre- and low-quality simulated reads, and 10 species were tested for real reads. Raven was the most robust assembler, obtaining complete and accurate genomes. All Miniasm/Racon and Raven assemblies of mediocre-quality reads provided accurate antimicrobial resistance (AMR) profiles, while the Raven assembly of Klebsiella variicola with low-quality reads was the only assembly with an accurate AMR profile among all assemblers and species. All assemblers functioned well for predicting virulence genes using mediocre-quality and real reads, whereas only the Raven assemblies of low-quality reads had accurate numbers of virulence genes. Regarding multilocus sequence typing (MLST), Miniasm/Racon was the most effective assembler for mediocre-quality reads, while only the Raven assemblies of Escherichia coli O157:H7 and K. variicola with low-quality reads showed positive MLST results. Miniasm/Racon and Raven were the best performers for MLST using real reads. The Miniasm/Racon and Raven assemblies showed accurate phylogenetic inference. For the pan-genome analyses, Raven was the strongest assembler for simulated reads, whereas Miniasm/Racon and Raven performed the best for real reads. Overall, the most robust and accurate assembler was Raven, closely followed by Miniasm/Racon.     

Dynamic analysis and optimal feedback control synthesis applied to biological waste treatment

The dynamics and optimal feedback control of biological waste treatment processes are examined. Modern feedback control theory is applied and results are obtained for both proportional and proportional plus integral control. A dynamic mathematical model of the system is developed by employing a Monod kinetic model, which is modified to account for endogenous metabolism, and a complete mixing flow model in the growth chamber. The feedback control is implemented via the flow rate so as to maintain desired effluent concentrations. External disturbances which cause changes in substrate concentration and/or cell concentration are considered and optimal control effort is applied. Results are also presented for impulse and step changes in the influent substrate concentration. It is found that by changing the values of the weighting factors in the objective function, it is possible to obtain good control of either the effluent substrate concentration or cell concentration.     

Real-Time Differential Pricing Scheme for Active Consumers with Electric Vehicles

Electric vehicles (EVs) are becoming increasingly popular because of their low operating costs. However, the anticipated increase in EV usage presents opportunities as well as challenges for utilities. This paper models EV charging behavior of active consumers who strategize their EV charging schedule to minimize the charging cost. The electric utility employs a temporal and spatially varying real-time pricing scheme that minimizes peak-to-average load ratio by influencing charging behavior of multiple active consumers. These active consumers decide their optimal charging strategy based on real-time electricity prices in order to minimize their EV charging cost. A model predictive control (MPC)-based optimal pricing scheme for electric utility and optimal charging strategy for active consumers is proposed. Effects of the proposed approach on load profile is verified using simulations.     

Effect of Fiber Length on Carbon Nanotube-Induced Fibrogenesis

Given their extremely small size and light weight, carbon nanotubes (CNTs) can be readily inhaled by human lungs resulting in increased rates of pulmonary disorders, particularly fibrosis. Although the fibrogenic potential of CNTs is well established, there is a lack of consensus regarding the contribution of physicochemical attributes of CNTs on the underlying fibrotic outcome. We designed an experimentally validated in vitro fibroblast culture model aimed at investigating the effect of fiber length on single-walled CNT (SWCNT)-induced pulmonary fibrosis. The fibrogenic response to short and long SWCNTs was assessed via oxidative stress generation, collagen expression and transforming growth factor-beta (TGF-β) production as potential fibrosis biomarkers. Long SWCNTs were significantly more potent than short SWCNTs in terms of reactive oxygen species (ROS) response, collagen production and TGF-β release. Furthermore, our finding on the length-dependent in vitro fibrogenic response was validated by the in vivo lung fibrosis outcome, thus supporting the predictive value of the in vitro model. Our results also demonstrated the key role of ROS in SWCNT-induced collagen expression and TGF-β activation, indicating the potential mechanisms of length-dependent SWCNT-induced fibrosis. Together, our study provides new evidence for the role of fiber length in SWCNT-induced lung fibrosis and offers a rapid cell-based assay for fibrogenicity testing of nanomaterials with the ability to predict pulmonary fibrogenic response in vivo.     

The Effect of Mn-rich Precipitates on the Strength of AZ31 Extrudates

The commercial magnesium alloy AZ31 has been subjected to a range of solution treatment regimes. These have then been extruded and their microstructure, texture, and precipitate populations characterized along with their mechanical properties. During the solution treatment, Mn-enriched particles develop and these remain largely unchanged throughout subsequent processing steps. A direct link between grain size and texture has been found, with coarser-grained specimens showing sharper textures. VPSC modeling has been used to quantify the effect of texture on the tensile yield strength, and it has been found that sharper textures have larger tensile yield strengths. Since coarser grain sizes have reduced Hall–Petch hardening, but have an additional texture-strengthening component, a region on the Hall–Petch plot for tension has been identified in which there is an insensitivity of strength to grain size. This has been quantitatively modeled and a texture-modified Hall–Petch plot for tension has been developed. The Mn-rich particles have also been shown to provide precipitate strengthening to the alloy of up to 40 MPa. The compressive behavior was clearer, with the compressive yield strength being directly correlated to grain size and unaffected by texture or precipitation hardening.