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Anti-drug vaccines previously failed clinical trials because they did not provide a sufficient titer or duration of antibodies (AB), but new adjuvants enhance both AB titers and efficacy duration. This clinical trial assessed AB titers after a single booster of commercial tetanus-diphtheria (Td) vaccine in 40 males randomized as 15 to Td alone and 25 to Td combined with the TLR5 adjuvant, Entolimod (Ent). Ent significantly increased ABs against diphtheria (DPT) (0.46 vs. 0.29 IU/mL increase; n = 40, p < 0.05), but against tetanus (TT) only if baseline TT AB was below 3 IU/mL (3.1 vs. 2.1 IU/mL; n = 20; p < 0.05). These 20 participants also showed a two-fold increase in anti-TT AB titer more often when given Ent than non-Ent (33% vs. 82%) (p < 0.03). Anti-Ent AB was low and appeared unlikely to reduce Ent efficacy after repeated Ent administration. Medical safety was excellent, and a TLR5 missense polymorphism reduced anti-DPT AB production, but Ent increased anti-DPT AB titers to levels induced in subjects with genetically “normal” TRL5 functioning. Further clinical testing of TLR5 adjuvants like Ent seems warranted for anti-drug vaccines.

The use of automated methods to estimate fractional vegetation cover (FVC) from digital photographs has increased in recent years given its potential to produce accurate, fast and inexpensive FVC measurements. Wide acceptance has been delayed because of the limitations in accuracy, speed, automation and generalization of these methods. This work introduces a novel technique, the Automated Canopy Estimator (ACE) that overcomes many of these challenges to produce accurate estimates of fractional vegetation cover using an unsupervised segmentation process. ACE is shown to outperform nine other segmentation algorithms, consisting of both threshold-based and machine learning approaches, in the segmentation of photographs of four different crops (oat, corn, rapeseed and flax) with an overall accuracy of 89.6%. ACE is similarly accurate (88.7%) when applied to remotely sensed corn, producing FVC estimates that are strongly correlated with ground truth values.

In this paper, the problem of estimating a scalar field (e.g., the spatial distribution of contaminants in an area) using a sensor network is considered. The sensors are assumed to have quantized measurements. We consider distributed estimation algorithms where each sensor forms its own estimate of the field, with sensors able to share information locally with its neighbours. Two schemes are proposed, called, respectively, measurement diffusion and estimate diffusion. In the measurement diffusion scheme, each sensor broadcasts to its neighbours the latest received measurements of every sensor in the network, while in the estimate diffusion scheme, each sensor will broadcast local estimates and Hessians to its neighbours. Information received from its neighbours will then be iteratively combined at each sensor to form the field estimates. Time-varying scalar fields can also be estimated using both the measurement diffusion and estimate diffusion schemes. Numerical studies illustrate the performance of the proposed algorithms, in particular demonstrating steady state performance close to that of centralized estimation.

Phenol is an important petrochemical that is conventionally used as a precursor for synthesizing an array of plastics and fine chemicals. As an emerging alternative to its traditional petrochemical production, multiple enzyme pathways have been engineered to date to enable its renewable biosynthesis from biomass feedstocks, each incorporating unique enzyme chemistries and intermediate molecules. Leveraging all three of the unique phenol biosynthesis pathways reported to date, a series of synthetic ‘metabolic funnels’ was engineered, each with the goal of maximizing net precursor assimilation and flux towards phenol via the parallel co-expression of multiple distinct pathways within the same Escherichia coli host. By constructing and evaluating all possible binary and tertiary pathway combinations, one ‘funnel’ was ultimately identified, which supported enhanced phenol production relative to all three individual pathways by 16 to 69%. Further host engineering to increase endogenous precursor availability then allowed for 26% greater phenol production, reaching a final titer of 554 ± 19 mg/L and 28.8 ± 0.34 mg/g yield on glucose. Lastly, using a diphasic culture including dibutyl phthalate for in situ phenol extraction, final titers were further increased to a maximum of 812 ± 145 mg/L at a yield of 40.6 ± 7.2 mg/g. The demonstrated ‘funneling’ pathway holds similar promise in support of phenol production by other, non-E. coli hosts, while this general approach can be readily extended towards a diversity of other value-added bioproducts of interest.