Cost modelling of Pseudomonas fluorescens and Pseudomonas chlororaphis as biocontrol for competitive exclusion of Salmonella enterica on tomatoes

Published research on process-based models for biocontrol of foodborne pathogens on produce is limited. The aim of this research was to develop cost model estimates for competitive exclusion (CE) process using Pseudomonas fluorescens and Pseudomonas chlororaphis (non-plant pathogenic and non-human pathogen) as biocontrol against Salmonella enterica on tomatoes. Cost estimates were based on material inputs, equipment, facilities, and projected processing conditions of post-harvest packaging of tomatoes. The microbiological data for inactivation of S. enterica was based on published papers. The small-scale processing facility was assumed to have a processing capacity of 2000 kg of tomatoes/hour for 16 h per day, operational 6 days a week, and for 3-months /year. The large-scale facility was assumed to have a processing capacity of 100,000 kg of tomatoes/hour. Estimated initial capital investment costs for small and large-scale models (production facility) were US$391,000 and US$2.1 million. Application of CE for biocontrol of S. enterica on tomatoes was estimated at US$0.0058–0.073/kg of tomatoes during commercial processing operations. This exceeds chlorine wash technology estimated at US$0.00046/kg and is competitive with gaseous chlorine dioxide at US$0.02–0.21/kg. For high-value produce, CE may complement existing technologies increase food safety, reduce storage loses, and extend shelf life of produce.     

Energy-barrier models for membrane transport.

Energy-barrier models are analyzed to find hidden assumptions and establish ranges of validity. The analysis proceeds by comparison with integrated results for model continuum membranes. The main conclusions are that a simple energy-barrier model has a wide range of validity, is remarkably accurate even when its conditions of validity are not strictly met, and is almost always superior to the analogous equations of irreversible thermodynamics. Its major limitations are a possible nonphysical divergence at high electric fields or volume flows caused by breakdown of the transition-state approximation, and the inability to treat multicomponent mixtures except in a pseudobinary (Nernst-Planck) approximation.     

Noise Normalizes Firing Output of Mouse Lateral Geniculate Nucleus Neurons

The output of individual neurons is dependent on both synaptic and intrinsic membrane properties. While it is clear that the response of an individual neuron can be facilitated or inhibited based on the summation of its constituent synaptic inputs, it is not clear whether subthreshold activity, (e.g. synaptic “noise”- fluctuations that do not change the mean membrane potential) also serve a function in the control of neuronal output. Here we studied this by making whole-cell patch-clamp recordings from 29 mouse thalamocortical relay (TC) neurons. For each neuron we measured neuronal gain in response to either injection of current noise, or activation of the metabotropic glutamate receptor-mediated cortical feedback network (synaptic noise). As expected, injection of current noise via the recording pipette induces shifts in neuronal gain that are dependent on the amplitude of current noise, such that larger shifts in gain are observed in response to larger amplitude noise injections. Importantly we show that shifts in neuronal gain are also dependent on the intrinsic sensitivity of the neuron tested, such that the gain of intrinsically sensitive neurons is attenuated divisively in response to current noise, while the gain of insensitive neurons is facilitated multiplicatively by injection of current noise- effectively normalizing the output of the dLGN as a whole. In contrast, when the cortical feedback network was activated, only multiplicative gain changes were observed. These network activation-dependent changes were associated with reductions in the slow afterhyperpolarization (sAHP), and were mediated at least in part, by T-type calcium channels. Together, this suggests that TC neurons have the machinery necessary to compute multiple output solutions to a given set of stimuli depending on the current level of network stimulation.     

Replacement of Ca by Ni in a Perovskite Titanate to Yield a Novel Perovskite Exsolution Architecture for Oxygen‐Evolution Reactions

For efficient catalysis and electrocatalysis well‐designed, high‐surface‐area support architectures covered with highly dispersed metal nanoparticles with good catalyst‐support interactions are required. In situ grown Ni nanoparticles on perovskites have been recently reported to enhance catalytic activities in high‐temperature systems such as solid oxide cells (SOCs). However, the micrometer‐scale primary particles prepared by conventional solid‐state reactions have limited surface area and tend to retain much of the active catalytic element within the bulk, limiting efficacy of such exsolution processes in low‐temperature systems. Here, a new, highly efficient, solvothermal route is demonstrated to exsolution from smaller scale primary particles. Furthermore, unlike previous reports of B‐site exsolution, it seems that the metal nanoparticles are exsolved from the A‐site of these perovskites. The catalysts show large active site areas and strong metal‐support interaction (SMSI), leading to ≈26% higher geometric activity (25 times higher mass activity with 1.4 V of E_on‐set) and stability for oxygen‐evolution reaction (OER) with only 0.72 µg base metal contents compared to typical 20 wt% Ni/C and even commercial 20 wt% Ir/C. The findings obtained here demonstrate the potential design and development of heterogeneous catalysts in various low‐temperature electrochemical systems including alkaline fuel cells and metal–air batteries.