Evaluation of the thiamine dose-response relationship for lake trout (Salvelinus namaycush) fry using an individual based model

Substantial natural reproduction of lake trout (Salvelinus namaycush) has not been achieved in the Great Lakes, except for Lake Superior and a few areas in Lake Huron, despite continued stocking efforts. Low thiamine levels in lake trout eggs, which can result in lethal and sublethal impacts (thiamine deficiency complex, TDC) on fry, may contribute to widespread recruitment failure in lake trout populations. We hypothesized that incorporation of sublethal impacts into dose-response curves would result in estimates of EC50s (median lethal concentrations) for fry greater than the estimates that rely only on acute mortality and that predation would exacerbate thiamine effects. To investigate the sublethal effects of TDC (prey capture success and predation mortality) on cohort growth and survival, we developed an individual-based model for lake trout fry. The model tracks daily activities, including consumption, respiration, growth, and mortality, of lake trout from hatch until fry reach a length of 33?mm when we assume fry feed naturally and thiamine effects are minimized. Model output with sublethal impacts resulted in an EC50 (7.3?nmol/g) that was greater than published studies that are limited to acute mortality (1.5?nmol/g). Furthermore, when we included interstitial and pelagic predation, the impact of sublethal effects shifted the EC50 values even higher (7.4–10?nmol/g). Simulation results indicate that low thiamine levels, in combination with moderate to high predation, can eliminate lake trout cohorts. Our simulations suggest that the sublethal effects of low thiamine can contribute to poor lake trout recruitment more than previously suspected.     

Balance between model detail and experimental information in steam methane reforming over a Ni/MgO-SiO2 catalyst

The optimization and full understanding of chemical reactions is aided by the construction of an adequate kinetic model. The development of such a kinetic model remains a challenging task. To tackle this challenge in the most efficient way, an iterative, systematic methodology, originally demonstrated for n-hexane hydroisomerization, is now extended aiming at finding the balance between the envisaged model detail and available information, often originating from time-consuming and expensive experiments. Steam methane reforming on the Ni/MgO-SiO₂ case study is used for this purpose, that is, the construction of a kinetic model that embeds a maximum amount of information contained in the dataset. The kinetic model is expanded stepwise from a power law model over a model with reactant adsorption toward a Langmuir–Hinshelwood–Hougen–Watson model. The performance of the initially underparameterized model improved significantly by adding reactant adsorption, yet including product adsorption led to overparameterization rather than enhanced model performance. © 2019 American Institute of Chemical Engineers AIChE J, 65: 1222–1233, 2019     

On Data Integration Problems With Manifolds

This article focuses on data integration problems where the predictor variables for some response variable partition into known subsets. This type of data is often referred to as multi-view data, and each subset of the predictors is called a data view. Accounting for data views can add practical value in terms of both interpretation and predictive performance. Many existing approaches for multi-view data rely on view-agreement principles, strong smoothness assumptions, or regularization penalties. The former approaches can be sensitive to modest noise in the response or predictor variables, while the latter approach is linear and can usually be out-performed. We develop semiparametric data integration methods to span key tradeoffs including the bias-variance tradeoff on prediction error, the possibility that the data may be fully viewed with no appreciable view relationships, and the use of sparse anchor point methods to detect and use manifolds (i.e., possibly nonelliptical structures) within views if they enhance performance. Theoretical results help justify the new technique, and its effectiveness and computational feasibility are demonstrated empirically. This new semiparametric methodology is available for public use through the supplemental R package mvltools. Additional supplementary material for this article is also available online.     

First-principles modeling for optimal design,operation, and integration of energy conversion and storage systems

Energy and electricity consumption is expected to increase in the foreseeable future. Concurrently, sustainability concerns of fossil-based energy resources have motivated the use of renewable and reusable energy resources, and the use of more efficient energy-converting and energy-consuming systems. Consequently, for the past decade, there have been major theoretical and experimental advances in (1) energy generation from renewable and reusable resources and (2) energy-consuming and energy-converting devices. This review article focuses on the recent theoretical advances in renewable energy conversion devices such as photovoltaic and fuel cells, and in energy storage devices such as rechargeable batteries, flow batteries, and supercapacitors. Due to similar chemistry, electrochemistry, and physics of these systems, modeling similarities between different energy systems are highlighted. This review puts into perspective how first-principles mathematical modeling has contributed to systematic advances in the optimal design, operation, and integration of these systems. © 2018 American Institute of Chemical Engineers AIChE J, 65: e16482 2019     

Acute health effects from exposure to indoor ultrafine particles—A randomized controlled crossover study among young mild asthmatics

Particulate matter is linked to adverse health effects, however, little is known about health effects of particles emitted from typical indoor sources. We examined acute health effects of short-term exposure to emissions from cooking and candles among asthmatics. In a randomized controlled double-blinded crossover study, 36 young non-smoking asthmatics attended three exposure sessions lasting 5 h: (a) air mixed with emissions from cooking (fine particle mass concentration): (PM_2.5: 96.1 μg/m³), (b) air mixed with emissions from candles (PM_2.5: 89.8 μg/m³), and c) clean filtered air (PM_2.5: 5.8 μg/m³). Health effects (spirometry, fractional exhaled Nitric Oxide [FeNO], nasal volume and self-reported symptoms) were evaluated before exposure start, then 5 and 24 h after. During exposures volatile organic compounds (VOCs), particle size distributions, number concentrations and optical properties were measured. Generally, no statistically significant changes were observed in spirometry, FeNO, or nasal volume comparing cooking and candle exposures to clean air. In males, nasal volume and FeNO decreased after exposure to cooking and candles, respectively. Participants reported additional and more pronounced symptoms during exposure to cooking and candles compared to clean air. The results indicate that emissions from cooking and candles exert mild inflammation in asthmatic males and decrease comfort among asthmatic males and females.     

Effect of Adventitious Carbon on Pit Formation of Monolayer MoS2

Forming pits on molybdenum disulfide (MoS₂) monolayers is desirable for (opto)electrical, catalytic, and biological applications. Thermal oxidation is a potentially scalable method to generate pits on monolayer MoS₂, and pits are assumed to preferentially form around undercoordinated sites, such as sulfur vacancies. However, studies on thermal oxidation of MoS₂ monolayers have not considered the effect of adventitious carbon (C) that is ubiquitous and interacts with oxygen at elevated temperatures. Herein, the effect of adventitious C on the pit formation on MoS₂ monolayers during thermal oxidation is studied. The in situ environmental transmission electron microscopy measurements herein show that pit formation is preferentially initiated at the interface between adventitious C nanoparticles and MoS₂, rather than only sulfur vacancies. Density functional theory (DFT) calculations reveal that the C/MoS₂ interface favors the sequential adsorption of oxygen atoms with facile kinetics. These results illustrate the important role of adventitious C on pit formation on monolayer MoS₂.