SUMMARY
A meaningful interpretation of geophysical measurements requires an assessment of the space of models that are consistent with the data, rather than just a single, ‘best’ model which does not ...convey information about parameter uncertainty. For this purpose, a trans‐dimensional Bayesian Markov chain Monte Carlo (MCMC) algorithm is developed for assessing frequency‐domain electromagnetic (FDEM) data acquired from airborne or ground‐based systems. By sampling the distribution of models that are consistent with measured data and any prior knowledge, valuable inferences can be made about parameter values such as the likely depth to an interface, the distribution of possible resistivity values as a function of depth and non‐unique relationships between parameters. The trans‐dimensional aspect of the algorithm allows the number of layers to be a free parameter that is controlled by the data, where models with fewer layers are inherently favoured, which provides a natural measure of parsimony and a significant degree of flexibility in parametrization. The MCMC algorithm is used with synthetic examples to illustrate how the distribution of acceptable models is affected by the choice of prior information, the system geometry and configuration and the uncertainty in the measured system elevation. An airborne FDEM data set that was acquired for the purpose of hydrogeological characterization is also studied. The results compare favourably with traditional least‐squares analysis, borehole resistivity and lithology logs from the site, and also provide new information about parameter uncertainty necessary for model assessment.
Metabolomics, in which small-molecule metabolites (the metabolome) are identified and quantified, is broadly acknowledged to be the omics discipline that is closest to the phenotype. Although ...appreciated for its role in biomarker discovery programs, metabolomics can also be used to identify metabolites that could alter a cell's or an organism's phenotype. Metabolomics activity screening (MAS) as described here integrates metabolomics data with metabolic pathways and systems biology information, including proteomics and transcriptomics data, to produce a set of endogenous metabolites that can be tested for functionality in altering phenotypes. A growing literature reports the use of metabolites to modulate diverse processes, such as stem cell differentiation, oligodendrocyte maturation, insulin signaling, T-cell survival and macrophage immune responses. This opens up the possibility of identifying and applying metabolites to affect phenotypes. Unlike genes or proteins, metabolites are often readily available, which means that MAS is broadly amenable to high-throughput screening of virtually any biological system.
Interferon gamma has long been studied as a critical mediator of tumor immunity. In recent years, the complexity of cellular interactions that take place in the tumor microenvironment has become ...better appreciated in the context of immunotherapy. While checkpoint inhibitors have dramatically improved remission rates in cancer treatment, IFN-γ and related effectors continue to be identified as strong predictors of treatment success. In this review, we provide an overview of the multiple immunosuppressive barriers that IFN-γ has to overcome to eliminate tumors, and potential avenues for modulating the immune response in favor of tumor rejection.
Autism spectrum disorder (ASD) is strongly associated with de novo gene mutations. One of the most commonly affected genes is SCN2A. ASD-associated SCN2A mutations impair the encoded protein NaV1.2, ...a sodium channel important for action potential initiation and propagation in developing excitatory cortical neurons. The link between an axonal sodium channel and ASD, a disorder typically attributed to synaptic or transcriptional dysfunction, is unclear. Here we show that NaV1.2 is unexpectedly critical for dendritic excitability and synaptic function in mature pyramidal neurons in addition to regulating early developmental axonal excitability. NaV1.2 loss reduced action potential backpropagation into dendrites, impairing synaptic plasticity and synaptic strength, even when NaV1.2 expression was disrupted in a cell-autonomous fashion late in development. These results reveal a novel dendritic function for NaV1.2, providing insight into cellular mechanisms probably underlying circuit and behavioral dysfunction in ASD.
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•Protein truncation in SCN2A/NaV1.2 bears considerable autism spectrum disorder risk•NaV1.2 governs action potential initiation in immature neocortical pyramidal cells•Mature Scn2a+/− neurons have impaired dendritic excitability and excitatory synapses•Cell-autonomous loss of NaV1.2 in mature cells alone impairs synaptic function
Haploinsufficiency in the gene SCN2A, which encodes the sodium channel NaV1.2, has strong autism association. Spratt et al. show that Scn2a contributes to dendritic excitability in mature neocortical pyramidal cells, where its loss impairs excitatory synaptic function and plasticity.
•A three-step adaptation of the TPB examined intention to make sustainable food choices (87).•TPB with ethics and morals offered increased predictive value over TPB alone (76).•TPB with ethics, ...health motives and morals best predicted sustainable food intention (84).•Positive moral attitude was the strongest predictor of sustainable food intention (82).
This study examined a three-step adaptation of the Theory of Planned Behaviour (TPB) applied to the intention of consumers to purchase sustainably sourced food. The sample consisted of 137 participants, of which 109 were female, who were recruited through a farmers market and an organic produce outlet in an Australian capital city. Participants completed an online questionnaire containing the TPB scales of attitude, subjective norms, perceived behavioural control and intention; measures of positive moral attitude and ethical self identity; and food choice motives. Hierarchical multiple regression was used to examine the predictive utility of the TPB in isolation (step 1) and the TPB expanded to include the constructs of moral attitude and ethical self-identity (step 2). The results indicated the expansion of the TPB to include these constructs added significantly to the predictive model measuring intention to purchase sustainably sourced food. The third step in the adaptation utilised this expanded TPB model and added a measure of retail channel (where consumers reported buying fresh produce) and 9 food choice motives, in order to assess the predictive utility of the inclusion of choice motivations in this context. Of the 8 food choice motives examined, only health and ethical values significantly predicted intention to purchase sustainably sourced food. However, with the addition of food choice motives, ethical self-identity was no longer a significant predictor of intention to purchase sustainably sourced food. Overall the adapted TPB model explained 76% of the variance in intention to purchase sustainably sourced food.
Nitric oxide (NO) is a short-lived, pleiotropic molecule that affects numerous critical functions in the body. Presently, there are markedly conflicting findings in the literature regarding NO and ...its role in carcinogenesis and tumor progression. NO has been shown to have dichotomous effects on cellular proliferation, apoptosis, migration, invasion, angiogenesis and many other important processes in cancer biology. It has been shown to be both pro- and antitumorigenic, depending on the concentration and the tumor microenvironment in question. NO is generated by three isoforms of NO synthase (NOS) that are widely expressed and sometimes upregulated in human tumors. Due to its vast array of physiological functions, it presents a huge challenge to researchers to discover its true potential in cancer biology and consequently, its use in anticancer therapies. In this study, we review the current knowledge in this area, with an emphasis placed on NO modulation as an anticancer therapy, focusing on NO-donating drugs and NOS inhibitors.
In this paper we employ a dataset of three dimensions—state, sector, and year—to estimate the short- and long-run price elasticities of state-level electricity demand in the United States. Our sample ...covers the period 2003–2015. We contribute to the literature by employing instrumental variable estimation approaches, using the between estimator, and pursuing panel specifications that enable us to control for multiple dimensions of fixed effects. We conclude that state-level electricity demand is very price inelastic in the short run, with a same-year elasticity of −0.1. The long-run elasticity is near −1, larger than often believed. Among the sectors, it is industry that has the largest long-run price elasticity of demand. This appears to in part be due to electricity-intensive industrial activities clustering in low-price states.
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