It has been shown that the same canonical cortical circuit model with mutual inhibition and a fatigue process can explain perceptual rivalry and other neurophysiological responses to a range of static stimuli. However, it has been proposed that this model cannot explain responses to dynamic inputs such as found in intermittent rivalry and rivalry memory, where maintenance of a percept when the stimulus is absent is required. This challenges the universality of the basic canonical cortical circuit. Here, we show that by including an overlooked realistic small nonspecific background neural activity, the same basic model can reproduce intermittent rivalry and rivalry memory without compromising static rivalry and other cortical phenomena. The background activity induces a mutual-inhibition mechanism for short-term memory, which is robust to noise and where fine-tuning of recurrent excitation or inclusion of sub-threshold currents or synaptic facilitation is unnecessary. We prove existence conditions for the mechanism and show that it can explain experimental results from the quartet apparent motion illusion, which is a prototypical intermittent rivalry stimulus.

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Abstract. The lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1), plays an important role in angiotension II (Ang II)-induced hypertensive renal injury associated with pro-inflammatory responses, tubular functional damage and cellular apotosis. In this study, we report on the role of LOX-1 in Ang II-induced oxidative functional damage and underlying signaling in human renal proximal tubular epithelial cells (HRPTEpiCs). The exposure to Ang II enhanced the expression of the NADPH oxidases (the p22phox, p47phox and Nox4 subunits), LOX-1 and the adhesion molecule, ICAM-1. It also promoted monocytic U937 cell adherences to HRPTEpiCs, increased reactive oxygen species formation and stimulated apotosis, which was concomitant with an increase in the activation of p38 and p44/42 mitogen-activated protein kinases (MAPK). Furthermore, the Ang II treatment disturbed the balance of the Bcl-2 family proteins, destabilized mitochondrial membrane potential, and subsequently triggered the release of cytochrome c into the cytosol, causing the activation of caspase-3. The NADPH oxidase inhibitors and LOX-1 small interfering RNA markedly ameliorated these detrimental effects by reducing LOX-1 expression and MAPK activation. The p38 and p44/42MAPK inhibitors also inhibited the Ang II-induced functional damage without affecting LOX-1 expression in the HRPTEpiCs. These observations suggest that LOX-1 mediates Ang II-induced renal tubular epithelial dysfunction. In addition, MAPK pathway activation occurs downstream of the Ang II/reactive oxygen species/ LOX-1 cascade.

Power side-channel attacks, which can deduce secret data via statistical analysis, have become a serious threat. Masking is an effective countermeasure for reducing the statistical dependence between secret data and side-channel information. However, designing masking algorithms is an error-prone process. In this paper, we propose a hybrid approach combing type inference and model-counting to verify masked arithmetic programs against side-channel attacks. The type inference allows an efficient, lightweight procedure to determine most observable variables whereas model-counting accounts for completeness. In case that the program is not perfectly masked, we also provide a method to quantify the security level of the program. We implement our methods in a tool QMVerif and evaluate it on cryptographic benchmarks. The experimental results show the effectiveness and efficiency of our approach.

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Abstract. To make predictions about the carbon cycling consequences of rising global surface temperatures, Earth system scientists rely on mathematical soil biogeochemical models (SBMs). However, it is not clear which models have better predictive accuracy, and a rigorous quantitative approach for comparing and validating the predictions has yet to be established. In this study, we present a Bayesian approach to SBM comparison that can be incorporated into a statistical model selection framework. We compared the fits of linear and nonlinear SBMs to soil respiration data compiled in a recent meta-analysis of soil warming field experiments. Fit quality was quantified using Bayesian goodness-of-fit metrics, including the widely applicable information criterion (WAIC) and leave-one-out cross validation (LOO). We found that the linear model generally outperformed the nonlinear model at fitting the meta-analysis data set. Both WAIC and LOO computed higher overfitting risk and effective numbers of parameters for the nonlinear model compared to the linear model, conditional on the data set. Goodness of fit for both models generally improved when they were initialized with lower and more realistic steady-state soil organic carbon densities. Still, testing whether linear models offer definitively superior predictive performance over nonlinear models on a global scale will require comparisons with additional site-specific data sets of suitable size and dimensionality. Such comparisons can build upon the approach defined in this study to make more rigorous statistical determinations about model accuracy while leveraging emerging data sets, such as those from long-term ecological research experiments.

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