Constraints on the active tectonics of the Friuli/NW Slovenia area from CGPS measurements and three-dimensional kinematic modeling

We use site velocities from continuous GPS (CGPS) observations and kinematic modeling to investigate the active tectonics of the Friuli/NW Slovenia area. Data from 42 CGPS stations around the Adriatic indicate an oblique collision, with southern Friuli moving NNW toward northern Friuli at the relative speed of 1.6 to 2.2 mm/a. We investigate the active tectonics using 3DMove, a three-dimensional kinematic model tool. The model consists of one indenter-shaped fault plane that approximates the Adriatic plate boundary. Using the ‘‘fault-parallel flow’’ deformation algorithm, we move the hanging wall along the fault plane in the direction indicated by the GPS velocities. The resulting strain field is used for structural interpretation. We identify a pattern of coincident strain maxima and high vorticity that correlates well with groups of hypocenters of major earthquakes (including their aftershocks) and indicates the orientation of secondary, active faults. The pattern reveals structures both parallel and perpendicular to the strike of the primary fault. In the eastern sector, which shows more complex tectonics, these two sets of faults probably form an interacting strike-slip system

Closed-loop Performance Optimization of Model Predictive Control with Robustness Guarantees

Model mismatch and process noise are two frequently occurring phenomena that can drastically affect the performance of model predictive control (MPC) in practical applications. We propose a principled way to tune the cost function and the constraints of linear MPC schemes to achieve good performance and robust constraint satisfaction on uncertain nonlinear dynamics with additive noise. The tuning is performed using a novel MPC tuning algorithm based on backpropagation developed in our earlier work. Using the scenario approach, we provide probabilistic bounds on the likelihood of closed-loop constraint violation over a finite horizon. We showcase the effectiveness of the proposed method on linear and nonlinear simulation examples

The mean field infinite range p=3 spin glass: equilibrium landscape and correlation time scales

We investigate numerically the dynamical behavior of the mean field 3-spin spin glass model: we study equilibrium dynamics, and compute equilibrium time scales as a function of the system size V. We find that for increasing volumes the time scales $\tau$ increase like $\ln \tau \propto V$. We also present an accurate study of the equilibrium static properties of the system.Comment: 6 pages, 9 figure

4D Spin Glasses in Magnetic Field Have a Mean Field like Phase

By using numerical simulations we show that the 4D $J=\pm 1$ Edwards Anderson spin glass in magnetic field undergoes a mean field like phase transition. We use a dynamical approach: we simulate large lattices (of volume $V$) and work out the behavior of the system in limit where both $t$ and $V$ go to infinity, but where the limit $V \to \infty$ is taken first. By showing that the dynamic overlap $q$ converges to a value smaller than the static one we exhibit replica symmetry breaking. The critical exponents are compatible with the ones obtained by mean field computations.Comment: Physrev format, 5 ps figures include

Extending Hybrid CSP with Probability and Stochasticity

Probabilistic and stochastic behavior are omnipresent in computer controlled systems, in particular, so-called safety-critical hybrid systems, because of fundamental properties of nature, uncertain environments, or simplifications to overcome complexity. Tightly intertwining discrete, continuous and stochastic dynamics complicates modelling, analysis and verification of stochastic hybrid systems (SHSs). In the literature, this issue has been extensively investigated, but unfortunately it still remains challenging as no promising general solutions are available yet. In this paper, we give our effort by proposing a general compositional approach for modelling and verification of SHSs. First, we extend Hybrid CSP (HCSP), a very expressive and process algebra-like formal modeling language for hybrid systems, by introducing probability and stochasticity to model SHSs, which is called stochastic HCSP (SHCSP). To this end, ordinary differential equations (ODEs) are generalized by stochastic differential equations (SDEs) and non-deterministic choice is replaced by probabilistic choice. Then, we extend Hybrid Hoare Logic (HHL) to specify and reason about SHCSP processes. We demonstrate our approach by an example from real-world.Comment: The conference version of this paper is accepted by SETTA 201

BVR-A deficiency leads to autophagy impairment through the dysregulation of AMPK/mTOR axis in the brain—Implications for neurodegeneration

Biliverdin reductase-A (BVR-A) impairment is associated with increased accumulation of oxidatively-damaged proteins along with the impairment of autophagy in the brain during neurodegenerative disorders. Reduced autophagy inhibits the clearance of misfolded proteins, which then form neurotoxic aggregates promoting neuronal death. The aim of our study was to clarify the role for BVR-A in the regulation of the mTOR/autophagy axis by evaluating age-associated changes (2, 6 and 11 months) in cerebral cortex samples collected from BVR-A knock-out (BVR-A−/−) and wild-type (WT) mice. Our results show that BVR-A deficiency leads to the accumulation of oxidatively-damaged proteins along with mTOR hyper-activation in the cortex. This process starts in juvenile mice and persists with aging. mTOR hyper-activation is associated with the impairment of autophagy as highlighted by reduced levels of Beclin-1, LC3β, LC3II/I ratio, Atg5–Atg12 complex and Atg7 in the cortex of BVR-A−/− mice. Furthermore, we have identified the dysregulation of AMP-activated protein kinase (AMPK) as a critical event driving mTOR hyper-activation in the absence of BVR-A. Overall, our results suggest that BVR-A is a new player in the regulation of autophagy, which may be targeted to arrive at novel therapeutics for diseases involving impaired autophagy

On the Use of Optimized Monte Carlo Methods for Studying Spin Glasses

We start from recently published numerical data by Hatano and Gubernatis cond-mat/0008115 to discuss properties of convergence to equilibrium of optimized Monte Carlo methods (bivariate multi canonical and parallel tempering). We show that these data are not thermalized, and they lead to an erroneous physical picture. We shed some light on why the bivariate multi canonical Monte Carlo method can fail.Comment: 6 pages, 5 eps figures include

The dysregulation of OGT/OGA cycle mediates Tau and APP neuropathology in down syndrome

Protein O-GlcNAcylation is a nutrient-related post-translational modification that, since its discovery some 30 years ago, has been associated with the development of neurodegenerative diseases. As reported in Alzheimer’s disease (AD), flaws in the cerebral glucose uptake translate into reduced hexosamine biosynthetic pathway flux and subsequently lead to aberrant protein O-GlcNAcylation. Notably, the reduction of O-GlcNAcylated proteins involves also tau and APP, thus promoting their aberrant phosphorylation in AD brain and the onset of AD pathological markers. Down syndrome (DS) individuals are characterized by the early development of AD by the age of 60 and, although the two conditions present the same pathological hallmarks and share the alteration of many molecular mechanisms driving brain degeneration, no evidence has been sought on the implication of O-GlcNAcylation in DS pathology. Our study aimed to unravel for the first time the role of protein O-GlcNacylation in DS brain alterations positing the attention of potential trisomy-related mechanisms triggering the aberrant regulation of OGT/OGA cycle. We demonstrate the disruption of O-GlcNAcylation homeostasis, as an effect of altered OGT and OGA regulatory mechanism, and confirm the relevance of O-GlcNAcylation in the appearance of AD hallmarks in the brain of a murine model of DS. Furthermore, we provide evidence for the neuroprotective effects of brain-targeted OGA inhibition. Indeed, the rescue of OGA activity was able to restore protein O-GlcNAcylation, and reduce AD-related hallmarks and decreased protein nitration, possibly as effect of induced autophagy

Paraoxonase-1 (PON-1) Arylesterase Activity Levels in Patients with Coronary Artery Disease: A Meta-Analysis

Aim: To review and compare the PON-1 arylesterase activity between coronary artery disease (CAD) and non-CAD patients. Methods: Data were obtained by searching MEDLINE and Scopus for all investigations published between January 1, 2000 and March 1, 2021 comparing PON-1 arylesterase activity between CAD and controls. Results: Twenty studies, based on 5417 patients, met the inclusion criteria and were included in the analysis. A random effect model revealed that PON-1 arylesterase activity was significantly lower in the CAD group compared to controls (SMD = -0.587, 95%CI = -0.776 to -0.339, p < 0.0001, I2 = 92.3%). In CAD patients, the PON-1 arylesterase activity was significantly higher among CAD patients without diabetes mellitus (DM) compared to those with diabetes (SMD: 0.235, 95% CI: 0.014 to 0.456, p = 0.03, I2 = 0%). Conclusions: PON-1 activity is significantly lower in CAD patients, and those without DM presented a significantly higher PON-1 arylesterase activity

SARS-CoV-2 sensing by RIG-I and MDA5 links epithelial infection to macrophage inflammation

SARS-CoV-2 infection causes broad-spectrum immunopathological disease, exacerbated by inflammatory co-morbidities. A better understanding of mechanisms underpinning virus-associated inflammation is required to develop effective therapeutics. Here we discover that SARS-CoV-2 replicates rapidly in lung epithelial cells despite triggering a robust innate immune response through activation of cytoplasmic RNA-sensors RIG-I and MDA5. The inflammatory mediators produced during epithelial cell infection can stimulate primary human macrophages to enhance cytokine production and drive cellular activation. Critically, this can be limited by abrogating RNA sensing, or by inhibiting downstream signalling pathways. SARS-CoV-2 further exacerbates the local inflammatory environment when macrophages or epithelial cells are primed with exogenous inflammatory stimuli. We propose that RNA sensing of SARS-CoV-2 in lung epithelium is a key driver of inflammation, the extent of which is influenced by the inflammatory state of the local environment, and that specific inhibition of innate immune pathways may beneficially mitigate inflammation-associated COVID-19