Model-Based Fault Detection and Estimation for Linear Time Invariant and Piecewise Affine Systems by Using Quadratic Boundedness

Quadratic boundedness is a notion of stability that is adopted to investigate the design of observers for dynamic systems subject to bounded disturbances. We will show how to exploit such observers for the purpose of fault detection. Toward this end, first of all we present the naive application of quadratic boundedness to construct state observers for linear time-invariant systems with state augmentation, i.e., where additional variables may be introduced to account for the occurrence of a fault. Then a Luenberger observer is designed to estimate the augmented state variable of the system in such a way to detect the fault by using a convenient threshold selection. Finally, such an approach is extended to piecewise affine systems by presenting a hybrid Luenberger observer and its related design based on quadratic boundedness. The design of all the observers for both linear time-invariant and piecewise affine systems can be done by using linear matrix inequalities. Simulation results are provided to show the effectiveness of the proposed approach

Polyply:A python suite for facilitating simulations of macromolecules and nanomaterials

Molecular dynamics simulations play an increasingly important role in the rational design of (nano)-materials and in the study of biomacromolecules. However, generating input files and realistic starting coordinates for these simulations is a major bottleneck, especially for high throughput protocols and for complex multi-component systems. To eliminate this bottleneck, we present the polyply software suite that provides 1) a multi-scale graph matching algorithm designed to generate parameters quickly and for arbitrarily complex polymeric topologies, and 2) a generic multi-scale random walk protocol capable of setting up complex systems efficiently and independent of the target force-field or model resolution. We benchmark quality and performance of the approach by creating realistic coordinates for polymer melt simulations, single-stranded as well as circular single-stranded DNA. We further demonstrate the power of our approach by setting up a microphase-separated block copolymer system, and by generating a liquid-liquid phase separated system inside a lipid vesicle

ENSEMBLES: a new multi-model ensemble for seasonal-to-annual predictions: Skill and progress beyond DEMETER in forecasting tropical Pacific SSTs

A new 46-year hindcast dataset for seasonal-to-annual ensemble predictions has been created using a multi-model ensemble of 5 state-of-the-art coupled atmosphere-ocean circulation models. The multi-model outperforms any of the single-models in forecasting tropical Pacific SSTs because of reduced RMS errors and enhanced ensemble dispersion at all lead-times. Systematic errors are considerably reduced over the previous generation (DEMETER). Probabilistic skill scores show higher skill for the new multi-model ensemble than for DEMETER in the 4–6 month forecast range. However, substantially improved models would be required to achieve strongly statistical significant skill increases. The combination of ENSEMBLES and DEMETER into a grand multi-model ensemble does not improve the forecast skill further. Annual-range hindcasts show anomaly correlation skill of ∼0.5 up to 14 months ahead. A wide range of output from the multi-model simulations is becoming publicly available and the international community is invited to explore the full scientific potential of these data

State estimation using a network of distributed observers with switching communication topology

State estimation of linear time-invariant (LTI) systems by using a network of distributed observers is studied in this paper. We assume that each observer has access to a local measurement which may be insufficient to provide the observability of the system, but the ensemble of all measurements in the network guarantees the observability. In this condition, the objective is to design a distributed state estimation approach such that, while the observers can exchange their estimated state vectors under a communication network, the estimated state vector of each observer converges to the state vector of the system. We consider a scenario when the communication links may fail and rebuild over time and the communication network does not stay connected constantly. Accordingly, the main contribution of the paper is to propose a distributed approach (with guarantees on the feasibility of the design) such that the state vector of the system is estimated by each observer if the union/joint of communication links in bounded intervals of time makes the network communication graph connected. Moreover, we also consider a scenario when the LTI system is subject to external disturbances and measurement noise. In this case, we derive sufficient conditions on the proposed approach such that if the communication topology stays connected during links failure, a desired performance to attenuate the effect of external disturbances and measurement noise on estimation errors is guaranteed. Simulation results show the effectiveness of the proposed estimation approach

Downscaling With an Unstructured Coastal-Ocean Model to the Goro Lagoon and the Po River Delta Branches

The Goro Lagoon Finite Element Model (GOLFEM) presented in this paper concentrates on the high-resolution downscaled model of the Goro Lagoon, along with five Po river branches and the coastal area of the Po delta in the northern Adriatic Sea (Italy) where crucial socio-economic activities take place. GOLFEM was validated by means of validation scores (bias – BIAS, root mean square error – RMSE, and mean absolute error – MAE) for the water level, current velocity, salinity and temperature measured at several fixed stations in the lagoon. The range of scores at the stations are: for temperature between −0.8 to +1.2°C, for salinity from −0.2 to 5 PSU, for sea level 0.1 m. The lagoon is dominated by an estuarine vertical circulation due to a double opening at the lagoon mouth and sustained by multiple sources of freshwater inputs. The non-linear interactions among the tidal forcing, the wind and the freshwater inputs affect the lagoon circulation at both seasonal and daily time scales. The sensitivity of the circulation to the forcings was analyzed with several sensitivity experiments done with the exclusion of the tidal forcing and different configurations of the river connections. GOLFEM was designed to resolve the lagoon dynamics at high resolution in order to evaluate the potential effects on the clam farming of two proposed scenarios of human intervention on the morphology of the connection with the sea. We calculated the changes of the lagoon current speed and salinity, and using opportune fitness indexes related to the clams physiology, we quantified analytically the effects of the interventions in terms of extension and persistence of areas of the clams optimal growth. The results demonstrate that the correct management of this kind of fragile environment relies on both long-term (intervention scenarios) and short-term (coastal flooding forecasts and potential anoxic conditions) modeling, based on a flexible tool that is able to consider all the recorded human interventions on the river connections. This study also demonstrates the importance of designing a seamless chain of models that are capable of integrating local effects into the coarser operational oceanographic models

Antiphospholipid reactivity against cardiolipin metabolites occurring during endothelial cell apoptosis.

We have recently shown that cardiolipin (CL) and its metabolites move from mitochondria to other cellular membranes during death receptor-mediated apoptosis. In this study, we investigate the immunoreactivity to CL derivatives occurring during endothelial apoptosis in patients with antiphospholipid syndrome (APS) and systemic lupus erythematosus (SLE). We compared the serum immunoreactivity to CL with that of its derivatives monolysocardiolipin (MCL), dilysocardiolipin (DCL), and hydrocardiolipin (HCL) by means of both enzyme-linked immunosorbent assay and thin-layer chromatography (TLC) immunostaining. In addition, we investigated the composition of phospholipid extracts from the plasma membrane of apoptotic endothelial cells and the binding of patients' sera to the surface of the same cells by using high-performance TLC and immunofluorescence analysis. The average reactivity to MCL was comparable with that of CL and significantly higher than that for DCL and HCL in patients studied, both in the presence or in the absence of beta2-glycoprotein I. Of relevance for the pathogenic role of these autoantibodies, immunoglobulin G from patients' sera showed an increased focal reactivity with the plasma membrane of endothelial cells undergoing apoptosis. Interestingly, the phospholipid analysis of these light membrane fractions showed an accumulation of both CL and MCL. Our results demonstrated that a critical number of acyl chains in CL derivatives is important for the binding of antiphospholipid antibodies and that MCL is an antigenic target with immunoreactivity comparable with CL in APS and SLE. Our finding also suggests a link between apoptotic perturbation of CL metabolism and the production of these antibodies

Protein-ligand binding with the coarse-grained Martini model

The detailed understanding of the binding of small molecules to proteins is the key for the development of novel drugs or to increase the acceptance of substrates by enzymes. Nowadays, computer-aided design of protein–ligand binding is an important tool to accomplish this task. Current approaches typically rely on high-throughput docking essays or computationally expensive atomistic molecular dynamics simulations. Here, we present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein–ligand interactions of small drug-like molecules. Remarkably, we achieve high accuracy without the need of any a priori knowledge of binding pockets or pathways. Our approach is applied to a range of systems from the well-characterized T4 lysozyme over members of the GPCR family and nuclear receptors to a variety of enzymes. The presented results open the way to high-throughput screening of ligand libraries or protein mutations using the coarse-grained Martini model

Autophagy modulation in lymphocytes from COVID-19 patients. new therapeutic target in SARS-COV-2 infection

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the novel coronavirus, causing coronavirus disease 2019 (COVID-19). During virus infection, several pro-inflammatory cytokines are produced, leading to the “cytokine storm.” Among these, interleukin (IL)-6, tumor necrosis factor‐α (TNF‐α), and IL-1β seem to have a central role in the progression and exacerbation of the disease, leading to the recruitment of immune cells to infection sites. Autophagy is an evolutionarily conserved lysosomal degradation pathway involved in different aspects of lymphocytes functionality. The involvement of IL-6, TNF‐α, and IL-1β in autophagy modulation has recently been demonstrated. Moreover, preliminary studies showed that SARS-CoV-2 could infect lymphocytes, playing a role in the modulation of autophagy. Several anti-rheumatic drugs, now proposed for the treatment of COVID-19, could modulate autophagy in lymphocytes, highlighting the therapeutic potential of targeting autophagy in SARS-CoV-2 infection

Multiscale modeling of molecular structure and optical properties of complex supramolecular aggregates

Supramolecular aggregates of synthetic dye molecules offer great perspectives to prepare biomimetic functional materials for light-harvesting and energy transport. The design is complicated by the fact that structure-property relationships are hard to establish, because the molecular packing results from a delicate balance of interactions and the excitonic properties that dictate the optics and excited state dynamics, in turn sensitively depend on this packing. Here we show how an iterative multiscale approach combining molecular dynamics and quantum mechanical exciton modeling can be used to obtain accurate insight into the packing of thousands of cyanine dye molecules in a complex double-walled tubular aggregate in close interaction with its solvent environment. Our approach allows us to answer open questions not only on the structure of these prototypical aggregates, but also about their molecular-scale structural and energetic heterogeneity, as well as on the microscopic origin of their photophysical properties. This opens the route to accurate predictions of energy transport and other functional properties