New sulfurated derivatives of cinnamic acids and rosmaricine as inhibitors of STAT3 and NF-kappa B transcription factors

A set of new sulfurated drug hybrids, mainly derived from caffeic and ferulic acids and rosmaricine, has been synthesized and their ability to inhibit both STAT3 and NF-kappa B transcription factors have been evaluated. Results showed that most of the new hybrid compounds were able to strongly and selectively bind to STAT3, whereas the parent drugs were devoid of this ability at the tested concentrations. Some of them were also able to inhibit the NF-kappa B transcriptional activity in HCT-116 cell line and inhibited HCT-116 cell proliferation in vitro with IC50 in micromolar range, thus suggesting a potential anticancer activity. Taken together, our study described the identification of new derivatives with dual STAT3/NF-kappa B inhibitory activity, which may represent hit compounds for developing multi-target anticancer agents

Preserving the positivity of the deformation gradient determinant in intergrid interpolation by combining RBFs and SVD: application to cardiac electromechanics

The accurate robust and efficient transfer of the deformation gradient tensor between meshes of different resolution is crucial in cardiac electromechanics simulations. We present a novel method that combines rescaled localized Radial Basis Function (RBF) interpolation with Singular Value Decomposition (SVD) to preserve the positivity of the determinant of the deformation gradient tensor. The method involves decomposing the evaluations of the tensor at the quadrature nodes of the source mesh into rotation matrices and diagonal matrices of singular values; computing the RBF interpolation of the quaternion representation of rotation matrices and the singular value logarithms; reassembling the deformation gradient tensors at quadrature nodes of the destination mesh, to be used in the assembly of the electrophysiology model equations. The proposed method overcomes limitations of existing interpolation methods, including nested intergrid interpolation and RBF interpolation of the displacement field, that may lead to the loss of physical meaningfulness of the mathematical formulation and then to solver failures at the algebraic level, due to negative determinant values. The proposed method enables the transfer of solution variables between finite element spaces of different degrees and shapes and without stringent conformity requirements between different meshes, enhancing the flexibility and accuracy of electromechanical simulations. Numerical results confirm that the proposed method enables the transfer of the deformation gradient tensor, allowing to successfully run simulations in cases where existing methods fail. This work provides an efficient and robust method for the intergrid transfer of the deformation gradient tensor, enabling independent tailoring of mesh discretizations to the particular characteristics of the physical components concurring to the of the multiphysics model.Comment: 24 pages; 11 figure

Comparison among Cognitive Radio Architectures for Spectrum Sensing

Recently, the growing success of new wireless applications and services has led to overcrowded licensed bands, inducing the governmental regulatory agencies to consider more flexible strategies to improve the utilization of the radio spectrum. To this end, cognitive radio represents a promising technology since it allows to exploit the unused radio resources. In this context, the spectrum sensing task is one of the most challenging issues faced by a cognitive radio. It consists of an analysis of the radio environment to detect unused resources which can be exploited by cognitive radios. In this paper, three different cognitive radio architectures, namely, stand-alone single antenna, cooperative and multiple antennas, are proposed for spectrum sensing purposes. These architectures implement a relatively fast and reliable signal processing algorithm, based on a feature detection technique and support vector machines, for identifying the transmissions in a given environment. Such architectures are compared in terms of detection and classification performances for two transmission standards, IEEE 802.11a and IEEE 802.16e. A set of numerical simulations have been carried out in a challenging scenario, and the advantages and disadvantages of the proposed architectures are discussed

Leitfaden Aquakulturanlagen Teil 2 : Stand der Technik zur Reduktion von Emissionen

Die in einer Aquakulturanlage mit Fütterung (= intensive Aquakulturanlage) verwendete Technik beeinflusst in hohem Masse deren Emissionen in Gewässer. Je nach Produktionsmethode ist diese Technik sehr unterschiedlich, was zur Folge hat, dass eine gesetzlich geforderte Reinigung nicht einheitlich für alle Produktionsmethoden festgelegt werden kann. Im Leitfaden werden verschiedenen Intensitätsstufen der Aquakulturproduktion definiert, die massgeblichen Technologien und deren Einfluss auf die Wasserqualität aufgezeigt sowie Optimierungshilfen geboten, um die wichtigsten Emissionsparameter von Fischzuchtanlagen kosteneffizient zu reduzieren/minimieren. Das Ziel des Leitfaden «Aquakulturanlagen, Teil 2» ist es eine Hilfestellung zu bieten, um die im Leitfaden «Aquakulturanlagen, Teil 1» geforderten Massnahmen, im Rahmen der Wirtschaftlichkeit und ohne Reduktion der Produktionsmenge, erreichen zu können. Dieses Dokument soll deshalb Betreibern wie Behörden als Hilfe dienen, die Emissionen von Aquakulturanlagen wo nötig kosteneffizient reduzieren zu können

3D Road Scene Interpretation for Autonomous Vehicle Driving

In this paper, the problem of 3D road scene interpretation for autonomous vehicle driving is addressed. In particular, the problems of road detection and obstacle avoidance in outdoor environments are investigated. A set of descriptive primitives (straight and circular line segments) is selected to describe 3D objects which commonly occur in road scenes, e.g., people, cars, trucks, houses, etc. First, these primitives are extracted directly from the input image of the scene, and then are grouped according to specific geometric relationships (symmetry, convergence, parallelism, closeness, etc.). Relational geometrical knowledge of the elements of a group can be used to index an object in a pure bottom-up way, so decreasing the recognition complexity by reducing the amount of data to be matched with an object model database. Results on a road image containing obstacles, which show the efficiency, accuracy and time performances of the proposed method are reported

Real-time whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations

Cardiac digital twins provide a physics and physiology informed framework to deliver predictive and personalized medicine. However, high-fidelity multi-scale cardiac models remain a barrier to adoption due to their extensive computational costs and the high number of model evaluations needed for patient-specific personalization. Artificial Intelligence-based methods can make the creation of fast and accurate whole-heart digital twins feasible. In this work, we use Latent Neural Ordinary Differential Equations (LNODEs) to learn the temporal pressure-volume dynamics of a heart failure patient. Our surrogate model based on LNODEs is trained from 400 3D-0D whole-heart closed-loop electromechanical simulations while accounting for 43 model parameters, describing single cell through to whole organ and cardiovascular hemodynamics. The trained LNODEs provides a compact and efficient representation of the 3D-0D model in a latent space by means of a feedforward fully-connected Artificial Neural Network that retains 3 hidden layers with 13 neurons per layer and allows for 300x real-time numerical simulations of the cardiac function on a single processor of a standard laptop. This surrogate model is employed to perform global sensitivity analysis and robust parameter estimation with uncertainty quantification in 3 hours of computations, still on a single processor. We match pressure and volume time traces unseen by the LNODEs during the training phase and we calibrate 4 to 11 model parameters while also providing their posterior distribution. This paper introduces the most advanced surrogate model of cardiac function available in the literature and opens new important venues for parameter calibration in cardiac digital twins

Pro-oxidant and pro-inflammatory effects of glycated albumin on cardiomyocytes.

Human serum albumin (HSA) is the most abundant circulating protein in the body and presents an extensive range of biological functions. As such, it is prone to undergo post-translational modifications (PTMs). The non-enzymatic early glycation of HSA, one of the several PTMs undergone by HSA, arises from the addition of reducing sugars to amine group residues, thus modifying the structure of HSA. These changes may affect HSA functions impairing its biological activity, finally leading to cell damage. The aim of this study was to quantitate glycated-HSA (GA) levels in the plasma of heart failure (HF) patients and to evaluate the biological effects of GA on HL-1 cardiomyocytes. Plasma GA content from HF patients and healthy subjects was measured by direct infusion electrospray ionization mass spectrometry (ESI-MS). Results pointed out a significant increase of GA in HF patients with respect to the control group (p < 0.05). Additionally, after stimulation with GA, proteomic analysis of HL-1 secreted proteins showed the modulation of several proteins involved, among other processes, in the response to stress. Further, stimulated cells showed a rapid increase in ROS generation, higher mRNA levels of the inflammatory cytokine interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α), and higher levels of the oxidative 4-HNE-protein adducts and carbonylated proteins. Our findings show that plasma GA is increased in HF patients. Further, GA exerts pro-inflammatory and pro-oxidant effects on cardiomyocytes, which suggest a causal role in the etiopathogenesis of HF