Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.

Awareness and early identification of hypertension is crucial in reducing the burden of cardiovascular disease (CVD). Artificial intelligence-based analysis of 12-lead electrocardiograms (ECGs) can already detect arrhythmias and hypertension. We performed an observational two-center study in order to develop a machine learning algorithm to proactively detect arterial hypertension from single-lead ECGs.

T-wave inversion through inhomogeneous voltage diffusion within the FK3V cardiac model.

The heart beats are due to the synchronized contraction of cardiomyocytes triggered by a periodic sequence of electrical signals called action potentials, which originate in the sinoatrial node and spread through the heart's electrical system. A large body of work is devoted to modeling the propagation of the action potential and to reproducing reliably its shape and duration. Connection of computational modeling of cells to macroscopic phenomenological curves such as the electrocardiogram has been also intense, due to its clinical importance in analyzing cardiovascular diseases.

Quantum neural networks for the discovery and implementation of quantum error-correcting codes.

We implement and use quantum neural networks that exploit bit-flip quantum error-correcting codes that correct bit-flip errors in arbitrary logical qubit states. We introduce conjugate layer quantum autoencoders and use them in order to restore states impacted by amplitude damping through the utilization of an approximative four-qubit error-correcting codeword. Our specific implementation avoids barren plateaus of the cost function and improves the training time.

Quantum targeted energy transfer through machine learning tools.

In quantum targeted energy transfer, bosons are transferred from a certain crystal site to an alternative one, utilizing a nonlinear resonance configuration similar to the classical targeted energy transfer. We use a computational method based on machine learning algorithms in order to investigate selectivity as well as efficiency of the quantum transfer in the context of a dimer and a trimer system. We find that our method identifies resonant quantum transfer paths that allow boson transfer in unison.

Deep learning-assisted frequency-domain photoacoustic microscopy.

Frequency-domain photoacoustic microscopy (FD-PAM) constitutes a powerful cost-efficient imaging method integrating intensity-modulated laser beams for the excitation of single-frequency photoacoustic waves. Nevertheless, FD-PAM provides an extremely small signal-to-noise ratio (SNR), which can be up to two orders of magnitude lower than the conventional time-domain (TD) systems. To overcome this inherent SNR limitation of FD-PAM, we utilize a U-Net neural network aiming at image augmentation without the need for excessive averaging or the application of high optical power.

Artificial intelligence-based opportunistic screening for the detection of arterial hypertension through ECG signals.

Objectives: Hypertension is a major risk factor for cardiovascular disease (CVD), which often escapes the diagnosis or should be confirmed by several office visits. The ECG is one of the most widely used diagnostic tools and could be of paramount importance in patients' initial evaluation.

Methods: We used machine learning techniques based on clinical parameters and features derived from the ECG, to detect hypertension in a population without CVD.

Development of an Oriented Co-Culture System Using 3D Scaffolds Fabricated via Non-Linear Lithography.

Damage in the Peripheral Nervous System (PNS) is related to numerous neurodegenerative diseases and has consequently drawn the attention of Tissue Engineering (TE), which is considered a promising alternative to already established methods such as surgery and autografts. TE focuses on the design, optimization, and use of scaffolds in vitro and in vivo. In this work, the authors used a novel scaffold geometry fabricated via Multiphoton Lithography (MPL), a commonly used fabrication method, for the mono- and co-cultures of glial Schwann (SW10) and neuronal Neuro-2a (N2a) cells.

Hybrid confocal fluorescence and photoacoustic microscopy for the label-free investigation of melanin accumulation in fish scales.

Lower vertebrates, including fish, can rapidly alter skin lightness through changes in melanin concentration and melanosomes' mobility according to various factors, which include background color, light intensity, ambient temperature, social context, husbandry practices and acute or chronic stressful stimuli. Within this framework, the determination of skin chromaticity parameters in fish species is estimated either in specific areas using colorimeters or at the whole animal level using image processing and analysis software. Nevertheless, the accurate quantification of melanin content or melanophore coverage in fish skin is quite challenging as a result of the laborious chemical analysis and the typical application of simple optical imaging methods, requiring also to euthanize the fish in order to obtain large skin samples for relevant investigations.

Analysis of chaotic dynamical systems with autoencoders.

We focus on chaotic dynamical systems and analyze their time series with the use of autoencoders, i.e., configurations of neural networks that map identical output to input.

Detection of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG-based approach.

Cardiac remodeling is recognized as an important aspect of cardiovascular disease (CVD) progression. Machine learning (ML) techniques were applied to basic clinical parameters and electrocardiographic features, in order to detect abnormal left ventricular geometry (LVG) even before the onset of left ventricular hypertrophy (LVH), in a population without established CVD. The authors enrolled 528 patients with and without essential hypertension, but no other indications of CVD.

Estimating the infection horizon of COVID-19 in eight countries with a data-driven approach.

The COVID-19 pandemic has affected all countries of the world producing a substantial number of fatalities accompanied by a major disruption in their social, financial and educational organization. The strict disciplinary measures implemented by China were very effective and thus were subsequently adopted by most world countries to various degrees. The infection duration and number of infected persons are of critical importance for the battle against the pandemic.

Shape-Dependent Single-Electron Levels for Au Nanoparticles.

The shape of metal nanoparticles has a crucial role in their performance in heterogeneous catalysis as well as photocatalysis. We propose a method of determining the shape of nanoparticles based on measurements of single-electron quantum levels. We first consider nanoparticles in two shapes of high symmetry: cube and sphere.

Nanoparticle shapes by using Wulff constructions and first-principles calculations.

Background: The majority of complex and advanced materials contain nanoparticles. The properties of these materials depend crucially on the size and shape of these nanoparticles. Wulff construction offers a simple method of predicting the equilibrium shape of nanoparticles given the surface energies of the material.

Thiolate adsorption on Au(hkl) and equilibrium shape of large thiolate-covered gold nanoparticles.

The adsorption of thiolates on Au surfaces employing density-functional-theory calculations has been studied. The dissociative chemisorption of dimethyl disulfide (CH(3)S-SCH(3)) on 14 different Au(hkl) is used as a model system. We discuss trends on adsorption energies, bond lengths, and bond angles as the surface structure changes, considering every possible Au(hkl) with h, k, l ≤ 3 plus the kinked Au(421).

Density functional theory based screening of ternary alkali-transition metal borohydrides: a computational material design project.

We present a computational screening study of ternary metal borohydrides for reversible hydrogen storage based on density functional theory. We investigate the stability and decomposition of alloys containing 1 alkali metal atom, Li, Na, or K (M(1)); and 1 alkali, alkaline earth or 3d/4d transition metal atom (M(2)) plus two to five (BH(4))(-) groups, i.e.