A comprehensive dataset of pattern electroretinograms for ocular electrophysiology research.

The Pattern Electroretinogram (PERG) is an essential tool in ophthalmic electrophysiology, providing an objective assessment of the central retinal function. It quantifies the activity of cells in the macula and the ganglion cells of the retina, assisting in the differentiation of macular and optic nerve conditions. In this study, we present the IOBA-PERG dataset, an extensive collection of 1354 transient PERG responses accessible on the PhysioNet repository.

ECGMiner: A flexible software for accurately digitizing ECG.

Background And Objective: The electrocardiogram (ECG) is the most important non-invasive method for elucidating information about heart and cardiovascular disease diagnosis. Typically, the ECG system manufacturing companies provide ECG images, but store the numerical data in a proprietary format that is not interpretable and is not therefore useful for automatic diagnosis. There have been many efforts to digitize paper-based ECGs.

Functional clustering of neuronal signals with FMM mixture models.

The identification of unlabeled neuronal electric signals is one of the most challenging open problems in neuroscience, widely known as Spike Sorting. Motivated to solve this problem, we propose a model-based approach within the mixture modeling framework for clustering oscillatory functional data called MixFMM. The core of the approach is the FMM (Frequency Modulated Möbius) waves, which are non-linear parametric time functions, flexible enough to describe different oscillatory patterns and simple enough to be estimated efficiently.

CIRCUST: A novel methodology for temporal order reconstruction of molecular rhythms; validation and application towards a daily rhythm gene expression atlas in humans.

The circadian system drives near-24-h oscillations in behaviors and biological processes. The underlying core molecular clock regulates the expression of other genes, and it has been shown that the expression of more than 50 percent of genes in mammals displays 24-h rhythmic patterns, with the specific genes that cycle varying from one tissue to another. Determining rhythmic gene expression patterns in human tissues sampled as single timepoints has several challenges, including the reconstruction of temporal order of highly noisy data.

A unique cardiac electrocardiographic 3D model. Toward interpretable AI diagnosis.

Mathematical models of cardiac electrical activity are one of the most important tools for elucidating information about heart diagnostics. In this paper, we present an efficient mathematical formulation for this modeling simple enough to be easily parameterized and rich enough to provide realistic signals. It relies on a five dipole representation of the cardiac electric source, each one associated with the well-known waves of the electrocardiogram signal.

Cerium dioxide nanoparticles modulate antioxidant defences and change vascular response in the human saphenous vein.

Nanoparticles have a promising future in biomedical applications and knowing whether they affect ex vivo vascular reactivity is a necessary step before their use in patients. In this study, we have evaluated the vascular effect of cerium dioxide nanoparticles (CeONPs) on the human saphenous vein in response to relaxing and contractile agonists. In addition, we have measured the protein expression of key enzymes related to vascular homeostasis and oxidative stress.

Compelling new electrocardiographic markers for automatic diagnosis.

Background And Objective: The automatic diagnosis of heart diseases from the electrocardiogram (ECG) signal is crucial in clinical decision-making. However, the use of computer-based decision rules in clinical practice is still deficient, mainly due to their complexity and a lack of medical interpretation. The objetive of this research is to address these issues by providing valuable diagnostic rules that can be easily implemented in clinical practice.

Dynamics of gliotoxin and bis(methylthio)gliotoxin production during the course of Aspergillus fumigatus infection.

Unlabelled: As recently described, fungal secondary metabolism activates during infection in response to a hostile host environment. Gliotoxin and bis(methylthio)gliotoxin are two recognized secondary metabolites produced by Aspergillus fumigatus with differential cytotoxicity and involved in virulence. We sought to describe the temporal dynamics of gliotoxin and bis(methylthio)gliotoxin during A.

Predicting COVID-19 progression from diagnosis to recovery or death linking primary care and hospital records in Castilla y León (Spain).

This paper analyses COVID-19 patients' dynamics during the first wave in the region of Castilla y León (Spain) with around 2.4 million inhabitants using multi-state competing risk survival models. From the date registered as the start of the clinical process, it is assumed that a patient can progress through three intermediate states until reaching an absorbing state of recovery or death.

Electrophysiological and Transcriptomic Features Reveal a Circular Taxonomy of Cortical Neurons.

The complete understanding of the mammalian brain requires exact knowledge of the function of each neuron subpopulation composing its parts. To achieve this goal, an exhaustive, precise, reproducible, and robust neuronal taxonomy should be defined. In this paper, a new circular taxonomy based on transcriptomic features and novel electrophysiological features is proposed.

A simple parametric representation of the Hodgkin-Huxley model.

The Hodgkin-Huxley model, decades after its first presentation, is still a reference model in neuroscience as it has successfully reproduced the electrophysiological activity of many organisms. The primary signal in the model represents the membrane potential of a neuron. A simple representation of this signal is presented in this paper.

Adaptation of the emerging pathogenic yeast to high caspofungin concentrations correlates with cell wall changes.

has emerged as a fungal pathogen that causes nosocomial outbreaks worldwide. Diseases caused by this fungus are of concern, due to its reduced susceptibility to several antifungals. exhibits paradoxical growth (PG; defined as growth at high, but not intermediate antifungal concentrations) in the presence of caspofungin (CPF).

The hidden waves in the ECG uncovered revealing a sound automated interpretation method.

A novel approach for analysing cardiac rhythm data is presented in this paper. Heartbeats are decomposed into the five fundamental P, Q, R, S and T waves plus an error term to account for artifacts in the data which provides a meaningful, physical interpretation of the heart's electric system. The morphology of each wave is concisely described using four parameters that allow all the different patterns in heartbeats to be characterized and thus differentiated This multi-purpose approach solves such questions as the extraction of interpretable features, the detection of the fiducial marks of the fundamental waves, or the generation of synthetic data and the denoising of signals.

Frequency Modulated Möbius Model Accurately Predicts Rhythmic Signals in Biological and Physical Sciences.

Motivated by applications in physical and biological sciences, we developed a Frequency Modulated Möbius (FMM) model to describe rhythmic patterns in oscillatory systems. Unlike standard symmetric sinusoidal models, FMM is a flexible parametric model that allows deformations to sinusoidal shape to accommodate commonly seen asymmetries in applications. FMM model parameters are easy to estimate and the model is easy to interpret complex rhythmic data.

Order restricted inference in chronobiology.

This paper is motivated by applications in oscillatory systems where researchers are typically interested in discovering components of those systems that display rhythmic temporal patterns. The contributions of this paper are twofold. First, a methodology is developed based on a circular signal plus error model that is defined using order restrictions.

Microarray Data Normalization and Robust Detection of Rhythmic Features.

Data derived from microarray technologies are generally subject to various sources of noise and accordingly the raw data are pre-processed before formally analysed. Data normalization is a key pre-processing step when dealing with microarray experiments, such as circadian gene-expressions, since it removes systematic variations across arrays. A wide variety of normalization methods are available in the literature.

A Bootstrap Based Measure Robust to the Choice of Normalization Methods for Detecting Rhythmic Features in High Dimensional Data.

Gene-expression data obtained from high throughput technologies are subject to various sources of noise and accordingly the raw data are pre-processed before formally analyzed. Normalization of the data is a key pre-processing step, since it removes systematic variations across arrays. There are numerous normalization methods available in the literature.

Circular Order Aggregation and its Application to Cell-cycle Genes Expressions.

The aim of circular order aggregation is to find a circular order on a set of n items using angular values from p heterogeneous data sets. This problem is new in the literature and has been motivated by the biological question of finding the order among the peak expression of a group of cell cycle genes. In this paper, two very different approaches to solve the problem that use pairwise and triplewise information are proposed.

Order restricted inference for oscillatory systems for detecting rhythmic signals.

Motivation: Many biological processes, such as cell cycle, circadian clock, menstrual cycles, are governed by oscillatory systems consisting of numerous components that exhibit rhythmic patterns over time. It is not always easy to identify such rhythmic components. For example, it is a challenging problem to identify circadian genes in a given tissue using time-course gene expression data.

Circular Order Aggregation and its Application to Cell-cycle Genes Expressions.

The aim of circular order aggregation is to find a circular order on a set of n items using angular values from p heterogeneous data sets. This problem is new in the literature and has been motivated by the biological question of finding the order among the peak expression of a group of cell cycle genes. In this paper, two very different approaches to solve the problem that use pairwise and triplewise information are proposed.

Circular piecewise regression with applications to cell-cycle data.

Applications of circular regression models appear in many different fields such as evolutionary psychology, motor behavior, biology, and, in particular, in the analysis of gene expressions in oscillatory systems. Specifically, for the gene expression problem, a researcher may be interested in modeling the relationship among the phases of cell-cycle genes in two species with differing periods. This challenging problem reduces to the problem of constructing a piecewise circular regression model and, with this objective in mind, we propose a flexible circular regression model which allows different parameter values depending on sectors along the circle.

Cell Wall Changes in Amphotericin B-Resistant Strains from Candida tropicalis and Relationship with the Immune Responses Elicited by the Host.

We have morphologically characterizedCandida tropicalisisolates resistant to amphotericin B (AmB). These isolates present an enlarged cell wall compared to isolates of regular susceptibility. This correlated with higher levels of β-1,3-glucan in the cell wall but not with detectable changes in chitin content.