A Matlab-based application for quantification of yeast cell growth on solid media.

Quantitative assessment of growth and survival is a suitable technique in studying biochemical, genetic and physiological processes in the cells. The budding yeast is one of the most widely used eukaryotic model organisms for studying cellular mechanisms and processes in evolutionarily distant species, including humans. Yeast growth can be evaluated on both liquid and solid media by measuring cell suspension turbidity and colony forming units, respectively.

Polydopamine-immobilized yeast cells for portable electrochemical biosensors applied in environmental copper sensing.

The coupling of biological organisms with electrodes enables the development of sustainable, low cost, and potentially self-sustained biosensors. A critical aspect is to obtain portable bioelectrodes where the biological material is immobilized on the electrode surface to be utilized on demand. Herein, we developed an approach for the rapid entrapment and immobilization of metabolically active yeast cells in a biocompatible polydopamine layer, which does not require a separate and time-consuming synthesis.

Inactivation of Accelerates -Dependent Osmoadaptation in .

Mitochondrial (an acronym for ReTroGrade) signaling plays a cytoprotective role under various intracellular or environmental stresses. We have previously shown its contribution to osmoadaptation and capacity to sustain mitochondrial respiration in yeast. Here, we studied the interplay between , the main positive regulator of the pathway, and , encoding the catalytic subunit of the Hap2-5 complex required for the expression of many mitochondrial proteins that function in the tricarboxylic acid (TCA) cycle and electron transport, upon osmotic stress.

Biological and technical challenges for implementation of yeast-based biosensors.

Biosensors are low-cost and low-maintenance alternatives to conventional analytical techniques for biomedical, industrial and environmental applications. Biosensors based on whole microorganisms can be genetically engineered to attain high sensitivity and specificity for the detection of selected analytes. While bacteria-based biosensors have been extensively reported, there is a recent interest in yeast-based biosensors, combining the microbial with the eukaryotic advantages, including possession of specific receptors, stability and high robustness.

Simple and Accurate Border Detection Algorithm for Melanoma Computer Aided Diagnosis.

The interest of the scientific community for computer aided skin lesion analysis and characterization has been increased during the last years for the growing incidence of melanoma among cancerous pathologies. The detection of melanoma in its early stage is essential for prognosis improvement and for guaranteeing a high five-year relative survival rate of patients. The clinical diagnosis of skin lesions is challenging and not trivial since it depends on human vision and physician experience and expertise.

A Data-Driven Approach to SAR Data-Focusing.

Synthetic Aperture RADAR (SAR) is a radar imaging technique in which the relative motion of the sensor is used to synthesize a very long antenna and obtain high spatial resolution. Several algorithms for SAR data-focusing are well established and used by space agencies. Such algorithms are model-based, i.

Modelling cognitive loads in schizophrenia by means of new functional dynamic indexes.

Functional connectivity analysis techniques have broadly applied to capture phenomenological aspects of the brain, e.g., by identifying characteristic network topologies for healthy and disease-affected populations, by highlighting several areas important for the global efficiency of the brain during some cognitive processing and at rest.

Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge.

Background: Early diagnosis of Alzheimer's disease (AD) and its onset in subjects affected by mild cognitive impairment (MCI) based on structural MRI features is one of the most important open issues in neuroimaging. Accordingly, a scientific challenge has been promoted, on the international Kaggle platform, to assess the performance of different classification methods for prediction of MCI and its conversion to AD.

New Method: This work presents a classification strategy based on Random Forest feature selection and Deep Neural Network classification using a mixed cohort including the four classes of classification problem, that is HC, AD, MCI and cMCI, to train the model.