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 Saccharomyces cerevisiae 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. Several software tools utilizing different parameters have been proposed to quantify yeast growth on solid media. Here, we developed a Matlab-based application which provides a rapid and robust quantitative yeast growth analysis from spot plating assay. Spot plating assay is a typical procedure to evaluate yeast growth in low-throughput laboratory settings, including growth on different nutrient sources or treatment with specific stressors. The app has a one-step installation process, a self-explanatory interface and shorter analysis steps compared with previous established methods, providing a useful tool for both expert and non-expert yeast researchers.

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. The reported approach allows obtaining the "electrical wire" of intact and active yeast cells with resulting current generation from glucose oxidation. Additionally, the electrochemical performance of the biohybrid yeast-based system has been characterized in the presence of CuSO4, a widely used pesticide, in the environmentally relevant concentration range of 20-100 µM. The system enabled the rapid preliminary monitoring of the contaminant based on variations in current generation, with a limit of detection of 12.5 µM CuSO4. The present approach for the facile preparation of portable yeast-based electrochemical biosensors paves the way for the future development of sustainable systems for environmental monitoring.

Inactivation of HAP4 Accelerates RTG-Dependent Osmoadaptation in Saccharomyces cerevisiae.

Mitochondrial RTG (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 RTG2, the main positive regulator of the RTG pathway, and HAP4, 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. Cell growth features, mitochondrial respiratory competence, retrograde signaling activation, and TCA cycle gene expression were comparatively evaluated in wild type and mutant cells in the presence and in the absence of salt stress. We showed that the inactivation of HAP4 improved the kinetics of osmoadaptation by eliciting both the activation of retrograde signaling and the upregulation of three TCA cycle genes: citrate synthase 1 (CIT1), aconitase 1 (ACO1), and isocitrate dehydrogenase 1 (IDH1). Interestingly, their increased expression was mostly dependent on RTG2. Impaired respiratory competence in the HAP4 mutant does not affect its faster adaptive response to stress. These findings indicate that the involvement of the RTG pathway in osmostress is fostered in a cellular context of constitutively reduced respiratory capacity. Moreover, it is evident that the RTG pathway mediates peroxisomes-mitochondria communication by modulating the metabolic function of mitochondria in osmoadaptation.

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. Here, we describe recently reported yeast-based biosensors highlighting their biological and technical features together with their status of development, that is, laboratory or prototype. Notably, most yeast-based biosensors are still in the early developmental stage, with only a few prototypes tested for real applications. Open challenges, including systematic use of advanced molecular and biotechnological tools, bioprospecting, and implementation of yeast-based biosensors in electrochemical setup, are discussed to find possible solutions for overcoming bottlenecks and promote real-world application of yeast-based biosensors.

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. Therefore, a computer method that makes an accurate extraction of important details of skin lesion image can assist dermatologists in cancer detection. In particular, the border detection is a critical computer vision issue owing to the wide range of lesion shapes, sizes, colours and skin texture types. In this paper, an automatic and effective pigmented skin lesion segmentation method in dermoscopic image is presented. The proposed procedure is adopted to extract a mask of the lesion region without the adoption of other signal processing procedures for image improvement. A quantitative experimental evaluation has been performed on a publicly available database. The achieved results show the method validity and its high robustness towards irregular boundaries, smooth transition between lesion and skin, noise and artifact presence.

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.e., the radiometric and geometric information about the specific sensor must be well known, together with the ancillary data information acquired on board the platform. In the development of low-cost and lightweight SAR sensors, to be used in several application fields, the precise mission parameters and the knowledge of all the specific geometric and radiometric information about the sensor might complicate the hardware and software requirements. Despite SAR data processing being a well-established imaging technique, the proposed algorithm aims to exploit the SAR coherent illumination, demonstrating the possibility of extracting the reference functions, both in range and azimuth directions, when a strong point scatterer (either natural or manmade) is present in the scene. The Singular Value Decomposition is used to exploit the inherent redundancy present in the raw data matrix, and phase unwrapping and polynomial fitting are used to reconstruct clean versions of the reference functions. Fairly focused images on both synthetic and real raw data matrices without the knowledge of mission parameters and ancillary data information can be obtained; as a byproduct, azimuth beam pattern and estimates of a few other parameters have been extracted from the raw data itself. In a previous paper, authors introduced a preliminary work dealing with this problem and able to obtain good-quality images, if compared to the standard processing techniques. In this work, the proposed technique is described, and performance parameters are extracted to compare the proposed approach to RD, showing good adherence of the focused images and pulse responses.

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. However, most of the known methods for quantifying functional coupling between fMRI time series are focused on linear correlation metrics. In this work, we propose a multidimensional framework to extract multiple descriptors of the dynamic interaction among BOLD signals in their phase space. A set of metrics is extracted from the cross recurrence plots of each couple of signals to form a multilayer connectivity matrix in which each layer is related to a specific complex dynamic phenomenon. The proposed framework is used to characterize functional abnormalities during a working memory task in patients with schizophrenia. Some topological descriptors are then extracted from both multilayer connectivity matrices and the most used Pearson-based connectivity networks to perform a binary classification task of normal controls and patients. The results show that the proposed connectivity model outperforms the statistical correlation-based connectivity in accuracy, sensitivity and specificity. Moreover, the statistical analysis of the selected features highlights that several dynamic metrics could better identify disease-related dynamic states in brain activity than the statistical correlation among physiological signals.

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. Moreover, we compare this approach with a novel classification strategy based on fuzzy logic learned on a mixed cohort including only HC and AD. EXPERIMENTS: A training set of 240 subjects and a test set including mixed cohort of 500 real and simulated subjects were used. The data included AD patients, MCI subjects converting to AD (cMCI), MCI subjects and healthy controls (HC). This work ranked third for overall accuracy (38.8%) over 19 participating teams. COMPARISON WITH EXISTING METHOD(S): The "International challenge for automated prediction of MCI from MRI data" hosted by the Kaggle platform has been promoted to validate different methodologies with a common set of data and evaluation procedures. CONCLUSION: DNNs reach a classification accuracy significantly higher than other machine learning strategies; on the other hand, fuzzy logic is particularly accurate with cMCI, suggesting a combination of these approaches could lead to interesting future perspectives.