Mechanisms by which PE21, an extract from the white willow Salix alba, delays chronological aging in budding yeast

To discover novel aging-delaying and longevity-extending chemical compounds of plant origin, we conducted a screen for plant extracts that can prolong yeast chronological lifespan. Our screen revealed six plant extracts that increase yeast chronological lifespan. One of these six plant extracts is PE21, an extract from the white willow Salix alba. We found that PE21 extends the longevity of chronologically aging yeast to a significantly greater extent than any of the presently known longevity-extending chemical compounds. We demonstrated that PE21 is a geroprotector which delays the onset and slows the progression of yeast chronological aging by eliciting a hormetic stress response. We showed that PE21 has the following effects on cellular processes known to define longevity in organisms across phyla: 1) PE21 amplifies mitochondrial respiration and membrane potential; 2) PE21 alters the pattern of age-related changes in the intracellular concentration of reactive oxygen species; 3) PE21 reduces oxidative damage to cellular proteins, membrane lipids, and mitochondrial and nuclear genomes; 4) PE21 enhances cell resistance to oxidative and thermal stresses; and 5) PE21 accelerates degradation of neutral lipids deposited in lipid droplets. We found that PE21 causes a remodeling of lipid metabolism in chronologically aging yeast, thereby instigating changes in the concentrations of several lipid classes. We demonstrated that such changes in the cellular lipidome initiate three mechanisms of aging delay and longevity extension. The first mechanism through which PE21 slows aging and prolongs longevity consists in its ability to decrease the intracellular concentration of free fatty acids. This postpones an age-related onset of liponecrotic cell death promoted by excessive concentrations of free fatty acids. The second mechanism of aging delay and longevity extension by PE21 consists in its ability to decrease the concentrations of triacylglycerols and to increase the concentrations of glycerophospholipids within the endoplasmic reticulum membrane. This activates the unfolded protein response system in the endoplasmic reticulum, which then decelerates an age-related decline in protein and lipid homeostasis and slows down an aging-associated deterioration of cell resistance to stress. The third mechanism underlying aging delay and longevity extension by PE21 consists in its ability to change concentrations of some lipids in the mitochondrial membranes. This alters certain catabolic and anabolic processes in mitochondria, thus amending the pattern of aging-associated changes in several key aspects of mitochondrial functionality

A variance equality test for two correlated complex Gaussian variables with application to spectral power comparison

Published versio

Statistical properties of the estimated degree of polarization

Published versio

Attenuation estimation from correlated sequences

Published versio

Ensemble estimation of polarization ellipse parameters

Accepted versio

Graphical modelling for brain connectivity via partial coherence.

Accepted versio

Design and implementation of low power consumption wireless sensor node

This work proposes an implementation of wireless sensor node characterized by its very low power consumption. The node comprises three main components: Xbee module, low power PIC microcontroller and digital sensor. The node can be set to sense then transmit data via one of two transmission methods: periodic and by interruptions. To evaluate the power consumption; currents in the node is measured during the different transmission stages for both methods. As a result, a significant reduction in the power consumption is shown particularly in sleep mode compared to conventional transmission methods. The characteristic of low power consumption makes the proposed node practically ecologic. It can also be fed with the extrem low power supplied by an energy harvesting system

Transformation of Fault Trees into Bayesian Networks Methodology for Fault Diagnosis

International audienceIn this article, we have shown an application of a decision support tool which is the FTBN, The combination of Bayesian Network (BN) with Fault Tree (FT) is an interesting approach to diagnose mechanical systems. Bayesian networks are tools provide robust probabilistic methods of reasoning under uncertainty, widely used in the field of reliability and fault diagnosis. While fault tree is a method of deductive analysis based on the realization of a tree that is used to identify combinations of failures, since both tools have a probabilistic aspect, the main purpose of this works is to give a methodological approach based on the transformation method of fault tree into bayesian network to model a mechanical systems, And more specifically the fault diagnosis.Fault tree construction allows building a Bayesians network. This step allows deriving the graphical structure of the bayesian network that represents the causal relationship between the different events, and exploits the mass of existing data (experience feedback database) of the system under study.In this paper a methodology approach is used to conduct quantification of conditionals probabilities of this Network, and performed a diagnosis on the out of balance trough modeled scenarios.The proposed methodology in our paper is centred on the presence or absence of the out of balance of the motor pump. Knowing that the source of this unbalance is caused by tows essentially events in the fault tree: Bending rotor and Break of vanes

Earthquake Magnitude and Frequency Forecasting in Northeastern Algeria using Time Series Analysis

This study uses two different time series forecasting approaches (parametric and non-parametric) to assess a frequency and magnitude forecasting of earthquakes above Mw 4.0 in Northeastern Algeria. The Autoregressive Integrated Moving Average (ARIMA) model encompasses the parametric approach, while the non-parametric method employs the Singular Spectrum Analysis (SSA) approach. The ARIMA and SSA models were then used to train and forecast the annual number of earthquakes and annual maximum magnitude events occurring in Northeastern Algeria between 1910 and 2019, including 287 main events larger than Mw 4.0. The SSA method is used as a forecasting algorithm in this case, and the results are compared to those obtained by the ARIMA model. Based on the root mean square error (RMSE) criterion, the SSA forecasting model appears to be more appropriate than the ARIMA model. The consistency between the observation and the forecast is analyzed using a statistical test in terms of the total number of events, denoted as N-test. As a result, the findings indicate that the annual maximum magnitude in Northeastern Algeria between 2020 and 2030 will range from Mw 4.8 to Mw 5.1, while between four and six events with a magnitude of at least Mw 4.0 will occur annually