Correlative metabolite profiling approach to understand antioxidant and antimicrobial activities from citrus essential oils

The objective of this study was to investigate the chemical composition of the essential oils from citrus species, as well as their antioxidant and antimicrobial activities. By gas chromatography-mass spectrometry, we identified thirty-seven metabolites and a range of unique compounds, contributing to the discrimination of the species. Different biological potentialities were detected depending on the species supported the significant effect of the genetic factor. C. aurantium and C. limon exhibited the highest antioxidant activities which make them a potential source of natural preservative. C. limon exhibited the highest antibacterial activities and C. reticulata the highest antifungal activities. Essential oils extracted from these agro-wastes could serve as natural food preservatives to prevent food pathogens and then extending the shelf life. A set of potential metabolites could be implicated in the observed biological activity, underlying that the antimicrobial activity is a complex trait.     

Teaching single-cell digital analysis using droplet-based microfluidics

Microfluidics allows the manipulation of small quantities of reagents in a high-throughput manner and is therefore highly amenable to single cell characterization and more generally to digital analysis, with applications in fields as varied as genomics, diagnostics, directed evolution, and drug screening. The growing place of microfluidics in biology laboratories encouraged us to develop a teaching method where advanced undergraduate or first-year graduate-level students are taught to fabricate droplet-based microfluidic devices, characterize them, and finally use them to perform a digital analysis of bacterial samples based on a phenotypic marker.     

Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses

The determination of deformation modulus of rock masses is one of the most difficult tasks in the field of rock mechanics. Due to the high cost and measurement difficulties of in situ tests in modulus determination, the predictive models using regression based statistical methods, back propagation neural networks (BPNN) and fuzzy systems are recently employed for the indirect estimation of the modulus. Among these methods, the BPNN has been reported to be very useful in modeling the rock material behavior, such as deformation modulus, by many researchers. Despite its extensive applications, design and structural optimization of BPNN are still done via a time-consuming reiterative trial-and-error approach. This research focuses on the efficiency of the genetic algorithm (GA) in design and optimizing the BPNN structure and its application to predict the deformation modulus of rock masses. GA is utilized to find the optimal number of neurons in hidden layer, learning rates and momentum coefficients of hidden and output layers of network. Then the result is compared with that of trial-and-error procedure. For the purpose, a database including 120 data sets was employed from four dam sites and power house locations in Iran. Taking advantages of performance criteria such as MSE, MAE, r, proved that the GA-ANN model gives superior predictions over the trial-and-error model.     

Effects of different food commodities on larval development and α-amylase activity of Plodia interpunctella (Hübner) (Lepidoptera: Pyralidae)

The present work was undertaken to study the influence of four commodities (wheat flour, dates, sorghum and barley) on Plodia interpunctella post-embryonic development. Larval weight, larval mortality, pupation and adult emergence were recorded. The study also aimed to find out the effect of these commodities on protein and glycogen production as well as on α-amylase activity. Results indicated that the weight of fourth instar larvae placed on dates increased gradually. Percentage mortality was low. Pupation and adult emergence were delayed. In contrast, the weight of larvae placed on wheat flour, sorghum or barley remained low. Pupation and adult emergence occurred sooner than among those placed on dates and the percentage mortality was highest for larvae placed on barley. Results also showed that protein content and α-amylase activity were lower for larvae placed on dates than for those placed on other commodities. The biochemical composition of different commodities showed that dates are a rich source of glucose, while their protein and starch contents were very low as compared to the other commodities. In contrast, wheat flour, sorghum and barley contained large amounts of starch and protein and low amounts of glucose. Thus, the reduction in α-amylase activity was probably due to the high levels of glucose in dates.     

Changes in EEG activity before and after exhaustive exercise in sedentary women in neutral and hot environments

This study examined the effect of hyperthermia on brain electrical activity measured with encephalography during prolonged exhaustive exercise in a group of sedentary women (VO₂max = 35 ± 4 mL kg min⁻¹). Two strenuous cycling exercises were performed either in neutral (N-Ex) or in heat (H-Ex) conditions. Tympanic temperature (Tty), heart rate (HR), body mass loss (BML), plasma volume decrease, and brain electrical activity [EEG: α (8–13 Hz) and β(13–30 Hz)-band and α/β index of fatigue: the ratio between EEG activity in the α band and β-band] were recorded throughout the cycling sessions. The Tty increase 1.0 °C in the N-Ex and 1.8 °C in H-Ex. HR increased in both sessions but with significantly higher values during the H-Ex session when compared with the N-Ex session (p < 0.001) (from 85 ± 4 beats min⁻¹ to 164 ± 6 beats min⁻¹ and from 83 ± 6 beats min⁻¹ to 181 ± 8 beats min⁻¹, respectively in N-Ex and in H-Ex). This was associated with a significantly higher BML (p < 0.05) and a higher plasma volume decrease in the H-Ex session (p < 0.01). The α/β index increased significantly during both trials particularly during the H-Ex session (p < 0.05). This was associated with a significant decrease of time to exhaustion (−34%). We suggest that exhausting work in the heat induced a change in gross brain activity (alpha/beta ratio) compared to a longer, less thermally demanding exposure. Fatigue in the heat could be attributed to central factors as well as thermal, cardiac and hydro-electrolytic impairment.     

Compositional specification of real time embedded systems by priority time Petri Nets

An important key challenge in Embedded Real Time Systems (ERTS) analysis is to provide a seamless scheduling strategy. Formal methods for checking the temporal characteristics and timing constraints at a high abstraction level have proven to be useful for making the development process reliable. In this paper, we present a Petri Net modeling formalism and an analysis technique which supports not only systems scheduling analysis but also the compositional specification of real time systems. The proposed Priority Time Petri Net gives determinism aspect to the model and accelerates its execution. Indeed, a compositional specification of a PTPN for complex application and multiprocessor architecture that solves the problem of hierarchy is presented.     

Influence of bacteria on lanthanide and actinide transfer from specific soil components (humus, soil minerals and vitrified municipal solid waste incinerator bottom ash) to corn plants: Sr-Nd isotope evidence

Experiments have been performed to test the stability of vitrified municipal solid waste (MSW) incinerator bottom ash under the presence of bacteria (Pseudomonas aeruginosa) and plants (corn). The substratum used for the plant growth was a humus-rich soil mixed with vitrified waste. For the first time, information on the stability of waste glasses in the presence of bacteria and plants is given. Results show that inoculated plant samples contained always about two times higher lanthanide and actinide element concentrations. Bacteria support the element transfer since plants growing in inoculated environment developed a smaller root system but have higher trace element concentrations. Compared with the substratum, plants are light rare earth element (LREE) enriched. The vitrified bottom ash has to some extent been corroded by bacteria and plant activities as indicated by the presence of Nd (REE) and Sr from the vitrified waste in the plants. (87)Sr/(86)Sr and (143)Nd/(144)Nd isotope ratios of plants and soil components allow the identification of the corroded soil components and confirm that bacteria accelerate the assimilation of elements from the vitrified bottom ash. These findings are of importance for landfill disposal scenarios, and similar experiments should be performed in order to better constrain the processes of microbially mediated alteration of the MSW glasses in the biosphere.     

Genetic Algorithm-based Adaptive Optimization for Target Tracking in Wireless Sensor Networks

In this paper, we address the problem of genetic algorithm optimization for jointly selecting the best group of candidate sensors and optimizing the quantization for target tracking in wireless sensor networks. We focus on a more challenging problem of how to effectively utilize quantized sensor measurement for target tracking in sensor networks by considering best group of candidate sensors selection problem. The main objective of this paper is twofold. Firstly, the quantization level and the group of candidate sensors selection are to be optimized in order to provide the required data of the target and to balance the energy dissipation in the wireless sensor network. Secondly, the target position is to be estimated using quantized variational filtering (QVF) algorithm. The optimization of quantization and sensor selection are based on the Fast and Elitist Multi-objective Genetic Algorithm (NSGA-II). The proposed multi-objective (MO) function defines the main parameters that may influence the relevance of the participation in cooperation for target tracking and the transmitting power between one sensor and the cluster head (CH). The proposed algorithm is designed to: i) avoid the problem lot of computing times and operation counts, and ii) reduce the communication cost and the estimation error, which leads to a significant reduction of energy consumption and an accurate target tracking. The computation of these criteria is based on the predictive information provided by the QVF algorithm. The simulation results show that the NSGA-II -based QVF algorithm outperforms the standard quantized variational filtering algorithm and the centralized quantized particle filter.     

Thermal performance study of a vacuum integrated solar storage collector (ISSC) with compound parabolic concentrator (CPC)

This paper deals with the energy performance of a new integrated solar storage collector (ISSC) with compound parabolic concentrator (CPC) conceived in the Thermal Process Laboratory in CRTEn Borj Cedria (North of Tunisia). The novelty in this system is the use of transparent vacuum insulation in the annulus between double half-Cylindrical Plexiglass, and the use of automated nocturnal insulation system, which suppresses heat loss during night. Also, the system is equipped with a mobile support permitting to have many collector orientations toward south, east-south, and west-south in order to maximize the incident solar flux. The experimental study of the ISSC system showed that the thermal loss coefficient of ISSC system is equal to 6.16 W/K for ISSC without nocturnal insulation and without vacuum, 4.69W/K for ISSC without nocturnal insulation and with vacuum, and 4.00 W/K for ISSC with nocturnal insulation and with vacuum. The thermal efficiency of the solar collector is equal to 42.92% for ISSC system fixed without vacuum, 45.95% for ISSC system fixed with vacuum, and 50.56% for ISSC system mobile with vacuum. In order to determine the long-term performance of the vacuum ISSC with CPC, the TRNSYS simulations were carried out by using the component modules modeling the ISSC with CPC concentrator (type 74 and type 60f). Comparison between experimental and predicted results for the temperature difference inside the storage tank during 3 days of January showed reasonable agreement. The numerical results for the ISSC system showed that the annual total energy collected (solar) and auxiliary energy were about 4670 and 1561 MJ, respectively. The annual total auxiliary energy represents about 33.4% of the annual total energy collected (solar). During the summer months (June, July, and August), no auxiliary is needed and the solar fraction (SF) is equal to 100%, where as the annual average SF is about 75%.     

Adaptive quantized target tracking in wireless sensor networks

This paper addresses target tracking in wireless sensor networks (WSN) where the observed system is assumed to evolve according to a probabilistic state space model. We propose to improve the use of the variational filtering (VF) by optimally quantizing the data collected by the sensors. Recently, VF has been proved to be suitable to the communication constraints of WSN. Its efficiency relies on the fact that the online update of the filtering distribution and its compression are executed simultaneously. However, this problem has been used only for binary sensor networks neglecting the transmission energy consumption in a WSN and the information relevance of sensor measurements. Our proposed method is intended to jointly estimate the target position and optimize the quantization level under fixed and variable transmitting power. At each sampling instant, the adaptive method provides not only the estimate of the target position by using the VF but gives also the optimal number of quantization bits per observation. The adaptive quantization is achieved by minimizing the predicted Cramér–Rao bound if the transmitting power is constant for all sensors, and optimizing the power scheduling under distortion constraint if this power is variable. The computation of the predicted Cramér–Rao bound is based on the target position predictive distribution provided by the VF algorithm. The proposed adaptive quantization scheme suggests that the sensors with bad channels or poor observation qualities should decrease their quantization resolutions or simply become inactive in order to save energy.