HAL Portal usj Université Saint-Joseph de Beyrouth-Liban
Not a member yet
1220 research outputs found
Sort by
Identification of the specific molecular and functional signatures of pre-beta-HDL: relevance to cardiovascular disease
No full textInternational audienceWhile low concentrations of high-density lipoprotein-cholesterol (HDL-C) are widely accepted as an independent cardiovascular risk factor, HDL-C-rising therapies largely failed, suggesting the importance of both HDL functions and individual subspecies. Indeed HDL particles are highly heterogeneous, with small, dense pre-beta-HDLs being considered highly biologically active but remaining poorly studied, largely reflecting difficulties for their purification. We developed an original experimental approach allowing the isolation of sufficient amounts of human pre-beta-HDLs and revealing the specificity of their proteomic and lipidomic profiles and biological activities. Pre-beta-HDLs were enriched in highly poly-unsaturated species of phosphatidic acid and phosphatidylserine, and in an unexpectedly high number of proteins implicated in the inflammatory response, including serum paraoxonase/arylesterase-1, vitronectin and clusterin, as well as in complement regulation and immunity, including haptoglobin-related protein, complement proteins and those of the immunoglobulin class. Interestingly, amongst proteins associated with lipid metabolism, phospholipid transfer protein, cholesteryl ester transfer protein and lecithin:cholesterol acyltransferase were strongly enriched in, or restricted to, pre-beta-HDL. Furthermore, pre-beta-HDL potently mediated cellular cholesterol efflux and displayed strong anti-inflammatory activities. A correlational network analysis between lipidome, proteome and biological activities highlighted 15 individual lipid and protein components of pre-beta-HDL relevant to cardiovascular disease, which may constitute novel diagnostic targets in a pathological context of altered lipoprotein metabolism
New modelling approach for the optimal sizing of an islanded microgrid considering economic and environmental challenges
No full textInternational audienceThe goal of this study is to optimize the sizing of PV/Batteries/Diesel generator/Electrical load Microgrid (MG) through targeting the minimization of cost and CO2 life cycle emissions of all the MG components. In order to reach these objectives, an Energy Management System (EMS) is designed. A Mixed Integer Linear Programming (MILP) generates the power flow distribution while minimizing CO2 life cycle emissions. The power flow computed by the MILP does not operate on real-time basis. The MILP algorithm takes into consideration the future usage cost of batteries and provides the EMS by ensuring that the storage system returns back to initial conditions at the end of every representative day. An economic function computes the capital expenditure (CAPEX) and the operation expenditure (OPEX) costs using both the power flow calculated by the MILP function and the sizing parameters generated from Genetic Algorithm (GA). The GA algorithm finds a set of sizing solutions that minimize life cycle CO2 emissions and CAPEX and OPEX of the microgrid. A new modelling strategy based on hourly allocation of costs and emissions is presented in this study. It allows taking sizing decisions on hourly basis while avoiding to specify MG elements replacement time and costs. A sensitivity analysis is conducted in this paper to show the robustness of the optimal sizing towards economic and emission parameters’ variation
Study of LoRaWAN Networks Reliability
No full textInternational audienceThe Internet of Things (IoT) is a rapidly evolving field that incorporates a wide range of technologies and applications, enabling the seamless integration of everyday objects into the digital world. The effective integration of IoT into various systems requires the implementation of lightweight solutions to overcome the challenges posed by highly dense networks and constrained resources, including computational power, memory capacity, and battery life. The present research is dedicated to investigating a specific context of the Internet of Things (Io’T), namely LoRaWAN, in which devices communicate with the access network using ALOHA-type access and spread spectrum technology. LoRaWAN advocates simplicity in order to reduce drastically the battery consumption, which severely degrades reliability. In this paper we introduce blind repetition in LoRaWAN: a packet is retransmitted a fixed number of times regardless of its good reception. Leveraging on existing data link layer functionalities, we compare this redundant mode to two existing modes, namely the unacknowledged mode and acknowledged mode. We run extensive simulations that consider the capture effect in LoRaWAN, in addition to the non-uniform distribution of devices. In such a realistic scenario, we perform thorough numerical simulations to quantify the discrepancy between the three modes, and identify the traffic conditions for which a given mode has precedence over the two others
Dual-sPLS : a Family of Dual Sparse Partial Least Squares Regressions for Feature Selection and Prediction with Tunable Sparsity; Evaluation on Simulated and Near-Infrared (NIR) Data
No full textInternational audienceRelating a set of variables X to a response y is crucial in chemometrics. A quantitative prediction objective can be enriched by qualitative data interpretation, for instance by locating the most influential features. When high-dimensional problems arise, dimension reduction techniques can be used. Most notable are projections (e.g. Partial Least Squares or PLS ) or variable selections (e.g. lasso). Sparse partial least squares combine both strategies, by blending variable selection into PLS. The variant presented in this paper, Dual-sPLS, generalizes the classical PLS1 algorithm. It provides balance between accurate prediction and efficient interpretation. It is based on penalizations inspired by classical regression methods (lasso, group lasso, least squares, ridge) and uses the dual norm notion. The resulting sparsity is enforced by an intuitive shrinking ratio parameter. Dual-sPLS favorably compares to similar regression methods, on simulated and real chemical data
Identification and apportionment of local and long-range sources of PM2.5 in two East-Mediterranean sites
No full textInternational audienceThe East Mediterranean and Middle East (EMME) region is a global climate hotspot that suffers from a lack of robust environmental data. This region, especially the Middle East, lacks source apportionment studies that help determine the different contributions of common regional airborne particulate matter sources. This work focuses on two sites in the East Mediterranean, that are, Zouk Mikael and Fiaa, Lebanon. The study shows the comprehensive chemical characterization of PM 2.5 samples collected over almost one year at two sites, serving as the source apportionment model, positive matrix factorization. Different sources were identified due to the integration of organic markers such as biogenic emissions, cooking, biomass burning, and diesel generators. Crustal dust and ammonium sulfate sources were the major contributors to PM 2.5 (43% and 46% at Zouk and Fiaa, respectively). Through cluster analysis, the former originated from the Arabian and Saharan Deserts, while the latter had different local and distant origins (industrial zones of Europe and Turkey), in addition to the contribution of Arabian and African countries to carbonaceous matter concentrations through refinery emissions. Meanwhile, local anthropogenic sources contributed to 36% at both sites, excluding ammonium sulfate. Traffic and industrial emissions, including energy production, contributed more to Zouk (27%) than Fiaa (13%). Sitespecific sources were also identified, with open waste burning at Fiaa contributing 16% and diesel generators at Zouk contributing 5%. Biogenic emissions contributed to 9-13%. These results will be important to policymakers to improve air quality in the EMME region while considering the potency of the PM in a region where the world health organization guidelines cannot be reached
Activity and Selectivity of Bimetallic Catalysts Based on SBA-15 for Nitrate Reduction in Water
No full textInternational audienceNitrate from the application of nitrogen-based fertilizers in intensive agriculture is a notorious waste product, though it lacks cost-effective solutions for its removal from potential drinking water resources. Catalytic reduction appears to be a promising technique for converting nitrates to benign nitrogen gas. Mesoporous silica SBA-15 is a frequently used catalyst support that has large surface areas and highly ordered nanopores. In this work, mesoporous silica SBA-15 bimetallic catalysts for nitrate reduction were investigated. The catalyst was optimized for the selection of promoter metal (Sn and Cu), noble metal (Pd and Pt) and loading ratios of these metals at different temperatures and reduction conditions. The catalysts prepared were characterized by FT-IR, N2 physisorption, XRD, SEM, and ICP. All catalysts showed the presence of cylindrical mesoporous channels and uniform pore structures that remained even after metals loading. In the presence of a CO2 buffer, the catalysts 4Pd-1Cu/SBA-15 and 1Pt-1Cu/SBA-15 reduced at 100?C under H2 and 1Pd-1Cu/SBA-15 reduced at 200°C under H2 demonstrated very high nitrate conversion. Furthermore, the forementioned Pd catalysts had higher N2 selectivity (88% - 87%) compared to Pt catalyst (80%). Nitrate conversion by the 4Pd-1Cu/SBA-15 catalyst was significantly decreased to 81% in the absence of CO2
Editorial: Risk assessment of mycotoxins in food
No full textInternational audienceSinapine is a phenolic compound found in mustard (Brassica juncea) seed meal. It has numerous beneficial properties such as antitumor, neuroprotective, antioxidant, and hepatoprotective effects, making its extraction relevant. In this study, the extraction of sinapine was investigated using three methods: (i) from a mustard seed meal defatted by a supercritical CO2 (SC-CO2) pretreatment, (ii) by the implementation of high-voltage electrical discharges (HVEDs), (iii) and by the use of ultrasound. The use of SC-CO2 pretreatment resulted in a dual effect on the valorization of mustard seed meal, acting as a green solvent for oil recovery and increasing the yield of extracted sinapine by 24.4% compared to the control. The combination of ultrasound and SC-CO2 pretreatment further increased the yield of sinapine by 32%. The optimal conditions for ultrasound-assisted extraction, determined through a response surface methodology, are a temperature of 75 °C, 70% ethanol, and 100% ultrasound amplitude, resulting in a sinapine yield of 6.90 ± 0.03 mg/g dry matter. In contrast, the application of HVEDs in the extraction process was not optimized, as it led to the degradation of sinapine even at low-energy inputs
The Environmental Exposures in Lebanese Infants (EELI) birth cohort: an investigation into the Developmental Origins of Health and Diseases (DOHaD)
No full textInternational audienceThe EELI Study is a longitudinal birth cohort launched in 2021 in Lebanon to examine the long-term impact of environmental exposures on the health of prospective Lebanese mothers and infants and disease outcomes. This article delineates the adopted study design and protocols, current progress, and contextual considerations for the planning and launching of a birth cohort in a resource-limited setting. A sample of n = 135 pregnant women expecting to give birth at the Hôtel-Dieu de France University Hospital has been recruited since the study launch. Over 500 variables have been recorded for each participant, and over 1000 biological specimens have been processed and stored in a biobank for further analysis. The EELI study establishes methodological and logistic basis to explore the concept of the exposome and its implementation and to establish a toolkit of the SOPs and questionnaires that can be employed by the other countries in the Eastern Mediterranean region
Youth Adoption of Innovative Digital Marketing and Cross-Cultural Disparities
No full textInternational audienceThis paper aims to explore Youth's attitudes towards digital marketing utility perception and its effect on behavioral patterns in a cross-cultural perspective. The unified theory of acceptance and use of technology (UTAUT 2) model was adopted together with three new variables from the reasoned action theory and the 5S Internet marketing model to propose a theoretical model on Youth's digital marketing adoption. A survey was conducted in Italy (N = 165) and Lebanon (N = 150), and PLS analysis was implemented for the empirical testing of the proposed research model. In the Italian sample, Hedonic motivation, social influence, facilitating conditions, and efficiency significantly predicted the behavioral intention of digital marketing which, in turn, was significantly related to use behavior. Subsequently, in the Lebanese sample, the subjective norms of hedonic motivation, social influence, experience and habit predicted behavioral intention, which was positively related with use behavior. The results led to the conclusion that national cultures still play an important role in affecting digital marketing adoption among younger generations, especially in less industrialized and technologically developed countries. Therefore, companies should keep this aspect in mind when innovating and developing digital marketing strategies targeting this generation.</div
Purification of Natural Pigments Violacein and Deoxyviolacein Produced by Fermentation Using Yarrowia lipolytica
No full textInternational audienceViolacein and deoxyviolacein are bis-indole pigments synthesized by a number of microorganisms. The present study describes the biosynthesis of a mixture of violacein and deoxyviolacein using a genetically modified Y. lipolytica strain as a production chassis, the subsequent extraction of the intracellular pigments, and ultimately their purification using column chromatography. The results show that the optimal separation between the pigments occurs using an ethyl acetate/cyclohexane mixture with different ratios, first 65:35 until both pigments were clearly visible and distinguishable, then 40:60 to create a noticeable separation between them and recover the deoxyviolacein, and finally 80:20, which allows the recovery of the violacein. The purified pigments were then analyzed by thin-layer chromatography and nuclear magnetic resonance