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Social Environment and Early Childhood Developmental Outcomes of Preterm Infants – A Prospective Cohort Study
No full textThis thesis explores the multifaceted dimensions of preterm birth, a leading cause of neonatal morbidity and mortality worldwide, through a series of interconnected studies that span from identifying determinants and risk factors to assessing the impact of socio-economic and healthcare crises on maternal and child health.
Utilizing a life course perspective, the first section constructs a multilevel life course conceptual framework which integrates biological, individual, psychological, family, community, and national level factors to map the complex interplay influencing the occurrence of preterm birth.
Subsequently, the thesis presents a prospective cohort study in Lebanon, a country facing economic collapse and healthcare challenges, to examine the association between social determinants, preterm birth, and developmental outcomes. Despite recruitment and follow-up challenges, findings indicate that lower quality of life, higher stress levels, and reduced social support correlate with preterm birth, while supportive social environments contribute to better developmental outcomes.
Finally, the thesis applies machine learning techniques to a large dataset to detect risk factors associated with preterm birth, aiming to develop a predictive model. Despite the complexity of predicting preterm births, the study highlights the potential of machine learning to enhance understanding and develop preventive strategies, demonstrating significant associations between various variables and preterm birth outcomes.
Together, these sections contribute to our understanding of the determinants and effects of preterm birth, advocating for comprehensive approaches that incorporate socio-economic and emotional well-being in prenatal care and policymaking
Comparison of the Levels of Particle Bound Polycyclic Aromatic Hydrocarbons between Pre and During Economic Crisis in Beirut
Get PDFThe presence of polycyclic aromatic hydrocarbons (PAHs) in urban areas due to human activities poses a significant risk to human health. A comparative study of three representative sites (AUB, BCD, and NSMU) in Beirut revealed varying levels of PAHs and PM2.5, with AUB showing the lowest concentrations. The demand for electricity, particularly during the hot summer season, and stagnant weather conditions contribute to high concentration of PAHs, in summer as well as in winter and fall. The study also found that the cancer risk associated with PAH exposure exceeded acceptable thresholds set by the Environmental Protection Agency (EPA), in the three sites, emphasizing the need for effective emission regulations. Additionally, source apportionment analysis identified diesel generators, incinerators, and gasoline/vehicular emissions as significant contributors to PAH pollution. Diesel generators played a prominent role in high socio- economic areas (BCD), while vehicular and traffic emissions were more influencing in low-to-middle income areas (NSMU).These findings demonstrate the need for targeted mitigation strategies to reduce PAH emissions and improve air quality in urban environments
The AKT/mTOR Pathway: A Potential Therapeutic Target in Acute Myeloid Leukemia
No full textAcute Myeloid Leukemia (AML) is a complex heterogeneous malignancy of myeloid origin and one of the most common adult leukemias accounting for the highest percent of acute leukemia related deaths. Despite important therapeutic advances, AML still associates with poor prognosis, high relapse rates, and resistance to chemotherapy. Early treatment modalities relied on intensive chemotherapy or investigational drugs for high-risk patients. Currently, therapy against AML encompasses more targeted approaches for specific mutations or disrupted pathways. The PI3K/AKT/mTOR pathway is activated in 60% of AML patients. Everolimus (EV), an mTOR inhibitor, improved the treatment of relapse-refractory AML patients, when combined with other therapeutic agents. In some subtypes of AML, all-trans retinoic acid (ATRA), a hormone playing a major role in differentiation, proved beneficial, alone or combined with other drugs, like arsenic trioxide (ATO).
We explored the effect of ATO, ATRA, EV, as single agents or as double or triple combinations on AML. Treated xenograft AML mice with ATO, ATRA, EV, ATO/ ATRA, or ATO/ ATRA /EV, were monitored for survival, or humanely sacrificed at day 28 to assess leukemic burden by immunophenotyping. The effect of single agents, select double, or triple combinations was assessed on cell growth and the AKT/mTOR pathway, in AML cell lines presenting different mutations.
We demonstrated that the triple combination ATO/ ATRA /EV significantly prolonged survival of AML xenografted mice and reached cure for some animals. This combination sharply reduced leukemic burden in the bone marrow of xenografted animals. At the cellular and molecular levels, ATO/ ATRA /EV induced growth arrest of different AML cell lines, and inactivated the AKT pathway, through abolishing the downstream mTOR pathway and reducing the expression of p-ERK in AML cells.
Collectively, our results demonstrate the importance of targeting the AKT/mTOR pathway in AML and warrant future clinical studies combining mTOR inhibitors with ATRA and ATO as a promising targeted therapy in AML.
It's Okay, Ya Kamal
No full textA children's book, created by AUB students from the Education Department, for the course EDUC218 as a final project
Learning Branching Strategies for Parameterized Vertex Cover
Get PDFThe Parameterized Vertex Cover (PVC) problem is a central problem on graphs, where given a graph G and a positive integer k the goal is to decide whether the graph contains a set of at most k vertices whose deletion destroys all edges of the graph. In other words, a set of vertices is called a vertex cover of a graph if after deleting those vertices we obtain an edgeless graph. The problem is one of Karp’s 21 NP-complete problems (Erickson, 2010)(Karp, 1972), meaning that the problem is computationally hard, takes exponential time to solve, and is not expected to be solvable in polynomial time unless P = NP. For most practical purposes, heuristics, approximation, or parameterized algorithms are the only reasonable way to solve large instances of the problem in a reasonable amount of time. We are interested in solving the problem exactly and one method for solving PVC is the Branch & Reduce paradigm. In a Branch & Reduce algorithm, we construct a search tree to solve a given instance by either branching on which vertices to include into a solution or applying reduction rules to reduce the search space. At a high level, the typical algorithm for PVC selects a vertex of highest degree and branches on either including said vertex in a solution or including all of its neighbors. Several reduction rules are also applied whenever possible. In this work, we investigate a new approach based on machine learning for optimizing vertex selection while solving PVC instances. We constructed a system that uses graph features as inputs to make inferences about the best weighting strategies to be applied on the different node features in order to select the best vertex to branch on. In our approach we utilize reinforcement learning technology to train our model. Our results show that we were able to outperform the high degree strategy in 85% of instances
Optimal Sizing and Siting of Solar Photovoltaic and Batteries in the Microgrid of a Lebanese Village
No full textThis thesis develops an optimization methodology based on Ordinal Optimization (OO) and DC Optimal Power Flow (DC-OPF) to determine the optimal sizing and placement of energy sources in a distribution network. The primary goal is to ensure reliable, affordable, and sustainable electricity supply with a cost-effective solution. The system consists of three types of energy sources: photovoltaic (PV) generator, battery energy storage (BES), and diesel generators (DG). The methodology consists of three main phases, which begins by sampling the extensive large search space of PV sizes and battery capacities to a relatively small subset of combinations denoted by Θ_N. This subset is then evaluated using a simple model over a year to determine the levelized cost of electricity (LCOE) of each design in this subset. The evaluated designs are then sorted in ascending order of LCOE to select the ones with the most appropriate sizes of PV, battery, and diesel generators. The top-ranked designs are then assessed using an accurate model based on DC-OPF, implemented by the MATPOWER 7.1 tool, from which we deduce the LCOE over a year of these top-s designs. Finally, the optimal design that has the minimum LCOE is identified. The DC-OPF simulates the system's operation and allow us to obtain the best distribution of PV and battery resources over the network. To minimize the use of diesel fuel and control CO2 emissions, we included the carbon tax in the evaluation of the LCOE. The functionality and performance of the developed methodology is tested on a standard IEEE 5-busbar network and on a real case distribution network for Younine, a village located in the West Beqaa district, where data on power consumption trends, available space and solar radiation were acquired.
On the Clustering of Demand and Weather Data for Electricity Generation Expansion Planning
No full textThis research investigates the performance of clustering algorithms for Electricity Generation Expansion Planning (GEP) with a focus on electricity demand and weather data. The primary objective is to identify the most effective algorithm for selecting representative periods that accurately reflect the variability in energy demand and renewable energy supply, influenced by weather conditions. The study examines the impact of different clustering algorithms, including Agglomerative Hierarchical, K-means, and K-medoids, across various settings by adjusting the number of representative periods and shifting the slice of the data. Through rigorous a priori and a posteriori analyses, the research evaluates the algorithms' performance in replicating the statistical characteristics and GEP decisions of the full dataset. It also assesses the impact of data slicing methods on the clustering outcomes. The results indicate that K-medoids algorithm stands out for its consistent accuracy in replicating the full dataset measures, making it the best performing algorithm in both a priori and a posteriori evaluation. This algorithm excels at capturing the essential statistical characteristics of the dataset, such as variance and correlations between demand and renewable energy outputs. However, it is notably susceptible to shifts in data slicing, which can significantly influence its performance. Shifts in the data slice often lead to variations in the algorithm's output, which highlights the delicate balance between data representation and the accuracy of clustering outcomes. The findings also highlight the dependency between initial data spread, which is a data characteristic, and the shifting effect on clustering. It is shown that a data possessing higher spread indicator will experience high shifting effect on both a priori and a posteriori measures. This study not only contributes to the theoretical understanding of clustering in GEP but also offers practical insights for energy policy and system design, emphasizing the critical role of accurate data representation in optimizing energy planning and operations. Furthermore, this study sheds light on a topic often neglected in common practices, showing that slice shifting might have considerable effect on outputs if the historical data possess a high spread indicator
Climate Adaptation Finance flows in the Middle East and North Africa
Get PDFThe policy brief delves into the pressing requirement for climate adaptation finance in the Middle East and North Africa (MENA) region. It underscores the rising climate threats and their detrimental effects on water scarcity, agricultural yield, and infrastructure. Furthermore, it evaluates the discrepancies in adaptation capabilities within the region, stresses the importance of amplifying adaptation funding, and scrutinizes the existing financial framework. Additionally, the brief explores inventive financial tools like debt-for-climate swaps and offers essential policy suggestions to bolster adaptation initiatives and financial structures.This policy brief delves into the paramount necessity for augmented climate adaptation finance streams within the Middle East and North Africa (MENA) region. Given that the region faces climate change repercussions at a rate twice that of the global average, the brief emphasizes the substantial threats to water resources, agriculture, and urban infrastructure. An assessment is made regarding the region's readiness for adaptation and the existing landscape of adaptation funding, highlighting a predominant reliance on debt-centered instruments. The brief advocates for a diversified array of financial mechanisms, heightened grant assistance, and innovative approaches such as debt-for-climate exchanges. It also furnishes essential policy recommendations to galvanize private sector investment, bolster the influence of sovereign wealth funds, and fortify regional collaboration to confront the urgent challenges of climate adaptation
Assessing the Effects of ONC 206 Alone and in Combination with Cisplatin (CDDP) on Ovarian Cancer cell lines
No full textOvarian cancer (OC) is considered the most lethal gynecologic malignancy worldwide with chemoresistance along with disease relapse, presenting major challenges. Therefore, developing novel therapies for OC is of utmost priority. Cisplatin (CDDP) is considered as the standard chemotherapy drug used in OC, though patients often develop resistance. Imipridones, a novel class of anti-cancer compounds, may be a novel therapeutic strategy that also aims to overcome this resistance. Therefore, this study aims to assess the effects of the Imipridone ONC 206, alone and in combination with CDDP on human OC cell lines. Methods: Two ovarian cancer cell lines, OVCAR-420 and SKOV-3, were used in this study, using both two-dimensional (2D) and three-dimensional assays (3D). The 2D assays included the MTT assay, trypan blue exclusion assay and wound healing assay to assess the effects of ONC206 alone and in combination with CDDP, on cell proliferation, viability and migratory ability, respectively. Additionally, the 3D sphere-forming assay was performed to examine the effects of both drugs on targeting the enriched population of OC stem cells. Results: Our MTT assay results demonstrated that ONC 206, both alone and in combination with CDDP, significantly inhibited the proliferation of OVCAR-420 and SKOV-3 cells. These findings were further validated using the trypan blue exclusion assay. A synergistic effect was observed when the two drugs were combined, enhancing their overall effectiveness. ONC 206 alone and in combination with CDDP resulted in incomplete wound closure in both cell lines Notably, in a 3D culture model, the sphere formation assay confirmed that ONC 206, either by itself or with CDDP, reduced both the size and sphere-forming ability of ovarian cancer stem cells. Conclusion: Our findings highlight the potential anti-cancer effects of Imipridones, particularly ONC206. Interestingly, ONC206 shows a remarkable synergy with cisplatin (CDDP), enhancing its efficacy even in resistant ovarian cancer cell lines, suggesting that this combination could overcome some of the limitations of traditional chemotherapy. As a result, ONC206 emerges as a promising candidate in future OC clinical trials
Coordination of Automotive Active Safety Systems via Global Chassis Envelope Control
No full textGround vehicles, characterized by their inherently over-actuated nature, serve as a foundation for the development of hierarchical control frameworks and control allocation schemes, which may be strategically harnessed and developed to achieve effective handling and enhanced traction on road surfaces via the coordination of their multiple active safety systems including active front steering (AFS), differential braking, and active suspension (AS).
To adhere to global chassis control, this thesis first introduces a global fuzzified model predictive control allocation (MPCA) scheme, integrating AFS, differential braking, and AS whilst considering their actuator dynamics. Fuzzy logic membership functions are set up to facilitate the appropriate coordination between these active safety systems.
Thereafter, stability regions defined in phase portraits connecting side slip and yaw rate are integrated into a model predictive controller and are used to derive a dynamically-changing allocation index. The decision to employ phase portraits stems from their ability to visualize ground vehicle dynamics and stability, which will be first used to enhance the lateral stability of ground vehicles within a hierarchical control framework. This results in effective resource allocation between front steering and braking, and thus significantly enhances lateral stability.
This research extends its focus onto explicitly defining and developing three-dimensional stability regions in the diagram relating the lateral and longitudinal accelerations to the yaw rate of the vehicle. This allows for the coordination of the active safety systems through a novel control architecture that employs this stability envelope in the design of the upper controller and within the control allocation scheme to attain the desired notion of global stability. This advancement not only addresses rollover risks but also improves both lateral and longitudinal handling performance in ground vehicles, whilst analyzing and employing measurements directly taken from the inertial measurement unit, thereby eliminating the need for state estimators in the stability regions.
To evaluate the proposed approaches, extensive simulations are conducted in the CarSim-Simulink environment for the various control schemes in corresponding appropriate testing maneuvers to verify the holistic design involving the a prediction of the actuator dynamics and involving a stability envelope on the global level of the vehicle. First, the obtained results demonstrated the advantages of integrating the actuator dynamics into the allocation scheme, which involve an anticipation of the effect of the actuator's dynamics on the control input. Specifically, 41.89 % and 36.38 % lower errors along the yaw rate and side-slip angle are the results of designing an MPCA scheme involved with a fuzzy logic system for a double lane change maneuver. The consideration of a 2D safety envelope in a model predictive control scheme demonstrates superior stability and handling performance, resulting in 20.6 % and 65.7 % smaller sideslip and yaw rate tracking errors, respectively, over the bench-marked nonlinear model predictive controller. The natural progression onto 3D to achieve global stability bestows a 62.63 % decrease in slip and 69.18 % lower roll angle in comparison to the commercially available controller