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Impact of Geopolitical Weaponization of Energy on Sustainability Policies of Sociopolitics, Environment, and Economy
Get PDFEnergy has long been used as a tool of foreign policy. It has been progressively interlinked with geopolitical relations being shaped by a multitude of factors such as market competition, geopolitical tensions, and regulatory frameworks. With energy diplomacy being increasingly evident in contemporary geopolitical relations as it utilizes energy resources and trade to achieve foreign policy objectives, this thesis tackles the implications that energy weaponization has on sustainability policies. Energy sustainability is one of the three energy policy objectives of any state. This research includes the analysis of the three policy objectives discussed in the ‘Impossible Energy Trinity’ that presents the tradeoff that states face between energy security, sovereignty, and sustainability. The included case study showcases the intersectionality of energy weaponization in policy making on economic, societal, and environmental levels and its implications. It is focused on Russia and the events that led to its current politically volatile nature as an energy producing and exporting state. The Russian invasion of Ukraine case is explored from the scope of the energy trinity which includes energy security, energy sovereignty, and energy sustainability in assessment of its current status. Accordingly, the impact of energy weaponization will be detailed though a particular focus on energy sustainability in Russia and the region. In order to assess this impact on energy sustainability, the three pillars of sustainability implicated will be discussed in both qualitative and quantitative methodologies. The subsequent policy differences within each of the three pillars are assessed over the period beginning with the Russian invasion of Ukraine on the 24th of February 2022 until January 2024
Mechanism of Action of DNA Polymerase Inhibitors in Triple-Negative Breast Cancer Cells and Anti-Tumor Effects in Intraductal Breast Cancer Mouse Model
No full textIntroduction: Breast cancer is the second leading cause of cancer-related fatalities globally and the most frequent malignancy among women. Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and is associated with high metastasis and poor prognosis. Due to the absence of specific TNBC targeted therapies, this breast cancer type is usually treated using conventional chemotherapy. However, TNBC therapy is commonly associated with drug resistance, a high relapse incidence, and pronounced side effects, emphasizing the urgency of developing novel, effective, and targeted therapeutic strategies. DNA polymerase 1 (POLA1) and histone deacetylase 11 (HDAC11) play key roles in cell proliferation, DNA synthesis and replication, and regulation of epigenetic mechanisms. POLA1 and HDAC11 levels are commonly elevated in human breast tumors as opposed to normal breast tissues. Therefore, POLA1 and HDAC11 present potential targets for drugs in breast cancer therapy. We have previously shown that the adamantyl retinoid ST1926 is a POLA1 inhibitor in the sub-µM range, impeding proliferation and inducing cell death in human TNBC cells while sparing normal ones. Additionally, we have synthesized two ST1926 analogues, MIR002 and GEM144, which demonstrated dual inhibitory activities against POLA1 and HDAC11. The three compounds showed potent anti-tumor activities in TNBC cells but GEM144 has improved pharmacological properties. Therefore, we selected GEM144 to further investigate its mechanism of action in TNBC cell lines. Methods: POLA1 and HDAC11 levels were measured by immunoblotting techniques in different types of human TNBC cell lines and a normal-like breast MCF10A cell line. In silico analysis was performed to determine the transcript levels of POLA1 and HDAC11 levels in human TNBC versus normal breast tissues and their effects on patient overall and disease-free survival. The mechanism of action of GEM144 was studied on cell viability and cell death using the trypan blue assay, DNA damage by immunoblotting techniques, and apoptosis by TUNEL assay on human TNBC cell lines. The breast intraductal mouse model, in female NSG mice, was selected to be used as the most suitable orthotopic mouse model for breast cancer. Results: GEM144 reduced viability and increased cell death in TNBC cell lines at low micromolar (µM) concentrations while sparing normal-like breast cells. The basal-like HCC1806 cells and the luminal androgen receptor (apocrine) MDA-MD-453 cells had IC50 values of 0.5 and 1.0 µM, respectively. DNA damage was detected by the increase in γH2AX and apoptosis was confirmed by TUNEL positivity and PARP cleavage in GEM144-treated TNBC cells. In silico analysis did not indicate statistically significant differences in the transcript levels of POLA1 and HDAC11 in human TNBC versus normal breast tissues nor in patient overall and disease-free survival. However, basal protein levels of POLA1 and HDAC11 were elevated in several TNBC cell lines versus normal-like breast cells. Importantly, we successfully established the breast intraductal mouse model at the American University of Beirut and we were able to track and quantitate tumor growth in vivo using the IVIS system. Conclusion: These results highlight the effectiveness of GEM144 in TNBC management. Future in vivo experiments, using the breast intraductal mouse model, will assess the effectiveness of GEM144 versus cisplatin standard chemotherapy in TNBC treatment
IMPACT-FORCE IDENTIFICATION USING DEEP LEARNING AND BAYESIAN INFERENCE WITH APPLICATION ON PIPELINE STRUCTURES
No full textStructures, ranging from bridges to pipelines, frequently encounter various impact events that can jeopardize their integrity. For example, a pipeline might experience impacts from falling debris during a construction project, or a bridge might suffer hits from over-height vehicles. In coastal areas, piers and offshore structures often face impacts from floating debris or boats. Each of these scenarios can induce stresses that, if undetected, might lead to critical failures. Therefore, it is essential to measure and analyze these impacts accurately to ensure the longevity and safety of such structures. Traditional methods such as hiring an inspector for this task are often costly, and complex, and can require pipeline shutdowns, leading to economic losses. To address these issues, this research employs a combination of deep learning and Bayesian inference techniques, offering a more efficient approach.
In this methodology, a novel two-stage approach is implemented to resolve the inverse problem of identifying impact forces on structures. Initially, a Convolutional Neural Network (CNN) classifier for each of the four sensors is employed to determine the impacting material (aluminum, rubber, plastic) impacting the structure. This classification stage utilizes ground truth data to accurately identify the nature of the impact.
Following this, the pre-classified data, categorized by the actual impacting materials, is directed into one of three 5-layer Artificial Neural Networks (ANNs), each designated for a different impacting material. These ANNs serve as surrogate models for Bayesian inference, which is used to infer both the impact force and position. By employing this method, the approach effectively creates a total of 12 distinct and specialized surrogate models, corresponding to each combination of sensor and tip type.
The testing phase of the ANNs demonstrates a low Mean Squared Error (MSE), indicating a precise prediction of the pipeline's acceleration frequency signals while the CNN tip classifier illustrated 99% and above F1 score in all sensing elements. Here, the Approximate Bayesian Computation with Subspace Simulation (ABC-SS) technique is utilized, with the ANN and CNN test results serving as input. This final step showed promising results with each sensor having over 90% precision with 7% uncertainty in inferring the impact force and 92% with 2% uncertainty in inferring its position along the length of the pipe (will be referred to as the depth).
A more robust inference of the impact force and depth location could be done by data fusion where each response coming from each sensor is combined by introducing a new combined distance metric. This way a higher precision and less uncertainty for the depth could be obtained with over 92% precision with 8% uncertainty for the impact force and over 98% precision with 1.8% uncertainty for the depth (location). The obtained results demonstrate the method's reliability and effectiveness in pinpointing impact forces and the location of the impact using even a single far-away sensor
The Impact of IMF Loans on the Economic Growth of MENA Region Countries
Get PDFThis thesis explores the complex interplay between International Monetary Fund (IMF) interventions
and their economic impacts on countries in the Middle East and North Africa
(MENA) region. Through an in-depth analysis of several MENA countries that have engaged
with the IMF, this research aims to dissect the effects of IMF programs and political stability
on economic growth, social welfare, paying close attention to the outcomes of program
conditionality.
Employing a methodological approach that integrates descriptive statistics and econometric
models like OLS, the study meticulously analyzes macroeconomic variables before and
after IMF loan interventions. It also addresses the critical issue of self-selection bias by using
polity indices as instrumental variables, ensuring a nuanced understanding of the IMF’s
impact. The data, sourced from the IMF and the World Economic Outlook database, and
PolityV website encompasses a range of economic indicators for seven countries within the
MENA region that have received IMF loans.
The thesis contributes to the academic discourse on international finance and economic
development by offering evidence-based policy recommendations aimed at enhancing the effectiveness of future IMF programs in the MENA region. It navigates the complexities of IMF
engagements, examining both the benefits and the challenges, and seeks to provide a comprehensive
overview of the multifaceted relationship between IMF loans and the socio-economic
dynamics within MENA. By doing so, it aspires to guide international financial institutions,
governments, and policymakers towards informed decision-making that aligns with long-term
development objectives, fostering sustainable growth while minimizing adverse effects. Additionally,
it aims to clear up the confusion around whether IMF loans are inherently bad for
countries, providing a balanced perspective on their impact
“Without Fear or Favor:” A Comparative Discourse Analysis of The New York Times’ Coverage of Palestine and Ukraine
No full textThis study aims to dissect the discrepancies within The New York Times coverage of global events which have long standing histories and find themselves on opposing sides of power imbalances, innate to the global order. At the beginning of the Russian-Ukrainian War in February 2022, there was an evident difference in the depictions in the media comparatively to similar conflicts globally. Having followed Israel’s occupation of Palestine closely, it was noteworthy to see how one conflict was reported compared to the other. This study explores the relationship between the online content of The New York Times, and its discursive practices and representation and the subsequent implications on power, knowledge production and perceptions for different audiences. This study explores this phenomenon by assessing coverage in Palestine in May 2021 comparatively with Ukraine in February 2022, respective to the narratives, reporting, and subsequent discourse in The New York Times, and how that discourse informs knowledge and power dynamics around two significant geopolitical conflicts which shape global relations in the current international affairs arena. The study finds that despite the stated intentions on truth and partiality, Times coverage raises problematic implications
Detection and Quantification of Nickel and Cadmium in Clay Agricultural Soil Using Hyperspectral Imaging and Artificial Intelligence-A Case Study in Lebanon
No full textHeavy metal contamination in agricultural soils poses serious environmental and health problems. Intensive efforts are employed to improve existing quantification methods of heavy metals in contaminated environments. Conventional detection techniques are time-consuming, tedious, and costly. The application of hyperspectral remote sensing in this field is possible and promising as a fast, nondestructive, and reliable detection technique. However, factors impacting the efficiency of image acquisition in detecting and quantifying heavy metals in agricultural soils were not thoroughly studied. This study proposes to assess the use of hyperspectral imaging and artificial intelligence for the detection of nickel (Ni) and cadmium (Cd) in agricultural clay soil collected from the Bekaa Valley, a major agricultural area in Lebanon, under different contamination levels and soil moisture content. The novelty of this study relies on the incorporation of HSI and AI for environmental monitoring in Lebanon.
Soil samples were contaminated with Ni and Cd individually, with concentrations ranging from 150 mg/kg to 4000 mg/kg and 2.5 mg/kg to 4000 mg/kg, respectively. The moisture content of raw and contaminated soil was varied from 5% to 75% based on soil water thresholds. Hyperspectral imaging was used to detect Ni and Cd contamination in the soil at different contamination and moisture content levels. The spectral curves showed an inverse correlation between Ni and Cd concentration and spectral reflectance. Based on continuum removal, Ni presence was well expressed near 2190 nm with a Pearson correlation factor of -0.79 and that of Cd near 1700 and 1750 nm with a correlation factor of -0.88 and -0.95 respectively. In addition, spectral changes due to the variation in soil moisture content were detected near 1400 and 1900 nm with a Pearson correlation of -0.88 and -0.81, respectively.
Machine learning and deep learning algorithms were used to develop univariate and multioutput models to predict the concentration of Ni and Cd in contaminated soil and assess the effect of soil moisture content on metals quantification. The models were constructed using partial least square regression (PLSR), support vector regression (SVR) and random forest regression (RFR) machine learning algorithms. In addition, artificial neural networks (ANN), convolution neural networks (CNN) and long short-term memory (LSTM) were used as deep learning algorithms. RFR had the highest prediction accuracy of 0.85 and 0.95 for Ni and Cd respectively. The prediction and validation results using other models were favorable except for PLSR due to data nonlinearity. In addition, the results showed that Cd was more predictable than Ni. Furthermore, to assess the effect of soil moisture content on heavy metal detection, multioutput heavy metal-moisture content machine and deep learning models were developed. The overall accuracy of all Ni and Cd model algorithms has decreased with the introduction of moisture to the model along with elevated RMSE values. With respect to RFR, the accuracy of Ni prediction was unchanged following water addition, whereas that of Cd has only slightly decreased from 0.95 for univariate to 0.90 for multioutput.
The results show the potential of using HSI and AI as a reliable and cost-effective approach for heavy metal pollution assessment in contaminated soils. The findings from this study may involve further investigations to examine its ex-situ applicability considering actual field conditions
Novel ST1926 Nanoparticle Drug Formulation Development and Therapeutic Potential in Colorectal Cancer
No full textNanomedicine has gained significant interest over the years as it provides effective drug delivery, enhanced stability, bioavailability, and permeability, thereby minimizing drug dosage and toxicity. It is a fast-evolving field that holds promise for the novel development of various therapeutic inventions, particularly in the treatment of cancer. The use of nanoparticle (NP) formulations in drug delivery has been applied to various cancer types and has shown to improve the ability of drugs to reach specific targeted sites in a controlled manner. Colorectal cancer (CRC), is among the most common types of cancers diagnosed worldwide. It imposes a health burden as it ranks third in terms of cancer incidence and second in terms of mortality worldwide. CRC results from the accumulation of aberrant genetic and epigenetic alterations within cells of the colon and/or rectum. Despite advances in CRC surgical techniques, chemotherapy, radiotherapy, and targeted therapy, disease resistance remains a major obstacle in the survival of patients, emphasizing the need for novel therapeutic modalities. We have previously shown that the adamantyl retinoid ST1926 displays potent anti-tumor activities in human CRC models. However, ST1926 is limited by its low bioavailability which resulted in it being halted in phase I clinical trials in cancer patients. Therefore, we developed novel ST1926-NPs and assessed their efficacy in CRC models. ST1926 was formulated into NPs using Flash NanoPrecipitation with a drug to polymer to co-stabilizer mass ratio of 1:2:0.2. Dynamic light scattering has shown that the Contin ST1926-NP diameter was 97 nm, with a polydispersity index of 0.206. Using viability, cell cycle, and cell death assays, we showed that ST1926-NPs exhibited potent anti-tumor activities in the human CRC HCT116 cells. In a CRC xenograft model, the tumor volumes of mice treated with ST1926-NP were significantly lower than those of the controls, even at very low concentrations of ST1926 in NPs. In conclusion, our research supports the use of ST1926-NP formulations in enhancing the stability and bioavailability of ST1926 in CRC, facilitating its further development in clinical settings
NOVEL ALGINATE AND ALGINATE SULFATE/POLYCAPROLACTONE NANOPARTICLES FOR ENHANCED GROWTH FACTORS DELIVERY IN WOUND HEALING APPLICATIONS
No full textDiabetes is a metabolic disorder characterized by hyperglycemia, affecting more than 460 million people worldwide. Uncontrolled diabetes can lead to secondary complications such as diabetic foot ulcers (DFUs) due to peripheral neuropathy and peripheral arterial disease. Non-healing DFUs can progress to gangrenes and may require amputations, affecting 11.2 per 1,000 patients per year and burdening the healthcare system with billions of dollars annually. DFUs are caused, in part, by the deficiency in growth factors (GFs), particularly connective tissue growth factor (CTGF), which disrupts efficient wound healing. Similarly, myocardial infarction (MI), the most severe and prevalent type of cardiovascular disease, is associated with an increased risk of heart failure and an overall worse prognosis in patients with low circulating levels of insulin-like growth factor 1 (IGF1). Exogenous delivery of CTGF and IGF1 can promote complete wound healing of DFUs and improve cardiac remodeling in MI. However, the delivery of GFs is limited by their low stability and short half-life, implicating the need for a nanocarrier to entrap and shield the GFs, promote their controlled release, and enhance their availability at the wound site.
In this thesis, we aimed to develop novel double-emulsion alginate (Alg) and heparin (HN)-mimetic alginate sulfate (AlgSulf2.0)/polycaprolactone (PCL) nanoparticles (NPs). These NPs were designed for the enhanced affinity binding and controlled delivery of CTGF and IGF1 with the ultimate aim of accelerating DFU healing and providing MI cardioprotection. First, we synthesized the NPs by the double emulsion solvent evaporation technique and assessed the NPs’ size, encapsulation efficiency (EE), cytotoxicity, and wound healing capacity in immortalized human adult epidermal cells (HaCaT). The sonication time and amplitude used for NP synthesis produced particles with a minimum size of 236 ± 25 nm. Treatment of HaCaT cells with up to 50 μg/mL of NPs showed no cytotoxic effects after 72 h. The highest bovine serum albumin EE (94.6 %, P = 0.028) and lowest burst release were attained with AlgSulf2.0/PCL NPs. Moreover, cells treated with AlgSulf2.0/CTGF exhibited the most rapid wound closure compared to controls while maintaining fibronectin synthesis. Second, based on the previous promising results, we explored the wound healing potential of CTGF-loaded Alg and AlgSulf2.0/PCL NPs in in vitro and in vivo settings. The NPs’ cytocompatibility, stability, and wound healing activity were assessed on HaCaT, primary human dermal fibroblasts (HDF), and a murine cutaneous wound model. The NPs were biocompatible, and their size was not affected by elevated temperatures, acidic pH, or protein-rich medium. We found that treatment of HaCaT and HDF cells with CTGF-loaded Alg and AlgSulf2.0/PCL NPs, respectively, induced rapid cell migration (76.12% and 79.49%, P < 0.05). Additionally, in vivo studies showed that CTGF-loaded Alg and AlgSulf2.0/PCL NPs resulted in the fastest and highest wound closure at early and late stages of wound healing, respectively (36.49%, P < 0.001 at day 1; 90.45%, P < 0.05 at day 10), outperforming free CTGF. Third, considering the previous encouraging findings, we were motivated to explore the versatile potential of the GF-affinity binding NPs. To this end, we examined the protective role of IGF1-loaded Alg and AlgSulf2.0/PCL NPs on an MI-mimetic cardiac hypoxia/reoxygenation (H/R) injury using rat neonatal cardiomyocytes (NCM). The NPs exhibited controlled release of IGF1 and did not induce any significant toxicity on NCM cells. Moreover, H/R injury led to a significant decrease in NCM cell viability (46.08%, P < 0.01) and metabolic activity (40.82%, P < 0.05), with significant 1.41- and 1.66-fold (P < 0.05) increase in the transcript levels of cell damage and cell survival markers. Conversely, treating NCM cells with IGF1-loaded Alg and AlgSulf2.0/PCL NPs after H/R resulted in more than 40% increase in cell viability outperforming free IGF1, implicating their ability to alleviate cell death post-MI.
Double-emulsion NPs based on Alg or the HN-mimetic AlgSulf represent a viable strategy for enhancing cutaneous wound healing in DFU and alleviating cell death post-MI. These novel polymeric NPs can be further expanded for the delivery of various HN-binding proteins, potentially aiding distinct pathological conditions characterized by GF deficiencies
Trends in Mortality Among Adolescents and Young Adults in Lebanon from 2017 to 2022
Get PDFAdolescence is a critical phase during which individuals develop habits that can significantly impact their future health and wellbeing. While youth is often perceived as a healthy period of life, it is alarming that over 1.4 million young people die annually, with a significant number of deaths occurring in low and middle-income countries. In Lebanon, there is a lack of recent studies that describe mortality trends and underlying causes of death (UCOD) among adolescents and young adults. Hence, this study aims to fill this crucial gap by examining mortality trends and common UCOD between 2017-2022.
Adolescent mortality data in Lebanon from 2017 to 2022 were collected from the Ministry of Public Health's (MOPH) database. The three parameters to describe the outcome of interest were time, place, and persons. A Pearson's chi-square test was used to assess trend changes, with a p-value of ≤0.05 indicating significance.
In Lebanon, a significant increase in mortality was observed among 10-14-year-old adolescents from 2017 to 2022. Young adults 20-24 had the highest percentage of death among all years compared to younger age groups. The highest percentage of deaths was noted in 2021 (18.5%), and males had a higher death rate than females (68.9% vs. 31.1%). The most common UCOD was external causes of death (35.7%), followed by cardiovascular/circulatory system diseases (20.4%) and neoplasms (9.2%). Adolescents aged 15-24 are more likely to die from external causes, while those aged 10-14 are more likely to die from neurological diseases. Mount Lebanon had the highest death rate (26%), with a significant increase in deaths in South Lebanon over the years.
In conclusion, as mortality rates increase, particularly among younger age groups, there is a need to prioritize adolescent and young adult health on the national agenda to develop and implement evidence-based policies and reduce the influence of the leading UCOD. This study serves as an essential quality indicator (QI) of the current data collected by the government surveillance system, highlighting the need to further enhance data validity and reporting.
Relations Between Mothers' Personal Values, Personality Traits, and Career Preferences
No full textWhen given the choice, some highly educated mothers of minor children prefer to stay at home to care for the family and the house, while others prefer to work outside the house in addition to caring for the children. Past research has investigated cultural, social, or organizational factors that push women to drop from the workforce after transitioning to motherhood. However, there were no studies that examined personality-level predictors of mother’s career preferences. There are two theoretical models that can be built on regarding relations between mothers’ personalities and career preferences. The Social Role Theory proposes that mothers’ career preferences are influenced by their endorsement of the traditional gender-based roles, while the Preference Theory proposes that mothers’ personal values are central determinants of their career preferences. Building on the Preference Theory, the aim of this study was to examine the associations between mothers’ personal values, personality traits, and career choices above and beyond their endorsement of traditional gender roles attitudes. Around 400 mothers of minor children holding at least a Bachelor’s degree in Lebanon were recruited through social media platforms and WhatsApp groups. Participants were asked to complete an online survey about their personal values, personality traits, gender role attitudes, work preferences, and demographics. Data was analyzed using multiple regression tests. It was found that the valuation of Achievement was a negative predictor of mothers’ preference for staying at home above and beyond their endorsement of traditional gender roles. It was also found that the valuation of Security and the personality trait of Neuroticism were positive predictors of mothers’ preference for staying at home above and beyond their endorsement of traditional gender roles. The results of the study lend support to the Preference Theory and call for the respect of mothers’ career preferences