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Synthesis of Ni/Cerium-Zirconium Mixed Metal Oxides Via Combustion Method for CO2 Methanation
No full textMethanation presents a promising strategy for CO2 utilization. However, the stability of CO2 requires the presence of an effective catalyst to overcome the kinetic barrier. Cerium-zirconium mixed metal oxides, known for their high oxygen vacancy concentration, surface basicity and nickel dispersion, serve as efficient supports in methanation reactions. In this study, a series of 20Ni/CexZr1-xO2 catalysts was synthesized using the rapid and energy-efficient solution combustion method and tested for CO2 methanation. The most promising Ce-to-Zr ratio was selected for further optimization by varying Ni content. All catalysts were thoroughly characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET) surface area analysis, and thermogravimetric analysis (TGA) to evaluate structure, morphology, textural properties, and thermal stability. Upon catalytic testing, the Ce-to-Zr ratio of 9:1 (20Ni/Ce0.9Zr0.1O2) resulted in the highest CO2 conversion of 90% with 100% methane selectivity at 325°C under GHSV=60,000 mL/g.h. Despite this, 20Ni/Ce0.75Zr0.25O2 showed low-temperature activity, attaining 74.3% conversion and 98.25% selectivity at 250 °C, while conversion in other catalysts, including 20Ni/Ce0.9Zr0.1O2, did not exceed 48% at the same temperature. Based on these findings, Ce0.75Zr0.25O2 support was selected for further optimization by varying Ni loading in xNi/Ce0.75Zr0.25O2 (x = 10, 20, 30, 40 wt%). The 40Ni/Ce0.75Zr0.25O2 catalyst exhibited the best performance, achieving 93% CO2 conversion at 300 °C. Upon reducing GHSV to 30,000 mL/g·h, conversion further improved to 95.22% at 275 °C. Additionally, the catalyst demonstrated good stability, maintaining activity over five days of continuous operation at 300 °C and a GHSV of 60,000 mL/g·h
Deep Learning for Malware Captioning: Explainable AI in Cybersecurity
No full textStatic malware detection using machine learning has achieved high accuracy, but the resulting models often operate as black boxes, limiting their practical utility. This thesis addresses a key challenge in this domain, the \emph{interpretability gap}, which refers to the disconnect between a model’s low-level feature attributions and a human analyst’s need for high-level semantic understanding. In this context, an \emph{explanation} refers to a set of input features that a post-hoc interpretability method identifies as most influential in the model’s prediction. To bridge the interpretability gap, we propose a tag-based explanation framework that maps these influential features to 11 different behavioral descriptors of malware, such as ransomware and dropper, using the SOREL dataset. We investigate three widely used explainability methods: Captum (Integrated Gradients), SHAP, and LIME, to assess their alignment with these human-understandable tags. The main contributions of this thesis include: (1) proposing a novel framework that transforms post-hoc explanations into semantically meaningful behavioral tags; (2) performing a systematic comparison of XAI methods to evaluate their consistency and interpretability; and (3) demonstrating that explanation-derived feature vectors can support accurate tag inference through supervised learning. We also decompose explanation outputs into functional feature categories to support structured interpretation and downstream integration with language models. Captum emerges as the most effective explainability method, enabling tag prediction with a general accuracy of 0.97, defined as predicting at least one correct tag, and a top-1 accuracy of 0.95, where the most confidently predicted tag matches one of the true labels
Structural Engineering in Metal Organic Frameworks (MOFs) for Energy and Environmental Applications
No full textThe continuous increase in worldwide energy consumption, together with the ongoing release of contaminants into water, pose substantial environmental problems. Metal organic frameworks (MOFs) emerged as promising porous crystalline materials due to their unique features, including chemical, thermal, and mechanical stability, high porosity, large surface area, and tunable properties. As a result, MOFs were employed in a wide range of applications such as catalysis, gas storage, separation, drug delivery, and water remediation. A zirconium subfamily of MOFs, namely UiO-66, has gained considerable interest because of its superior chemical and thermal stability compared to other reported MOFs. Besides, it presents the ability to be structurally modified to optimize specific applications without altering the main building blocks and the topology of the framework. Herein, two structural engineering strategies will be used to enhance the adsorption properties of UiO-66, which include (i) linker functionalization and (ii) defect formation. The synthesized isostructural UiO-66 MOFs will be fully characterized using powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and Brunauer–Emmett–Teller (BET) surface area analysis. The first part of the study will be focused on investigating MOFs as adsorbents for water remediation, along with the determination of the thermodynamic and kinetic parameters of the adsorption phenomenon. This study seeks to understand the function of the different UiO-66-based MOF nanocrystals in the adsorption process, aiming to guide the future development of these materials for water remediation applications. In the second part, structurally tuned Zr-MOFs, and Al-MOFs, both incorporating Lewis acidic sites, will be synthesized, fully characterized, and tested as potential esterification catalysts towards biodiesel additives production. Therefore, we aim to enhance the adsorption and catalytic properties of the selected MOF systems to meet real-world needs for a greener sustainable future
Neuroinflammation in Immunodeficient Mice Following Traumatic Brain Injury
No full textBackground: Traumatic Brain Injury (TBI) is a cause of disability and death worldwide. It is
characterized by primary mechanical damage followed by secondary neuroinflammatory
damage, that involves oxidative stress and immune activation, which exacerbates the brain tissue injury. The immune system plays an immense role in TBI. Both innate immune
responses (mediated by microglia, neutrophils, and kallikrein-kinin system) and adaptive
immune responses (mediated by T and B lymphocytes) play crucial roles in
neuroinflammation. The role of adaptive immunity in modulating inflammatory responses
after TBI has gained attention, with lymphocytes implicated in amplifying inflammatory
cascades. The recombination activating gene 1 (RAG1) is essential for developing mature T and B lymphocytes, and knocking out RAG1 results in mice lacking adaptive immune
responses. However, the precise role of adaptive immunity in TBI remains unclear.
Hypothesis & Aim: We hypothesized that the absence of the RAG1 gene and adaptive
immune responses lead to a reduction in neuroinflammatory responses following TBI. To
investigate this hypothesis, we aimed to compare key inflammatory responses in RAG1
knockout mice versus wildtype (WT) mice subjected to TBI.
Methods: For the purpose of this study, moderate TBI was induced in 48 male/female WT
and RAG1-/- mice (n=3/group) using controlled cortical impact (CCI) (4 m/s, 1.5 mm depth,
1s dwell time) targeting the right parietal cortex. Sham controls received craniotomy only.
Mice were sacrificed on days 3 or 7 post-injury. Cortical RNA was extracted for RT-qPCR
analysis of inflammatory genes and normalized to GAPDH. All procedures were approved
by AUB's IACUC and complied with ethical guidelines for animal research.
Results: Our study revealed that RAG1 significantly modulates neuroinflammatory responses following TBI in a time- and sex-dependent manner. Both genotypes and sexes showed astrocyte activation (GFAP↑ at D3 and D7). RAG1+/+ mice exhibited sustained
neuroinflammation, with prolonged elevation of pro-inflammatory cytokines (TNF-α, IL-1β,
IL-6, IFN-γ), microglial markers (Galectin-3, CD68), and oxidative enzymes (NOX1&4)
through D7, particularly in females. However, RAG1-/- mice demonstrated resolution of
these responses. This suggests that adaptive immunity perpetuates secondary injury. Notably, RAG1 was required for anti-inflammatory signaling (IL-10, TGF-β1), since their levels didn’t increase in RAG1-/- females, unlike RAG1+/+ females. Therefore, it has a dual role in both exacerbating early inflammation and facilitating later repair. In addition, sex differences were prominent, with RAG1+/+ females showing delayed but amplified
cytokine/KKS activation whereas males had a persistent oxidative stress post-TBI.
Conclusion: Our findings demonstrate that adaptive immunity plays a critical and complex
role in TBI since it is responsible for both detrimental and reparative processes. Knocking
out the RAG1 gene attenuated pathological inflammatory processes and reduced sustained microglial activation, pro-inflammatory cytokine production, and oxidative stress,
particularly in females. However, RAG1 deficiency also impaired beneficial adaptive
immune functions due to the blunted anti-inflammatory responses, especially in RAG1-/-
females. These findings reveal that while RAG1 drives neuroinflammation, it is still essential for anti-inflammatory effects and repair. Therefore, this highlights the dual nature of adaptive immunity in TBI pathophysiology
Neuro-Shazam: Unlocking Complex Auditory Signal Recognition through Combination-Sensitive Neurons
No full textBiologically relevant stimuli are often highly complex in nature, containing multiple spectral and temporal components that carry information crucial for survival. Several studies suggest a strong relationship between accurate perception of communication sounds and the fidelity of the spectrotemporal auditory processing mechanisms. Combination sensitivity in central auditory neurons reflects a highly selective form of spectrotemporal integration. In this process, specialized neurons, generally known as combination-sensitive neurons (CSNs), integrate auditory inputs in a highly nonlinear fashion across both temporal and spectral domains. This non-linear integration of sound elements is a hallmark of complex auditory processing, in which the neuronal response to a combination of stimuli is much larger than the sum of the individual responses. The precise temporal coincidence of inputs, occurring within few tens of milliseconds, serves as the fundamental mechanism driving this process. Bats, among other species that exhibit this neural behavior, rely on such rapid coincidence to enhance their integration capabilities for effective echolocation. Yet one of the most complex examples of combination sensitive behavior exist in species of songbirds. Neurons in higher-order brain areas are found to be highly tuned to the selective combination of song-syllables, exhibiting a more complex processing behavior, with extended integration periods of hundreds of milliseconds, paralleling the complex processing demands of human speech. In this complex system, CSNs play a key role in allowing songbirds to recognize their own song, differentiate between conspecifics, and decode the subtle social information encoded in song. Despite their crucial role in songbird auditory processing, the mechanisms underlying the combination-sensitive neurons are mostly unexplored. In particular, it is not known how such extended temporal integration can be achieved by these neurons and yet remain sensitive to precisely timed inputs. With these complexities in mind, we developed three biophysically realistic conductance-based neural network models, representing different underlying mechanisms. Our network models are largely based on well-established principles of spectrotemporal processing and coincidence detection, which are particularly important in complex auditory processing. Our first network extends the mechanism of postsynaptic facilitation/priming by adding network-level mechanisms that allow the precisely timed convergence of both inhibitory and excitatory inputs onto the combination-sensitive neuron, which in turn drive spiking activity reflective of their integrated influence. Our second network model examines a scheme in which network-level mechanisms, specifically precisely timed offset responses, gate the integration of a dual-inhibitory inputs at the level of CSN, giving rise to its own offset response, signifying successful combination. Our third network model extends temporal coding principles to show the dynamic transition of the CSN between subthreshold excitatory temporal integration and coincidence detection mode, gated by the shift from asynchronous to precisely timed synchronous inputs, where a subsequent, spectrally distinct input elicits a rapid fluctuation that enables coincidence detection. For each network model, we conducted in-depth simulations to explore how this precise behavior is orchestrated by the interplay of synaptic and intrinsic mechanisms in the combination-sensitive network. We have identified key intrinsic parameters, like the T-type calcium channel conductance and the hyperpolarization-activated current (Ih), that strongly impact the generation of successful network behavior. We further explored how dynamic modulation of these parameters, in response to synaptic influences, further contributing to the selective spectrotemporal integration exhibited by these networks. Overall, our findings suggest that songbirds employ a two-stage process of auditory temporal coding, unlike the single-stage process described previously in several species, including bats. In songbirds, the classical coincidence detection window is preceded by a pre-coincidence integration stage. The first stage is gated by a network of interconnected neurons that can hold on to the carried information from the first stimulus, allowing for an extended temporal integration window. Under a precisely timed release of the held input, the system then transitions into a high-precision coincidence detection mode, during which combination-sensitive neurons temporally integrate incoming sensory information within a narrow window. This work provides a generalized framework for understanding the principles of temporal coding and combination sensitivity across sensory modalities but also provides valuable insights to guide future experimental investigations.
Historical Writing and the State: Sultans in the Biographical Writing of 16th and 17th Century Historians of Bilād al-Shām
Full text linkBy the 1970’s, the history of the Arab lands under Ottoman rule had been finally seen as elusive and yet to be thoroughly explored by scholars who had grown increasingly dissatisfied with the nationalist interpretations of the Ottoman past. This thesis follows on this approach of revisiting Arab-Ottoman history through the sources that we have left of the four-centuries long Ottoman rule in the Arab world. Particularly, it aims at examining the early modern attitude of Arab religious scholars of Bilād al-Shām towards the figure they were subject to, the sultan. It shifts the focus from studying the relationships of the Arabs with the provincial governments towards examining the relationship with the central government through the particular lens of determining attitudes of ulema of Bilād al-Shām towards the Ottoman ruling figure.
Biographical dictionaries covering the 16th and until the mid-17th century, and which have entries on Ottoman sultans, were utilized in this research. These works were authored by the learned elite of the society of Bilād al-Shām and reflect their own dispositions, and perhaps to a lesser degree, that of the general public. Their evaluation of the Ottoman sultans is uniformly positive, founded on the conformity of the basis of the Ottoman sultans’ rule and general conduct on one hand with the defining aspects of legitimacy in Islamic legal and political theory on the other. The thesis touches upon the topic of uniform social and political standards across the regions of Bilād al-Shām and Anatolia, highlighting that broadly similar pillars of legitimation are shared between Ottoman Turkish scholars and their counterparts in Bilād al-Shām, based on a shared Islamic social fabric
The Reception of Avicenna’s Distinction Between Essence and Existence in the 13th Century Islamic World: The Qūnawī-Ṭūsī Correspondence
No full textThe broad focus of this thesis is a correspondence between Sufi mystic Ṣadr al-Dīn al Qūnawī (d.1274) and Persian philosopher and polymath Naṣīr al-Dīn al-Ṭūsī (d.1274). More specifically, this thesis looks at a particular part of this correspondence, the first question (al-masʾala l-ūlā) by al-Qūnawī, and al-Ṭūsī’s reply (jawāb), providing an in depth analysis of the background and context. In this question, al-Qūnawī solicits the Avicennian doctrine of the distinction between essence and existence in everything else other than God and explores how it relates to the Necessary of Being. Upon investigation, this distinction proves to be problematic on a metaphysical level when it comes to God, in light of the identification of God’s essence and existence: how can God, who is utterly unique, simple, and one, be said to exist, at the same time when all other contingent beings are said to exist? Al-Qūnawī’s critique will hinge on this problem of unity vs. multiplicity as he puts forward an ontology of utter oneness of being, the theory of waḥdat al-wujūd. Although he did not coin the term himself, this theory is associated with al-Qūnawī’s master, Ibn al-ʿArabī, and has been used to denote his school of thought. Despite the astute points al-Qūnawī makes, al-Ṭūsī is not convinced and replies to him, bringing forward yet a new ontological understanding of Being, i.e., his concept of al wujūd al-maqūl bi-l-tashkīk, being predicated ambiguously. While al-Ṭūsī thinks of himself as defending a rational philosophical Avicennian paradigm, we will come to appreciate his original contributions to a line of thought that culminates in Mullā Ṣadrā’s thought over 350 years later
Next-Generation Point-of-Care Cancer Detection: Bioelectronic Mapping of the Malignancy Spectrum
Full text linkCirculating tumor cells (CTCs) detach from primary tumors, enter the bloodstream, and contribute to disease progression through “metastasis”—the leading cause of cancer-related deaths. CTCs carry vital information about cancer progression; however, their capture and detection remain a major challenge for point-of-care (POC) cancer diagnostics, therapeutic monitoring, and prognostic assessment. This primarily arises from the lack of universal markers capable of isolating CTCs from blood and identifying their metastatic potential. In this work, we aim to advance next-generation POC cancer detection by developing a bioelectronic platform for mapping CTCs across their metastatic spectrum and establishing a novel core technology for their isolation and detection. To achieve this, we utilized in-vitro model of metastasis progression derived from the same cell lineage by varying the expression of Connexin 43 in MDA-MB-231 triple-negative breast cancer cells.
In our work, via in-house developed single-cell force microscopy assay, we demonstrated that malignancy correlates with cellular biophysical properties. Highly malignant cells exhibited increased elasticity, cellular softening, and reduced adhesion forces up to ~150 nN, in contrary to cells with lower metastatic potential that showed cellular stiffening, viscous membrane character, and enhanced adhesion of ~350 nN, an almost 60% increase in adhesion strength. Notably, these differences were observed only when cells were in clusters, and the effect disappeared at single-cell state. A first of its kind measurement. Furthermore, we investigated real-time biomechanical responses to Docetaxel (DTX), a microtubule-targeting chemotherapeutic agent, across different metastatic states using Quartz Crystal Microbalance with Dissipation Monitoring (QCM-D). Treatment with 20 nM DTX increased cellular stiffness, with distinct response magnitudes and kinetics between the metastatic cell variants, which correlated with cell aggressiveness and malignancy levels. Additionally, we designed and fabricated a Dielectrophoretic Impedance Spectroscopy (DEPIS) array, with low-impedance and high-sensitivity using additive-manufacturing technology. This array was optimized through finite element analysis simulations to maximize efficiency for CTC capture and characterization in solution. Our results revealed an interplay between cancer cell biophysical and dielectric properties, which correlate with their metastatic states. Highly metastatic cells displayed membrane capacitances of 16.88 ± 3.24 mF m−2, higher than those of less metastatic subtypes with membrane capacitances below 14.3 ± 2.54 mF m−2. These capacitance variations corresponded to distinct crossover frequencies—an essential metric for cell sorting. Additionally, impedance measurements at 1 kHz revealed significant differences in double-layer capacitance among the metastatic subgroups, highlighting DEPIS as a non-invasive and rapid tool for CTC sorting, capture, and classification.
Finally, we designed and developed a novel all-planar, high-performance organic electrochemical transistor (OECT) with high reproducibility, amplification (3.8 mS) and rapid response times (0.08 ms). Our OECT-based cancer biosensor revealed that cancer cells with different metastatic states modulate the drain current (IDS) differently. Cells with lower metastatic potential caused a greater attenuation of IDS up to 35% compared to 12% modulation with cells in the higher metastatic spectrum, correlating with their higher adhesion strength and lower membrane capacitance, as established in our previous studies.
This work will pave the way for a next-generation POC platform for cancer detection. The unique cellular fingerprints identified can serve as biophysical and bioelectronic biomarkers for distinguishing and sorting CTCs. Our approach holds great promise for liquid biopsy-based cancer diagnostics and monitoring, offering a powerful tool for precision medicine applications
Enhancing Bioavailability and Therapeutic Potential of Plant-Based Ibuprofen Formulation
No full textApproximately 50% of the active pharmaceutical ingredients (APIs) given orally have low solubility, limiting their bioavailability. One such extensively used examples is ibuprofen (2- (4- (2-methylpropyl) phenyl) propionic acid), a non-steroidal anti-inflammatory drug (NSAID) classified as the Biopharmaceutics Classification System (BCS) II due to its low solubility, high permeability, and limited bioavailability. An important trend in the pharmaceutical industry is the growing use of finely subdivided materials, drugs, to increase dissolution, solubility, and bioavailability. Many APIs are thermosensitive, and therefore nonthermal alternative procedures that can enhance the physicochemical properties of drug-excipient combinations have gained popularity. One such technology is the mechanical formulation through
milling, which is becoming increasingly popular. A notable advancement in pharmaceuticals involves the reduction of the percentage of APIs by substituting them with organic compounds. These compounds achieve
similar therapeutic goals and fulfill the dietary requirements of consumers. Despite the considerable therapeutic potential of ibuprofen in the treatment of various diseases, its applicability, safety, and long-term use are limited by several factors. In particular, ibuprofen can increase the risk of stomach bleeding and ulcers, posing safety concerns for individuals with kidney or heart disease. Consequently, there is an ongoing demand for plant-based combinations that mitigate the limitations associated with APIs. The model selected for this study is ibuprofen and ginger. Where ginger, a traditional herbal remedy with a long history of treating inflammatory conditions, complements the therapeutic goals of ibuprofen. Ginger shares common attributes 2 with ibuprofen, including antioxidant activity, anti-inflammatory properties, and antibacterial effects. Two phases of the screening processes will be conducted after the establishment of parameters that impact the ibuprofen milling process. These parameters include milling duration, frequency, temperature, ball-to-powder ratio, and the ratio of ibuprofen to plant-based material. The first phase, ball mill parameter optimization, involves fixing these parameters while varying them one at a time. The second phase involves varying the ratios of the components. Several analytical tests will be performed to evaluate the results before and after milling. These include X-ray diffraction (XRD), dynamic light scattering (DLS), scanning electron microscopy (SEM), thermogravimetric analysis (TGA) and differential scanning calorimetry
(DSC), and high-performance liquid chromatography (HPLC) to track the release profiles of the prepared samples in various acidic, hydrochloric acid (HCl), and slightly basic, phosphate buffer solution (PBS) medium. The results indicated changes in the physical characteristics of ibuprofen after milling. Analysis of the release profiles for unmilled, milled, and various combinations of ibuprofen-ginger showed that the dissolution rate of milled ibuprofen was improved,
showing an enhancement 57% over the unmilled form. Furthermore, among the combinations tested, the mixture containing 80% ibuprofen and ginger 20% exhibited the most effective release, achieving an ibuprofen release of 76% in PBS and 25% in HCl. Together, the findings suggest that grinding reduces the size of the particles and enhances the solubility, while the inclusion of ginger further improves the efficiency of ibuprofen release. We anticipate that this study will pave the way for a series of future projects examining the dynamic interplay between APIs and organic ingredients. By exploring various combinations, our aim is to optimize therapeutic efficacy while minimizing potential side effects. This comprehensive approach holds promise for the advancement of pharmaceutical formulations and the improvement of patient outcomes