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COMPUTATIONAL CHEMISTRY FOR IMPROVED NATURAL COMPOUNDS-TARGET AFFINITY PREDICTIONS
No full textThe rapid evolution of pathogens underscores an urgent need for accelerated therapeutic development strategies. With an emphasis on natural compounds, this work expands the field of drug repositioning by employing machine learning(ML) techniques to forecast compound-protein interactions that may have therapeutic consequences. Our method makes use of several pre-trained Drug-Target Affinity (DTA) models, such as GraphDTA, MLT-LE, and DeepDTA, to predict binding affinities between protein targets listed in BindingDB and natural products sourced from the COCONUT database. This integration aims to create a robust database facilitating the repurposing of naturally occurring compounds, which are often overlooked in traditional synthetic drug pipelines
THE EMERGING ROLE OF ACADEMIC DEANS IN THE TRANSFORMING HIGHER EDUCATION LANDSCAPE OF KAZAKHSTAN
No full textWithin Kazakhstan’s fast-changing higher education landscape, the nature of academic deanship is also rapidly changing (Arntzen, 2016; Wepner et al., 2015). While deanship has traditionally been associated with curriculum design, student admissions, and faculty hiring, it now also requires engagement in strategic planning, human resource management, budgetary issues, fundraising, and communication with business and industry (Cleverley-Thompson, 2016; Gmelch et al., 2012). Thus, the role of the dean has become a more complex and multifaceted position in academia. Therefore, this study aimed to explore academic deanship in Kazakhstani public universities to find out how deans perceive and experience their jobs, what roles they play as school administrators, and what challenges they face in their positions. For this purpose, this study employed semi-structured in-depth interviews conducted with 15 academic deans from seven public universities (national and regional universities) in Kazakhstan.
The study’s findings indicate that the dean’s executive behavior in public universities is rule-bound and context-dependent due to the inherited centralized governance system in Kazakhstan as a post-Soviet republic. These factors explain top-down control, tight control, hierarchical relationships, and centralized decision-making in universities. Under these conditions, the dean’s autonomy in managing academic schools are restricted in the issues associated with finances, decision-making, and problem solving (Hartley et al., 2016; Sagintayeva et al., 2017; Yembergenova et al., 2021). This suggests that deans who are selected as administrators are limited to fully carry out their administrative and managerial functions. These discrepancies in which deans operate explain their role conflict and role ambiguity in the workplace.
These findings contribute to the understanding of academic deanship in the context of the post-Soviet higher education system, highlighting that the dean’s managerial potential
remains unrecognized (Cleverley-Thompson, 2016; Wepner et al., 2015). Therefore, the practical implications emphasize the need for empowering deans in Kazakhstani public universities by delegating them greater authority. This will strengthen grassroots leadership among deans, enhancing their administrative-managerial status. Regarding the theoretical implications, executive behavioral theory showed that the dean’s managerial behavior is prone to norm-following behavior, suggesting that this theory is more suitable for analyzing decentralized university governance systems. Role conflict and ambiguity theory allowed to examining the dean’s roles from different perspectives, such as individual and institutional levels, offering deeper insights into understanding the phenomenon under study
YOUTH AND GODLESSNESS IN SOVIET KAZAKHSTAN, 1921-1933
No full textThis thesis examines the changing relationship between the Komsomol and religion, both Islam and Christianity, in early Soviet Kazakhstan. It demonstrates that the marginalization of Kazakh youth in the Komsomol during the New Economic Policy (1921-1928) (NEP) was connected to their limited ability or willingness to participate in campaigns of cultural transformation, but that the reintroduction of mass political violence during collectivization allowed a cohort of Kazakh youth to achieve parity with Russians on the anti-religious front. In conditions of limited resources, poor communications, and low literacy, the legitimization of decentralized violence acted as the great equalizer. While there already exists abundant anglophone literature on the role of Russian youth in overturning the NEP consensus, the role of Kazakh and Central Asian youth is still to be thoroughly investigated. My thesis is an attempt to address this gap in the social history of Kazakhstan
OSKORBLIMENTY (ОСКОРБЛИМЕНТЫ): LINGUISTIC PATTERNS IN BACKHANDED COMPLIMENTS
No full textThe boundary between genuine positive expression and concealed negativity in communication poses challenges for speakers. In the modern world, this distinction is further complicated as most individuals seek to project specific public images, reinforced by societal norms promoting politeness. This capstone project focuses on the relatively unexplored phenomenon of “backhanded compliments,” expressions that are somewhere in between compliments and insults.
My research uses the theoretical framework of im/politeness, referring to Birner’s (1987) term of face, the public self-image that individuals want to have. One’s face can be enhanced, maintained, threatened, or even lost, depending on their actions or the actions of others towards them. According to Birner (2013), all speech acts have a performative nature, so verbal expressions certainly have a potential to harm or enhance one’s face. The use of backhanded compliments particularly allows speakers to maintain their faces while threatening the faces of the hearers. For example, within the the speech act, “You look good today, I did not recognize you!” at least two things are happening: (1) the act threatens the hearer’s face in the way that it hints that the hearer does not look this good usually; (2) this backhanded comment, unlike insults, is “safe” for the utterers, as they can always “shield” themselves saying that they were just complimenting the hearer, thus maintaining their own face. Here, it is essential to understand the terms of illocutionary force and perlocutionary effect (Birner, 2013), where the former means the meaning the speaker puts into their words, and the latter denotes how the hearer interprets and reacts to those words. In the case of backhanded compliments, what a speaker meant to say (illocutionary force), what was actually said (locutionary act), and what a hearer heard (perlocutionary effect) often bear no mutual respondence. In my capstone, I closely study all these three components of backhanded compliments
EXPERIMENTAL INVESTIGATION OF DATA TRANSMISSION USING POWERLINE COMMUNICATION
No full textPowerline Communication (PLC) has emerged as a promising technology for data transmission, offering a cost-effective and versatile solution for various applications. The primary objective of the project is to assess the performance of PLC network through the development of experimental setups using PLC adapters. The study tests PLC under different scenarios in real-world conditions. As a result of this project, conclusions were made whether the performance of PLC network is comparable to that of Ethernet network
COMPUTATIONAL ANALYSIS OF FLUID STRUCTURE INTERACTION (FSI) IN HORIZONTAL AXIS WIND TURBINES (HAWTS)
No full textWind power plays a crucial role in the worldwide shift towards sustainable and renewable
sources of energy. Wind turbine power generation performances made them a widely
adopted method for electricity production, playing a crucial role in the world’s energy
resources. Accordingly, optimizing the wind turbine blade’s design is essential for
increasing wind turbine performance and reducing expenses. The main aim of this capstone
project is to analyze the Fluid Structure Interaction of the HAWTs and to achieve the most
effective design of the turbine, in terms of power generation performance and resource
requirement by optimizing the blades using low fidelity methods.
In engineering and scientific studies, low-fidelity and high-fidelity simulation and
optimization have become common concepts, especially in the field of wind turbine design
and analysis. These concepts are essential in order to study computational structure and
controlling the resource demand, as well as improving the operation of the turbines. This
paper focuses on applying low-fidelity optimization techniques with QBlade, which is a
commonly used open-source software for creating aerodynamic simulations of horizontal axis wind turbines. A low-fidelity simulation can involve simplified fluid dynamics
calculations and simplified structural models, in the context of wind turbine design, in
order to forecast the turbine’s effectiveness. Compared to the high-fidelity simulations, low
fidelity simulations are economically and computationally reasonable. It means that, in the
process of optimization of design parameters of the wind turbine more design alternatives
are available in order to reach the most effective parameters.
Computational Analysis of Fluid-Structure Interaction (FSI) within Horizontal Axis
Wind Turbines (HAWTs) will be studied on the NREL 5MW and NREL Phase VI
turbine. In order to get the optimization results of these wind turbine blades, low fidelity
optimization methods, such as Betz and Schmitz theories will be used
PESTILENCE, PUS AND POWER: RUSSIAN IMPERIAL AND SOVIET RULE IN CENTRAL ASIA OF THE XIX AND XX CENTURIES THROUGH THE PRISM OF EPIDEMICS AND PUBLIC HEALTH
No full textASSESSING THE ROLE OF EBV PROTEINS IN AMYLOID-BETA AGGREGATION ASSOCIATED WITH INDUCTION OF ALZHEIMER'S DISEASE
No full textBackground: Alzheimer’s disease represents the most prevalent form of neurocognitive decline. The key distinguishing pathological markers within the central nervous system involve the aggregation of senile plaques resulting from a two-step cleavage of the amyloid precursor protein by beta- and gamma-secretase enzymes sequentially. Previous studies have demonstrated a positive correlation between individuals who have mononucleosis due to EBV infection and their increased vulnerability to Alzheimer's disease. Hence, a new outlook on the disease etiology known as the "infectious hypothesis" has directed attention toward the Epstein-Barr virus (EBV), a double-stranded DNA virus, in terms of its potential contribution to plaque formation and inflammation associated with Alzheimer's disease.
Methods: H4 neuroglioma and U118 glioblastoma cell lines were directly infected with EBV containing supernatant. The expression of APP and Tau mRNA was detected by qPCR. Protein levels were measured using ELISA with anti-APP antibodies, both before and after viral infection. Virus-free H4 and U118 cell lines were used as controls for comparative statistical analysis of mRNA and protein levels of the APP gene.
Results: Infected U118 cell growth was maintained for more than 20 days, while H4 cells died out after the 7th day post-infection. qPCR results showed a consistent decrease in wild-type APP, APP-KPI, and APP-770 mRNA levels throughout the infection period, while Tau protein exhibited a statistically significant decrease in its expression level. In H4 cells, there was a decrease in WT APP and APP-KPI, while tau protein showed an enhanced mRNA level compared to the control.
Conclusion: Direct EBV infection of glial cells resulted in alterations in the expression of Alzheimer's disease hallmark genes (WT APP, APP-770, APP-751, and Tau) in both H4 and U118 cell lines in a time-dependent manner
ADVANCING BLOOD SAMPLE ANALYSIS: INCORPORATING EXPERT OPINIONS AND EXPLAINABLE AI IN MULTI-LABEL DISEASE PREDICTION
No full textBlood sample analysis plays a crucial role in modern medical practice,
aiding in the detection of a wide array of diseases. Despite its
significance, the potential of blood samples for predicting various
diseases has remained largely unexplored. Our project aimed to dive
into evaluate the efficacy of blood samples in predicting a broad spectrum
of disease using large-scale MIMIC III medical dataset. Given
the sparse nature of the data, we combine imputation with multi-task
models for which we identify and utilize meaningful auxiliary tasks
and are thus able to reach an average state-of-the-art ROC-AUC score
of 81% across the 50 most prevalent diseases within the dataset. To
further validate our findings, we sought the expertise of five medical
doctors, who independently rated the predictability of these diseases
from blood samples. Spearman’s rho analysis revealed a substantial
agreement ( = 0.61) between the doctors’ ratings and the actual ROCAUC
values of our machine learning models. In order to add transparency
and reliability, we employed the Local Interpretable Modelagnostic
Explanations (LIME) method to identify the most predictive
blood sample features. These findings were rigorously cross-checked
with medical experts, affirming the robustness and credibility of our
predictive models. Our study represents a significant advancement in
the field of medical diagnostics, showcasing the untapped potential of
blood sample analysis in disease prediction. By integrating cuttingedge
machine learning techniques with expert validation, we pave the
way for enhanced patient care and improved healthcare outcomes