Matching Items (182)
Description
The objective of this meta-analysis is to holistically evaluate the existing body of literature on the anti-neoplastic potential of snake and bee venom. In recent years, venom-based therapeutics have emerged as a promising solution for combating cancer, generating a notable rise in publications on the topic. Consequently, this comprehensive study

The objective of this meta-analysis is to holistically evaluate the existing body of literature on the anti-neoplastic potential of snake and bee venom. In recent years, venom-based therapeutics have emerged as a promising solution for combating cancer, generating a notable rise in publications on the topic. Consequently, this comprehensive study aims to assess the current state of research and identify trends that may guide future investigations. Following the guidelines established by PRISMA, a total sample of 26 research papers were extracted from the electronic databases, PubMed and Scopus. These papers were categorized based on their publication dates, and research questions were formulated regarding three main topics: venom type, cancer-targeting mechanism, and cancer type. Statistical analysis of the research questions was performed using 2x2 contingency tables for a chi-square test. The results of the analysis reveal a statistically significant increase in publications focused on cell death mechanisms and breast cancer in recent years. This increase in publications reflects a growing interest in the potential for venom to induce apoptosis in cancer cells and target the unique biological properties of breast cancer. Overall, this meta-analysis offers insight into the evolving sphere of venom-based cancer research, providing a glimpse into the potential trajectory of this field.
ContributorsHolder, Marina (Author) / Amdam, Gro (Thesis director) / Mana, Miyeko (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / Economics Program in CLAS (Contributor)
Created2023-12
Description
Weight stigma is a prevalent issue that has detrimental effects on health for both adolescents and parents. Adolescents are in a formative stage of life, so it is important to understand how parents may impact adolescents’ own experience with weight stigma. Past research has examined adolescent coping, body image, and

Weight stigma is a prevalent issue that has detrimental effects on health for both adolescents and parents. Adolescents are in a formative stage of life, so it is important to understand how parents may impact adolescents’ own experience with weight stigma. Past research has examined adolescent coping, body image, and associated stigma in the context of the parent-child relationship. This cross-sectional study examined self-reported weight stigma experience and internalization within 42 parent/adolescent dyads to provide greater understanding of how adolescents and parents are experiencing and internalizing weight stigma independently and transversely.
ContributorsMillett, Emma (Author) / McEntee, Mindy (Thesis director) / Adams, Marc (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2022-12
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Description
A remarkable phenomenon in contemporary physics is quantum scarring in classically chaoticsystems, where the wave functions tend to concentrate on classical periodic orbits. Quantum scarring has been studied for more than four decades, but the problem of efficiently detecting quantum scars has remained to be challenging, relying mostly on human visualization of wave

A remarkable phenomenon in contemporary physics is quantum scarring in classically chaoticsystems, where the wave functions tend to concentrate on classical periodic orbits. Quantum scarring has been studied for more than four decades, but the problem of efficiently detecting quantum scars has remained to be challenging, relying mostly on human visualization of wave function patterns. This paper develops a machine learning approach to detecting quantum scars in an automated and highly efficient manner. In particular, this paper exploits Meta learning. The first step is to construct a few-shot classification algorithm, under the requirement that the one-shot classification accuracy be larger than 90%. Then propose a scheme based on a combination of neural networks to improve the accuracy. This paper shows that the machine learning scheme can find the correct quantum scars from thousands images of wave functions, without any human intervention, regardless of the symmetry of the underlying classical system. This will be the first application of Meta learning to quantum systems. Interacting spin networks are fundamental to quantum computing. Data-based tomography oftime-independent spin networks has been achieved, but an open challenge is to ascertain the structures of time-dependent spin networks using time series measurements taken locally from a small subset of the spins. Physically, the dynamical evolution of a spin network under time-dependent driving or perturbation is described by the Heisenberg equation of motion. Motivated by this basic fact, this paper articulates a physics-enhanced machine learning framework whose core is Heisenberg neural networks. This paper demonstrates that, from local measurements, not only the local Hamiltonian can be recovered but the Hamiltonian reflecting the interacting structure of the whole system can also be faithfully reconstructed. Using Heisenberg neural machine on spin networks of a variety of structures. In the extreme case where measurements are taken from only one spin, the achieved tomography fidelity values can reach about 90%. The developed machine learning framework is applicable to any time-dependent systems whose quantum dynamical evolution is governed by the Heisenberg equation of motion.
ContributorsHan, Chendi (Author) / Lai, Ying-Cheng (Thesis advisor) / Yu, Hongbin (Committee member) / Dasarathy, Gautam (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2022
Description

Insect pheromones are crucial for survival and reproduction because they influence insect behavior, communication, and interactions within and outside the colony. Honey bees (Apis mellifera) have one of the most complex pheromonal communication systems. One pheromone, known as Queen Mandibular Pheromone (QMP), is released by the queen bee to regulate

Insect pheromones are crucial for survival and reproduction because they influence insect behavior, communication, and interactions within and outside the colony. Honey bees (Apis mellifera) have one of the most complex pheromonal communication systems. One pheromone, known as Queen Mandibular Pheromone (QMP), is released by the queen bee to regulate physiology, behavior, and gene expression in the female worker caste. The pheromone acts as a signal of queen presence that suppresses worker reproduction. In the absence of reproduction, young workers focus on taking care of the queen and larvae, known as nurse tasks, while older workers forage. In nurse bees, QMP has fundamental physiological impacts, including increasing abdominal lipid stores and increasing the protein content of hypopharyngeal glands (HPG). The HPG are worker-specific glands that can synthesize royal jelly used in colony nourishment. In workers, larger HPG signifies the ability to secrete royal jelly, while shrunken glands are characteristic of foragers that do not make jelly. While it is known that QMP increases abdominal lipid stores, the underlying mechanism is unclear: Does the pheromone simply make workers consume more pollen which provides lipids and protein, or does QMP also increase lipogenesis? In this study, I measured abdominal lipogenesis as fatty acid synthase (FAS) activity and monitored abdominal protein content and HPG size in caged, nurse-aged worker bees. In cages, workers were exposed to QMP or not, and they were provided with a lipid less diet in a full factorial design experiment. I found that QMP did not influence abdominal FAS activity or protein, but significantly increased HPG size. The data also revealed a significant positive correlation between abdominal protein and HPG size. My results do not support the idea that QMP modulates lipogenesis in worker bees, but my data can be interpreted to reflect that QMP mobilizes abdominal protein for the production of jelly in the HPG. This finding is in line with a previous study revealing a role of honey bee Brood Pheromone in mobilization of a major protein used in jelly production. Overall, my results support a fundamental role of QMP in worker metabolic processes associated with colony nourishment.

ContributorsOreshkova, Angela (Author) / Amdam, Gro (Thesis director) / Scofield, Sebastian (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor) / School of Life Sciences (Contributor)
Created2023-05
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Description
Few-layer black phosphorous (FLBP) is one of the most important two-dimensional (2D) materials due to its strongly layer-dependent quantized bandstructure, which leads to wavelength-tunable optical and electrical properties. This thesis focuses on the preparation of stable, high-quality FLBP, the characterization of its optical properties, and device applications.Part I presents an

Few-layer black phosphorous (FLBP) is one of the most important two-dimensional (2D) materials due to its strongly layer-dependent quantized bandstructure, which leads to wavelength-tunable optical and electrical properties. This thesis focuses on the preparation of stable, high-quality FLBP, the characterization of its optical properties, and device applications.Part I presents an approach to preparing high-quality, stable FLBP samples by combining O2 plasma etching, boron nitride (BN) sandwiching, and subsequent rapid thermal annealing (RTA). Such a strategy has successfully produced FLBP samples with a record-long lifetime, with 80% of photoluminescence (PL) intensity remaining after 7 months. The improved material quality of FLBP allows the establishment of a more definitive relationship between the layer number and PL energies. Part II presents the study of oxygen incorporation in FLBP. The natural oxidation formed in the air environment is dominated by the formation of interstitial oxygen and dangling oxygen. By the real-time PL and Raman spectroscopy, it is found that continuous laser excitation breaks the bonds of interstitial oxygen, and free oxygen atoms can diffuse around or form dangling oxygen under low heat. RTA at 450 °C can turn the interstitial oxygen into dangling oxygen more thoroughly. Such oxygen-containing samples show similar optical properties to the pristine BP samples. The bandgap of such FLBP samples increases with the concentration of the incorporated oxygen. Part III deals with the investigation of emission natures of the prepared samples. The power- and temperature-dependent measurements demonstrate that PL emissions are dominated by excitons and trions, with a combined percentage larger than 80% at room temperature. Such measurements allow the determination of trion and exciton binding energies of 2-, 3-, and 4-layer BP, with values around 33, 23, 15 meV for trions and 297, 276, 179 meV for excitons at 77K, respectively. Part IV presents the initial exploration of device applications of such FLBP samples. The coupling between photonic crystal cavity (PCC) modes and FLBP's emission is realized by integrating the prepared sandwich structure onto 2D PCC. Electroluminescence has also been achieved by integrating such materials onto interdigital electrodes driven by alternating electric fields.
ContributorsLi, Dongying (Author) / Ning, Cun-Zheng (Thesis advisor) / Vasileska, Dragica (Committee member) / Lai, Ying-Cheng (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2022
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Description

In this research, I surveyed existing methods of characterizing Epilepsy from Electroencephalogram (EEG) data, including the Random Forest algorithm, which was claimed by many researchers to be the most effective at detecting epileptic seizures [7]. I observed that although many papers claimed a detection of >99% using Random Forest, it

In this research, I surveyed existing methods of characterizing Epilepsy from Electroencephalogram (EEG) data, including the Random Forest algorithm, which was claimed by many researchers to be the most effective at detecting epileptic seizures [7]. I observed that although many papers claimed a detection of >99% using Random Forest, it was not specified “when” the detection was declared within the 23.6 second interval of the seizure event. In this research, I created a time-series procedure to detect the seizure as early as possible within the 23.6 second epileptic seizure window and found that the detection is effective (> 92%) as early as the first few seconds of the epileptic episode. I intend to use this research as a stepping stone towards my upcoming Masters thesis research where I plan to expand the time-series detection mechanism to the pre-ictal stage, which will require a different dataset.

ContributorsBou-Ghazale, Carine (Author) / Lai, Ying-Cheng (Thesis director) / Berisha, Visar (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2022-05
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Description
Predicting nonlinear dynamical systems has been a long-standing challenge in science. This field is currently witnessing a revolution with the advent of machine learning methods. Concurrently, the analysis of dynamics in various nonlinear complex systems continues to be crucial. Guided by these directions, I conduct the following studies. Predicting critical

Predicting nonlinear dynamical systems has been a long-standing challenge in science. This field is currently witnessing a revolution with the advent of machine learning methods. Concurrently, the analysis of dynamics in various nonlinear complex systems continues to be crucial. Guided by these directions, I conduct the following studies. Predicting critical transitions and transient states in nonlinear dynamics is a complex problem. I developed a solution called parameter-aware reservoir computing, which uses machine learning to track how system dynamics change with a driving parameter. I show that the transition point can be accurately predicted while trained in a sustained functioning regime before the transition. Notably, it can also predict if the system will enter a transient state, the distribution of transient lifetimes, and their average before a final collapse, which are crucial for management. I introduce a machine-learning-based digital twin for monitoring and predicting the evolution of externally driven nonlinear dynamical systems, where reservoir computing is exploited. Extensive tests on various models, encompassing optics, ecology, and climate, verify the approach’s effectiveness. The digital twins can extrapolate unknown system dynamics, continually forecast and monitor under non-stationary external driving, infer hidden variables, adapt to different driving waveforms, and extrapolate bifurcation behaviors across varying system sizes. Integrating engineered gene circuits into host cells poses a significant challenge in synthetic biology due to circuit-host interactions, such as growth feedback. I conducted systematic studies on hundreds of circuit structures exhibiting various functionalities, and identified a comprehensive categorization of growth-induced failures. I discerned three dynamical mechanisms behind these circuit failures. Moreover, my comprehensive computations reveal a scaling law between the circuit robustness and the intensity of growth feedback. A class of circuits with optimal robustness is also identified. Chimera states, a phenomenon of symmetry-breaking in oscillator networks, traditionally have transient lifetimes that grow exponentially with system size. However, my research on high-dimensional oscillators leads to the discovery of ’short-lived’ chimera states. Their lifetime increases logarithmically with system size and decreases logarithmically with random perturbations, indicating a unique fragility. To understand these states, I use a transverse stability analysis supported by simulations.
ContributorsKong, Lingwei (Author) / Lai, Ying-Cheng (Thesis advisor) / Tian, Xiaojun (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Alkhateeb, Ahmed (Committee member) / Arizona State University (Publisher)
Created2023
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Description
Background: Studies have examined student fruit/vegetable (FV) consumption, selection, and waste related to lunch duration and found that longer duration at lunch was associated with greater consumption, selection, and reduced waste. However, few studies have investigated the relationship between time to eat and FVs. The aim of this research is

Background: Studies have examined student fruit/vegetable (FV) consumption, selection, and waste related to lunch duration and found that longer duration at lunch was associated with greater consumption, selection, and reduced waste. However, few studies have investigated the relationship between time to eat and FVs. The aim of this research is to analyze the relationship between objective time to students took to eat (“time to eat”) as it relates to their fruit and vegetable consumption, selection, and plate waste.in elementary, middle, and high schools. Methods: A secondary analysis of cross-sectional study of 37 Arizona schools to discover the differences in the selection, consumption, and waste of FVs from students (Full N = 2226, Elementary N = 630, Middle School N = 699, High School N = 897) using objective time to eat measures. Zero-inflated negative binomial regressions examined differences in FV grams selected, consumed, and wasted adjusted for sociodemographics including race, ethnicity, eligibility for free or reduced lunch, academic year, and sex and clustering for students within schools. Results are presented across school level (elementary, middle, and high school). Results: The average time taken to eat ranged from 10-12 minutes for all students. The association of time to eat and lunch duration were not closely related (r=0.03, p = 0.172). In the count model for every additional minute spent, there was a 0.5% greater likelihood of selecting FVs for elementary kids among those who took any FVs. In the zero-inflated model, it was found that there was a statistically significant relationship between time spent eating and the selection of fruits and vegetables. For the total sample and high schoolers, a minute more of eating time was associated with a 4.3% and 8.8% greater odds of selecting FV. This means that longer eating time increased the likelihood of choosing fruits and vegetables. The results indicated that the longer students took to eat, the higher the likelihood of consuming more of FVs. Each 10 more minutes spent eating (i.e., time to eat) is associated with a 5% increase in grams of FV selected relative to mean (for those that chose FV) over 1 week this equates to 32 g increase of FV selected. However, for middle schoolers, the time to eat was not found to be significant in relation to the grams of fruits and vegetables consumed. There was some significance in the sociodemographic factors such as gender (all) and other (middle school). There was a relationship between time taken to eat and waste as a proportion for fruits and vegetables. For example, among those among the students who wasted something (as a proportion of selection), each additional 10 minutes of eating time was associated with a .6% decrease in waste relative to the mean (for those who chose fruits and vegetables) over a week, resulting in a decrease in waste percentage of 16.5%. Among high schoolers, males had a slightly higher odds of wasting a proportion of fruits and vegetables. Conclusions: This study aimed to examine the association between the time students take to eat during lunch and their fruit and vegetable (FV) consumption, selection, and plate waste. The findings revealed that the time to eat was related to FV consumption, depending on the school level. However, it was not significantly associated with FV selection or waste. The study emphasized the need for further research on time to eat, distinguishing it from the duration of lunch. Longer lunch periods and adequate time could influence better food choices, increased FV consumption, and reduced waste. The study highlighted the importance of interventions and school policies promoting healthier food choices and providing sufficient time for students to eat. Future research should validate these findings and explore the impact of socialization opportunities on promoting healthier eating habits. Understanding the relationship between lunch duration, time to eat, and students' dietary behaviors can contribute to improved health outcomes and inform effective strategies in school settings.
ContributorsDandridge, Christina Marie (Author) / Adams, Marc (Thesis advisor) / Whisner, Corrie (Committee member) / Bruening, Meg (Committee member) / Arizona State University (Publisher)
Created2023
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Description
A notable challenge when assembling synthetic gene circuits is that modularity often fails to function as intended. A crucial underlying reason for this modularity failure is the existence of competition for shared and limited gene expression resources. By designing a synthetic cascading bistable switches (Syn-CBS) circuit in a single strain

A notable challenge when assembling synthetic gene circuits is that modularity often fails to function as intended. A crucial underlying reason for this modularity failure is the existence of competition for shared and limited gene expression resources. By designing a synthetic cascading bistable switches (Syn-CBS) circuit in a single strain with two coupled self-activation modules to achieve successive cell fate transitions, nonlinear resource competition within synthetic gene circuits is unveiled. However, in vivo it can be seen that the transition path was redirected with the activation of one switch always prevailing over that of the other, contradictory to coactivation theoretically expected. This behavior is a result of resource competition between genes and follows a ‘winner-takes-all’ rule, where the winner is determined by the relative connection strength between the two modules. Despite investigation demonstrating that resource competition between gene modules can significantly alter circuit deterministic behaviors, how resource competition contributes to gene expression noise and how this noise can be controlled is still an open issue of fundamental importance in systems biology and biological physics. By utilizing a two-gene circuit, the effects of resource competition on protein expression noise levels can be closely studied. A surprising double-edged role is discovered: the competition for these resources decreases noise while the constraint on resource availability adds its own term of noise into the system, denoted “resource competitive” noise. Noise reduction effects are then studied using orthogonal resources. Results indicate that orthogonal resources are a good strategy for eliminating the contribution of resource competition to gene expression noise. Noise propagation through a cascading circuit has been considered without resource competition. It has been noted that the noise from upstream genes can be transmitted downstream. However, resource competition’s effects on this cascading noise have yet to be studied. When studied, it is found that resource competition can induce stochastic state switching and perturb noise propagation. Orthogonal resources can remove some of the resource competitive behavior and allow for a system with less noise.
ContributorsGoetz, Hanah Elizabeth (Author) / Tian, Xiaojun (Thesis advisor) / Wang, Xiao (Committee member) / Lai, Ying-Cheng (Committee member) / Arizona State University (Publisher)
Created2022
Description

Maternal morbidity and mortality rates in the United States continues to rise, with a wide range of contributing factors such as mental illness, cardiovascular disease and systemic inequality. This metastudy provides a holistic view of the research that has been published on the issue of U.S. maternal healthcare from 2000-2022.

Maternal morbidity and mortality rates in the United States continues to rise, with a wide range of contributing factors such as mental illness, cardiovascular disease and systemic inequality. This metastudy provides a holistic view of the research that has been published on the issue of U.S. maternal healthcare from 2000-2022. The patterns of publications on specific topics over time can tell us what is perceived as a current major cause by physicians, public leaders, researchers, and the public. A deeper dive into systemic inequality as a cause of maternal morbidity and mortality highlights it as a major contributor to these high rates, but that progress is slowly being made through the implementation of detection and prevention tactics, as well as accessible prenatal programs and care.

ContributorsRettig, Lelia (Author) / Amdam, Gro (Thesis director) / Bang, Christofer (Committee member) / Barrett, The Honors College (Contributor) / School of Human Evolution & Social Change (Contributor) / School of Life Sciences (Contributor)
Created2023-05