| 1 |
Author(s):
Cecilia Zheng.
Page No : 1-12
|
Combined Influence of Multiple Factors on Lung Cancer and Anxiety: A Probit Model Analysis
Abstract
Lung cancer remains a leading cause of cancer-related mortality worldwide, significantly impacting public health. Concurrently, anxiety is a prevalent psychological disorder known to influence the development and progression of various cancers. This research paper aims to examine the combined influence of various factors, including demographic characteristics, environmental exposures, and physical conditioning data, on the incidence of lung cancer and anxiety. Given the binary nature of both lung cancer and anxiety outcomes, the analyses will first employ separate binary probit regression models to identify significant predictors for each condition independently. Then, joint modeling techniques will be implemented with dual purposes. First, by comparing the results of the individual and joint models, the reliability and robustness of the findings will be enhanced through cross-validation. Second, joint models will enable an investigation into potential endogeneity between lung cancer and anxiety. Addressing this endogeneity is crucial as it can potentially improve model robustness and provide deeper insights into the interrelationship between these two health outcomes. For the study purpose, the demographic factors considered include age and gender. Environmental factors encompass smoking history, alcohol consumption, and peer pressure. Physical conditioning data includes pre-existing health conditions such as yellow fingers, anxiety, chronic disease, fatigue, allergy, wheezing, coughing, shortness of breath, swallowing difficulty, and chest pain. By leveraging advanced statistical modeling techniques, this research seeks to uncover nuanced relationships and potential causal pathways that may exist between lung cancer and anxiety. The findings from this study will contribute to the existing body of knowledge by providing additional case study showing the relationship among a multitude of factors on lung cancer and anxiety, respectively. Also, the endogeneity checking among lung cancer and anxiety may enhance the efficiency of models done by others in the future.
The research paper's analysis on lung cancer incidences reveals that age and wheezing are significant predictors. In examining anxiety levels, smoking emerged as a significant predictor, indicating a higher likelihood of anxiety among smokers. In addition, the joint probit models confirmed these findings, with age and wheezing significantly predicting lung cancer incidence, and smoking significantly predicting anxiety levels. No significant endogeneity was observed between lung cancer and anxiety, suggesting that these health outcomes are influenced by different sets of factors. These findings underscore the importance of considering demographic, environmental, and physical conditioning data in understanding and addressing lung cancer and anxiety, and checking their potential endogeneity
| 2 |
Author(s):
Muchen Xu.
Page No : 13-24
|
Exploration of Potential Biomarkers for Diagnosing Dementia through Machine Learning Techniques
Abstract
Dementia, marked by cognitive decline and neuropsychiatric symptoms, significantly impacts individuals and society, especially with an aging global population. Despite the need for early diagnosis and intervention, current diagnostic methods are costly and invasive. This study investigates blood-based microRNAs (miRNAs) as non-invasive, cost-effective biomarkers for early dementia diagnosis and subtype differentiation. Using machine learning techniques, we analyzed serum miRNA expression profiles from the Gene Expression Omnibus database (GSE120584), which includes samples from dementia and cognitively normal controls. Our approach involved Support Vector Machine (SVM), Random Forest (RF), Recursive Feature Elimination (RFE), and Neural Networks for feature selection. Logistic Regression was used for classification. Pathway analysis was further performed on the target genes of the identified miRNA biomarkers to explore biological insights behind these biomarkers. We identified miRNAs such as miR-6777-3p, miR-1471, and miR-6806-5p as potential biomarkers for dementia diagnosis and miRNAs like miR-4290 and miR-3184-3p for subtype differentiation. Among the miRNA biomarkers, miR-371b-3p, miR-1539, and miR-4290 are newly discovered biomarkers that have not been mentioned in any studies before. Additionally, this study demonstrates the power of integrating deep learning with traditional machine learning techniques to find new outcomes. This study also reveals the connection between dementia and infectious diseases on a molecular level, providing new therapeutic insights in dementia.
| 3 |
Author(s):
Giselle Haddad.
Page No : 25-31
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Unraveling the Role of TREM2 and CD33 in Alzheimer’s Disease
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by the accumulation of amyloid plaques and neurofibrillary tangles. This study investigates the role of two key genes, TREM2 and CD33, in AD pathogenesis. Gene expression profiles from human brain samples and transgenic mouse models were analyzed using data from the Gene Expression Omnibus. Differential gene expression analysis revealed significant upregulation of TREM2 and CD33 in AD samples compared to controls. Analysis of KEGG pathway, a database composed of biological pathway maps, showed TREM2 involvement in osteoclast differentiation and CD33 in hematopoietic cell lineage. String database analysis highlighted both genes' roles in regulating tumor necrosis factor production and cytokine production. Furthermore, TREM2 variants in AD patients were examined, identifying six variants (H157Y, R98W, D87N, T66M, Y38C, and Q33X) exclusive to AD samples. The R47H variant showed the strongest correlation with AD (p<0.01). The following SNPs of CD33 were identified as contributors to AD development: rs3826656, rs3865444, and rs12459419. These findings suggest that TREM2 and CD33 play crucial roles in AD progression, potentially through modulation of microglial function and inflammatory responses. These genes may serve as promising targets for developing novel AD therapeutics and biomarkers.
| 4 |
Author(s):
Abigail G. Medin.
Page No : 32-42
|
Robbing Students of Future Earnings and America’s Economic Growth: The Lack of Financial Literacy Courses in Public Schools
Abstract
This study evaluates the benefits and opportunities of a high school financial literacy course mandate. Growing American credit card debt and personal bankruptcies have demonstrated a need for financial literacy. This knowledge would empower individuals to have better credit management and budgeting practices, more favorable loans/interest rates, and the ability to grow their wealth. An analysis of interest rate distribution, financial literacy surveys, and the success of other financial education methods have shown the potential return and benefits in the long run for high school graduates of just a single semester course. Data from the Brooking Institute and Harvard Business School has proven that financial education taught at a younger age results in greater economic stability. Therefore, students who take a personal finance course will be less reliant on welfare programs and contribute more to society. With the apparent financial disparities in education and wealth across many groups, high school presents a unique opportunity to address most students before their paths diverge effectively.
| 5 |
Author(s):
Rohan Jha, Rishabh Jha.
Page No : 43-49
|
Identification of Key Impacting Sectors Driving S&P 500 Variation using Statistical Modeling
Abstract
We analyze the Standard and Poor's 500 (S&P 500) variation with all eleven S&P sectors. There
are effectively nine sectors because we combined real estate with financials, and communication with
technology. We performed regression analysis and found that all sectors, except utilities, are statistically
significant predictors of the S&P 500 variation, with p-values less than 0.05. However, the impact of the
technology sector is lower at only ~10% because its impact is strongly correlated with other sectors except
energy and financial. We subsequently used only three independent variables: the technology, energy, and
financials sectors. The regression analysis revealed they are statistically significant with p-values less
than 10
-10 and an R-square greater than 0.98. The technology sector covers over 50% of S&P variations,
the financials sector covers ~35%, and energy comprises the remaining ~15%. These were validated by
taking different frequencies such as monthly and weekly over time spans of the last 20, 15, and 8 years
of data. Thus, we analyzed S&P 500 variability with key sectors like technology, financials, and
energy. Due to economic, market, and technological interconnection, most sectors are related. Financials
offer access to capital and energy is a significant part of the cost across sectors. The energy sector is also
driven by global supply and demand dynamics, geopolitics, and OPEC (Organization of the Petroleum
Exporting Countries) policies
| 6 |
Author(s):
Brinda Avadhanam.
Page No : 50-54
|
A Review: Polycystic Ovary Syndrome and Implications for Infertility
Abstract
Polycystic Ovary Syndrome (PCOS) is a hormonal disorder in which individuals have small cysts that develop on their ovaries, sometimes inhibiting menstruation and ovulation. Individuals with PCOS have excessive levels of androgens, which can cause hormonal imbalances, irregular menstruation, and symptoms of hyperandrogenism. PCOS can also cause infertility since this hormonal imbalance inhibits ovulation in the body. One way to treat infertility is In Vitro Fertilization (IVF), in which the ovaries are stimulated to create multiple egg-containing follicles from which eggs are retrieved, fertilized outside of the body, then inserted into the uterus. Individuals with PCOS are also more likely to develop type 2 diabetes, cardiovascular complications, and endometrial cancer. Some ways of treating PCOS include oral contraceptives to balance hormones, and drugs like metformin to decrease insulin and androgen production. Infertility-specific medications, like clomiphene and follistim, stimulate the production of estrogen and FSH, which thereby increases fertility.
| 7 |
Author(s):
Bitong Sun, Katherine A. Xie.
Page No : 55-60
|
Optimizing CO2 Photoreduction Through Metal-Organic Frameworks: The Impact of Metal Selection and Composite Structures
Abstract
The rising atmospheric carbon dioxide (CO2) concentration poses significant environmental challenges, including global warming and associated climate change. Metal-organic frameworks (MOFs) have emerged as promising materials for addressing and mitigating CO2 levels due to their high surface area, tunable pore sizes, and customizable metal nodes. This review focuses on the influence of metal selection and MOF composites on the efficiency and stability of MOFs in CO2 photoreduction reactions. The findings underscore the potential of MOF-based catalysts in developing sustainable solutions for CO2 reduction, offering a pathway to mitigate environmental impacts while advancing renewable energy technologies.
| 8 |
Author(s):
Sophia Baer.
Page No : 61-74
|
A Comparative Analysis of State Pollinator Protection Laws
Abstract
Various states in the U.S. have passed laws to regulate pesticide use for agriculture and prohibit excessive harm to pollinators. However, current online public information on pollinator protection laws in America is largely lacking. This study establishes an inventory of the state legislation that has banned neonicotinoid pesticides and protected pollinators. The results of this review indicate that only sixteen states have enacted neonicotinoid bans, none of which are complete bans. Sixteen states had no portion of their agricultural website dedicated to pollinator conservation and did not address bees or pollinators at all except when referencing pest control. These same states had no legislation or ban on neonicotinoids. In sixteen states neonicotinoids are labeled as restricted use, however, finding information on neonicotinoid regulation and defining restricted use was difficult. Overall, there is a lack of clarity and consistency across state legislation. There are inconsistent policies across the U.S., but the states that have more progressive policies also are more conscious of the environment and conservation. The implementation of federal legislation would be most effective at unifying the states and creating widespread pollinator conservation.
| 9 |
Author(s):
Xinyun Zhang.
Page No : 75-85
|
The Use of CNN Network In The Generation Of Clear Line Contours of Thangka Art.
Abstract
Thangka has been a crucial aspect of Buddhism for thousands of years. It acts as a visualization of the myths and legends that shaped Buddhism and attracted many devotees to worship. Furthermore, Thangka also serves as a reflection of the cultural, political and social aspect of Tibetan society in history. However the paintings, especially the outlines, became extremely fragile after years of harsh environments. We utilized Deep learning, specifically the Convolutional neural network (CNN) as our main method for processing data. After numerous training the training loss gradually approaches 0, which means that the model no longer needs additional training. Under the CNN network we used max pooling which discards trivial information, rectified linear (ReLU) activation function, residential Network (ResNet), and other methods in our research to achieve a clear outline of Thangka. Through episodes of convolution and ReLU function, the image generated becomes gradually more defined in layer 1. However, as the image becomes processed continuously, it becomes gradually more abstract. Thus, the best results could be found in layer 1. Our study aims to use the results from the CNN network convolution to aid those who are studying the Thangka art in distinguishing the outlines of the image.
| 10 |
Author(s):
Camylla Morell.
Page No : 86-90
|
Exploring the Effect of Music on Focus and Attention
Abstract
The impact music has on focus and memory have been studied with a number of different factors. With the arrival of Mozart Theory and other hypotheses, this topic has been seen from a different angle, that is an indirect effect. In this paper, we will discuss the impact that different types of music have on various factors such as memory, attention, focus, and cognitive performance. We expected to find that the lyrics and rhythm of the song are the most impactful part of the song. Secondly, we will get this information by reviewing different studies related to the topic. This literature review revealed the varying effects that music has on memory and focus while studying. Furthermore, this variability is largely attributed to the tempo and presence of lyrics. As a result, some studies show that music is linked to the prevention of mind wandering and can indirectly help focus by increasing mood. In contrast, another study reported that music in general, especially with lyrics is detrimental to attention and focus.
| 11 |
Author(s):
Julia Shante Petri, Emilian Christopher Stamatopoulos.
Page No : 91-99
|
Recent Advances in Induced Pluripotent Stem Cells-based Hypertrophic Cardiomyopathy Study
Abstract
Hypertrophic cardiomyopathy (HCM) is a heritable cardiovascular disorder marked by abnormal thickening of the heart muscle, leading to left ventricular outflow tract obstruction and compromised blood flow. It is associated with a range of symptoms including arrhythmias, chest pain, and sudden cardiac death, with no current effective treatments available. The pathogenic mechanisms underlying HCM remain elusive and induced pluripotent stem cells (iPSCs) reprogrammed from patient somatic cells have emerged as a powerful tool for uncovering pathogenic mechanisms of HCM. This review examines recent advancements in the iPSC-based HCM modeling and highlights how isogenic iPSCs generated by Crispr/Cas9 overcome genetic background variability associated with the traditional iPSC-HCM models. Isogenic iPSCs not only enhance our understanding of single-causative genetic mechanisms of HCM but also will offer potential insights into complex cases involving multiple genetic mutations in the future.
| 12 |
Author(s):
Dongyang Yu.
Page No : 100-104
|
Exploring The Intersection of Geometry and Ballet
Abstract
Ballet originated in the courts of the Italian Renaissance in the 15th century is still a popular dance art today. It is characterized by diversity, and the expression of emotion varies from situation to situation. Geometry plays a crucial role in ballet formation, human form, and movement proportions. This paper focuses on the relationship between ballet and geometry, deeply discusses how geometric principles such as line, triangle, circle, symmetry and asymmetry are integrated to ballet choreography and performance. By analyzing ballets such as Swan Lake and Giselle, this paper highlights how dancers use geometric concepts to create visually compelling formations that emphasize harmony and unity. These geometric elements are shown to enhance the precision, beauty, and storytelling in ballet performances.
| 13 |
Author(s):
Emma T. Wen.
Page No : 105-111
|
Is Artificial Intelligence Creative?
Abstract
Artificial Intelligence (AI) has been quickly popularized within the past few years, leading to a conversation on whether or not AI are creative. Human creativity is multifaceted, with many theories focusing on specific aspects of creativity with the measurement evaluating the originality and usefulness of ideas. AI generates its output by imitating and learning from data that it is given, which leads to problems within the product. Yet, AI does perform well at select creativity tests. We discuss various creativity theories and tests within humans and AI, and find that AI’s lack of key factors, such as intentionality, differentiates it from human creativity. We argue that AI doesn’t exhibit true creativity and instead only emulates it.
| 14 |
Author(s):
Junting Zhao, Richard Qiang Hao.
Page No : 115-122
|
Recent Advances in Maturation of Induced pluripotent Stem Cells-derived Cardiomyocytes
Abstract
Induced pluripotent stem cells -derived cardiomyocytes (iPSC-CMs) increasingly become an important tool for studying genetic heart diseases, drug screening and heart cell therapy. However, iPSC-CMs differentiated by the traditional ways are immature and the challenge of producing fully matured iPSC-CMs that functionally resemble adult cardiomyocytes persists. Immature iPSC-CMs exhibit characteristics typical of fetal-stage cells, including less developed contractility, electrophysiological properties, and metabolic processes. These limitations restrict their clinical applications, especially in regenerative medicine. Over the past decade, numerous strategies have been developed to enhance the maturation of iPSC-CMs. These include prolonged culture periods, biochemical induction using specific hormones and energy substrates, and the application of biophysical stimuli such as electrical and mechanical stimulation. In addition, advances in biomaterials, such as extracellular matrices and hydrogels, have been crucial in replicating the physiological environment needed to support iPSC-CM development. Together, these methods aim to produce cardiomyocytes that more closely mimic adult heart cells in structure and function. This review explores recent progress in optimizing the maturation of iPSC-CMs and highlights their potential impact on future applications in cell therapy and heart disease modeling.
| 15 |
Author(s):
Xiaochuan Liang.
Page No : 123-131
|
A Study of Applying Pix2Pix to Reduce the Cost and Damage of Radiotherapy of Cancerous Livers
Abstract
Radiation therapy is widely used to treat cancerous cells; however, it is costly and has significant
side effects if not properly used. During treatment, some organs, such as the liver, may shift slightly due
to the patient’s respiration, making accurate real-time tracking and contouring essential to minimize
damage to healthy tissues. Current tracking systems usually include precise real-time positioning and
contouring of the treatment area, which require costly imaging technologies to run throughout the
entire therapy session, increasing the overall treatment cost and limiting the availability of advanced
equipment like MRIs to hospitals. This research studies the application of Pix2Pix, a Conditional
Generative Adversarial Network (GAN), as an alternative to traditional contouring methods in
liver cancer radiotherapy. The result shows that a well-trained Pix2Pix can help accurately track
liver movement during respiration. With AI computing power becoming increasingly affordable, I
anticipate that GANs like Pix2Pix can be industrialized to make cancer treatment more accessible
and cost-effective.
| 16 |
Author(s):
eJenn Huang.
Page No : 132-144
|
The Impact of Mental Health on Academic Performance: Comparative Insights from Original and Simulated Data
Abstract
Mental health challenges, including depression, anxiety, and stress, are increasingly affecting
students worldwide, with significant implications for academic performance. This study investigates
the relationship between mental health and academic success, specifically focusing on CGPA
(Cumulative GPA), while considering demographic and school-related variables such as age, gender,
course, and year of study. The data utilized for this research originates from the Kaggle “Student
Mental Health” dataset, consisting of 101 student responses. To address the limitation of a small
sample size, the dataset was expanded to 10,000 entries using bootstrapping and numeric perturbation.
Various statistical methods, including ANOVA, Chi-Square tests, multinomial logistic regression, and
random forest feature importance rankings, were applied to both the original and simulated datasets.
The results indicate that age has no significant effect on CGPA, while mental health variables such as
depression and treatment exhibit significant associations with academic performance in the original
dataset. The simulated dataset, however, showed exaggerated relationships, emphasizing the need for
careful validation when using simulated data. Feature importance rankings identified “Course” and
“Current Year” as the most critical predictors of CGPA, with mental health variables ranking lower.
These findings highlight the complex interplay between mental health and academic performance
and call for enhanced mental health support within educational systems.
| 17 |
Author(s):
Nathan Ma.
Page No : 145-154
|
Exploring Gender-Based Differences in Banking Transactions: A Data-Driven Analysis Using Multiple Linear Models and Permutation Feature Importance
Abstract
This study examines gender-based differences in financial behavior, focusing on various bank
transactions and demographic factors using a large dataset from Kaggle, consisting of over one million
entries. By applying multiple linear regression models and permutation feature importance rankings,
the study explores how different variables, such as age, geographic location, and transactional timing,
influence banking patterns across genders. The findings reveal that age is a significant predictor
of transaction behavior for males, while it is less impactful for females. Geographic location,
particularly “Location_West” and “Location_Other,” plays a crucial role for males but has minimal
influence on females. For females, transactional timing, indicated by “TransactionHour,” shows
more importance in predicting banking behaviors. In contrast, gender itself does not significantly
affect transaction outcomes when controlling for other variables. Overall, the study highlights the
importance of demographic and contextual factors, such as age and geography, over inherent genderbased differences. These insights provide valuable guidance for financial institutions aiming to tailor
their services more effectively to meet the needs of diverse customer segments. The results emphasize
the need for data-driven approaches to better understand gender-specific financial behavior and
improve service offerings.
| 18 |
Author(s):
Joshua Lin.
Page No : 155-160
|
A Comparative Analysis of HBOT and L-HBOT: An Innovative Possibility to Enhance HBOT?
Abstract
Oxygen is a vital component of wound healing. When trauma occurs, causing a wound, it is
difficult to get oxygen to the wound site because of the damage that is present. Hyperbaric oxygen
chamber therapy (HBOT) is employed to combat hypoxia in wounds by having the patient inhale high
concentrations of oxygen at elevated atmospheric pressures, accelerating the rate at which oxygen
dissolves into the bloodstream at a faster rate. HBOT has been used to treat a variety of conditions
like diabetic foot ulcers, Crohn’s disease, and chronic wounds. Yet, the limited body of literature and
the risk of complications have limited the application of HBOT. Medical professionals have proven
the significance of oxygen on wound healing from stimulating most of the vital functions to initiating
the production of certain genes, oxygen is the keystone for successful complete wound healing. On
the other hand, the risk of oxygen toxicity is one of the main contraindications of HBOT. Contrary
to common knowledge, recent studies have shown that HBOT administered at a lower atmospheric
pressure with a lower oxygen concentration can decrease this risk significantly. Low-pressure
hyperbaric oxygen therapy (L-HBOT) may still be as effective as traditional high-pressure hyperbaric
oxygen therapy (H-HBOT) without the unpredictable complications that medical professionals are
unsure of. In all, this review aims to analyze recent studies to provide a clear comparison between
these two variations of HBOT.
| 19 |
Author(s):
Anika Pallapothu.
Page No : 161-171
|
Comparative Analysis of Gender Bias in Text-Based and Audio-Based NLP Models: Insights from Asian Linguistic and Cultural Contexts
Abstract
This study examines gender biases in Natural Language Processing (NLP) models, focusing on
text-based and audio-based systems within Asian linguistic and cultural contexts. It highlights how
gender roles and cultural norms in Asian backgrounds influence these biases, using examples like
Google Translate, Siri, and Alexa. The research focuses on analyzing datasets that reflect Asian
languages and cultural norms, examining how gender roles, stereotypes, and historical patterns
manifest in NLP models. The study employed comprehensive strategies, including analyzing word
embeddings and model outputs. This helps identify stereotypes linking gender to certain professional
traits, particularly in text-based models. It also examines the performance of audio-based NLP
models in speech recognition, voice commands, and interpretation, highlighting accuracy issues,
especially for profiles that deviate from standard demographics in the training data. The study
analyzes word embeddings and model outputs to identify gender-related stereotypes in professional
traits, highlighting persistent biases, especially in speech recognition models with lower accuracy for
non-standard demographics. The findings suggest the need for strategies to curb biases and ensure
equitable NLP outcomes that promote inclusion and diversity among users. The research is vital
for NLP developers, scholars, and AI teams, as it explores text- and audio-based models, revealing
findings that help reduce biases and promote equity in AI language systems.
| 20 |
Author(s):
Justin Yeh.
Page No : 172-185
|
The Correlation Between the Development of Renewable Energy, Non-Renewable Energy, and Economic Growth in Taiwan
Abstract
This paper aims to explore the correlation between the development of renewable energy and
economic growth in Taiwan along with the correlation between the development of non-renewable
energy and economic growth. I analyzed Taiwan’s current energy situation and understand the energy
problems that Taiwan faces, and discussed policies that impact Taiwan’s economy, energy production,
and consumption. I found that Taiwan could achieve stronger GDP growth through increased
employment, investments, consumer spending, and technological advancements of renewable energy
production rather than non-renewable energy production. I found a correlation of 0.902 between
Taiwan’s GDP and renewable energy production, as opposed to a correlation of -0.0655 between nonrenewable energy production and Taiwan’s GDP. This high correlation between the production of
renewable energy and GDP growth in Taiwan confirms that the nation should increase its investment
in renewable energy production rather than non-renewable energy production. By comprehending
the current situations and current policies, this paper attempts to provide an explanation of Taiwan’s
energy issues, assist future energy policymakers, bring awareness to Taiwan’s energy development,
promote sustainability goals, and provide a general background of Taiwan’s energy development.
This paper also aims to provide well-rounded research to help investors make informed decisions
about renewable energy.
| 21 |
Author(s):
Diya Sammanna, Ashley N. Pearson.
Page No : 186-195
|
The Relationship Between CD8 T Cells and HPV-Positive Head and Neck Cancer
Abstract
HPV-specific head and neck cancer (HNSCC) develops secondary to infection with the human
papillomavirus (HPV). HNSCC mostly affects younger and older age groups who live in North
America and Europe. T cells play a crucial role in identifying and killing cancer cells in an antigenspecific manner, but cancer cells often evade this natural immunologic detection system. Importantly,
the impact of antigen-specific differentiation of T cells in HNSCC is unknown. In this paper, we
hypothesized that the location of the tumor and expression of the marker PD-1 is associated with
stronger T cell effector functions and T cell activation. To test this hypothesis, we analyzed single cell
RNA sequencing (scRNA-seq) for expression of gene programs associated with immunosuppression
and T cell effector functions. We found that the lymph node had an increased expression of
immunosuppressive genes and that PD-1+ T cells had increased expression of genes associated with
effector functions. This work suggests that researchers should focus on improving T cell activation in
the lymph node to enhance the anti-tumor immune response in HNSCC.