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Anishka Vissamsetty - Cryptojacking Detection: A Novel Two-Step Approach Integrating Machine Learning and DNS Monitoring
The rapid emergence of cryptocurrencies and blockchain technology has introduced not only innovative opportunities, but also cybersecurity challenges. One such threat is cryptojacking, in which cybercriminals exploit victims' computing power for unauthorized cryptocurrency mining. This study proposes a novel two-step approach for efficient cryptojacking detection: the first step employs machine learning to identify fluctuations in CPU and memory utilization, with a focus on accurate detection by considering network traffic in the second step. DNS logs are analyzed to identify new domains during CPU spikes, cross-referenced against a curated dataset. The proposed model achieved an accuracy of 89% using gradient-boosted decision trees on real-world data. This fusion of machine learning and network monitoring provides a potent defense against the growing menace of cryptojacking, with practical applications spanning corporate environments, cloud infrastructure, and personal devices.
Aarohi Sonputri - Small Molecule Stabilization of the CARD11 G-quadruplex Represses Transcription: Developing a Therapeutic Target for Diffuse Large B Cell Lymphoma
Diffuse large B cell lymphoma (DLBCL) involves abnormal B cell growth in the lymphatic system. Abnormalities such as recurrent genetic mutations cause critical components of the BCR signaling pathway to be overactive. This constitutes an oncogenic defect that drives uncontrolled B cell growth. Caspase recruitment domain-containing protein 11 (CARD11) is a critical BCR pathway scaffold protein and its recurrent gain-of-function mutations are frequently found in DLBCL. This study aims to investigate the potential of CARD11 gene silencing as a therapeutic for DLBCL treatment by targeting the DNA secondary structure- G-quadruplexes (G4s) formed within the gene, as G4s usually act as physical barriers to gene expression. Using circular dichroism (CD), stable G4’s were identified within the highly guanine-rich promoter region. Small molecules were screened using a fluorescence-resonance energy transfer (FRET) assay to identify compounds that stabilize G4 structures. To quantify the effects of G4 stabilization on gene expression, qPCR was then used to determine that stabilization of G4s led to repression of transcription and subsequent reduction in mRNA levels of the oncogene CARD11 and a few others,, which are crucial for DLBCL progression These findings highlight that stabilizing the G-quadruplex structures formed in the CARD11 promoter region could inhibit DLBCL growth by silencing CARD11 gene expression and downstream oncogenic signals in the BCR pathway.
Judy Zhao - Naringenin’s Mitigation of Rotenone-Induced Cytotoxicity: a Potential New Candidate for Treating Parkinson’s Disease
In the past five years, the prevalence of Parkinson's disease (PD) has increased by over 50% with an estimated 10 million people affected worldwide. Alpha-synuclein (αS) expression is a major hallmark of PD and has been linked to the downregulation of mitochondrial complex 1, neuronal death, and sporadic PD. Recent research has focused on polyphenols as potential targets for developing new treatments for PD because of their antioxidant properties. This study investigates the potential of the polyphenol naringenin to decrease αS expression, which would increase dopamine release in rotenone-induced PD. We tested naringenin against rotenone-induced cytotoxicity through caspase assays, colorimetric assays, and ELISA assays. We found that naringenin was able to mitigate both caspase activity and cytotoxicity in HTB-11 and U937 cells, representing neuronal and immune cells respectively. These findings suggest that naringenin has a protective effect on cells challenged by rotenone, potentially capable of slowing down the progression of PD.
yunseo lee - Remediating Carcinogenic Contamination Produced by Textile Waste Using Wood-decay Fungus
With 92 million tonnes of clothing discarded each year, the textile industry is responsible for 20% of global water pollution. Additionally, the manufacturing and disposal process, mostly occurring in developing nations, involves numerous toxins that may cause lung cancer as workers inhale them. However, wood-decay fungus, which secretes an enzyme capable of decomposing common soil pollutants, may offer a potential solution. Therefore, this research was conducted to study the feasibility of using wood-decay fungus as a bioremediator solution for fiber-related pollutants. In our experiment, contaminated water created by soaking worn-out T-shirts in distilled water revealed low dissolved oxygen (DO) and high dissolved carbon dioxide (COD) levels. In addition, when exposed to the contaminated water, lung cancer cell line A549 experienced increased cell growth and a long band of fragmented DNA. However, Lentinula edodes and Trametes versicolor (wood-decay fungi) placed in the contaminated water remediated the existing pollution. L. edodes was excluded from further experiments, as it suppressed the growth of other plants/biomass, revealing that it may be dangerous to be used as a solution in marine environments. Furthermore, A549 cells treated with T. versicolor-treated contaminated water had decreased lung cancer cell growth and less DNA damage. This data was collected by placing a net containing dry fungi next to running textile wastewater or by adding T. versicolor in dyeing and bleaching processes. In conclusion, T. versicolor remediates clothing waste pollutants in water, inhibits the proliferation of lung cancer cells, and prevents DNA damage. Understanding the effect of wood-decay fungi on clothing waste pollutants and may be significant in discovering new approaches to minimizing environmental impacts and protecting the health of garment workers in the textile industry.
kristine lu & Chao lu - Epigenetic Regulation of Head and Neck Squamous Cell Carcinoma: Insights from Transcriptome Analysis of Nsd1 Knockout 3D Organoid Model
Head and Neck Squamous Cell Carcinoma (HNSCC) are a prevalent form of cancer, affecting the oral cavity, larynx, and pharynx. HNSCC is commonly associated with tobacco use, alcohol consumption, and Human Papilloma Virus (HPV) infection. HPV Positive HNSCC generally exhibits better prognosis than HPV Negative HNSCC, which is frequently diagnosed at advanced stages with poor outcomes. Epigenetics, the study of heritable changes in gene expression that do not involve the sequence of base pairs in DNA, play a crucial role in HNSCC development. Specifically, the mutation of the gene NSD1, a gene that mediates histone modification, is often positively correlated with improved survival rates in HPV(-) HNSCC. This study aims to investigate the impact of Nsd1 mutations on gene expression and pathways using murine oral-derived organoids, a 3D model that mimics head and neck squamous cancer cells' complexities. Through RNA sequencing and analysis, we compared wildtype organoids to organoids where we knocked out Nsd1 at different stages of cancer progression. Our results suggest that Nsd1 knockout leads to reduced inflammation, potentially contributing to weakened immune responses, and affects genes associated with Extracellular Matrix Organization, potentially influencing cancer cell migration and metastasis. Additionally, Nsd1 may alter epithelial-mesenchymal transition, impacting cancer aggressiveness. This study provides valuable insights into the role of Nsd1 in HNSCC and identifies potential targets for future research and therapeutic interventions.
cloris shi & Evan Layher - Decision Bias in Recognition Memory: How Memory-Selective Neurons Encode Criterion Shifts
Humans make recognition-based decisions by assessing stimuli familiarity while considering strategic biases. For example, in the legal system, people balance eyewitness memory against the consequences of mistakenly identifying the wrong suspect. Under uncertain memory, individuals can flexibly shift criteria to optimize decisions based on the situation. Prior single-neuron research has characterized memory-selective (MS) neurons that accurately distinguish new and familiar stimuli, but it remains unclear whether these neurons respond differently under various decision biases. Here, we recorded extracellular action potentials of single neurons across frontal and temporal cortices while subjects performed an image recognition task with criterion manipulations. Firing rate patterns of MS neurons were decoded using a Support Vector Machine (SVM) classifier to identify selectivity during pre-stimulus and stimulus periods. In all recorded brain regions, we identified MS, visually-selective, and criterion-selective neurons. Furthermore, MS neurons encoded criterion shifts and image categories, suggesting that memory is integrated in parallel with other stimuli within the single neurons. These findings reveal that MS neurons are influenced by decision biases and other stimulus features that encompass the nuances of memory-based decision-making.
Linda Zeng - A Generative-Adversarial Approach to Low-Resource Language Translation via Data Augmentation
Language and culture preservation are serious challenges, both socially and technologically. In response to this issue, this paper takes a data-augmenting approach to low-resource machine translation, helping to diversify the field and preserve underrepresented cultures. Since low-resource languages, such as Aymara and Quechua, do not have many available translations that machine learning software can use as a reference, machine translation models frequently make errors when translating to and from low-resource languages. Because models learn the syntactic and lexical patterns underlying translations through processing the training data, insufficient amount of data hinders them from producing accurate translations. In this paper, I propose the novel application of a generative-adversarial network (GAN) to automatically augment low-resource language data. A GAN consists of two competing models, one learning to generate sentences from noise and the other interpreting whether a given sentence is real or generated. The paper shows that even when training on a very small amount of language data (< 20,000 sentences) in a simulated low-resource setting, such a model is able to generate original, coherent sentences, such as “ask me that healthy lunch im cooking up,” and “my grandfather work harder than your grandfather before.” This GAN architecture is effective in augmenting low-resource language data to improve the accuracy of machine translation and provides a reference for future experimentation with GANs.
shaochi chuang - Investigating the Origins of Niche Shift in Bagheera kiplingi
As a predominantly herbivorous forager among a wide range of predators, the jumping spider Bagheera kiplingi’s diet of Beltian bodies, a detachable nutrient-filled tip found on certain species of Vachellia trees, is unique among the more than 6,000 members of the family Salticidae. The jumping capabilities of Salticidae spiders is widely accepted to be an evolutionary trait designed to help them capture far-away prey. Therefore, the herbivory of B. kiplingi presents a fascinating area of study as its ability to digest high-fiber, nutrient-poor plant material could provide key insights into the evolutionary processes behind niche shifts. Analysis of the dietary habits of Bagheera prosper, B. kiplingi's closest relative, characterizes this species as an obligate carnivore. Moreover, polymerase chain reaction using nifH primers has resulted in the successful amplification of DNA from surface-sterilized B. kiplingi, but not from B. prosper or Frigga crocuta (another spider species that has been found on Vachellia collinsii plants). These results document the first discovery of nitrogen-fixing activity within an arachnid and support the hypothesis that B. kiplingi benefits from the presence of symbiotic bacteria in its gut to supplement a low-nitrogen diet. Additionally, behavioral analysis of B. kiplingi’s diet in controlled settings suggests that they require regular inoculations of ant larvae in order to survive on plant material, supporting the hypothesis that B. kiplingi obtains a portion of its microbiome through consuming ant larvae. This hypothesis is further reinforced by an analysis of B. kiplingi’s mouth structures via scanning electron microscopy indicating that they do not have physical adaptations that are generally associated with herbivory. Meanwhile, video behavioral analysis of B. kiplingi behavior in comparison to B. prosper and F. crocuta provides evidence for the optimal foraging theory and the locomotor crossover hypothesis.
Ann Lee - Inhibition of Superbug Formation by Blocking Transmission of Bacterial Necrosignal Using Kimchi LAB Metabolites
Multidrug resistance of superbugs has become one of the greatest threats to global health. Previously, it was understood that bacteria that survive direct exposure to antibiotics acquire antibiotic resistance traits and become superbugs. However, a 2020 study by Bhattacharyya et al. introduced a novel mechanism of superbug formation: dying bacteria transfer resistance-enhancing factor AcrA to living bacteria through a process called necrosignaling. In this study, we investigate whether the metabolites released by kimchi lactic acid bacteria (LAB) of various fermentation stages could block the transfer of necrosignals and therefore inhibit the creation of superbugs. To test this hypothesis, dying E. coli was applied on one side of LB agar plates and kimchi metabolites were injected into their borders. Agar diffusion tests were performed with E. coli on the other side of the agar plates. It was found that E. coli evolved into multi-resistant bacteria under necrosignal conditions, and kimchi LAB metabolites of all three fermentation stages blocked the transmission of these signals, resulting in a reduction in bacterial growth. Notably, LAB metabolites of kimchi in the second, or moderate, fermentation stage were the most effective. These results demonstrate that the LAB metabolites found in kimchi block the transmission of necrosignals, prompting further research into the use of kimchi postbiotics as a potential health food or medical treatment to counter multidrug resistance.
aaron lee - Improving Solar Flare Prediction using Deep Learning: Solar Flare Anticipation Algorithm (SOFAA)
High-energy solar superstorms can disrupt commercial, telecommunications, and energy infrastructure, posing a considerable risk to electronics in everyday life. Scientists have created deep-learning algorithms that can predict future solar superstorms, but these methods fail to incorporate numerical and visual data simultaneously. To increase the accuracy of deep-learning algorithms predicting solar flares, this paper proposes a deep-learning algorithm, SOFAA (Solar Flare Anticipation Algorithm), combining numerical telemetry and photos taken of the sun from the SDO (Solar Dynamics Observatory) and SOHO (Solar & Heliospheric Observatory) satellites. The SOFAA used data from the SOHO’s total solar irradiance (TSI) measurements, measurements of energy from helium and hydrogen ions and electrons, and the SDO’s solar images in the Fe IX ion spectrum. All data was taken in 2017, from January 1 to December 31. By utilizing GoogLeNet and multilayer perceptron models, the SOFAA achieved a loss value of 0.1255, demonstrating the ability to predict TSI values indicative of a solar flare a day in advance. In short, the incorporation of both numerical and visual data can give more accurate predictions of solar flare activity.
Alaina Shinde - The Use of iPSC-Derived Neural Organoids to Investigate Autism Spectrum Disorders
Autism spectrum disorders (ASD), characterized by impaired social communication, behavioral abnormalities, and restricted interests, affect an estimated 1 in 59 children worldwide. This review provides a comprehensive background on ASD, its historical context, genetic bases, and the influence of epigenetic and environmental factors. Neural organoids, 3D models that mimic human brain structure and functionality, are utilized with intrinsic organoid protocols and guided differentiation to gain insights into ASD modeling, risk gene identification, and environmental effects. Neural organoids also offer promise in drug screening and personalized treatment development for ASD. Despite limitations and ethical concerns, neural organoids stand as a transformative tool in understanding the molecular and cellular bases of ASD, ultimately paving the way for more effective interventions and support for affected individuals.
clare peng - Exploring the Genetic, Neurobiological, and Psychological Mechanisms Involved in the Connection Between Anxious Attachment and Problematic Internet Use
Internet addiction, a behavioral disorder characterized by excessive and pathological internet use, is a growing concern with profound implications for mental health in today's digital age. Anxious-ambivalent attachment, a subtype of insecure attachment rooted in adverse early life experiences, influences regulation of emotions and interpersonal relationships. Several studies have established a significant correlation between attachment anxiety and problematic internet use (PIU), as individuals exhibiting this attachment style are more prone to internet addiction tendencies. The potential mechanisms underlying the association between internet addiction and anxious-attachment style hold significant importance in the fields of psychology and addiction research. While the specific biological origins of this phenomenon are currently unknown, this review explores the intricate relationships between attachment styles, genetic factors such as monoamine neurotransmitters (dopamine and serotonin), the personality trait of neuroticism, and cortisol stress responses in the context of internet addiction. By establishing these connections, we will be able to better understand the factors contributing to PIU and develop targeted interventions and preventive strategies to combat this increasingly critical public health concern. In this review, we will address the gap in knowledge regarding possible mechanisms driving the association between internet addiction and anxious-ambivalent attachment style.