Application of Flow Cytometry as Novel Technology in Studying Lipid Oxidation in Oil-in-Water Emulsions

The body of literature on the impact of emulsion particle size on oxidation rates is unclear. This could be because emulsions are typically polydisperse and the oxidation rate of individual droplets is impossible to discern. Flow cytometry is a technique for studying individual cells and their subpopulations using fluorescence technologies. It is possible that individual emulsion droplets could also be characterized by flow cytometry as a novel approach for studying lipid oxidation. Typical emulsion droplets are too small to be visualized by flow cytometer, so emulsions were prepared to have droplets \u3e 2 μm; weighting agent and xanthan gum were added to minimize creaming during storage. A radical-sensitive lipid-soluble fluorescence probe (BODIPY665/676) was added to the lipid used to prepare the emulsion so that the susceptibility of individual emulsion droplets could be determined. The results showed that in a polydisperse emulsion system, small droplets were oxidized faster than large droplets. Using mixtures of emulsions with and without prooxidants, it was possible to see the transfer of prooxidants between droplets, a process that is influenced by surfactant and salt concentrations. For example, surfactants micelles can transfer prooxidants to neighboring non-oxidized droplets and cause fluorescence loss when surfactant concentration was higher than critical micelle concentration (CMC). Transfer of prooxidants was promoted by adding NaCl and free fatty acid which could be attributed to the lower CMC. This study showed the potential for applying flow cytometry on oxidation of individual emulsion droplets

DataScope: Predictive Diagnosis in IIoT-enabled Smart Manufacturing

This a collaborative project between Elizabethtown College and CPNet, LLC that is looking to help apply predictive modeling with CPNet’s domain knowledge to one of CPNet’s clients\u27 IIoT manufacturing problems. CPNet has provided us with datasets taken from one of their clients in the hope that we can build a model that will be able to predict when a part within the machines they are looking at will fail and subsequently shut the machine down. We are trying to take their data and turn it into information that the company can take preemptive action on and save them downtime during operation. In this project, we designed a health index that we used to create y-labels that we did not have in our dataset. We did this so that we could solve the remaining useful life problem of our project, which is where we attempt to determine how long the machine has to run before a failure or maintenance is needed based off of the health index using machine learning. We then went on to build several machine learning models to solve our problem. First, we used traditional machine learning models such as Polynomial Regression, Tree-based Regression, Ridge, Regression and XGBoost. Later we turned to Neural Networks and built a Multilayer Perceptron, a Convolutional Neural Network and a Recurrent Neural Network. From our experiments traditional machine learning models outperformed the neural networks. This was to be expected since the dataset wasn’t that large, but it was worth testing regardless. Also, from our experiments it is apparent that a part of our health index the CPK (CPK analysis will be explained in greater detail later in the paper.) will need to be worked on in the future to provide better y-labels. All in all, this project did show that this problem is worth exploring in the future since the solution could be quite valuable in smart manufacturing

Finite element simulation on the reflection and transmission of the lamb waves across a micro defect of plates

This paper presents a theoretical and finite element (FE) investigation of the generation and propagation characteristics of the fundamental Lamb waves symmetrical mode S0 and anti-symmetrical mode A0 after testing with different types of defects in the plates. The reflection and transmission of Lamb waves at a micro symmetry defect and asymmetry defect are analyzed numerically in the two-dimension (2D) model. Mode conversion of Lamb waves can occur upon encountering the asymmetry discontinuities leading to newly-converted modes apart from wave reflection and transmission. When testing the symmetry defects, the reflection and transmission waves have no modal separation phenomenon. To describe the mode conversion and reflection and transmission degree, and evaluate the micro defect severity, a series of defects are simulated to explore the relationships of defect reflection and transmission with the length and depth of a defect in the 2D FE model. In the three-dimension (3D) FE model, the straight-crest Lamb waves and circular-crest Lamb waves are simulated and researched by contrast analysis. Then the straight-crest Lamb waves are motivated to study the scattering laws of Lamb waves interacting with the circle hole defects and rectangular hole defects. S0 mode and SH0 mode are contained in the scattering waves after S0 mode testing the through holes defects. Corresponding mode energy percentages were analyzed at different micro defect severities changed in different ways. Simulation results illustrated that the modal energy percentages varied in a different character and provided support for the analytically determined results of Lamb waves in the non-destructive testing and evaluation

NetSec: Real-time and Scalable Malware Traffic Detection within IoT Networks

Detecting malicious network traffic in real time has become a crucial requirement at smart communities for elderly care and medical facilities with the prevalence of Internet-of-things (IoT) devices. Existing machine learning based solutions for network traffic malware detection often fail to scale with the exponential increase of IoT devices at the facility and to detect malicious traffic with desirable low latency. In this paper we seek to fill the gap by designing a scalable end-to-end network traffic analyzing system that permits real-time malware detection. By leveraging distributed systems such as Apache Kafka and Apache Spark, the system has demonstrated scalable performance as the number of IoT devices grow. Using Intel’s oneAPI software stack for both machine learning and deep learning models, the model inference speed is boosted by three-fold

Effects of UV-C Light Exposure and Refrigeration on Phenolic and Antioxidant Profiles of Subtropical Fruits (Litchi, Longan, and Rambutan) in Different Fruit Forms

The objectives of this study were to investigate how UV-C irradiation and refrigeration affect shelf-life and antioxidant level of litchi, longan, and rambutan. Three forms (whole, dehulled, and destoned) of fresh fruits were treated by refrigeration and UV-C irradiations. After processing, deterioration rate, phenolics compounds, and antioxidant capacity were quantified. The deterioration rate was recorded as decay index. The results showed that both refrigeration and UV-C exposure extended the shelf-life of the fruits. The refrigeration enriched antioxidant levels of litchi but caused nutritional degradation in longan and rambutan; UV-C radiation enriched litchi antioxidant contents but was related to reduction of antioxidant capacity in longan and rambutan. Removing hulls and stones was associated with the decrease of antioxidants in litchi. The effects on antioxidant levels varied from fruit to fruit, resulting from hormesis phenomenon. The change of phytochemical levels was hypothesized as an accumulative process. The effects of fruit forms were not consistent in different fruits, which could be multifactorially influenced

Effects of crumb rubber and styrene-butadiene rubber additives on the properties of asphalt binder and the Marshall performance properties of asphalt mixtures

The primary aim of this study is to evaluate the impact of incorporating crumb rubber (CR) and styrene-butadiene rubber (SBR) additives, ranging from 0% to 5% by weight of bitumen, on the performance of a bituminous concrete mixture using the wet process. Laboratory experiments, including the Marshall test, were conducted to establish the optimum bitumen content (OBC) for the hot mix. The study focuses on determining the optimal proportions of CR and SBR to achieve maximum strength. The results show that increasing the proportions of both CR and SBR leads to significant improvements in strength, with the maximum stability recorded at 16.14 kN and a flow of 1.23 mm for a mix containing 5% CR and 4% SBR. The findings further suggest an inverse relationship between CR content and strength, while an increasing SBR content enhances strength. Consequently, the optimal proportions for incorporating CR and SBR additives are identified as 5% and 4%, respectively

Spatiotemporal Scan and Age-Period-Cohort Analysis of Hepatitis C Virus in Henan, China: 2005–2012

Background: Studies have shown that hepatitis C virus (HCV) infection increased during the past decades in China. However, little evidence is available on when, where, and who were infected with HCV. There are gaps in knowledge on the epidemiological burden and evolution of the HCV epidemic in China. Methods: Data on HCV cases were collected by the disease surveillance system from 2005 to 2012 to explore the epidemic in Henan province. Spatiotemporal scan statistics and age-period-cohort (APC) model were used to examine the effects of age, period, birth cohort, and spatiotemporal clustering. Results: 177,171 HCV cases were reported in Henan province between 2005 and 2012. APC modelling showed that the HCV reported rates significantly increased in people aged > 50 years. A moderate increase in HCV reported rates was observed for females aged about 25 years. HCV reported rates increased over the study period. Infection rates were greatest among people born between 1960 and 1980. People born around 1970 had the highest relative risk of HCV infection. Women born between 1960 and 1980 had a five-fold increase in HCV infection rates compared to men, for the same birth cohort. Spatiotemporal mapping showed major clustering of cases in northern Henan, which probably evolved much earlier than other areas in the province. Conclusions: Spatiotemporal mapping and APC methods are useful to help delineate the evolution of the HCV epidemic. Birth cohort should be part of the criteria screening programmes for HCV in order to identify those at highest risk of infection and unaware of their status. As Henan is unique in the transmission route for HCV, these methods should be used in other high burden provinces to help identify subpopulations at risk

RadOnc-GPT: A Large Language Model for Radiation Oncology

This paper presents RadOnc-GPT, a large language model specialized for radiation oncology through advanced tuning methods. RadOnc-GPT was finetuned on a large dataset of radiation oncology patient records and clinical notes from the Mayo Clinic in Arizona. The model employs instruction tuning on three key tasks - generating radiotherapy treatment regimens, determining optimal radiation modalities, and providing diagnostic descriptions/ICD codes based on patient diagnostic details. Evaluations conducted by comparing RadOnc-GPT outputs to general large language model outputs showed that RadOnc-GPT generated outputs with significantly improved clarity, specificity, and clinical relevance. The study demonstrated the potential of using large language models fine-tuned using domain-specific knowledge like RadOnc-GPT to achieve transformational capabilities in highly specialized healthcare fields such as radiation oncology

The Radiation Oncology NLP Database

We present the Radiation Oncology NLP Database (ROND), the first dedicated Natural Language Processing (NLP) dataset for radiation oncology, an important medical specialty that has received limited attention from the NLP community in the past. With the advent of Artificial General Intelligence (AGI), there is an increasing need for specialized datasets and benchmarks to facilitate research and development. ROND is specifically designed to address this gap in the domain of radiation oncology, a field that offers many opportunities for NLP exploration. It encompasses various NLP tasks including Logic Reasoning, Text Classification, Named Entity Recognition (NER), Question Answering (QA), Text Summarization, and Patient-Clinician Conversations, each with a distinct focus on radiation oncology concepts and application cases. In addition, we have developed an instruction-tuning dataset consisting of over 20k instruction pairs (based on ROND) and trained a large language model, CancerChat. This serves to demonstrate the potential of instruction-tuning large language models within a highly-specialized medical domain. The evaluation results in this study could serve as baseline results for future research. ROND aims to stimulate advancements in radiation oncology and clinical NLP by offering a platform for testing and improving algorithms and models in a domain-specific context. The ROND dataset is a joint effort of multiple U.S. health institutions. The data is available at https://github.com/zl-liu/Radiation-Oncology-NLP-Database.Comment: 10 pages, 7 figures, 6 table

Utilization of post-exposure prophylaxis potentially contributed to the changes of risk behaviors among men who have sex with men in China

BackgroundThe HIV infection status among men who have sex with men (MSM) in China is a cause for concern. Post-exposure prophylaxis (PEP) serves as a highly effective biomedical preventive measure against HIV infection. Substantial evidence has established an association between PEP utilization and risk behaviors among MSM, but whether the utilization of PEP has an impact on risk behaviors remains unknown. This study sought to elucidate the impact of PEP usage on risk behaviors among MSM and provide recommendations for developing targeted HIV prevention programs.MethodsA cohort study was conducted in Qingdao, China, from April 2021 to January 2022. Participants were enlisted by volunteers from community-based organizations through a snowball sampling method. Face-to-face interviews were conducted to collect sociodemographic and behavioral information of participants. The study encompassed a retrospective investigation, baseline survey, and follow-up survey, representing periods before, during, and after PEP usage, respectively. Generalized estimating equations, fitting a Poisson regression model, were applied to scrutinize changes in risk behaviors of MSM during and after PEP usage, in comparison to before PEP usage.ResultsA total of 341 MSM were recruited in the cohort study, with 179 individuals completing the follow-up survey. In comparison to before PEP usage, there was a significant increase in the proportion of Rush Popper usage (17.6% vs. 23.8% vs. 29.6%) and commercial sexual partners (10.9% vs. 17.6% vs. 21.8%) among MSM during and after PEP usage. Before PEP usage, 88.7% of MSM reported having ≥3 temporary sexual partners in the last 6 months. This proportion exhibited no significant change during PEP usage (91.8%), but it significantly increased to 97.8% after PEP usage (P < 0.05). Notably, there was a significant decrease in group sex during and after PEP usage compared to before PEP usage (30.8% vs. 21.4% vs. 21.2%).ConclusionThe utilization of PEP may impact risk behaviors among MSM, potentially leading to increased Rush Popper usage, temporary sexual partners, and commercial sexual partners after PEP usage, accompanied by a decrease in group sex. Further research is imperative to elucidate the impact of PEP utilization on MSM and develop targeted HIV prevention programs