Pyridoimidazolium Cationic Dyes: Theory, Synthesis, and Sub-cellular Localization

(PICs, Figure 1) are a new class of fluorescent dyes which are prepared by an exceedingly flexible methodology. Using computational chemical methods, the excitation and emission maxima of these dyes were simulated. This was best accomplished through geometry optimization (MM+) followed by calculating the electronic spectrum with ZINDO/S. This protocol has been found to be effective and has the potential to greatly streamline the design of new longer wavelength dyes. Several new longer wavelength dyes have now been identified as synthetic targets. The presence of amino groups on a known fluorescent ring system is well established to markedly affect the optical properties of the dye. Methodology had been developed toward the amination of different position on the 2-(2-pyridyl)-carboxyl-quinoline ring system, leading to the testing of 3 new amino substituted fluorescent dyes, including 4-amino-2-(2-pyridyl)-quinoline. This is also the precursor to 4-isothiocyanato-2-(2-pyridyl)-quinoline, which is a potentially useful cellular tag for proteins. The behavior of green fluorescent PICs in live smooth muscle cells was probed using confocal microscopy. Several dyes were proven to be effective in staining the mitochondria. A novel red (488/675nm) nuclear envelope stain was identified and investigated

Transforming encounters: A review of the drivers and mechanisms of macrofaunal plastic fragmentation in the environment

Plastic has infiltrated every ecosystem on the planet, making encounters between this anthropogenic pollutant and fauna inevitable. Abiotic environmental breakdown involving light, oxygen, temperature and mechanical forces is well-characterized, while biotic degradation mechanisms are less well-understood. Reports of the role of macrofauna in the fragmentation of plastic debris are increasing. This review explores the driving factors for macrofaunal fragmentation, as well as the physiological mechanisms by which plastic items are fragmented. The presence, and access to plastic within an organism’s habitat are the key determinants of macrofaunal plastic degradation. Foraging strategies, along with burrowing and nesting behaviors increase the likelihood of macrofauna interacting with plastics. Though this type of fragmentation can occur externally, it often follows ingestion, which in itself can be driven by resemblance to food. Four physical mechanisms of macrofaunal plastic fragmentation were identified, namely biting, drilling, grazing and grinding. Biting, restricted to the mouthparts of an organism, was the most common form of macrofaunal fragmentation reported in literature. Similarly, the use of specialized mouthparts for drilling or grazing can produce secondary plastic particles. Lastly, grinding, through manipulation by the gizzard or gastric mill following ingestion can significantly reduce the size of the plastic material. Prolonged and/or repeated interactions with plastics pose the risk of increased wear on the mouthparts and digestive organs involved. Through mechanisms that directly affect the plastic’s structural integrity, physical fragmentation by macrofauna can amplify overall plastic degradation rates and the formation of micro- and nanoplastics in the environment, while long internal retention times can contribute to their dispersal, trophic transfer, and the organism’s exposure to plastic additives. To more fully understand the extent of macrofaunal plastic fragmentation and allow predictive modeling, we suggest the reporting of evidence in a unified and systematic way. Our findings further highlight the urgency for the implementation of a global plastic waste management system to reduce the burden of micro- and nanoplastics

Clinical Trial Prediction via Natural Language Processing and Graph Mining

This MQP was completed by a single student, who created a baseline model for clinical trial prediction. The goal of this MQP was to predict the clinical trial phase based on historical medical data. To do this, the student used the ElementTree API in Python to parse over 100 thousand XML files into a database. The database was then cleaned using a combination of Pandas, pairwise & listwise deletion, and reformatting values. The database was then split into training and testing data: the former used for fitting the Machine Learning model known as CatBoost (a gradient boosting machine specifically designed for data with many categorical variables), whereas the latter was used for evaluating the model performance. The model was then analyzed to find how much each factor influences the clinical trial phase. It was discovered that the source, or from where the clinical trial originates, has the most impact on how well the clinical trial proceeds through phases

Stock Market Simulation 2120

The goal of this project was to conduct a simulation of the stock market to figure out which trading strategy would be most effective. A neural network program was designed to analyze stock market patterns for the prediction of trends. A six-week simulation was completed using three different trading techniques: day trading, position trading, and trading guided by neural network. The returns of the three methods were strategies are as follows: 64.38% for Neural Network, 61.69% for position trading, and 48.64% for day trading. In comparison, S&P 500 only gained 5.65% for the same period of time. This project gave the student insight into how the stock market works so that he could make informed trading decisions in the future

A Framework to identify People, Devices and Services in Cyber-physical system of systems

Many online services and their service providers require the electronic proof of identity for the secure authentication of citizens. Internet of Things (IoT)- and Cyber-Physical Systems (CPS)-services and devices are increasing and these are used in different areas. Furthermore, the increasing distribution of online services and IoT devices need to be monitored, especially in critical infrastructure. The proposal of a framework to authenticate and identify people, devices and services can be a useful tool to improve security and trust in CPS, linking them with identified people by utilizing and combining tools which do exist in isolation. IoT frameworks and identity protocols combined with responsible people, hardware, smartphone-applications, and certification authorities can provide secure authentication, trustworthy communication and the management of identities and permissions. This position paper proposes an IoT-framework for (critical infrastructure) service providers and public administration to authenticate, identify and manage their running devices and services as well as people, their electronic identification and sent records. This can improve the reaction time and processes additionally providing trust and secure communication between people, devices and services, especially for authorities in critical infrastructure areas, where humans and their safety are particularly important.Forschung Burgenlan