12 research outputs found
Improving Traffic Flow and Reducing Congestion Using Predictive Analytic
No full textTraffic congestion is a significant issue in urban areas, leading to increased travel times, fuel consumption, and environmental pollution. Traditional traffic management methods, such as fixed traffic signal timings and manual interventions, often fail to adapt dynamically to changing traffic conditions. This study explores the application of predictive analytics and machine learning techniques to optimize traffic flow and reduce congestion. By leveraging historical traffic data, real-time traffic monitoring systems, and external factors such as weather conditions and road incidents, predictive models can be developed to forecast congestion levels and suggest optimal traffic management strategies.
The methodology involves data collection from multiple sources, processioning for quality enhancement, and the application of machine learning algorithms, including decision trees, random forests, and neural networks, to predict traffic patterns. These models will help in dynamic traffic signal control, congestion mitigation, and route optimization. The anticipated outcome is a smart traffic management system that enhances urban mobility, reduces delays, and contributes to sustainable transportation infrastructure. This research can potentially aid city planners, transportation agencies, and policymakers implement data-driven solutions for improving urban traffic efficiency
Novel microelectrode arrays for in-vitro analysis of neural activity
No full textMicroelectrode arrays (MEAs) are extensively used for measuring neural activity in-vitro given their ability to monitor several neurons simultaneously unlike techniques such as patch clamp. However, MEAs still have limitations in acquiring high spatial resolution data due to limited number of channels that can be parallelly scanned, the need for bulky anti-aliasing filters, and limitations in signal-to-noise ratio (SNR) arising from thermal noise. Commercially available MEAs rely on resistive or self-capacitive sensing scheme, but this research proposes a new approach to increase the number of sensing locations while reducing the channels and to increase SNR. Fundamental design aspects of a MEA such as the shape and size of electrodes are revisited. By employing traditional lithographic fabrication techniques, these arrays with various geometries are fabricated and characterized. Neural cultures are seeded on these novel MEAs to record neural activity in the electrical domain and concurrently Ca+2 Imaging is performed to correlate and verify the activity of a neuron
Sulphur-based spinel material as a cathode for Magnesium-ion battery
No full textLi-ion batteries have major disadvantages. One of which is the growth of dendrites (in case Li metal based batteries), a major safety issue. In addition to that, Li-ion batteries also use elements such as Co, and Li, which are regionally scarce. Mg-ion batteries are one of the alternatives for Li-ion batteries. However, the electrolyte and cathode materials for Mg-ion batteries, are still in developmental stages.In this study, the use of a sulphur-based spinel (also known as thiospinel) material as a cathode is explored. After literature review, MgMn2S4 and MgTi2S4 were identified as suitable cathode materials. Following which, the MgMn2S4 spinel is doped with Ti in the place of Mn at different doping ratios and the resulting combinations are evaluated for their stability, average intercalation voltage, volume change, spinel inversion and migration barriers. Two combinations MgMnTiS4 and MgMn0.75Ti1.25S4 are found to be stable with respect to the end members i.e. MgMn2S4 and MgTi2S4. Average voltages of 1.702 and 1.527 V (vs. Mg/Mg2+) are observed for MgMnTiS4 and MgMn0.75Ti1.25S4. However, spinel inversion is observed in MgMnTiS4. A volume change of 21.2, and 20% is observed in MgMnTiS4 and MgMn0.75Ti1.25S4, respectively
Author Correction: Small Molecule Accurate Recognition Technology (SMART) to Enhance Natural Products Research
No full textAn amendment to this paper has been published and can be accessed via a link at the top of the paper
Investigation of Implantable Multichannel Neurostimulators
No full textabstract: There is a strong medical need and important therapeutic applications for improved wireless bioelectric interfaces to the nervous system. Multichannel devices are desired for neural control of robotic prosthetics that interface to remaining nerves in limb stumps of amputees and as alternatives to traditional wired arrays used in for some types of brain stimulation. This present work investigates a new approach to ultrasound-powering of implantable microelectronic devices within the tissue that may better support such applications. These devices are of ultra-miniature size that is enabled by a wireless technique. This study investigates two types of ultrasound-powered neural interfaces for multichannel sensory feedback in neurostimulation. The piezoceramics lead zirconate titanate (PZT) ceramic and polyvinylidene fluoride (PVDF) polymer were the primary materials used to build the devices. They convert ultrasound to electricity that when rectified by a diode produce a current output that is neuro stimulatory to peripheral nerve or the neurons in the brain. Multichannel devices employ a form of spatial multiplexing that directs focused ultrasound towards localized and segmented regions of PVDF or PZT that allows independent channels of nerve actuation. Different frequencies of ultrasound were evaluated for best results. Firstly, a 2.25 MHz frequency signal that is reasonably penetrating through body tissue to an implant several centimeters deep and also a 5 MHz frequency more suited to application for actuation of devices within a less than a centimeter of nerve. Results show multichannel device performance to have a complex inter-relationship with frequency, size and thickness, angular incidence, channel separations, and number of folds (layers connected in series and parallel). The output electrical port impedances of PVDF devices were examined in relationship to that of stimulating electrodes and tissue interfaces. Miniature multichannel devices were constructed using an unreported method of employing state of the art laser cutting systems. The results show that PVDF based devices have advantages over PZT, because of better acoustic coupling with tissue, known better biocompatibility, and better separation between multiple channels. However, the PZT devices proved to be better overall in terms of compactness and higher outputs for a given ultrasound power level.Dissertation/ThesisMasters Thesis Bioengineering 201
S-Taliro: A Tool for Temporal Logic Falsification for Hybrid Systems
No full textabstract: S-Taliro is a fully functional Matlab toolbox that searches for trajectories of minimal robustness in hybrid systems that are implemented as either m-functions or Simulink/State flow models. Trajectories with minimal robustness are found using automatic testing of hybrid systems against user specifications. In this work we use Metric Temporal Logic (MTL) to describe the user specifications for the hybrid systems. We then try to falsify the MTL specification using global minimization of robustness metric. Global minimization is carried out using stochastic optimization algorithms like Monte-Carlo (MC) and Extended Ant Colony Optimization (EACO) algorithms. Irrespective of the type of the model we provide as an input to S-Taliro, the user needs to specify the MTL specification, the initial conditions and the bounds on the inputs. S-Taliro then uses this information to generate test inputs which are used to simulate the system. The simulation trace is then provided as an input to Taliro which computes the robustness estimate of the MTL formula. Global minimization of this robustness metric is performed to generate new test inputs which again generate simulation traces which are closer to falsifying the MTL formula. Traces with negative robustness values indicate that the simulation trace falsified the MTL formula. Traces with positive robustness values are also of great importance because they indicate how robust the system is against the given specification. S-Taliro has been seamlessly integrated into the Matlab environment, which is extensively used for model-based development of control software. Moreover the toolbox has been developed in a modular fashion and therefore adding new optimization algorithms is easy and straightforward. In this work I present the architecture of S-Taliro and its working on a few benchmark problems.Dissertation/ThesisM.S. Computer Science 201
Discovery of DTX3L inhibitors through a homogeneous FRET-based assay that monitors formation and removal of poly-ubiquitin chains
No full textAbstract
Ubiquitination is a reversible protein post-translational modification in which consequent enzymatic activity results in the covalent linking of ubiquitin to a target protein. Once ubiquitinated, a protein can undergo multiple rounds of ubiquitination on multiple sites or form poly-ubiquitin chains. Ubiquitination regulates various cellular processes, and dysregulation of ubiquitination has been associated with more than one type of cancer. Therefore, efforts have been carried out to identify modulators of the ubiquitination cascade. Herein, we present the development of a FRET-based assay that allows us to monitor ubiquitination activity of DTX3L, a RING-type E3 ubiquitin ligase. Our method shows a good signal window with a robust average Z’ factor of 0.76 on 384-well microplates, indicating a good assay for screening inhibitors in a high-throughput setting. From a validatory screening experiment, we have identified the first molecules that inhibit DTX3L with potencies in the low micromolar range. We also demonstrate that the method can be expanded to study deubiquitinases, such as USP28, that reduce FRET due to hydrolysis of fluorescent poly-ubiquitin chains.Abstract
Ubiquitination is a reversible protein post-translational modification in which consequent enzymatic activity results in the covalent linking of ubiquitin to a target protein. Once ubiquitinated, a protein can undergo multiple rounds of ubiquitination on multiple sites or form poly-ubiquitin chains. Ubiquitination regulates various cellular processes, and dysregulation of ubiquitination has been associated with more than one type of cancer. Therefore, efforts have been carried out to identify modulators of the ubiquitination cascade. Herein, we present the development of a FRET-based assay that allows us to monitor ubiquitination activity of DTX3L, a RING-type E3 ubiquitin ligase. Our method shows a good signal window with a robust average Z’ factor of 0.76 on 384-well microplates, indicating a good assay for screening inhibitors in a high-throughput setting. From a validatory screening experiment, we have identified the first molecules that inhibit DTX3L with potencies in the low micromolar range. We also demonstrate that the method can be expanded to study deubiquitinases, such as USP28, that reduce FRET due to hydrolysis of fluorescent poly-ubiquitin chains
MultiOMICs landscape of SARS-CoV-2-induced host responses in human lung epithelial cells
No full textCOVID-19 pandemic continues to remain a global health concern due to the emergence of newer variants. Several multiOmics studies have produced extensive evidence on host-pathogen interactions and potential therapeutic targets. Nonetheless, an increased understanding of host signaling networks regulated by post-translational modifications and their ensuing effect on the cellular dynamics is critical to expanding the current knowledge on SARS-CoV-2 infections. Through an unbiased transcriptomics, proteomics, acetylomics, phosphoproteomics, and exometabolome analysis of a lung-derived human cell line, we show that SARS-CoV-2 Norway/Trondheim-S15 strain induces time-dependent alterations in the induction of type I IFN response, activation of DNA damage response, dysregulated Hippo signaling, among others. We identified interplay of phosphorylation and acetylation dynamics on host proteins and its effect on the altered release of metabolites, especially organic acids and ketone bodies. Together, our findings serve as a resource of potential targets that can aid in designing novel host-directed therapeutic strategies
Author Correction: Small Molecule Accurate Recognition Technology (SMART) to Enhance Natural Products Research
No full textAssessment of protein-protein interfaces using graph neural networks
No full text2021 Summer.Includes bibliographical references.Proteins are fundamental building blocks of cellular function. They systematically interact with other proteins to make life happen. Understanding these protein-protein interactions is important for obtaining a detailed understanding of protein function and to enable the process of drug and vaccine design. Experimental methods for studying protein interfaces including X-ray crystallography, NMR, and Cryo-electron microscopy, are expensive, time consuming, and sometimes unsuccessful due to the unstable nature of many protein-protein interactions. Computational docking experiments are a cheap and fast alternative. Docking algorithms produce a large number of potential solutions that are then ranked by quality. However, current scoring methods are not good enough for finding a docking solution that is close to the native structure. That has led to the development of machine learning methods for this task. These methods typically involve extensive engineering of features to describe the protein complex, and are not very successful at identifying good quality solutions among the top ranks. In this thesis, we propose a scoring technique that uses graph neural networks that function at the atomic level to learn the interfaces of docked proteins without the need for feature engineering. We evaluate our model and show that it performs better than commonly used docking methods and deep learning methods that use 3D CNNs
