<p>Natural products, along with natural product derivatives, are known to be at the root of the development of many pharmaceuticals, oftentimes showing unique bioactivity against interesting targets. Specifically, natural products containing furans show activity against a variety of diseases including fungal infections, and cancers. It is hypothesized that unknown natural products containing furans could show more potent or other biological activities. However, it is challenging to discover and isolate these small molecules from cell supernatant. The work described herein showcases the development of a molecular probe that can covalently attach to furan moieties via a [4 + 2] Diels-Alder cycloaddition, making them easily identifiable on liquid chromatography mass spectroscopy (LC-MS). The molecular probe, which undergoes this reaction with a variety of furans, was designed with both a UV-tag and a mass tag to enable easy identification. The probe has been tested with a variety of purified furans, including natural products, methylenomycin furan (MMF) hormones, and MMF derivatives. Moreover, work has begun to test the molecular probe in cell supernatants. </p>

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Indiana University-Purdue University Indianapolis (IUPUI) / Component-Based Software Development (CSBD) has gained widespread importance in recent times, due to its wide-scale applicability in software development. System developers can now pick and choose from the pre-existing components to suit their requirements in order to build their system. For the purpose of developing a quality-aware system, finding the suitable components offering services is an essential and critical step. Hence, Service Discovery is an important step in the development of systems composed from already existing quality-aware software services. Currently, there is a plethora of new-age devices, such as PDAs, and cell phones that automate daily activities and provide a pervasive connectivity to users. The special characteristics of these devices (e.g., mobility, heterogeneity) make them as attractive choices to host services. Hence, they need to be considered and integrated in the service discovery process. However, due to their limitations of battery life, intermittent connectivity and processing capabilities this task is not a simple one. This research addresses this challenge of including resource constrained devices by enhancing the UniFrame Resource Discovery System (URDS) architecture. This enhanced architecture is called Mobile Device Enabled Service Discovery System (MDE-URDS). The experimental validation of the MDE-URDS suggests that it is a scalable and quality-aware system, handling the limitations of mobile devices using existing and well established algorithms and protocols such as Mobile IP.

<div>By Machine Learning in the Open World, we are trying to build models that can be used in a more realistic setting where there could always be something "unknown" happening. Beyond the traditional machine learning tasks such as classification and segmentation where all classes are predefined, we are dealing with the challenges from newly emerged classes, irrelevant classes, outliers, and class imbalance.</div><div>At the beginning, we focus on the Non-Exhaustive Learning (NEL) problem from a statistical aspect. By NEL, we assume that our training classes are non-exhaustive, where the testing data could contain unknown classes. And we aim to build models that could simultaneously perform classification and class discovery. We proposed a non-parametric Bayesian model that learns some hyper-parameters from both training and discovered classes (which is empty at the beginning), then infer the label partitioning under the guidance of the learned hyper-parameters, and repeat the above procedure until convergence.</div><div>After obtaining good results on applications with plain and low dimensional data such flow-cytometry and some benchmark datasets, we move forward to Non-Exhaustive Feature Learning (NEFL). For NEFL, we extend our work with deep learning techniques to learn representations on datasets with complex structural and spatial correlations. We proposed a metric learning approach to learn a feature space with good discrimination on both training classes and generalize well on unknown classes. Then we developed some variants of this metric learning algorithm to deal with outliers and irrelevant classes. We applied our final model to applications such as open world image classification, image segmentation, and SRS hyperspectral image segmentation and obtained promising results.</div><div>Finally, we did some explorations with Out of Distribution detection (OOD) to detect irrelevant sample and outliers to complete the story.</div>

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Autonomously monitoring and analyzing the behavior of the crowd is an open research topic in the transportation field because of its criticality to the safety of people. Real-time identification, tracking, and prediction of crowd behavior are primordial to ensure smooth crowd management operations and the welfare of the public in many public areas, such as public transport stations and streets. This being said, enabling such systems is not a straightforward procedure. First, the complexity brought by the interaction and fusion from individual to group needs to be assessed and analyzed. Second, the classification of these actions might be useful in identifying danger and avoiding any undesirable consequences. The adoption of the Internet-of-things (IoT) in such systems has made it possible to gather a large amount of data. However, it raises diverse compatibility and trustworthiness challenges, among others, hindering the use of conventional service discovery and network navigability processes for enabling crowd management systems. In fact, as the IoT network is known for its highly dynamic topology and frequently changing characteristics (e.g., the devices' status, such as availability, battery capacity, and memory usage), traditional methods fail to learn and understand the evolving behavior of the network so as to enable real-time and context-aware service discovery to assign and select relevant IoT devices for monitoring and managing the crowd. In large-scale IoT networks, crowd management systems usually collect large data streams of images from different heterogeneous sources (e.g., CCTVs, IoT devices, or people with their smartphones) in an inadvertent way. Due to the limitations and challenges related to communication bandwidth, storage, and processing capabilities, it is unwise to transfer unselectively all the collected images since some of these images either contain duplicate information, are inaccurate, or might be falsely submitted by end-users; hence, a filtering and quality check mechanism must be put in place. As images can only provide limited information about the crowd by capturing only a snapshot of the scene at a specific point in time with limited context, an extension to deal with videos to enable efficient analysis such as crowd tracking and identification is essential for the success of crowd management systems. In this thesis, we propose to design a smart image enhancement and quality control system for resource pooling and allocation in the Internet-of-Things applied to crowd management systems. We first rely on the Social IoT (SIoT) concept, which defines the relationships among the connected objects, to extract accurate information about the network and enable trustworthy and context-aware service exchange and resource allocation. We investigate the service discovery process in SIoT networks and essentially focus on graph-based techniques while overviewing their utilization in SIoT and discussing their advantages. We also propose an alternative to these scalable methods by introducing a low-complexity context-aware Graph Neural Network (GNN) approach to enable rapid and dynamic service discovery in a large-scale heterogeneous IoT network to enable efficient crowd management systems. Secondly, we propose to design a smart image selection procedure using an asymmetric multi-modal neural network autoencoder to select a subset of photos with high utility coverage for multiple incoming streams in the IoT network. The proposed architecture enables the selection of high-context data from an evolving picture stream and ensures relevance while discarding images that are irrelevant or falsely submitted by smartphones, for example. The approach uses the photo's metadata, such as geolocation and timestamps, along with the pictures' semantics to decide which photos can be submitted and which ones must be discarded. To extend our framework beyond just images and deal with real-time videos, we propose a transformer-based crowd management monitoring framework called V3Trans-Crowd that captures information from video data and extracts meaningful output to categorize the crowd's behavior. The proposed 3D Video Transformer is inspired from Video Swin-Transformer/VIVIT and provides an improved hierarchical transformer for multi-modal tasks with spatial and temporal fusion layers. Our simulations show that due to its ability to embed the devices' features and relations, the GNN is capable of providing more concise clusters compared to traditional techniques, allowing for better IoT network learning and understanding. Moreover, we show that the GNN approach speeds up the service lookup search space and outperforms the traditional graph-based techniques to select suitable IoT devices for reporting and monitoring. Simulation results for three different multi-modal autoencoder architectures indicate that a hierarchical asymmetric autoencoder approach can yield better results, outperforming the mixed asymmetric autoencoder and a concatenated input autoencoder, while leveraging user-side rendering to reduce bandwidth consumption and computational overhead. Also, performance evaluation for the proposed V3Trans-Crowd model has shown great results in terms of accuracy for crowd behavior classification compared to state-of-the-art methods such as C3D pre-trained, I3D pre-trained, and ResNet 3D pre-trained on the Crowd-11 and MED datasets.

Enterprise networks are traditionally mapped via layers two or three, providing a view of what devices are connected to different parts of the network infrastructure. A method was developed to map connections at layer four, providing a view of interconnected systems and services instead of network infrastructure. This data was graphed and displayed in a web application. The information proved beneficial in identifying connections between systems or imbalanced clusters when troubleshooting problems with enterprise applications.

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Trichomonas vaginalis is a sexually-transmitted parasite that is the causative agent in the disease trichomoniasis. Resistance to the only FDA-approved medication to this disease, metronidazole, has been on the increase giving rise to the need for finding targets for new inhibitors to exploit. New inhibitors can target enzymes such as 4-coumarate:CoA ligase and S-adenosylhomocysteine hydrolase. Another potential target is a cathepsin D-like protease found in T. vaginalis . This aspartic protease in humans is responsible for degrading proteins in the lysosome, and degrading hemoglobin in P. falciparum as the homologue plasmepsin. Searching the gene database, only one cathepsin-D like protease was discovered throughout the organism's genome. Utilizing RT-PCR, this gene is found to be expressed in two different strains of the organism. Transfection of an epitope-tagged version of this cathepsin D-like protease into T. vaginalis was accomplished, and subsequent immunofluorescence of this tagged version shows it to be localized in intracellular compartments, which can be colocalized using the SNARE and VAMP proteins found in T. vaginalis .