Direct Quantification of Deubiquitinating Enzyme Activity in Single Intact Cells

Challenges in drug efficacy occur during the treatment of most types of cancer due to the heterogeneity of the tumor microenvironment. This has led to the development of personalized medicine. Due to the clinical success of the proteasome inhibitors Bortezomib and Carfilzomib in treatment of multiple myeloma, interest has shifted towards molecularly-targeted chemotherapeutics for ubiquitin-proteasome system (UPS). Deubiquitinating enzymes (DUBs) are an essential part of this pathway which have been found to promote Bortezomib resistance in multiple myeloma patients. Unfortunately, there is a lack of specific, high throughput biochemical assays to characterize DUB activity in patient samples before and after treatment with DUB inhibitors. Therefore, there is a need for novel biochemical assays for quantifying DUB activity in a single cell level. In this research, a long-lived, cell permeable, peptide-based reporter was developed to directly quantify DUB activity in intact single cells. A hallmark of this reporter is the use of a β-hairpin ‘protectide’ which can confer its stability to enzyme substrates. First, a study was performed to determine if the arginine-rich β-hairpin sequence motif could behave as a cell penetrating peptide (CPP). Chapter 2 highlights these findings and confirms that the RWRWR β-hairpin sequence could be rapidly internalized into intact cells. Chapter 3 summarizes findings from another study that investigated the use of incorporating D-chirality amino acids into CPPs. The D-chirality peptides were confirmed to be highly stabled under intracellular conditions and were found to be rapidly internalized. Next, a microfluidic droplet trapping array was developed to encapsulate and analyze the intracellular fluorescence of intact single cells. Chapter 4 summarizes the development of this device and its use to identify distinct subpopulations that emerge with respect to the CPP uptake. Finally, Chapter 5 utilizes the findings from Chapter 2 to develop a long-lived, cell permeable, fluorescent, peptide-based reporter for DUB activity. This chapter confirms the utility of this reporter in both cell lysates and intact cells. In summary, a set of toolkits including CPPs, a microfluidics droplet trapping array, and peptide-based DUB reporter were developed to provide a new platform for drug screening and personalized diagnostics

Robust Opponent Modeling in Real-Time Strategy Games using Bayesian Networks

Opponent modeling is a key challenge in Real-Time Strategy (RTS) games as the environment is adversarial in these games, and the player cannot predict the future actions of her opponent. Additionally, the environment is partially observable due to the fog of war. In this paper, we propose an opponent model which is robust to the observation noise existing due to the fog of war. In order to cope with the uncertainty existing in these games, we design a Bayesian network whose parameters are learned from an unlabeled game-logs dataset; so it does not require a human expert’s knowledge. We evaluate our model on StarCraft which is considered as a unified test-bed in this domain. The model is compared with that proposed by Synnaeve and Bessiere. Experimental results on recorded games of human players show that the proposed model can predict the opponent’s future decisions more effectively. Using this model, it is possible to create an adaptive game intelligence algorithm applicable to RTS games, where the concept of build order (the order of building construction) exists

Damage and restoration of historical urban walls: literature review and case of studies

Within this work, the causes of collapses and damages occurred in masonry artefacts have been evaluated to properly identify suitable monitoring and restoration methods. In this regard, a comprehensive literature review has been performed. Based on the results, moisture has been found to be a critical parameter, that affects the structural health of masonry artefacts. Various non-destructive methods were employed to measure moisture and monitor the materials involved, including Infrared Thermography, Electrical Resistivity Tomography, Ground Penetrating Radar, Laser Scanning and Digital Terrestrial Photogrammetry, Global Navigation Satellite Systems, Unilateral Nuclear Magnetic Resonance, Laser-Induced Fluorescence technique, Acoustic Imaging and Acoustic Tomography, Geographic Information System, on-site survey process and computer modeling of the structure with specific FEM software. Finally, the implementation of tie-beams, Fiber Reinforced Polymers layers, ventilation, draining systems, and high-quality materials are proposed as solutions for controlling the moisture effect and retrofitting

X-CapsNet For Fake News Detection

News consumption has significantly increased with the growing popularity and use of web-based forums and social media. This sets the stage for misinforming and confusing people. To help reduce the impact of misinformation on users' potential health-related decisions and other intents, it is desired to have machine learning models to detect and combat fake news automatically. This paper proposes a novel transformer-based model using Capsule neural Networks(CapsNet) called X-CapsNet. This model includes a CapsNet with dynamic routing algorithm paralyzed with a size-based classifier for detecting short and long fake news statements. We use two size-based classifiers, a Deep Convolutional Neural Network (DCNN) for detecting long fake news statements and a Multi-Layer Perceptron (MLP) for detecting short news statements. To resolve the problem of representing short news statements, we use indirect features of news created by concatenating the vector of news speaker profiles and a vector of polarity, sentiment, and counting words of news statements. For evaluating the proposed architecture, we use the Covid-19 and the Liar datasets. The results in terms of the F1-score for the Covid-19 dataset and accuracy for the Liar dataset show that models perform better than the state-of-the-art baselines

Damage and restoration of historical urban walls: literature review and case of studies

Within this work, the causes of collapses and damages occurred in masonry artefacts have been evaluated to properly identify suitable monitoring and restoration methods. In this regard, a comprehensive literature review has been performed. Based on the results, the moisture has found to be a critical parameter, which affects the structural health of masonry artefacts. Diverse non-destructive methods were employed to measure the moisture and monitor the materials involved: the Infrared Thermography, the Electrical Resistivity Tomography and the Ground Penetrating Radar, the Laser Scanning and Digital Terrestrial Photogrammetry, the Global Navigation Satellite Systems, the Unilateral Nuclear Magnetic Resonance, the Laser-Induced Fluorescence technique, the Acoustic Imaging and the Acoustic Tomography, the Geographic Information System and on-site survey process as well as computer modeling of the structure with specific FEM software. Finally, the implementation of tie-beams, Fiber Reinforced Polymers layers, ventilation, draining systems, and high-quality materials are proposed as solutions for controlling the moisture effect and retrofitting

Identity-preserving Editing of Multiple Facial Attributes by Learning Global Edit Directions and Local Adjustments

Semantic facial attribute editing using pre-trained Generative Adversarial Networks (GANs) has attracted a great deal of attention and effort from researchers in recent years. Due to the high quality of face images generated by StyleGANs, much work has focused on the StyleGANs' latent space and the proposed methods for facial image editing. Although these methods have achieved satisfying results for manipulating user-intended attributes, they have not fulfilled the goal of preserving the identity, which is an important challenge. We present ID-Style, a new architecture capable of addressing the problem of identity loss during attribute manipulation. The key components of ID-Style include Learnable Global Direction (LGD), which finds a shared and semi-sparse direction for each attribute, and an Instance-Aware Intensity Predictor (IAIP) network, which finetunes the global direction according to the input instance. Furthermore, we introduce two losses during training to enforce the LGD to find semi-sparse semantic directions, which along with the IAIP, preserve the identity of the input instance. Despite reducing the size of the network by roughly 95% as compared to similar state-of-the-art works, it outperforms baselines by 10% and 7% in Identity preserving metric (FRS) and average accuracy of manipulation (mACC), respectively