Different Tides: A Journey of Loss, Loneliness, and Friendship

This project is an exploration of story, producing an animatic for the original story Different Tides. Different Tides is a story about loss and separation, and how we carry with us a piece of everyone that has ever been close to us. This thesis will explore the themes of loneliness and loss that pervade the short, as well as the methods of visually expressing those themes. The goal of this thesis is to produce an animatable story, complete with storyboards, animatic, and color script, as well as explore the psychology behind loss and friendship

Radiosensitization of gliomas by intracellular generation of 5-fluorouracil potentiates prodrug activator gene therapy with a retroviral replicating vector.

A tumor-selective non-lytic retroviral replicating vector (RRV), Toca 511, and an extended-release formulation of 5-fluorocytosine (5-FC), Toca FC, are currently being evaluated in clinical trials in patients with recurrent high-grade glioma (NCT01156584, NCT01470794 and NCT01985256). Tumor-selective propagation of this RRV enables highly efficient transduction of glioma cells with cytosine deaminase (CD), which serves as a prodrug activator for conversion of the anti-fungal prodrug 5-FC to the anti-cancer drug 5-fluorouracil (5-FU) directly within the infected cells. We investigated whether, in addition to its direct cytotoxic effects, 5-FU generated intracellularly by RRV-mediated CD/5-FC prodrug activator gene therapy could also act as a radiosensitizing agent. Efficient transduction by RRV and expression of CD were confirmed in the highly aggressive, radioresistant human glioblastoma cell line U87EGFRvIII and its parental cell line U87MG (U87). RRV-transduced cells showed significant radiosensitization even after transient exposure to 5-FC. This was confirmed both in vitro by a clonogenic colony survival assay and in vivo by bioluminescence imaging analysis. These results provide a convincing rationale for development of tumor-targeted radiosensitization strategies utilizing the tumor-selective replicative capability of RRV, and incorporation of radiation therapy into future clinical trials evaluating Toca 511 and Toca FC in brain tumor patients

Finitely generated ideals of the ring of integer-valued polynomials

Throughout this paper, Z denoes the integers, Q the rational numbers, and D the collection of polynomials over Q having the property thatf(a) E Z for every a in Z. After first being studied by Polya [2 1 ] and Skolem [23], the domain D has been the subject of several more recent papers [2-14, 16, 17 ]. In particular, Brizolis established in [4] that D is a Priifer domain with each finitely generated ideal I determined by the values at integers of the polynomials in I. Specifically, he showed that if I= (f,(r),...,&(f))D, then g(t) E I if and only if g(u) E (f,(a),...,fJ(u))Z for every a E Z. In this paper we continue the study of the finitely generated ideals of D. While our initial efforts were directed toward answering a question of Brizolis [4] as to whether or not each finitely generated ideal of D can be gnerated by two elements, in time we became interested in giving a more explicit description of finite generating sets for ideals of D. Our methods are constructive, and we feel that we have had some success in accomplising this goal

GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems

Given the convenience of collecting information through online services, recommender systems now consume large scale data and play a more important role in improving user experience. With the recent emergence of Graph Neural Networks (GNNs), GNN-based recommender models have shown the advantage of modeling the recommender system as a user-item bipartite graph to learn representations of users and items. However, such models are expensive to train and difficult to perform frequent updates to provide the most up-to-date recommendations. In this work, we propose to update GNN-based recommender models incrementally so that the computation time can be greatly reduced and models can be updated more frequently. We develop a Graph Structure Aware Incremental Learning framework, GraphSAIL, to address the commonly experienced catastrophic forgetting problem that occurs when training a model in an incremental fashion. Our approach preserves a user's long-term preference (or an item's long-term property) during incremental model updating. GraphSAIL implements a graph structure preservation strategy which explicitly preserves each node's local structure, global structure, and self-information, respectively. We argue that our incremental training framework is the first attempt tailored for GNN based recommender systems and demonstrate its improvement compared to other incremental learning techniques on two public datasets. We further verify the effectiveness of our framework on a large-scale industrial dataset.Comment: Accepted by CIKM2020 Applied Research Trac

Message Passing for Complex Question Answering over Knowledge Graphs

Question answering over knowledge graphs (KGQA) has evolved from simple single-fact questions to complex questions that require graph traversal and aggregation. We propose a novel approach for complex KGQA that uses unsupervised message passing, which propagates confidence scores obtained by parsing an input question and matching terms in the knowledge graph to a set of possible answers. First, we identify entity, relationship, and class names mentioned in a natural language question, and map these to their counterparts in the graph. Then, the confidence scores of these mappings propagate through the graph structure to locate the answer entities. Finally, these are aggregated depending on the identified question type. This approach can be efficiently implemented as a series of sparse matrix multiplications mimicking joins over small local subgraphs. Our evaluation results show that the proposed approach outperforms the state-of-the-art on the LC-QuAD benchmark. Moreover, we show that the performance of the approach depends only on the quality of the question interpretation results, i.e., given a correct relevance score distribution, our approach always produces a correct answer ranking. Our error analysis reveals correct answers missing from the benchmark dataset and inconsistencies in the DBpedia knowledge graph. Finally, we provide a comprehensive evaluation of the proposed approach accompanied with an ablation study and an error analysis, which showcase the pitfalls for each of the question answering components in more detail.Comment: Accepted in CIKM 201

Early response predicts subsequent response to olanzapine long-acting injection in a randomized, double-blind clinical trial of treatment for schizophrenia

<p>Abstract</p> <p>Background</p> <p>In patients with schizophrenia, early non-response to oral antipsychotic therapy robustly predicts subsequent non-response to continued treatment with the same medication. This study assessed whether early response predicted later response when using a long-acting injection (LAI) antipsychotic.</p> <p>Methods</p> <p>Data were taken from an 8-week, randomized, double-blind, placebo-controlled study of olanzapine LAI in acutely ill patients with schizophrenia (n = 233). Early response was defined as ≥30% improvement from baseline to Week 4 in Positive and Negative Syndrome Scale (PANSS_0-6) Total score. Subsequent response was defined as ≥40% baseline-to-endpoint improvement in PANSS_0-6Total score. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and predictive accuracy were calculated. Clinical and functional outcomes were compared between Early Responders and Early Non-responders.</p> <p>Results</p> <p>Early response/non-response to olanzapine LAI predicted later response/non-response with high sensitivity (85%), specificity (72%), PPV (78%), NPV (80%), and overall accuracy (79%). Compared to Early Non-responders, Early Responders had significantly greater improvement in PANSS_0-6Total scores at all time points and greater baseline-to-endpoint improvement in PANSS subscale scores, Quality of Life Scale scores, and Short Form-36 Health Survey scores (all p ≤ .01). Among Early Non-responders, 20% demonstrated response by Week 8. Patients who lacked early improvement (at Week 4) in Negative Symptoms and Disorganized Thoughts were more likely to continue being non-responders at Week 8.</p> <p>Conclusions</p> <p>Among acutely ill patients with schizophrenia, early response predicted subsequent response to olanzapine LAI. Early Responders experienced significantly better clinical and functional outcomes than Early Non-responders. Findings are consistent with previous research on oral antipsychotics.</p> <p>Clinical Trials Registry</p> <p>F1D-MC-HGJZ: Comparison of Intramuscular Olanzapine Depot With Placebo in the Treatment of Patients With Schizophrenia <url>http://clinicaltrials.gov/ct2/show/NCT00088478?term=olanzapine+depot&rank=3</url></p> <p>Registry identifier - <a href="http://www.clinicaltrials.gov/ct2/show/NCT00088478">NCT00088478</a></p

Surface Aggregation of Urinary Proteins and Aspartic Acid-Rich Peptides on the Faces of Calcium Oxalate Monohydrate Investigated by In Situ Force Microscopy

The growth of calcium oxalate monohydrate in the presence of Tamm-Horsfall protein (THP), osteopontin, and the 27-residue synthetic peptides (DDDS)6DDD and (DDDG)6DDD (D = aspartic acid, S = serine, and G = glycine) was investigated via in situ atomic force microscopy. The results show that these four growth modulators create extensive deposits on the crystal faces. Depending on the modulator and crystal face, these deposits can occur as discrete aggregates, filamentary structures, or uniform coatings. These proteinaceous films can lead to either the inhibition of or an increase in the step speeds (with respect to the impurity-free system), depending on a range of factors that include peptide or protein concentration, supersaturation, and ionic strength. While THP and the linear peptides act, respectively, to exclusively increase and inhibit growth on the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left( {\bar{1}01} \right)$$\end{document} face, both exhibit dual functionality on the (010) face, inhibiting growth at low supersaturation or high modulator concentration and accelerating growth at high supersaturation or low modulator concentration. Based on analyses of growth morphologies and dependencies of step speeds on supersaturation and protein or peptide concentration, we propose a picture of growth modulation that accounts for the observations in terms of the strength of binding to the surfaces and steps and the interplay of electrostatic and solvent-induced forces at the crystal surface