Resolving the Complexity of Some Fundamental Problems in Computational Social Choice

This thesis is in the area called computational social choice which is an intersection area of algorithms and social choice theory.Comment: Ph.D. Thesi

Molecular contest between potato and the potato cyst nematode Globodera pallida: modulation of Gpa2-mediated resistance

Gpa2 recognition specificity Among all the multicellular animals, nematodes are the most numerous. In soil, a high variety of free living nematodes feeding on bacteria can be found as well as species that parasitize insects, animals or plants. The potato cyst nematode (PCN) Globodera pallida is an important pest of cultivated potato. Upon infection of the roots, the nematode induces a feeding cell complex or so-called syncytium, on which the immobilized nematode fully depends for its development and reproduction. Due to the sophisticated feeding manner and ability to survive for a long time in the absence of a host plant, the best way to control these soil-born pathogens is the exploitation host resistance. Natural resistance to nematodes is based on single dominant resistance genes (R) or quantitative trait loci (QTL). Several nematode resistance genes have been identified and mapped. This includes the potato gene Gpa2 (Van der Vossen et al., 2000) that confers resistance against the population D383 of G. pallida. The Gpa2 gene is highly homologous to Rx1, which confers resistance against potato virus X (Bendahmane et al., 1999). Both genes encode a protein with a nucleotide-binding leucinerich repeat (NB-LRR) domains and a short coiled-coil domain at the N-terminus, which are in 88% identical at the amino acid level. The vast majority of the differences between Gpa2 and Rx1 is found in the predicted solvent exposed regions of the LRR domain. In chapter 2, we have shown that the LRR domain is essential for the recognition specificities of Gpa2 and Rx1, whereas the CC-NBS domains can be exchanged without affecting the specificity. In chapter 5, we have used a series of chimeric constructs in which segments of the Gpa2 LRR were replaced by the corresponding segments from Rx1. These constructs allowed us to narrow down the region required for nematode recognition to a stretch of residues between 808 and 912 amino acid residues in Gpa2, including 10 amino acids that differ between Gpa2 and Rx1. Furthermore, a computer-aided 3D model of the LRR domain is presented in which 7 of the Gpa2 specific amino acid residues map in a cluster onto the concave surface of the horseshoe-like structure of the LRR domain. Gpa2-mediated nematode resistance The research described in chapter 3 aimed to understand the mechanisms underlying Gpa2- mediated resistance to the potato cyst nematode G. pallida. The extreme resistance response conferred by the close homologue Rx1 results in the blocking of the potato virus X (PVX) at the infection sites and hence, the prevention of systemic spreading throughout the plant. Surprisingly, an entirely different defense mechanism was observed for resistant potato plants infected with juveniles of the avirulent Globodera pallida population D383. In susceptible plants, both the virulent population Rookmaker and the avirulent population D383 formed normal developing syncytia and nematodes were able to complete their life cycle as described in previous studies. Infection of resistant plants with the avirulent population showed no differences between susceptible and resistant potato plants in the early stages of G.pallida parasitism (root entering, migration, syncytium initiation). Syncytium induction took place in parenchyma cells, but rarely in other tissues. In samples collected 7 days later, however, the first necrotic cells in the surrounding of the syncytium were noticed including symptoms of degradation in the ultra structure of the syncytium itself in case of resistant plants infected with avirulent nematodes. Samples collected 10 days post infection had already a layer of necrotic cells, which separates the syncytium from the vascular bundle. At 14 days post infection, it was observed that the parenchyma cells not incorporated directly in the syncytia started to divide fast. Groups of hyperplastic cells surrounding the degrading syncytium resulted in pushing it away to the outer part of the root. This unique phenomenon, which was not observed before, can be part of the Gpa2-mediated defense response or a secondary reaction to the presence of necrotic, dead cells and a way to exclude them from the healthy conductive tissue of the root. Transcriptional regulation of the Gpa2 promoter To look in more details into the transcriptional regulation and expression of Gpa2, the native promoter was fused to the reporter gene GUS and this construct was introduced into susceptible potato. In chapter 3, the activity of the Gpa2 promoter was observed and shown to be restricted to the vascular system and the root tips in uninfected plants. Roots were challenged with G.pallida and the localization of the GUS expression was observed at the infection sites at different parasitic stages. During infection with virulent nematodes - but not the avirulent ones - this activity seems to be down regulated in vicinity of the syncytium. Such a local inhibition of Gpa2 promoter activity is in line with observations made on resistant roots when necrotic cells were only present around the feeding cell complex, distantly from the feeding nematode. The effector protein RBP-1 elicits a Gpa2 dependent HR Recently, a RBP-1protein with strong similarity to the SPRY domain of the Ran-binding protein RanBPM in juveniles of G. pallida was identified as a putative Gpa2 elicitor. Transient expression of RBP-1 in N. benthamiana leaves elicits a Gpa2-dependent cell death typical for the R-gene associated hypersensitive response (HR). Total RNA isolated from two populations of G.pallida, D383 (avr to Gpa2) and Rookmaker (vir to Gpa2) was converted into cDNA and screened for the presence of RBP-1s. This screening allowed the identification of in total 10 classes of closely related homologs of RBP-1. All identified classes were tested for their ability to elicit the Gpa2-dependent HR in an agroinfiltration assay. The capacity to induce an Gpa2-dependent HR was shown to correlate with a single amino acid substitution in RBP-1. No response was observed for two classes, which were obtained from the virulent population (RBP-1ROOK2, RBP-1ROOK4). For the other homologous RBP-1 classes – both deriving from the virulent and avirulent population - the response was ranging from a mild to a strong and fast HR. Both in-active RBP-1 variants have a serine substitution at position 166 (S166P) within the SPRY domain. When this residue was projected on a computer aided 3D model of RBP, we noticed that this amino acid is in a loop extending from the protein core. Replacing the proline into a serine is predicted to change the shape of the loop and hence, to affect the potential surface for protein-protein interactions. Non-eliciting RBP-1 variants suppress RBP-induced Gpa2 activation It was shown that the non-eliciting variants (RBP-1ROOK2 and RBP-1ROOK4) can suppress the activation of a Gpa2-mediated HR by the eliciting RBP-1 variants. This effect was specific for the Gpa2-mediated HR, and not observed with a Rx1-induced HR. As autoactive mutants of Gpa2 and Rx1-mediated cell death are not blocked by the inactive variants of RBP-1, the mechanism of suppression or inhibition likely operates on a functional Gpa2 protein, instead of downstream Gpa2-activated signaling pathways. Further research is required to resolve the mechanism underlying the possible competitive interactions of the active and the inactive RBP-1 variants on the Gpa2-mediated HR. Essentially, two possible models that could explain this phenomenon. First, the inactive variants could physically out compete the active RBP-1s. The binding target of active and inactive variants of RBP-1 variants could be directly in the Gpa2 protein or in the virulence target monitored by Gpa2. Alternatively, the inactive variants of RBP-1 may intercept active RBP-1 variants by forming an inactive heterodimer complex rendering it essentially undetectable for the Gpa2 protein. <br/

2016 GREAT Day Program

SUNY Geneseo’s Tenth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1010/thumbnail.jp

Sequential decision making in artificial musical intelligence

Over the past 60 years, artificial intelligence has grown from a largely academic field of research to a ubiquitous array of tools and approaches used in everyday technology. Despite its many recent successes and growing prevalence, certain meaningful facets of computational intelligence have not been as thoroughly explored. Such additional facets cover a wide array of complex mental tasks which humans carry out easily, yet are difficult for computers to mimic. A prime example of a domain in which human intelligence thrives, but machine understanding is still fairly limited, is music. Over the last decade, many researchers have applied computational tools to carry out tasks such as genre identification, music summarization, music database querying, and melodic segmentation. While these are all useful algorithmic solutions, we are still a long way from constructing complete music agents, able to mimic (at least partially) the complexity with which humans approach music. One key aspect which hasn't been sufficiently studied is that of sequential decision making in musical intelligence. This thesis strives to answer the following question: Can a sequential decision making perspective guide us in the creation of better music agents, and social agents in general? And if so, how? More specifically, this thesis focuses on two aspects of musical intelligence: music recommendation and human-agent (and more generally agent-agent) interaction in the context of music. The key contributions of this thesis are the design of better music playlist recommendation algorithms; the design of algorithms for tracking user preferences over time; new approaches for modeling people's behavior in situations that involve music; and the design of agents capable of meaningful interaction with humans and other agents in a setting where music plays a roll (either directly or indirectly). Though motivated primarily by music-related tasks, and focusing largely on people's musical preferences, this thesis also establishes that insights from music-specific case studies can also be applicable in other concrete social domains, such as different types of content recommendation. Showing the generality of insights from musical data in other contexts serves as evidence for the utility of music domains as testbeds for the development of general artificial intelligence techniques. Ultimately, this thesis demonstrates the overall usefulness of taking a sequential decision making approach in settings previously unexplored from this perspectiveComputer Science

Relationships among Constructs of L2 Chinese Reading and Language Background.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

On the Recognition of Emotion from Physiological Data

This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

2012 Research Day Abstract Listing

Abstracts of oral and poster presentations from student researchers, presented at UNC\u27s Annual Research Conference during Academic Excellence Week