No description available.

The ability to recall a spatial layout from multiple orientations – spatial flexibility – is a challenging cognitive process, especially when the global configuration cannot be viewed from a single vantage point, as spatial information must first be integrated before it can be flexibly recalled. The current study examined if experiencing the transition between multiple viewpoints enhances spatial flexibility for both non-integrated (Exp. 1) and integrated environments (Exp. 2), if the type of transition matters, and if action provides an additional advantage over passive visual flow. In Experiment 1, participants viewed an array of dollhouse furniture from four viewpoints that presented the global configuration from multiple orientations. In Experiment 2, the array was viewed piecemeal, from four viewpoints that presented the global configuration in partial chunks. The control condition presented the dollhouse as a series of static views, whereas in the remaining conditions, visual flow was continuous. Participants viewed the natural transition between viewpoints, and either passively experienced the transitions (i.e., by watching the dollhouse rotate or being rolled around it), or actively generated them (i.e., by rotating the dollhouse or walking around it). Across both experiments, continuous visual flow significantly enhanced spatial flexibility when paired with observer movement around the dollhouse, either active or passive. Furthermore, when participants had to integrate spatial information across discrete learning experiences (Exp. 2), active movement provided a significant advantage above passive experience. These findings suggest that array stability is key to flexible spatial memory, with action providing an additional boost to spatial integration. / Psychology

When spatial updating tasks are performed in a real-world setting, participants usually complete it with ease (e.g., Klatzky et al., 1998). However, in virtual reality (VR), when tasks are presented using optic flow, participants tend to exhibit one of two response patterns, with some participants correctly updating their headings (“turners”) and others pointing consistently in the opposite direction (“non-turners”) (e.g., Gramann et al., 2005). While research has looked at the stability and pointing characteristics of these two groups (e.g., Gramann et al., 2012; Riecke, 2008), we still do not know why non-turners exist. The following thesis studied two potential sources of cue conflict—stationary versus central visual information and sensorimotor interference—that could impact participants’ strategies using the Starfield task (Gramann et al., 2012). Occluding stationary peripheral information increased pointing errors, especially for turners. It is thus possible that turners require the peripheral information to correctly parse and process the central optic flow. Alternatively, manipulating body orientation to decrease sensorimotor interference seemed to decrease error and increase strategy consistency for both turners and non-turners. It is possible that the orientation changes allowed participants to ignore the stationary body- based cues, thereby improving spatial updating. Although these manipulations did not remove the non-turner group altogether, they provided important insights into how cue conflicts may play a role in spatial updating for VR tasks. / Thesis / Master of Science (MSc)

Bayesian hierarchical models are useful for modeling spatial data because they have flexibility to accommodate complicated dependencies that are common to spatial data. In particular, intrinsic conditional autoregressive (ICAR) models are commonly assigned as priors for spatial random effects in hierarchical models for areal data corresponding to spatial partitions of a region. However, selection of prior distributions for these spatial parameters presents a challenge to researchers. We present and describe ref.ICAR, an R package that implements an objective Bayes intrinsic conditional autoregressive prior on a vector of spatial random effects. This model provides an objective Bayesian approach for modeling spatially correlated areal data. ref.ICAR enables analysis of spatial areal data for a specified region, given user-provided data and information about the structure of the study region. The ref.ICAR package performs Markov Chain Monte Carlo (MCMC) sampling and outputs posterior medians, intervals, and trace plots for fixed effect and spatial parameters. Finally, the functions provide regional summaries, including medians and credible intervals for fitted values by subregion. / Master of Science / Spatial data is increasingly relevant in a wide variety of research areas. Economists, medical researchers, ecologists, and policymakers all make critical decisions about populations using data that naturally display spatial dependence. One such data type is areal data; data collected at county, habitat, or tract levels are often spatially related. Most convenient software platforms provide analyses for independent data, as the introduction of spatial dependence increases the complexity of corresponding models and computation. Use of analyses with an independent data assumption can lead researchers and policymakers to make incorrect, simplistic decisions. Bayesian hierarchical models can be used to effectively model areal data because they have flexibility to accommodate complicated dependencies that are common to spatial data. However, use of hierarchical models increases the number of model parameters and requires specification of prior distributions. We present and describe ref.ICAR, an R package available to researchers that automatically implements an objective Bayesian analysis that is appropriate for areal data.

Glioblastoma is one of the most devastating forms of primary brain tumor, with a survival of 14.6 months from the diagnosis. Despite the aggressive treatments used in the clinic, consisting of surgical resection followed by radiotherapy and concurrent chemotherapy using temozolomide, the absence of novel treatments and the development of resistance to standard-of-care therapies continue to position glioblastoma as the most challenging brain tumor in adults. The main factor contributing to the incurability of glioblastoma is the extensive heterogeneity. This heterogeneity, which is evident both at intratumoral and inter-patient levels, represents a substantial obstacle to the achievement of effective treatments. In this context, the use of single-cell resolution appears one of the most powerful means for understanding the intricacies and unravelling the heterogeneity of glioblastoma. Although single-cell RNA sequencing studies have provided and still provide valuable insights, they lack the essential spatial context that is critical for unravelling the heterogeneity of glioblastoma. This limitation impedes the understanding of interactions among distinct subpopulations and their intricate relationships with the neuronal microenvironment. To gain insights into the heterogeneity of glioblastoma, we used the spatially resolved RNA sequencing technology to analyse glioblastoma samples deriving from 3 different patients. For each patient, we focused on four distinct tumor regions, i. e. the proliferating tumor area, the necrotic core, the infiltrating area, and a distal healthy area. Cancer cells identified in the infiltrating regions exhibited a unique pattern of cell subpopulations, with the oligodendrocytes as the most represented in this area. In addition, we managed to generate patient-derived glioblastoma organoids from nearly all areas, with the tumor regions displaying the highest growth rates. These patient-derived organoids, which represent fundamental models useful to faithfully replicate the disease in vitro, may be employed in future analyses. Finally, we also derived glioblastoma stem cell cultures from the different tumor regions, with the proliferating tumor area showing the highest rate of success. In the second part, we aimed to gain a deeper understanding of the molecular mechanisms that underlie the development of glioblastoma, which is crucial for developing effective treatments, and characterise ELAVL2 role, highlighting its potential role as a potential tumor suppressor in glioblastoma. Collectively, our findings suggest that ELAVL2 promotes the exit of glioblastoma stem cells from quiescence, boosting their self-renewal capacity while also facilitating neuronal differentiation. The in vivo validation of our results using an orthotopic human glioblastoma stem cell xenograft model and a D. melanogaster genetic model strongly supports our findings and points to deletion of ELAVL2 as a factor that increases aggressiveness in glioblastoma stem cells and in vivo.

Game-space presents the development of a student game-hall on the campus of Virginia Polytechnic Institute and State University in Blacksburg, Virginia. The architectural theory that guided the development asserts that an intelligent translation of a building's physical and conceptual needs into a matrix of well defined layers provides a strong foundation for the creation of a cultivated space. / Master of Architecture

No / Natural visual scenes are a rich source of information. Objects often carry luminance, colour, motion, depth and textural cues, each of which can serve to aid detection and localization of the object within a scene. Contemporary neuroscience presumes a modular approach to visual analysis in which each of these attributes are processed within ostensibly independent visual streams and are transmitted to geographically distinct and functionally dedicated centres in visual cortex (van Essen & Maunsell, 1983; Zihl, von Cramon & Mai, 1983; Maunsell & Newsome, 1987; Tootell, Hadjikhani, Mendola, Marrett & Dale, 1998). In the present study we ask how the visual system localizes objects within this framework. Specifically, we investigate how the visual system assigns a unitary location to objects defined by multiple stimulus attributes, where such attributes provide conflicting positional cues. The results show that conflicting sources of visual information can be effortlessly combined to form a global estimate of spatial position, yet, this conflation of visual attributes is achieved at a cost to localization accuracy. Furthermore, our results suggest that the visual system assigns more perceptual weight (Landy, 1993; Landy & Kojima, 2001) to visual attributes which are reliably related to object contours.

No description available.

The concept of proximity is an important aspect of human reasoning. Despite the diversity of applications that require proximity measures, the most intuitive notion is that of spatial nearness. The aim of this thesis is to investigate the underpinnings of the notion of nearness, propose suitable formalisations and apply them to the processing of GIS data. More particularly, this work offers a framework for spatial proximity that supports the development of more intuitive tools for users of geographic data processing applications. Many of the existing spatial reasoning formalisms do not account for proximity at all while others stipulate it by using natural language expressions as symbolic values. Some approaches suggest the association of spatial relations with fuzzy membership grades to be calculated for locations in a map using Euclidean distance. However, distance is not the only factor that influences nearness perception. Hence, previous work suggests that nearness should be defined from a more basic notion of influence area. I argue that this approach is flawed, and that nearness should rather be defined from a new, richer notion of impact area that takes both the nature of an object and the surrounding environment into account. A suitable notion of nearness considers the impact areas of both objects whose degree of nearness is assessed. This is opposed to the common approach of only taking one of both objects, seen as a reference to assess the nearness of the other to it, into consideration. Cognitive findings are incorporated to make the framework more relevant to the users of Geographic Information Systems (GIS) with respect to their own spatial cognition. GIS users bring a wealth of knowledge about physical space, particularly geographic space, into the processing of GIS data. This is taken into account by introducing the notion of context. Context represents either an expert in the context field or information from the context field as collated by an expert. In order to evaluate and to show the practical implications of the framework, experiments are conducted on a GIS dataset incorporating expert knowledge from the Touristic Road Travel domain.

The concept of proximity is an important aspect of human reasoning. Despite the diversity of applications that require proximity measures, the most intuitive notion is that of spatial nearness. The aim of this thesis is to investigate the underpinnings of the notion of nearness, propose suitable formalisations and apply them to the processing of GIS data. More particularly, this work offers a framework for spatial proximity that supports the development of more intuitive tools for users of geographic data processing applications. Many of the existing spatial reasoning formalisms do not account for proximity at all while others stipulate it by using natural language expressions as symbolic values. Some approaches suggest the association of spatial relations with fuzzy membership grades to be calculated for locations in a map using Euclidean distance. However, distance is not the only factor that influences nearness perception. Hence, previous work suggests that nearness should be defined from a more basic notion of influence area. I argue that this approach is flawed, and that nearness should rather be defined from a new, richer notion of impact area that takes both the nature of an object and the surrounding environment into account. A suitable notion of nearness considers the impact areas of both objects whose degree of nearness is assessed. This is opposed to the common approach of only taking one of both objects, seen as a reference to assess the nearness of the other to it, into consideration. Cognitive findings are incorporated to make the framework more relevant to the users of Geographic Information Systems (GIS) with respect to their own spatial cognition. GIS users bring a wealth of knowledge about physical space, particularly geographic space, into the processing of GIS data. This is taken into account by introducing the notion of context. Context represents either an expert in the context field or information from the context field as collated by an expert. In order to evaluate and to show the practical implications of the framework, experiments are conducted on a GIS dataset incorporating expert knowledge from the Touristic Road Travel domain.