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Neuroblastoma (NB) is a childhood cancer with a highly complex nature. High-risk NB patients undergo intensive treatment regimens that are often followed by long-term side effects. This, in addition to the emergence of resistant cancer cells, highlights a need for novel therapeutic targets and treatment strategies to improve outcome in NB. P21 is a cyclin-dependent kinase inhibitor considered to play a role in tumor resistance and aggressiveness due to its involvement in cell cycle and/or apoptosis. This project aimed to explore the expression of endogenous p21 in high-risk NB cell lines and whether p21 could be a therapeutic target for high-risk NB. Endogenous p21 levels were investigated using RT-qPCR and quantitative immunocytochemistry in eight high-risk NB cell lines. A small molecular inhibitor of p21, UC2288, was used in these cell lines to investigate tumour cell viability following p21 inhibition. In addition, combination treatment with UC2288 and the chemotherapy drug cisplatin was performed on resistant NB cell lines. Our results show variable expression of p21, where cell lines with high endogenous p21 expression showed sensitivity to single agent treatment with cisplatin or UC2288. Moreover, resistant NB cell lines showed lower endogenous p21 expression, however, combination treatment with UC2288 and cisplatin showed reduced viability, indicating sensitivity to combination treatment. This project highlights the potential of using p21 as a therapeutic target as well as a predictive biomarker in high-risk NB.

There are a number of available statistical methods for adaptive designs, among which the combination method of Bauer and Kohne's (1994) is well known and widely used. In this work, we revisit the the Bauer-Kohne method in three ways: overall FWER control for single-hypothesis in a two-stage adaptive design, overall FWER control for two-hypothesis in a two-stage adaptive design, and overall FDR control for multiple-hypothesis in a two-stage adaptive design. We first take the Bauer-Kohne method in a more direct manner to have more flexibility in the choice of the early rejection and acceptance boundaries as well as the second stage critical value based on the chosen combination function. Our goal is not to develop a new method, but focus primarily on developing a comprehensive understanding of two-stage designs. Rather than tying up the early rejection and acceptance boundaries by considering the second stage critical value to be the same as that of the level á combination test, as done in the original Bauer-Kohne method, we allow the second-stage critical value to be determined from prefixed early rejection and acceptance boundaries. An explicit formula is derived for the overall Type I error probability to determine the second stage critical value from these stopping boundaries not only for Fisher's combination function but also for other types of combination function. Tables of critical values corresponding to several different choices of early rejection and acceptance boundaries and these combination functions are presented. A dataset from a clinical study is used to apply the different methods based on directly computed second stage critical values from pre fixed stopping boundaries and discuss the outcomes in relation to those produced by the original Bauer-Kohne method. We then extend the Bauer-Kohne method to two-hypothesis setting and propose a stepwise-combination method for a two-stage adaptive design. In particular, we modify Holm's step-down procedure (1979) and suggest a step-down combination method to control the overall FWER at a desired level á. In many scientific studies requiring simultaneous testing of multiple null hypotheses, it is often necessary to carry out the multiple testing in two stages to decide which of the hypotheses can be rejected or accepted at the first stage and which should be followed up for further testing having combined their p-values from both stages. Unfortunately, no multiple testing procedure is available yet to perform this task meeting pre-specified boundaries on the first-stage p-values in terms of the false discovery rate (FDR) and maintaining a control over the overall FDR at a desired level. Our third goal in this work is to present two procedures, extending the classical Benjamini-Hochberg (BH) procedure and its adaptive version incorporating an estimate of the number of true null hypotheses from single-stage to a two-stage setting. These procedures are theoretically proved to control the overall FDR when the pairs of first- and second-stage p-values are independent and those corresponding to the null hypotheses are identically distributed as a pair (p1, p2) satisfying the p-clud property of Brannath, Posch and Bauer (2002, Journal of the American Statistical Association, 97, 236 -244). We consider two types of combination function, Fisher's and Simes', and present explicit formulas involving these functions towards carrying out the proposed procedures based on pre-determined critical values or through estimated FDR's. Simulations were carried to compare the proposed methods with class BH procedure using first stage data only and full data from both stages respectively. Our simulation studies indicate that the proposed procedures can have significant power improvement over the single-stage BH procedure based on the first stage data, at least under independence, and can continue to control the FDR under some dependence situations. Application of the proposed procedures to a real gene expression data set produces more discoveries compared to the single-stage BH procedure using the first stage data and full data as well. / Statistics

The ability to remember spatial locations is critical to human functioning, both in an evolutionary and an everyday sense. And yet, spatial memories and judgments often show systematic errors. Explanations for such errors have ranged from assumptions that memories are nonmetric, to the use of imperfect inferences, to the optimal combination of multiple sources of information. More recently, bias has been explained through the Category Adjustment Model - a Bayesian model in which fine-grained and categorical information are optimally combined (Huttenlocher, Hedges, & Duncan, 1991). However, experiments testing this model have largely used locations contained in simple geometric shapes. Use of this paradigm raises the issue of whether the results generalize to location memory in the complex natural world, as it should if it is to provide an over-arching framework for thinking about spatial memory. Here, this issue is addressed using a novel extension of the location memory paradigm that allows for testing of location memory in an everyday, 3D environment. The results support two predictions of the Category Adjustment Model - that memory for locations is biased toward central values, and that the magnitude of error increases with the retention interval. Future directions for testing the model in an increasingly ecologically valid manner are discussed. / Psychology

This paper examines the integration of geometric and feature cues in spatial memory with a modified version of the spatial reorientation task. Proponents of the geometric module position argue that there is an encapsulated reorientation module, and that true reorientation is accomplished only via geometry (Lee & Spelke, 2010). Features can be used in a second and separate step, but only to locate a goal rather than as a reorientation cue. In contrast, proponents of the adaptive combination approach argue that geometry and feature cues are integrated in spatial memory and that both cues can be used to facilitate reorientation (Newcombe & Ratliff, 2007). The present experiment provides support for the adaptive combination position. The first phase of the experiment was a reorientation task in a trapezoid enclosure (unambiguous geometry) with a narrow feature strip (unambiguous feature). During training, participants started facing a set direction, were disoriented, and then were asked to return to the original facing direction using the geometry, the feature, or both. During these training trials, participants were biased in the direction of the feature, indicating that both features and geometry might be integrated in a Bayesian fashion in spatial memory. The test trials provided additional support for integration and the adaptive combination position. There were four conflict test conditions where the feature moved in relation to the geometry present in training. When the feature moved a small distance in the direction of the geometry, when the feature moved to a novel wall, or when the feature moved a small distance away from the geometry of an acute corner angle, participants selected the correct geometric position. In contrast, when the feature moved a small distance away from the geometry of an obtuse corner angle or when the feature moved a moderate distance away from the geometry of either an acute or an obtuse angle, participants integrated their response and selected and area between the training geometry and feature. The modularity position predicts choices exclusively at the geometry position. This was not found. The adaptive combination position predicts that features and geometry will sometimes be used independently and crucially can be integrated. The data support this position. In sum, the present research provides the first evidence for integration of geometric and feature cues in adult spatial memory during reorientation, and supports the adaptive combination position. Additionally, a mini spatial battery was administered to participants. Correlations of individual differences in spatial skills with reorientation in this paradigm are discussed. / Psychology

In recent years, computational neuroscientists have suggested that human behaviour, including perception, occurs in a manner consistent with Bayesian inference. According to the Bayesian ideal observer model, the observer combines cues from multiple sensory streams as a weighted average based on each cue’s reliability. Most cue-combination research has focused on integration of cues between sensory modalities or within the visual modality. Cue combination within the tactile modality has been relatively rarely studied, and it is still not known whether cues from individual digits combine optimally. In this thesis, we use the ideal observer model to determine whether cues from three different digits are combined optimally. We predicted that cues from multiple digits would be combined according to the optimal cue combination model. To test our hypothesis, we devised a two-interval forced choice (2IFC) task where participants had to discriminate the distal/proximal location of a 1-mm thick edge across the fingerpad(s) of the index (D2), middle (D3), and ring (D4) fingers. We used a Bayesian adaptive method, the ψ method, to compute participants’ psychometric functions for single-digit (D2, D3, and D4) and multiple-digit (D23, D24, D34, and D234) conditions. We determined the stimulus level ∆x, the distance (mm) between the distal and proximal stimuli locations, at 76% correct probability. This distance corresponds to a sensitivity index d'=1 and is the σ value of the participant’s stimulus measurement distribution. We then used the single-digit σ values to predict optimal cue combination for the multiple-digits combinations. We did not observer optimal cue-combination between the digits in this study. We outline potential implications the results of this experimental have on determining how the nervous system combines cues between digits, focusing on theoretical and experimental updates to the experiment that might result in the observation of optimal cue combination between digits. / Thesis / Master of Science (MSc)

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23603号 / 医博第4790号 / 新制||医||1055(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 上野 英樹, 教授 金子 新, 教授 河本 宏 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

El aprendizaje autom�atico es un �area de investigaci�on que proporciona algoritmos y t�ecnicas que son capaces de aprender autom�aticamente a partir de experiencias pasadas. Estas t�ecnicas son esenciales en el �area de descubrimiento de conocimiento de bases de datos (KDD), cuya fase principal es t�ÿpicamente conocida como miner�ÿa de datos. El proceso de KDD se puede ver como el aprendizaje de un modelo a partir de datos anteriores (generaci�on del modelo) y la aplicaci�on de este modelo a nuevos datos (utilizaci�on del modelo). La fase de utilizaci�on del modelo es muy importante, porque los usuarios y, muy especialmente, las organizaciones toman las decisiones dependiendo del resultado de los modelos. Por lo general, cada modelo se aprende de forma independiente, intentando obtener el mejor resultado (local). Sin embargo, cuando varios modelos se usan conjuntamente, algunos de ellos pueden depender los unos de los otros (por ejemplo, las salidas de un modelo pueden ser las entradas de otro) y aparecen restricciones. En este escenario, la mejor decisi�on local para cada problema tratado individualmente podr�ÿa no dar el mejor resultado global, o el resultado obtenido podr�ÿa no ser v�alido si no cumple las restricciones del problema. El �area de administraci�on de la relaci�on con los clientes (CRM) ha dado origen a problemas reales donde la miner�ÿa de datos y la optimizaci�on (global) deben ser usadas conjuntamente. Por ejemplo, los problemas de prescripci�on de productos tratan de distinguir u ordenar los productos que ser�an ofrecidos a cada cliente (o sim�etricamente, elegir los clientes a los que se les deber�ÿa de ofrecer los productos). Estas �areas (KDD, CRM) carecen de herramientas para tener una visi�on m�as completa de los problemas y una mejor integraci�on de los modelos de acuerdo a sus interdependencias y las restricciones globales y locales. La aplicaci�on cl�asica de miner�ÿa de datos a problemas de prescripci�on de productos, por lo general, ha / Bella Sanjuán, A. (2012). Model Integration in Data Mining: From Local to Global Decisions [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16964