Effect size estimation and robust classification for irregularly sampled functional data

Functional data arise frequently in numerous scientific fields with the development of modern technology. Accordingly, functional data analysis to extract information on curves or functions is an important area for investigation. In this thesis, we address two key issues: measuring an effect size of variable of the interest in functional analysis of variance (fANOVA) model and the development of robust probabilistic classifier in functional response model. We especially consider irregular functional data in our study, where curves are collected over varying or non-overlapping intervals. First, we develop an approach to quantify the effect size on functional data, perform functional ANOVA hypothesis test, and conduct power analysis. We develop an approach to quantify the effect size on functional data, perform functional ANOVA hypothesis test, and conduct power analysis. We introduce the functional signal-to-noise ratio ($fSNR$), visualize the magnitude of effects over the interval of interest, and perform bootstrapped inferences. It can be applicable when the individual curves are sampled at irregularly spaced points or collected over varying intervals. The proposed methods are applied in the analysis of functional data from inter-laboratory quantitative ultrasound measurements, and in a reanalysis of Canadian weather data. Moreover, we represent the asymptotic power of functional ANOVA test as a function of proposed measure. The agreement between the asymptotic and empirical results is examined and found to be quite good even for small sample sizes. The asymptotic lower bound of power can be reasonably used to determine sample size in planning experimental design. Second, we build a robust probabilistic classifier for functional data, which predicts the membership for given input as well as provides informative posterior probability distribution over a set of classes. This method combines Bayes formula and semiparametric mixed effects model with robust tuning parameter. We aim to make the method robust to outlying curves especially in providing robust degree of certainty in prediction, which is crucial in medical diagnosis. It can be applicable to various practical structures, such as unequally and sparsely collected samples or repeatedly measured curves retaining between-curve correlation, with very flexible spatial covariance function. As an illustration we conduct simulation studies to investigate the sensitivity behaviors of probability estimates to outlying curves under Gaussian assumption and compare our proposed classifier with other functional classification approaches. The performance is evaluated by imposing more penalty for being confident but false prediction. The value of the proposed approach hinges on its simple, flexible, and computational efficiency. We illustrate the issues and methodology in ultrasound quantitative ultrasound, backscatter coefficient vs. frequency functional data, commonly obtained as irregular form and public dataset with artificial contamination. We also show how to implement proposed classifier in R

Electrophysiology of glioma: a Rho GTPase-activating protein reduces tumor growth and spares neuron structure and function

Background. Glioblastomas are the most aggressive type of brain tumor. A successful treatment should aim at halting tumor growth and protecting neuronal cells to prevent functional deficits and cognitive deterioration. Here, we exploited a Rho GTPase-activating bacterial protein toxin, cytotoxic necrotizing factor 1 (CNF1), to interfere with glioma cell growth in vitro and vivo. We also investigated whether this toxin spares neuron structure and function in peritumoral areas. Methods. We performed a microarray transcriptomic and in-depth proteomic analysis to characterize the molecular changes triggered by CNF1 in glioma cells. We also examined tumor cell senescence and growth in vehicle-and CNF1-treated glioma-bearing mice. Electrophysiological and morphological techniques were used to investigate neuronal alterations in peritumoral cortical areas. Results. Administration of CNF1 triggered molecular and morphological hallmarks of senescence in mouse and human glioma cells in vitro. CNF1 treatment in vivo induced glioma cell senescence and potently reduced tumor volumes. In peritumoral areas of glioma-bearing mice, neurons showed a shrunken dendritic arbor and severe functional alterations such as increased spontaneous activity and reduced visual responsiveness. CNF1 treatment enhanced dendritic length and improved several physiological properties of pyramidal neurons, demonstrating functional preservation of the cortical network. Conclusions. Our findings demonstrate that CNF1 reduces glioma volume while at the same time maintaining the physiological and structural properties of peritumoral neurons. These data indicate a promising strategy for the development of more effective antiglioma therapies

The clinical- and cost-effectiveness of functional electrical stimulation and ankle-foot orthoses for foot drop in Multiple Sclerosis: a multicentre randomized trial

Objective: To compare the clinical- and cost-effectiveness of ankle-foot orthoses (AFOs) and functional electrical stimulation (FES) over 12 months in people with Multiple Sclerosis with foot drop. Design: Multicentre, powered, non-blinded, randomized trial. Setting: Seven Multiple Sclerosis outpatient centres across Scotland. Subjects: Eighty-five treatment-naïve people with Multiple Sclerosis with persistent (>three months) foot drop. Interventions: Participants randomized to receive a custom-made, AFO (n = 43) or FES device (n = 42). Outcome measures: Assessed at 0, 3, 6 and 12 months; 5-minute self-selected walk test (primary), Timed 25 Foot Walk, oxygen cost of walking, Multiple Sclerosis Impact Scale-29, Multiple Sclerosis Walking Scale-12, Modified Fatigue Impact Scale, Euroqol five-dimension five-level questionnaire, Activities-specific Balance and Confidence Scale, Psychological Impact of Assistive Devices Score, and equipment and National Health Service staff time costs of interventions. Results: Groups were similar for age (AFO, 51.4 (11.2); FES, 50.4(10.4) years) and baseline walking speed (AFO, 0.62 (0.21); FES 0.73 (0.27) m/s). In all, 38% dropped out by 12 months (AFO, n = 21; FES, n = 11). Both groups walked faster at 12 months with device (P < 0.001; AFO, 0.73 (0.24); FES, 0.79 (0.24) m/s) but no difference between groups. Significantly higher Psychological Impact of Assistive Devices Scores were found for FES for Competence (P = 0.016; AFO, 0.85(1.05); FES, 1.53(1.05)), Adaptability (P = 0.001; AFO, 0.38(0.97); FES 1.53 (0.98)) and Self-Esteem (P = 0.006; AFO, 0.45 (0.67); FES 1 (0.68)). Effects were comparable for other measures. FES may offer value for money alternative to usual care. Conclusion: AFOs and FES have comparable effects on walking performance and patient-reported outcomes; however, high drop-outs introduces uncertainty