Psychosocial Functioning in Women with Symptoms of Persistent Genital Arousal

Persistent genital arousal disorder (PGAD) is characterized by physiological sexual arousal (vasocongestion, sensitivity of the genitals and nipples) that is described as distressing, and sometimes painful. Although awareness of PGAD is growing, there continues to be a lack of systematic research on this condition. The vast majority of published reports are case studies. Little is known about the symptom characteristics, biological factors, or psychosocial functioning associated with the experience of persistent genital arousal (PGA) symptoms. This study sought to characterize a sample of women with PGA (Study One); compare women with and without PGA symptoms on a series of biopsychosocial factors (Study Two); and undertake an exploratory comparison of women with PGA, painful PGA, and genital pain (Study Three)—all within a biopsychosocial framework. Symptom-free women, women with PGA symptoms, painful PGA, and genital pain, completed an online survey of biological factors (medical history, symptom profiles), psychological factors (depression, anxiety) and social factors (sexual function, relationship satisfaction). Study One found that women report diverse symptoms associated with PGA, with almost half reporting painful symptoms. In Study Two, women with symptoms of PGA reported significantly greater impairment in most domains of psychosocial functioning as compared to symptom-free women. In particular, catastrophizing of vulvar sensations was related to symptom ratings (i.e., greater severity, distress) and psychosocial outcomes (i.e., greater depression and anxiety). Finally, Study Three found that women with PGA symptoms reported some overlap in medical comorbidities and symptom expression as those with combined PGA and vulvodynia and those with vulvodynia symptoms alone; however, there were also a number of significant differences in their associated physical symptoms. These studies indicate that PGA symptoms have negative consequences for the psychosocial functioning of affected women. As such, future research and clinical care may benefit from a biopsychosocial approach to PGA symptoms. These studies highlight areas for more targeted research, including the role of catastrophizing in PGA symptom development and maintenance, and the potential conceptualization of both PGA and vulvodynia (and potentially other conditions) under a general umbrella of ‘genital paraesthesias’ (i.e., disorders characterized by abnormal sensations, such as tingling and burning).

Linear Hyperspectral Unmixing Using L0-norm Approximations and Nonnegative Matrix Factorization

Spectral unmixing (SU) is a technique to characterize mixed pixels of the hyperspectral images measured by remote sensors. Most of the existing spectral unmixing algorithms are developed using the linear mixing models. Since the number of endmembers/materials present at each mixed pixel is normally scanty compared with the number of total endmembers (the dimension of spectral library), the problem becomes sparse. This thesis introduces sparse hyperspectral unmixing methods for the linear mixing model through two different scenarios. In the first scenario, the library of spectral signatures is assumed to be known and the main problem is to find the minimum number of endmembers under a reasonable small approximation error. Mathematically, the corresponding problem is called the $\ell_0$-norm problem which is NP-hard problem. Our main study for the first part of thesis is to find more accurate and reliable approximations of $\ell_0$-norm term and propose sparse unmixing methods via such approximations. The resulting methods are shown considerable improvements to reconstruct the fractional abundances of endmembers in comparison with state-of-the-art methods such as having lower reconstruction errors. In the second part of the thesis, the first scenario (i.e., dictionary-aided semiblind unmixing scheme) will be generalized as the blind unmixing scenario that the library of spectral signatures is also estimated. We apply the nonnegative matrix factorization (NMF) method for proposing new unmixing methods due to its noticeable supports such as considering the nonnegativity constraints of two decomposed matrices. Furthermore, we introduce new cost functions through some statistical and physical features of spectral signatures of materials (SSoM) and hyperspectral pixels such as the collaborative property of hyperspectral pixels and the mathematical representation of the concentrated energy of SSoM for the first few subbands. Finally, we introduce sparse unmixing methods for the blind scenario and evaluate the efficiency of the proposed methods via simulations over synthetic and real hyperspectral data sets. The results illustrate considerable enhancements to estimate the spectral library of materials and their fractional abundances such as smaller values of spectral angle distance (SAD) and abundance angle distance (AAD) as well.