A note on the effective medium theory of random resistive-reactive medium

The effective medium theory for a system with randomly distributed point conductivity and polarisability is reformulated, with attention to cross-terms involving the two disorder parameters. The treatment reveals a certain inconsistency of the conventional theory owing to the neglect of the Maxwell-Wagner effect. The results are significant for the critical resistivity and dielectric anomalies of a binary liquid mixture at the phase separation point.

Computing the Stochastic Complexity of Simple Probabilistic Graphical Models

Minimum Description Length (MDL) is an information-theoretic principle that can be used for model selection and other statistical inference tasks. There are various ways to use the principle in practice. One theoretically valid way is to use the normalized maximum likelihood (NML) criterion. Due to computational difficulties, this approach has not been used very often. This thesis presents efficient floating-point algorithms that make it possible to compute the NML for multinomial, Naive Bayes and Bayesian forest models. None of the presented algorithms rely on asymptotic analysis and with the first two model classes we also discuss how to compute exact rational number solutions.

Reactive state selectivity in photodimerization through micellar counter-ion effects: photodimerization of acenaphthylenes

Photodimerization of acenaphthylene and 5,6-dichloroacenaphthylene solubilized in sodium dodecylsulphate (SDS), cetyltrimethylammonium chloride (CTAC), dodecyltrimethylammonium chloride (DTAC), cetyltrimethylammonium bromide (CTAB) and Triton X-100 micelles gives a mixture of cis and trans dimers. The magnitude of the cis:trans ratio is sensitive to the type of micelle used. In CTAB micelles the heavy atom effect of the bromide counter-ions leads to an increased triplet-derived trans dimer yield, whereas in micelles with light atom counter-ions (CTAC, DTAC and SDS) the singlet-derived cis dimer predominates.

A Probabilistic Approach to the Primary Visual Cortex

What can the statistical structure of natural images teach us about the human brain? Even though the visual cortex is one of the most studied parts of the brain, surprisingly little is known about how exactly images are processed to leave us with a coherent percept of the world around us, so we can recognize a friend or drive on a crowded street without any effort. By constructing probabilistic models of natural images, the goal of this thesis is to understand the structure of the stimulus that is the raison d etre for the visual system. Following the hypothesis that the optimal processing has to be matched to the structure of that stimulus, we attempt to derive computational principles, features that the visual system should compute, and properties that cells in the visual system should have. Starting from machine learning techniques such as principal component analysis and independent component analysis we construct a variety of sta- tistical models to discover structure in natural images that can be linked to receptive field properties of neurons in primary visual cortex such as simple and complex cells. We show that by representing images with phase invariant, complex cell-like units, a better statistical description of the vi- sual environment is obtained than with linear simple cell units, and that complex cell pooling can be learned by estimating both layers of a two-layer model of natural images. We investigate how a simplified model of the processing in the retina, where adaptation and contrast normalization take place, is connected to the nat- ural stimulus statistics. Analyzing the effect that retinal gain control has on later cortical processing, we propose a novel method to perform gain control in a data-driven way. Finally we show how models like those pre- sented here can be extended to capture whole visual scenes rather than just small image patches. By using a Markov random field approach we can model images of arbitrary size, while still being able to estimate the model parameters from the data.

A probabilistic approach to the problem of electron localization in disordered systems and sharpness of the mobility edge

By applying the theory of the asymptotic distribution of extremes and a certain stability criterion to the question of the domain of convergence in the probability sense, of the renormalized perturbation expansion (RPE) for the site self-energy in a cellularly disordered system, an expression has been obtained in closed form for the probability of nonconvergence of the RPE on the real-energy axis. Hence, the intrinsic mobility mu (E) as a function of the carrier energy E is deduced to be given by mu (E)= mu 0exp(-exp( mod E mod -Ec) Delta ), where Ec is a nominal 'mobility edge' and Delta is the width of the random site-energy distribution. Thus mobility falls off sharply but continuously for mod E mod >Ec, in contradistinction with the notion of an abrupt 'mobility edge' proposed by Cohen et al. and Mott. Also, the calculated electrical conductivity shows a temperature dependence in qualitative agreement with experiments on disordered semiconductors.

Multi-state-choice automata model for expedient learning in random environment

A new automata model Mr,k, with a conceptually significant innovation in the form of multi-state alternatives at each instance, is proposed in this study. Computer simulations of the Mr,k, model in the context of feature selection in an unsupervised environment has demonstrated the superiority of the model over similar models without this multi-state-choice innovation.

A Reactive Inverse PN Algorithm for Collision Avoidance among Multiple Unmanned Aerial Vehicles

While performing a mission, multiple Unmanned Aerial Vehicles (UAVs) need to avoid each other to prevent collisions among them. In this paper, we design a collision avoidance algorithm to resolve the conflict among UAVs that are on a collision course while flying to heir respective destinations. The collision avoidance algorithm consist of each UAV that is on a collision course reactively executing a maneuver that will, as in `inverse' Proportional Navigation (PN), increase Line of Sight (LOS) rate between them, resulting in a `pulling out' of collision course. The algorithm is tested for high density traffic scenarios as well as for robustness in the presence of noise.

Polymorphic low penetrance genes and breast cancer : The role of genes involved in metabolism of xenobiotics, estrogens and reactive oxygen species

Various endogenous and exogenous factors have been reported to increase the risk of breast cancer. Many of those are related to prolonged lifetime exposure to estrogens. Furthermore, a positive family history of breast cancer and certain benign breast diseases are known to increase the risk of breast cancer. The role of lifestyle factors, such as use of alcohol and smoking has been an area of intensive study. Alcohol has been found to increase the risk of breast cancer, whereas the role of smoking has remained obscure. A multitude of enzymes are involved in the metabolism of estrogens and xenobiotics including the carcinogens found in tobacco smoke. Many of the metabolic enzymes exhibit genetic polymorphisms that can lead to inter-individual differences in their abilities to modify hazardous substrates. Therefore, in presence of a given chemical exposure, one subgroup of women may be more susceptible to breast carcinogenesis, since they carry unfavourable forms of the polymorphic genes involved in the metabolism of the chemical. In this work, polymorphic genes encoding for cytochrome P450 (CYP) 1A1 and 1B1, N-acetyl transferase 2 (NAT2), sulfotransferase 1A1 (SULT1A1), manganese superoxide dismutase (MnSOD) and vitamin D receptor (VDR) were investigated in relation to breast cancer susceptibility in a Finnish population. CYP1A1, CYP1B1 and SULT1A1 are involved in the metabolism of both estrogens and xenobiotics, whereas NAT2 is involved only in the latter. MnSOD is an antioxidant enzyme protecting cells from oxidative damage. VDR, in turn, mediates the effects of the active form of vitamin D (1,25(OH)2D3, calcitriol) on maintenance of calcium homeostasis and it has anti-proliferative effects in many cancer cells. A 1.3-fold (95% CIs 1.01-1.73) increased risk of breast cancer was seen among women who carried the NAT2 slow acetylator genotype and a 1.5-fold (95% CI 1.1-2.0) risk was found in women with a MnSOD variant A allele containing genotypes compared to women with the NAT2 rapid acetylator genotype or to those with the MnSOD VV genotype, respectively. Instead, women with the VDR a allele containing genotypes were found to be at a decreased risk for breast cancer (OR 0.73; 95% CI 0.54-0.98) compared to women with the AA genotype. No significant overall associations were found between SULT1A1 or CYP genotypes and breast cancer risk, whereas a combination of the CYP1B1 432Val allele containing genotypes with the NAT2 slow acetylator genotypes posed a 1.5-fold (95% CI 1.03-2.24) increased risk. Moreover, NAT2 slow acetylator genotype was found to be confined to women with an advanced stage of breast cancer (stages III and IV). Further evidence for the association of xenobiotic metabolising genes with breast cancer risk was found when active smoking was taken into account. Women who smoked less than 10 cigarettes/day and carried at least one CYP1B1 432Val variant allele, were at 3.1-fold (95% CI 1.32-7.12) risk of breast cancer compared to women who smoked the same amount but did not carry the variant allele. Furthermore, the risk was significantly increased with increasing number of the CYP1B1 432Val alleles (p for trend 0.005). In addition, women who smoked less than 5 pack-years and carried the NAT2 slow acetylator genotype were at a 2.6-fold (95% CI 1.01-6.48) increased risk of breast cancer compared to women who smoked the same amount but carried the NAT2 rapid acetylator genotype. Furthermore, the combination of the CYP1B1 432Val allele and the NAT2 slow acetylator genotype increased the risk of breast cancer by 2.5-fold (95% CI 1.11-5.45) among ever smokers. Instead, the MnSOD A allele was found to be a risk factor among postmenopausal long-term smokers (>15 years of smoking) (OR 5.1; 95% CI 1.4-18.4) or among postmenopausal women who had smoked more than 10 cigarettes/day (OR 5.5; 95% CI 1.3-23.4) compared to women who had similar smoking habits but carried the MnSOD V/V genotype. Similarly, within subgroups of postmenopausal women who were using oral contraceptives, hormone replacement therapy or alcohol, women carrying the MnSOD A allele genotypes seemed to be at increased risk of breast cancer compared to women with the MnSOD V/V genotype. A positive family history of breast cancer and high parity were shown to be inversely associated with breast cancer risk among women carrying the VDR ApaI a allele or among premenopausal women carrying the SULT1A1*2 allele, respectively.

Effect of Nucleic Acid Reactive Antibodies on Tumor Cells Grown in Vivo

Nucleic acid reactive antibodies have been reported to inhibit various nucleio acid mediated functions in cell free systems. These antibodies were also shown to inhibit the growth of transformed cells in culture due to the high rate of endocytosis in transformed cells as compared to normal cells. In this report, we have tested the possibility of nucleic acid reactive antibodies inhibiting the growth of tumor cells in vivo. The life span of mice bearing Dalton's lymphoma ascites tumor cells was increased, when they were immunized with conjugates of guanosine-BSA, GMP-BSA and tRNA-MBSA complex before transplanting the tumor cells. A similar effect was also observed when mice were injected intraperitoneally with antibodies to guanosine oi GMP along with the tumor cells. The specificity was ascertained, as immunization with non-specific antigens did not show any significant effect on tumor bearing mice. The results shows that nucleic acid. reactive antibodies inhibit the growth of tumor cells in vivo.

Effect of oxygen partial pressure on the structural and optical properties of dc reactive magnetron sputtered molybdenum oxide films

Molybdenum oxide films (MoO3) were deposited on glass and crystalline silicon substrates by sputtering of molybdenum target under various oxygen partial pressures in the range 8 × 10−5–8 × 10−4 mbar and at a fixed substrate temperature of 473 K employing dc magnetron sputtering technique. The influence of oxygen partial pressure on the composition stoichiometry, chemical binding configuration, crystallographic structure and electrical and optical properties was systematically studied. X-ray photoelectron spectra of the films formed at 8 × 10−5 mbar showed the presence of Mo6+ and Mo5+ oxidation states of MoO3 and MoO3−x. The films deposited at oxygen partial pressure of 2 × 10−4 mbar showed Mo6+ oxidation state indicating the films were nearly stoichiometric. It was also confirmed by the Fourier transform infrared spectroscopic studies. X-ray diffraction studies revealed that the films formed at oxygen partial pressure of 2 × 10−4 mbar showed the presence of (0 k 0) reflections indicated the layered structure of α-phase MoO3. The electrical conductivity of the films decreased from 3.6 × 10−5 to 1.6 × 10−6 Ω−1 cm−1, the optical band gap of the films increased from 2.93 to 3.26 eV and the refractive index increased from 2.02 to 2.13 with the increase of oxygen partial pressure from 8 × 10−5 to 8 × 10−4 mbar, respectively.