Stochastic Models of Human Kidney Cancer

This dissertation is focused on the development of stochastic models for carcinogenesis of human kidney cancer. Based on recent biological studies, we have developed a multiple-pathway stochastic model for the human pediatric kidney cancer - Wilms\u27 tumor. To account for hereditary cancer cases and the development of non-hereditary cancers through two different pathways in the stochastic model, we have also developed a generalized mixture model. In this mixture model, two mixing probability distributions were applied, which are a multinomial distribution to explain the genetic segregation of the stage-limiting tumor suppressor genes and a binomial distribution to account for the development of non-hereditary cancers through two pathways. We have applied this model to fit and analyze the SEER data of Wilms\u27 tumor from NCI/NIH. Our results indicate that the proposed model involving hereditary and non-hereditary cancer cases fitted the data better than the single-pathway model with hereditary cancer cases. We have also derived a biologically supported stochastic model for human adult kidney cancer - renal cell carcinoma (RCC) involving three pathways. These pathways are: 3-stage pathway for pRCC , 4-stage pathway for ccRCC and 5-stage pathway for chRCC. To account for different individuals in the population at risk of developing renal cell carcinoma through different pathways, we have also presented a mixture model of three pathways. We have used this model to fit and analyze the SEER data of renal cell carcinoma from NCI/NIH. Our results indicate that the model not only provides a logical avenue to incorporate biological information but also fits the data well. These models not only would provide more insights into human kidney cancer but also would provide useful guidance for its prevention and control and for prediction of future cancer cases

Langevin Simulations Of First-order Phase Transitions In Fluids And Materials

In this thesis, we study dynamical aspects associated with (a) phase separation in binary mixtures, which involve their time evolution and time-dependent correlation behaviour, and (b) pattern formation in martensitic transformations. Using a Langevin model, we show for the first time how the predictions of scaling the dimensional arguments for two and three dimensional binary mixtures may be validated numerically within the context of a single model. In particular, we show how domain growth exponents result by an appropriate choice of the crossover lengths in the diffusive, viscous and inertial regimes. We also find that the small-wavenumber-scaling of the structure function is correlated with the velocity-velocity spectrum in fluids. We study the decay of the local autocorrelation and two-time correlation in both binary alloys and binary fluids. We show that the difference between theoretical predictions and numerical simulations for the decay exponent for conserved systems can be related to the sensitivity of the exponent to the amplitude of initial conditions. In simulating martensitic transformations in shape memory alloys, we show the formation of twin martensites and tweed precursors in corresponding regimes with a Langevin model. The Langevin model predicts a hierarchical structure near the habit plane, which provides an explanation of the shape memory mechanism

Direct evidence of a blocking heavy atom effect on the water-assisted fluorescence enhancement detection of Hg²⁺ based on a ratiometric chemosensor

At the current stage of chemosensor chemistry, the critical question now is whether the heavy atom effect caused by HTM ions can be blocked or avoided. In the present work, we provide unequivocal evidence to confirm that the heavy atom effect of Hg²⁺ is inhibited by water and other solvent molecules based on results using the chemosensor L. Most importantly, the heavy atom effect and blocking thereof were monitored within the same system by the use of ratiometric fluorescence signal changes of the pyrene motif. These observations not only serve as the foundation for the design of new ‘turn-on’ chemosensors for HTM ions, but also open up new opportunities for the monitoring of organic reactions

Pertussis Toxin-sensitive Activation of Phospholipase C by the C5a and fMet-Leu-Phe Receptors

Signal transduction pathways that mediate C5a and fMet-Leu-Phe (fMLP)-induced pertussis toxin (PTx)-sensitive activation of phospholipase C (PLC) have been investigated using a cotransfection assay system in COS-7 cells. The abilities of the receptors for C5a and fMLP to activate PLC beta 2 and PLC beta 3 through the Gbeta gamma subunits of endogenous Gi proteins in COS-7 cells were tested because both PLC beta 2 and PLC beta 3 were shown to be activated by the beta gamma subunits of G proteins in in vitro reconstitution assays. Neither of the receptors can activate endogenous PLC beta 3 or recombinant PLC beta 3 in transfected COS-7 cells. However, both receptors can clearly activate PLC beta 2 in a PTx-sensitive manner, suggesting that the receptors may interact with endogenous PTx-sensitive G proteins and activate PLC beta 2 probably through the Gbeta gamma subunits. These findings were further corroborated by the results that PLC beta 3 could only be slightly activated by Gbeta 1gamma 1 or Gbeta 1gamma 5 in the cotransfection assay, whereas the Gbeta gamma subunits strongly activated PLC beta 2 under the same conditions. PLC beta 3 can be activated by Galpha q, Galpha 11, and Galpha 16 in the cotransfection assay. In addition, the Ggamma 2 and Ggamma 3 mutants with substitution of the C-terminal Cys residue by a Ser residue, which can inhibit wild type Gbeta gamma -mediated activation of PLC beta 2, were able to inhibit C5a or fMLP-mediated activation of PLC beta 2. These Ggamma mutants, however, showed little effect on m1-muscarinic receptor-mediated PLC activation, which is mediated by the Gq class of G proteins. These results all confirm that the Gbeta gamma subunits are involved in PLC beta 2 activation by the two chemoattractant receptors and suggest that in COS-7 cells activation of PLC beta 3 by Gbeta gamma may not be the primary pathway for the receptors

Bayesian modeling of ChIP-chip data using latent variables

<p>Abstract</p> <p>Background</p> <p>The ChIP-chip technology has been used in a wide range of biomedical studies, such as identification of human transcription factor binding sites, investigation of DNA methylation, and investigation of histone modifications in animals and plants. Various methods have been proposed in the literature for analyzing the ChIP-chip data, such as the sliding window methods, the hidden Markov model-based methods, and Bayesian methods. Although, due to the integrated consideration of uncertainty of the models and model parameters, Bayesian methods can potentially work better than the other two classes of methods, the existing Bayesian methods do not perform satisfactorily. They usually require multiple replicates or some extra experimental information to parametrize the model, and long CPU time due to involving of MCMC simulations.</p> <p>Results</p> <p>In this paper, we propose a Bayesian latent model for the ChIP-chip data. The new model mainly differs from the existing Bayesian models, such as the joint deconvolution model, the hierarchical gamma mixture model, and the Bayesian hierarchical model, in two respects. Firstly, it works on the difference between the averaged treatment and control samples. This enables the use of a simple model for the data, which avoids the probe-specific effect and the sample (control/treatment) effect. As a consequence, this enables an efficient MCMC simulation of the posterior distribution of the model, and also makes the model more robust to the outliers. Secondly, it models the neighboring dependence of probes by introducing a latent indicator vector. A truncated Poisson prior distribution is assumed for the latent indicator variable, with the rationale being justified at length.</p> <p>Conclusion</p> <p>The Bayesian latent method is successfully applied to real and ten simulated datasets, with comparisons with some of the existing Bayesian methods, hidden Markov model methods, and sliding window methods. The numerical results indicate that the Bayesian latent method can outperform other methods, especially when the data contain outliers.</p

Synthesis of a ditopic homooxacalix[3]arene for fluorescence enhanced detection of heavy and transition metal ions

A pyrene-appended ratiometric fluorescent chemosensor L based on a synthetic approach of insulating the fluorophore from the ionophore by a specific molecular spacer has been synthesised and characterised. The fluorescence spectra changes of L suggested that the chemosensor can detect heavy and transition metal (HTM) ions ratiometrically and with variable sensitivity according to the substituents present. ¹H NMR titration experiments indicated that the three triazole ligands prefer binding with Hg²⁺, Pb²⁺ and Zn²⁺, resulting in a conformational change that produces monomer emission of the pyrene accompanied by the excimer quenching. However, the addition of Fe³⁺, which may be accommodated by the cavity of L, makes the pyrene units move closer to each other, and a discernible increase in the emission intensity of the static excimer is observed. Therefore, it is believed that the ditopic scaffold of the calix[3]arene as a specific molecular spacer here plays an important role in the blocking of the heavy atom effect of HTM ions by insulating the fluorophore from the ionophore given the long distance between the metal cation and the pyrene moiety

Complete Cross-triplet Loss in Label Space for Audio-visual Cross-modal Retrieval

The heterogeneity gap problem is the main challenge in cross-modal retrieval. Because cross-modal data (e.g. audiovisual) have different distributions and representations that cannot be directly compared. To bridge the gap between audiovisual modalities, we learn a common subspace for them by utilizing the intrinsic correlation in the natural synchronization of audio-visual data with the aid of annotated labels. TNN-CCCA is the best audio-visual cross-modal retrieval (AV-CMR) model so far, but the model training is sensitive to hard negative samples when learning common subspace by applying triplet loss to predict the relative distance between inputs. In this paper, to reduce the interference of hard negative samples in representation learning, we propose a new AV-CMR model to optimize semantic features by directly predicting labels and then measuring the intrinsic correlation between audio-visual data using complete cross-triple loss. In particular, our model projects audio-visual features into label space by minimizing the distance between predicted label features after feature projection and ground label representations. Moreover, we adopt complete cross-triplet loss to optimize the predicted label features by leveraging the relationship between all possible similarity and dissimilarity semantic information across modalities. The extensive experimental results on two audio-visual double-checked datasets have shown an improvement of approximately 2.1% in terms of average MAP over the current state-of-the-art method TNN-CCCA for the AV-CMR task, which indicates the effectiveness of our proposed model.Comment: 9 pages, 5 figures, 3 tables, accepted by IEEE ISM 202