The Emergent Landscape of Detecting EGFR Mutations Using Circulating Tumor DNA in Lung Cancer.

The advances in targeted therapies for lung cancer are based on the evaluation of specific gene mutations especially the epidermal growth factor receptor (EGFR). The assays largely depend on the acquisition of tumor tissue via biopsy before the initiation of therapy or after the onset of acquired resistance. However, the limitations of tissue biopsy including tumor heterogeneity and insufficient tissues for molecular testing are impotent clinical obstacles for mutation analysis and lung cancer treatment. Due to the invasive procedure of tissue biopsy and the progressive development of drug-resistant EGFR mutations, the effective initial detection and continuous monitoring of EGFR mutations are still unmet requirements. Circulating tumor DNA (ctDNA) detection is a promising biomarker for noninvasive assessment of cancer burden. Recent advancement of sensitive techniques in detecting EGFR mutations using ctDNA enables a broad range of clinical applications, including early detection of disease, prediction of treatment responses, and disease progression. This review not only introduces the biology and clinical implementations of ctDNA but also includes the updating information of recent advancement of techniques for detecting EGFR mutation using ctDNA in lung cancer

Multistage Infrastructure System Design: An Integrated Biofuel Supply Chain against Feedstock Seasonality and Uncertainty

A biofuel supply chain consists of various interdependent components from feedstock resources all the way to energy demand sites. This study focuses on the design of an efficient biofuel supply chain system against seasonal variations and uncertainties of feedstock supply in an integrative manner. By integrating planning and operational decisions in a stochastic programming framework, we aim at finding an effective design strategy for biofuel supply chain that is economically viable and hedges well against a wide range of future uncertainties. A solution algorithm based on scenario decomposition is designed to overcome computational challenges involved in large-scale applications. A California case study is implemented to demonstrate the applicability of the proposed methods in evaluating the economic potential, the infrastructure needs, and the risk of wastes-based bioethanol production

Identification of Yeast Genes Affecting Production of Hydrogen Sulfide and Volatile Thiols from Cysteine Treatment during Fermentation

Hydrogen sulfide (H2S), well-known for its undesirable rotten-egg odour, is often produced during fermentation by the yeast Saccharomyces cerevisiae when nitrogen becomes depleted. However, an early burst of H2S generated by yeast from cysteine could contribute to the formation of the fruity varietal thiols 3-mercaptohexan-1-ol (3MH) and 3-mercaptohexyl acetate (3MHA) through reaction with (E)-2-hexenal, which is otherwise rapidly metabolised. The goal of this project is to identify genes and pathways leading to H2S generation from cysteine and thus enhance the tropical aromas in wine that appeal to many consumers. Using a candidate gene approach, TUM1 was for the first time identified to play a crucial role in the early production of H2S from cysteine. Overexpressing TUM1 elevated production of H2S, while its deletion reduced the H2S by half. Furthermore, deletion of either MET17 or MET2 led to an additional delayed burst of H2S, suggesting that a portion of the H2S generated from cysteine is fed directly into the sulfate assimilation pathway. Triple deletants of STR2, STR3 and individual MET genes, were shown to require both MET17 and TUM1 to bypass the transsulfuration pathway and grow on high concentrations of cysteine as the sole sulfur source. These results illustrate that cysteine is not converted to sulfate or sulfite, but rather to sulfide via a novel pathway requiring the action of Tum1p. The failure to identify a specific QTL associated with H2S formation from cysteine using a set of 96 fully sequenced M2 x F15 progeny, suggests multiple genes affect the trait. To identify additional genes, a modified version of bismuth-containing indicator agar resembling grape juice was developed and used to screen both AWRI1631 wine yeast and BY4741 deletion collections. Both Δlst4 and Δlst7 strains were observed to form lighter coloured colonies and produce significantly less H2S than the wild-type on high concentrations of cysteine. Further investigations revealed that deleting genes involved in cysteine transportation such as AGP1, GNP1, MUP1, STP1 and DAL81 all resulted in reduced production of H2S from cysteine. These findings demonstrated, for the first time, that genes involved in regulating cysteine uptake could affect H2S formation from cysteine and therefore selecting wine yeasts with ability to take up supplemented cysteine efficiently could maximise aromatic thiol production. Preliminary results indicate that the higher levels of 3MH/A could be achieved by modulating TUM1 and cysteine supplementation. In addition, polysulfides, that may affect the sensory quality of wine, were detected for the first time in yeast undergoing fermentation on high concentrations of cysteine by the fluorescent probe SSP4. Finally, an up-to-date review of recent study on sulfur metabolism in S. cerevisiae is presented, which includes suggestions for future research in this field. In conclusion, these findings not only have greatly advanced our current understanding of S. cerevisiae cysteine catabolism, but also could be applied to develop better yeast strains, as well as novel winemaking practices to enhance tropical aromas of wines.Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food & Wine, 201

Development and numerical implementation of nonlinear viscoelastic-viscoplastic model for asphalt materials

Hot mix asphalt (HMA) is a composite material which consists of aggregates, air voids and asphalt materials. The HMA response is typically described to be viscoelastic-viscoplastic, and its response is a function of temperature, stress/strain rate, and stress/strain level. Many researches have shown that the viscoelastic response of asphalt mixtures can be nonlinear once the stress/strain value exceeds a certain threshold level. This study presents a nonlinear viscoelastic-viscoplastic model for describing the behavior of asphalt materials under various conditions. A new method is developed in this study for separating the viscoelastic response from the viscoplastic response. The first part of this study focuses on the implementation of Schapery nonlinear viscoelastic model in finite element (FE) using a user-defined material subroutine (UMAT) within the ABAQUS commercial software. The FE implementation employs the recursive-iterative integration algorithm, which can improve the convergence and save the calculating time. The verification of the nonlinear viscoelastic model is achieved by analyzing (1) the response of asphalt mixtures tested in the Simple Shear Test (SST) at several temperatures and stress levels, (2) the response of unaged and aged asphalt binders tested in the Dynamic Shear Rheometer (DSR), and (3) the response of asphalt binders in the multiple stress creep recovery test (MSCR). In the second part of this study, the nonlinear viscoelastic-viscoplastic constitutive relationship is implemented using UMAT. The viscoplastic component of the model employs Perzyna’s theory with Extended Drucker-Prager yield surface which is modified to account for the difference in material response under compression and extension stress states. The study includes parametric analysis to illustrate the effect of nonlinear viscoelastic parameters and viscoplastic parameters on the asphalt mix response. The capability of the model in describing the fatigue and permanent deformation distresses of asphalt pavements is illustrated using finite element simulations. The constitutive model developed in this study can describe the behavior of asphalt materials (asphalt binder, asphalt mastic and mixtures) under various testing conditions. This study also achieved the FE implementation of a nonlinear viscoelasticviscoplastic constitutive model that can simulate the fatigue and permanent deformation distresses of asphalt pavement structures

Data mining of the GAW14 simulated data using rough set theory and tree-based methods

Rough set theory and decision trees are data mining methods used for dealing with vagueness and uncertainty. They have been utilized to unearth hidden patterns in complicated datasets collected for industrial processes. The Genetic Analysis Workshop 14 simulated data were generated using a system that implemented multiple correlations among four consequential layers of genetic data (disease-related loci, endophenotypes, phenotypes, and one disease trait). When information of one layer was blocked and uncertainty was created in the correlations among these layers, the correlation between the first and last layers (susceptibility genes and the disease trait in this case), was not easily directly detected. In this study, we proposed a two-stage process that applied rough set theory and decision trees to identify genes susceptible to the disease trait. During the first stage, based on phenotypes of subjects and their parents, decision trees were built to predict trait values. Phenotypes retained in the decision trees were then advanced to the second stage, where rough set theory was applied to discover the minimal subsets of genes associated with the disease trait. For comparison, decision trees were also constructed to map susceptible genes during the second stage. Our results showed that the decision trees of the first stage had accuracy rates of about 99% in predicting the disease trait. The decision trees and rough set theory failed to identify the true disease-related loci

Dynamic Power Index Adjustment Based On Battery Level

This disclosure describes techniques for dynamic adjustment of output power index of a wireless remote controller device based on a detected battery level of the device. The battery voltage level of the device is periodically measured. When the level falls below a predetermined threshold, the output power index is adjusted to ensure that the total transmit power from the controller device lies within a specified range. Dynamic adjustment of transmit power via the power index adjustment enables the controller device to have a transmit power that lies between the power spectral distribution (PSD) target and the PSD limit (maximum) over a range of battery voltage values

One-dimensional polymers in random environments: stretching vs. folding

In this article we study a \emph{non-directed polymer model} on $\mathbb Z$, that is a one-dimensional simple random walk placed in a random environment. More precisely, the law of the random walk is modified by the exponential of the sum of ``rewards'' (or penalities) $\beta \omega_x -h$ sitting on the range of the random walk, where $(\omega_x)_{x\in \mathbb Z}$ are i.i.d.\ random variables (the disorder), and where $\beta\geq 0$ (disorder strength) and $h\in \mathbb{R}$ (external field) are two parameters. When $\beta=0,h>0$, this corresponds to a random walk penalized by its range; when $\beta>0, h=0$, this corresponds to the ``standard'' polymer model in random environment, except that it is non-directed. In this work, we allow the parameters $\beta,h$ to vary according to the length of the random walk, and we study in detail the competition between the \emph{stretching effect} of the disorder, the \emph{folding effect} of the external field (if $h\ge 0$), and the \emph{entropy cost} of atypical trajectories. We prove a complete description of the (rich) phase diagram. For instance, in the case $\beta>0, h=0$ of the non-directed polymer, if $\omega_x$ ha a finite second moment, we find a transversal fluctuation exponent $\xi=2/3$, and we identify the limiting distribution of the rescaled log-partition function.Comment: 28 pages, 5 figure