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Search Results: 1 - 10 of 286583 matches for " Luke E. K. Achenie "
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Life Cycle Costs and Life Cycle Assessment for the Harvesting, Conversion, and the Use of Switchgrass to Produce Electricity
Nuttapol Lerkkasemsan,Luke E. K. Achenie
International Journal of Chemical Engineering , 2013, DOI: 10.1155/2013/492058
Abstract: This paper considers both LCA and LCC of the pyrolysis of switchgrass to use as an energy source in a conventional power plant. The process consists of cultivation, harvesting, transportation, storage, pyrolysis, transportation, and power generation. Here pyrolysis oil is converted to electric power through cocombustion in conventional fossil fuel power plants. Several scenarios are conducted to determine the effect of selected design variables on the production of pyrolysis oil and type of conventional power plants. The set of design variables consist of land fraction, land shape, the distance needed to transport switchgrass to the pyrolysis plant, the distance needed to transport pyrolysis oil to electric generation plant, and the pyrolysis plant capacity. Using an average agriculture land fraction of the United States at 0.4, the estimated cost of electricity from pyrolysis of 5000 tons of switchgrass is the lowest at $0.12 per kwh. Using natural gas turbine power plant for electricity generation, the price of electricity can go as low as 7.70 cent/kwh. The main advantage in using a pyrolysis plant is the negative GHG emission from the process which can define that the process is environmentally friendly. 1. Introduction Our dependence on fossil fuel has increased over the past century due to increasing energy consumption. The U.S. Department of Energy [1] stated that transportation energy demand is increasing at an annual rate of 0.2 percent from year 2010 to 2035. Total electricity consumption is also increasing at an annual rate of 0.8 percent from 3879 billion kilowatt-hours in 2010 to 4775 billion kilowatt-hours in 2035. On the other hand, the world oil reservoir is decreasing. From BP’s estimates [2], world oil production has already reached its maximum and is expected to drop. At the present production rate, the world oil reservoir will last for forty-one years. Renewable energy such as biooil will be an alternative source to make up the reduction of oil production rate. Faaij [3] reported that fossil fuel dominated the world’s energy uses, supplying 80% of the total energy requirement. However, 10–15% of this demand could be covered by biomass resource. Biomass is an important energy resource for developing countries accounting for 50–90% of their total energy requirement. Advantages of biomass energy include potential to reduce GHG emissions, substitution for depleting global crude oil reservoir, potential impacts on waste management, and the conversion of waste resources into clean energy. Waste resources include natural forests wood,
The New and Computationally Efficient MIL-SOM Algorithm: Potential Benefits for Visualization and Analysis of a Large-Scale High-Dimensional Clinically Acquired Geographic Data
Tonny J. Oyana,Luke E. K. Achenie,Joon Heo
Computational and Mathematical Methods in Medicine , 2012, DOI: 10.1155/2012/683265
Abstract: The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen’s SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen’s SOM.
Optimization of the bioconversion of glycerol to ethanol using Escherichia coli by implementing a bi-level programming framework for proposing gene transcription control strategies based on genetic algorithms  [PDF]
Carol Milena Barreto-Rodriguez, Jessica Paola Ramirez-Angulo, Jorge Mario Gomez-Ramirez, Luke Achenie, Andres Fernando Gonzalez-Barrios
Advances in Bioscience and Biotechnology (ABB) , 2012, DOI: 10.4236/abb.2012.34049
Abstract: In silico approaches for metabolites optimization have been derived from the flood of sequenced and annotated genomes. However, there exist still numerous degrees of freedom in terms of optimization algorithm approaches that can be exploited in order to enhance yield of processes which are based on biological reactions. Here, we propose an evolutionary approach aiming to suggest different mutant for augmenting ethanol yield using glycerol as substrate in Escherichia coli. We found that this algorithm, even though is far from providing the global optimum, is able to uncover genes that a global optimizer would be incapable of. By over-expressing accB, eno, dapE, and accA mutants in ethanol production was augmented up to 2 fold compared to its counterpart E. coli BW25113.
Computational models in plant-pathogen interactions: the case of Phytophthora infestans
Andrés Pinzón, Emiliano Barreto, Adriana Bernal, Luke Achenie, Andres F González Barrios, Raúl Isea, Silvia Restrepo
Theoretical Biology and Medical Modelling , 2009, DOI: 10.1186/1742-4682-6-24
Abstract: Deterministic logistics models have been widely used to study pathogenicity mechanisms since the early 1950s, and have focused on processes at higher biological resolution levels. In recent years, owing to the availability of high throughput biological data and computational resources, interest in stochastic modeling of plant-pathogen interactions has grown. Stochastic models better reflect the behavior of biological systems. Most modern approaches to plant pathology modeling require molecular kinetics information. Unfortunately, this information is not available for many plant pathogens, including P. infestans. Boolean formalism has compensated for the lack of kinetics; this is especially the case where comparative genomics, protein-protein interactions and differential gene expression are the most common data resources.Control and management of plant diseases and the identification of factors that contribute to the spread a given plant pathogen attack are at the basis of phytopathology. Mathematical models and computational simulations have been used, along with molecular and physiological approaches, to solve these and other issues.In the early 1990s the use of stochastic models in plant pathology was reviewed [1,2], mostly focused on epidemics. In this work we update topics not fully covered in previous reviews as well as associated experimental approaches that characterize the systems biology era [3]. Most of the review will focus on the Phytophthora infestans - Solanum tuberosum pathosystem, but its discussion will be general enough as to be applicable to any other plant pathogen system. A brief discussion of boolean networks and how this approach could drive the modeling of the compatible interaction between P. infestans and S. tuberosum is also introduced.Plants use various strategies to resist infection by a particular pathogen [4]. These strategies are part of the plant's innate immune system and can be grouped into two broad categories [5]. The first reco
Implications of Therapy-Induced Selective Autophagy on Tumor Metabolism and Survival
Luke R. K. Hughson,Vincent I. Poon,Jaeline E. Spowart,Julian J. Lum
International Journal of Cell Biology , 2012, DOI: 10.1155/2012/872091
Abstract: Accumulating evidence indicates that therapies designed to trigger apoptosis in tumor cells cause mitochondrial depolarization, nuclear damage, and the accumulation of misfolded protein aggregates, resulting in the activation of selective forms of autophagy. These selective forms of autophagy, including mitophagy, nucleophagy, and ubiquitin-mediated autophagy, counteract apoptotic signals by removing damaged cellular structures and by reprogramming cellular energy metabolism to cope with therapeutic stress. As a result, the efficacies of numerous current cancer therapies may be improved by combining them with adjuvant treatments that exploit or disrupt key metabolic processes induced by selective forms of autophagy. Targeting these metabolic irregularities represents a promising approach to improve clinical responsiveness to cancer treatments given the inherently elevated metabolic demands of many tumor types. To what extent anticancer treatments promote selective forms of autophagy and the degree to which they influence metabolism are currently under intense scrutiny. Understanding how the activation of selective forms of autophagy influences cellular metabolism and survival provides an opportunity to target metabolic irregularities induced by these pathways as a means of augmenting current approaches for treating cancer. 1. Introduction In order to evade barriers against cancer progression and treatment resistance, tumor cells undergo metabolic adaptations and develop mechanisms to resist apoptosis [1]. Apoptosis resistance in tumor cells can occur through multiple changes, none of which are mutually exclusive. For example, tumor cells enhance antiapoptotic signaling pathways and upregulate the removal or repair of damaged DNA as well as denatured proteins. Overcoming stressors that activate apoptosis requires higher rates of energy production and necessitates that tumor cells make metabolic changes to sustain antiapoptotic signaling, DNA repair mechanisms, and elevated protein turnover. While anticancer therapies that target these essential processes have been proven effective [2–4], improved outcomes may be achieved by combining them with metabolic inhibition. Metabolic inhibitors have been shown to improve the efficacy of standard therapies in various cancer types [5–8]. Furthermore, the increase in toxicity that is achieved when metabolic inhibitors are combined with standard therapies is often well tolerated clinically, supporting the feasibility of this approach for treating cancer [9, 10]. As a result, there is a need to increase the
When Cells Suffocate: Autophagy in Cancer and Immune Cells under Low Oxygen
Katrin Schlie,Jaeline E. Spowart,Luke R. K. Hughson,Katelin N. Townsend,Julian J. Lum
International Journal of Cell Biology , 2011, DOI: 10.1155/2011/470597
Abstract: Hypoxia is a signature feature of growing tumors. This cellular state creates an inhospitable condition that impedes the growth and function of all cells within the immediate and surrounding tumor microenvironment. To adapt to hypoxia, cells activate autophagy and undergo a metabolic shift increasing the cellular dependency on anaerobic metabolism. Autophagy upregulation in cancer cells liberates nutrients, decreases the buildup of reactive oxygen species, and aids in the clearance of misfolded proteins. Together, these features impart a survival advantage for cancer cells in the tumor microenvironment. This observation has led to intense research efforts focused on developing autophagy-modulating drugs for cancer patient treatment. However, other cells that infiltrate the tumor environment such as immune cells also encounter hypoxia likely resulting in hypoxia-induced autophagy. In light of the fact that autophagy is crucial for immune cell proliferation as well as their effector functions such as antigen presentation and T cell-mediated killing of tumor cells, anticancer treatment strategies based on autophagy modulation will need to consider the impact of autophagy on the immune system. 1. Introduction In many tumors, cell growth and proliferation exceeds the development of local vasculature supplying oxygen and nutrients. As a result, tumors form disorganized angiogenic vessels that cause the percent of oxygen within the tumor to range heterogeneously from anoxic (<0.5% O2) and hypoxic (0.5–1.5% O2) to normoxic (>1.5% O2) levels [1, 2]. Cancer cells in close proximity to vasculature contribute to tumor hypoxia by rapidly utilizing oxygen and nutrients that arrive at the tumor site. This can result in either chronic or cycling hypoxia depending on how quickly cancer cells consume oxygen once new vascular networks are formed [3, 4]. To circumvent the effects of oxygen deprivation, the transcription factor hypoxia inducible factor-1α (HIF-1α) is stabilized in cells under hypoxia. HIF-1α allows for adaptation to hypoxia by promoting a metabolic switch from oxidative phosphorylation to glycolysis and by initiating angiogenesis [5]. Collectively, the hypoxic and nutrient-depleted tumor microenvironment impacts the metabolism, survival, and function of all cells exposed to it. As a result of hypoxic stress, cells in the tumor microenvironment activate autophagy, a cell survival process that degrades and recycles cellular constituents. Autophagy can be induced by various stressors including nutrient starvation, growth factor withdrawal, hypoxia, and
A Practical Target Tracking Technique in Sensor Network Using Clustering Algorithm  [PDF]
Luke K. Wang, Chien-Chang Wu
Wireless Sensor Network (WSN) , 2012, DOI: 10.4236/wsn.2012.411038
Abstract: Sensor network basically has many intrinsic limitations such as energy consumption, sensor coverage and connectivity, and sensor processing capability. Tracking a moving target in clusters of sensor network online with less complexity algorithm and computational burden is our ultimate goal. Particle filtering (PF) technique, augmenting handoff and K-means classification of measurement data, is proposed to tackle the tracking mission in a sensor network. The handoff decision, an alternative to multi-hop transmission, is implemented for switching between clusters of sensor nodes through received signal strength indication (RSSI) measurements. The measurements being used in particle filter processing are RSSI and time of arrival (TOA). While non-line-of-sight (NLOS) is the dominant bias in tracking estimation/accuracy, it can be easily resolved simply by incorporating K-means classification method in PF processing without any priori identification of LOS/NLOS. Simulation using clusters of sensor nodes in a sensor network is conducted. The dependency of tracking performance with computational cost versus number of particles used in PF processing is also investigated.
miRNA signature associated with outcome of gastric cancer patients following chemotherapy
Chang Kim, Hark K Kim, R Luke Rettig, Joseph Kim, Eunbyul T Lee, Olga Aprelikova, Il J Choi, David J Munroe, Jeffrey E Green
BMC Medical Genomics , 2011, DOI: 10.1186/1755-8794-4-79
Abstract: Biopsy samples were collected prior to chemotherapy from 90 gastric cancer patients treated with CF and from 34 healthy volunteers. At the time of disease progression, post-treatment samples were additionally collected from 8 clinical responders. miRNA expression was determined using a custom-designed Agilent microarray. In order to identify a miRNA signature for chemotherapy resistance, we correlated miRNA expression levels with the time to progression (TTP) of disease after CF therapy.A miRNA signature distinguishing gastric cancer from normal stomach epithelium was identified. 30 miRNAs were significantly inversely correlated with TTP whereas 28 miRNAs were significantly positively correlated with TTP of 82 cancer patients (P<0.05). Prominent among the upregulated miRNAs associated with chemosensitivity were miRNAs known to regulate apoptosis, including let-7g, miR-342, miR-16, miR-181, miR-1, and miR-34. When this 58-miRNA predictor was applied to a separate set of pre- and post-treatment tumor samples from the 8 clinical responders, all of the 8 pre-treatment samples were correctly predicted as low-risk, whereas samples from the post-treatment tumors that developed chemoresistance were predicted to be in the high-risk category by the 58 miRNA signature, suggesting that selection for the expression of these miRNAs occurred as chemoresistance arose.We have identified 1) a miRNA expression signature that distinguishes gastric cancer from normal stomach epithelium from healthy volunteers, and 2) a chemoreresistance miRNA expression signature that is correlated with TTP after CF therapy. The chemoresistance miRNA expression signature includes several miRNAs previously shown to regulate apoptosis in vitro, and warrants further validation.miRNAs are short (~22 nucleotide), non-coding RNAs that regulate gene expression primarily by translational repression or transcriptional degradation [1]. miRNAs have great potential as cancer biomarkers because of their tissue-speci
Superconductivity and Field-Induced Magnetism in Pr$_{2-x}$Ce$_x$CuO$_4$ Single Crystals
J. E. Sonier,K. F. Poon,G. M. Luke,P. Kyriakou,R. I. Miller,Ruixing Liang,C. R. Wiebe,P. Fournier,R. L. Greene
Physics , 2003, DOI: 10.1103/PhysRevLett.91.147002
Abstract: We report muon-spin rotation/relaxation (muSR) measurements on single crystals of the electron-doped high-T_c superconductor Pr$_{2-x}$Ce$_x$CuO$_4$. In zero external magnetic field, superconductivity is found to coexist with Cu spins that are static on the muSR time scale. In an applied field, we observe a Knight shift that is primarily due to the magnetic moment induced on the Pr ions. Below the superconducting transition temperature T_c, an additional source of static magnetic order appears throughout the sample. This finding is consistent with antiferromagnetic ordering of the Cu spins in the presence of vortices. We also find that the temperature dependence of the in-plane magnetic penetration depth in the vortex state resembles that of the hole-doped cuprates at temperatures above ~ 0.2 T_c.
Weak quasistatic magnetism in the frustrated Kondo lattice Pr_2Ir_2O_7
D. E. MacLaughlin,Y. Ohta,Y. Machida,S. Nakatsuji,G. M. Luke,K. Ishida,R. H. Heffner,Lei Shu,O. O. Bernal
Physics , 2008, DOI: 10.1016/j.physb.2008.11.167
Abstract: Muon spin relaxation experiments have been performed in the pyrochlore iridate Pr_2Ir_2O_7 for temperatures in the range 0.025-250 K. Kubo-Toyabe relaxation functions are observed up to > 200 K, indicating static magnetism over this temperature range. The T -> 0 static muon spin relaxation rate Delta(0) ~ 8 mus^-1 implies a weak quasistatic moment (~0.1 mu_B). The temperature dependence of Delta is highly non-mean-field-like, decreasing smoothly by orders of magnitude but remaining nonzero below ~150 K. The data rule out ordering of the full Pr^3+ CEF ground-state moment (3.0 mu_B) down to 0.025 K. The weak static magnetism is most likely due to hyperfine-enhanced ^141Pr nuclear magnetism. The dynamic relaxation rate lambda increases markedly below ~20 K, probably due to slowing down of spin fluctuations in the spin-liquid state. At low temperatures lambda is strong and temperature-independent, indicative of a high density of low-lying spin excitations as is common in frustrated antiferromagnets.
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