bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimization of Looped Airfoil Wind Turbine (LAWT™) Design Parameters for Maximum Power Generation
Authors: Banerjee Subhodeep , Song Binhe , Syrovy George , Agarwal Ramesh K. 
Published: November 2015

The looped airfoil wind turbine (LAWT™) is a patented new technology by EverLift Wind Tecnology, Inc. for generating power from wind. It takes advantage of the superior lift force of a linearly traveling wing compared to the rotating blades in conventional wind turbine configurations. Compared to horizontal and vertical axis wind turbines, the LAWT™ can be manufactured with minimal cost because it does not require complex gear systems and its blades have a constant profile along their length [1]. These considerations make the LAWT™ economically attractive for small-scale and decentralized power generation in rural areas. Each LAWT™ is estimated to generate power in the range of 10 kW to 1 MW. Due to various advantages, it is meaningful to determine the maximum power generation of a LAWT™ by optimizing the structural layout.
In this study, CFD simulations were conducted using ANSYS Fluent to determine the total lift and drag coefficient for a cascade of airfoils. The k-kl-ω turbulence model was used to account for flow in the laminar-turbulent transition region. Given the lift and drag coefficients and the kinematics of the system, an analytical formula for the power generation of the LAWT™ was developed. General formulas were obtained for the average lift and drag coefficients so that the total power could be predicted for any number of airfoils in LAWT™. The spacing between airfoils was identified as the key design parameter that affected the power generation of the LAWT™. The results show that a marked increase in total power can be achieved if the optimum spacing between the airfoils is used.
CFD Analysis of the Pump Performance With Different Liquid Viscosity
Authors: Strongin Mikhail P. 
Published: November 2015

The pumping of liquids with different viscosity is used in many industrial applications from automotive to food processing. Change of viscosity may have high influence on the pump performance. Therefore, it is important to compare efficiency of the same pump by pumping different liquids with large variety of viscosity through them.
In the present work the behavior of flows in the centrifugal pump with viscosity in the range from 10−3 to 1 kg/(m*s) is studied.
The pump model consists of suction, impeller, and discharge parts, which were studied as a single entity. This setup naturally permits analysis of the effects of non-uniformity of velocity. Full geometry was considered due to the absence of symmetry on the volute part of the pump.
K-ω SST turbulence closure model was used for these simulations. Commercial code Fluent 15.07 was chosen for Computational Fluid Dynamics (CFD) calculations. Multiple Reference Frame (MRF) model for steady state calculations was selected with the total of 3.4 million cells. Control transient calculations were done with sliding mesh approach. Transient and steady state cases showed a difference in the head within 5%.
The results show the drop of the pump head (∼ 4%) and efficiency (∼ 9%) with change the Re number on the inlet from 500,000 to 50,000 or kinematic viscosity from 10−6 to 10−5 m2/s respectively with the same flow rate. Model results show that at Re = 5,000 head dropped by 15% and efficiency by 35% in comparison to the case where Re = 500,000.
Moreover, the calculations showed that the blockage effect of large flow for high flow rate (>1.7 BEP) for Re < 5,000 appeared on the volute discharge side. Blockage effect was due to cavitation.
CFD simulations of the influence of liquid viscosity on the pump performance can help prepare practical recommendations for designers.
The Design of a Degassing Pump Based on Numerical and Experimental Research
Authors: Xie Dongjie , Wu Peng , Wu Dazhuan 
Published: November 2015

In the food industry, it is necessary to remove the gas in liquid, in order to prevent the juice from oxidation and prolong guarantee period. At present, vacuum tanks are widely used in beverage productive process to remove air. However, the vacuum tanks are usually bulky and of high energy consumption.
In this study, the purposes of degassing pump design are: (1) effectively removing free gas and dissolved gas in liquid, (2) conveying the liquid, (3) more energy efficient than vacuum tank. In this process, numerical method is adopted to simulate the gas-liquid separation with eulerian multiphase model and population balance model (PBM) using the commercial code FLUENT.
Structures of pulp pump and gas-liquid cylindrical cyclone separator are used for reference. The degassing pump mainly consists of inlet tube, centrifuge drum, exhaust tube, impeller, volute and outlet tube etc.
The numerical simulation results show that there is a huge low pressure area in the center of the drum. Free gas and dissolved gas are separated into the low pressure region under the effect of centrifugal force and local low pressure.
Bubble diameter has a great impact on the degassing effect. PBM which considers bubbles coalescence and breakup is adopted here to calculate the diameter of bubbles. The drum diameter has an extremely influence on the inlet pressure of degassing pump. The centrifuge drum is installed in front of impeller, so pre-swirl of the fluid inside impeller inlet is strong. The hydraulic performance of pump slightly declines when centrifuge drum is added in numerical calculation.
The degassing pump is in manufacturing and will be tested in future.
Abstract A1-14: Discovery of a functional SNP in an estrogen receptor binding site in the IGF1R gene
Authors: Bahreini Amir , Bahreini Amir , Santanas Lucas , Wang Peilu , Benos Panayiotis V. , Lee Adrian V. , Lee Adrian V. , Oesterreich Steffi , Oesterreich Steff 
Published: November 2015

Abstract
Estrogen receptor (ER) positive luminal breast cancer represents approx 70% of all newly diagnosed breast cancers. ER is known to drive breast cancer development and progression, and targeting ER has been one of the most successful targeted therapies in oncology. Given the paucity of functional DNA sequence variants (DSVs) within the ER in primary breast tumors, we asked whether DSVs in ER binding sites could contribute to altered hormone response through changes in ER recruitment to DNA.
We developed a pipeline to extract DSVs in ER binding sites, and to assess their impact on ER binding in a genome-wide manner, using ChIP-seq data from hormone responsive breast cancer cells. We utilized MACS for peak calling and GATK for calling DSVs including SNVs and short indels. Using 22,143 ER binding sites which overlapped in at least six data sets, we detected 1,409,406 DSVs from 441,855,039 reads. Position weight matrix (PWM) was used to scan the genome for potential ERE motifs, and identified 126 potential DSVs which were predicted to change ER binding. Of those, 29 DSV-containing EREs were in proximity or within estrogen regulated genes. Intriguingly, the top hit was a previously reported SNP within intron 2 of the IGF1R gene (rs62022087), which our data predicted to increase the affinity for ER binding. The ERE lies in a regulatory region characterized by active histone marks such as H3K29ac and H3k4Me1, and recruitment of a number of transcription factors including c-myc, FoxA1, GATA-1, and finally DNase I hypersensitive sites. Further analysis of published ChIP-seq studies on 6 ER+ cell lines and 15 primary tumors revealed that the presence of rs62022087 was associated with the enrichment of reads over the ER binding site, suggesting that the DSV indeed increases binding affinity of ER. Our further in-vitro functional studies confirmed that ER is more preferentially binding to mutant allele vs wild-type allele.
In summary, we have shown that our pipeline is capable of predicting potential regulatory DSVs in ER binding sites. Studies are ongoing to understand functional and clinical relevance of rs62022087, especially with respect to crosstalk between estrogen and IGF1 signaling.
Citation Format: Amir Bahreini, Amir Bahreini, Lucas Santanas, Peilu Wang, Panayiotis V. Benos, Adrian V. Lee, Adrian V. Lee, Steffi Oesterreich, Steff Oesterreich. Discovery of a functional SNP in an estrogen receptor binding site in the IGF1R gene. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-14.
Abstract A1-28: Chromosome fusions triggered by noncoding RNA
Authors: Bracht John , Wang Xing , Shetty Keerthi , Chen Xiao , Nowacki Mariusz , McCloud Sierra , Clay Derek , Landweber Laura 
Published: November 2015

Abstract
Chromosomal fusion is a frequent occurrence in cancer cells and can promote transformation through the generation of oncogenic gene products. There are two non-exclusive models for how these chromosome fusions may occur: (1) the instability of the cancer genome produces chromosomal fusions randomly and those promoting transformation enjoy a selective advantage, or (2) some non-random mechanism drives chromosome fusion. Evidence for (2) comes from recurrent fusion events and correlative human data linking noncoding RNA expression with chromosome fusion. To help address this important fundamental question, we functionally tested the role of noncoding RNA, specifically piRNAs, in promoting chromosomal fusion in the ciliate, Oxytricha trifallax, a microbial eukaryote. We microinjected custom piRNAs into cells and observed chromosome fusion in four cases (of four tested), including a double fusion event and a chromosome circularization event (self-fusion). Furthermore, these RNA-mediated inter-and-intra-chromosomal fusions are heritable over multiple sexual generations, illustrating the ability of noncoding RNAs to program genome architecture, including chromosome fusion and circularization.
Citation Format: John Bracht, Xing Wang, Keerthi Shetty, Xiao Chen, Mariusz Nowacki, Sierra McCloud, Derek Clay, Laura Landweber. Chromosome fusions triggered by noncoding RNA. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-28.
Abstract A1-48: Whole genome expression based drug repurposing
Authors: Geeleher Paul , Bhutra Steven , Wang Jacqueline , Huang R. Stephanie 
Published: November 2015

Abstract
Currently, personalizing cancer chemotherapy relies on pathology and more recently molecular biomarker-based approaches. However, as the driving biology is normally not fully understood, the majority of existing biomarkers do not capture a substantial proportion of variability in drug response. This partly explains the commonly observed lack of reproducibility of findings (e.g. from many conventional gene expression signatures) when these markers are applied to new datasets. We developed an approach to predict in vivo drug sensitivity that leverages whole-genome gene expression microarray data and allows the expression of every gene to influence the prediction by a small amount. Our approach builds statistical models from gene expression and drug sensitivity data in a very large panel of cell lines, then applies these models to gene expression data from primary tumor biopsies. In this study, we applied this approach to tumor samples collected in The Cancer Genome Atlas (TCGA). We derived predicted sensitivity for over one hundred drugs in each of the tumor samples. As a proof-of-concept, we demonstrated that a targeting agent (lapatinib) designed against a specific tumor marker (HER2 positive) is indeed predicted to be more sensitive in HER2 positive breast cancers when compared to the other type of cancers. Meanwhile, we identified other agents that exhibit similar or superior sensitivity when compared to commonly prescribed agents in different disease settings. These findings warrant further evaluation of these agents to be repurposed for possible new indications. Interestingly, some of our derived drug sensitivity predictive estimates are correlated with observed survival outcomes in certain cancer patients. When screening all tumor types based on their molecular profiles, we defined several classes of drugs that maybe differentially effective based on tumor molecular profiles. In conclusion, a genome-wide expression drug sensitivity model built in cell lines can be a powerful approach in repurposing drug in cancer treatment.
Citation Format: Paul Geeleher, Steven Bhutra, Jacqueline Wang, R. Stephanie Huang. Whole genome expression based drug repurposing. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-48.
Abstract A1-54: Pan-cancer analysis of the etiology and consequences of intratumor heterogeneity
Authors: Andor Noemi , Graham Trevor A. , Petritsch Claudia , Ji* Hanlee P. , Maley* Carlo C. 
Published: November 2015

Abstract
This abstract is being presented as a short talk in the scientific program. A full abstract is printed in the Proffered Abstracts section (PR03) of the Conference Proceedings.
Citation Format: Noemi Andor, Trevor A. Graham, Claudia Petritsch, Hanlee P. Ji*, Carlo C. Maley*. Pan-cancer analysis of the etiology and consequences of intratumor heterogeneity. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-54.
Abstract A1-56: Applying TCGA data for breast cancer diagnostics and pathway analysis
Authors: Andersen Jannik , Shah Parantu , Ibbs Matthew , Filas Violetta , Korski Konstanty , Marszalek Andrzej , Wu Yuanqing , Chang Edward , Toniatti Carlo , Draetta Giulio , Wiznerowicz Maciej 
Published: November 2015

Abstract
The main objective of our project is to obtain a comprehensive insight into oncogenic signaling in order to develop novel diagnostic tools for molecular subtypes of breast cancer (BRCA). We have provided about 100 BRCA samples along with full pathology and clinical annotations to the TCGA program. These cancer samples have been extensively characterized using all available genomics platforms and the obtained molecular data contributed to the molecular characterization of BRCA. This integrated effort identified four major molecular subtypes of breast cancer that can be identified across five profiling platform including genomics, transcriptomics and proteomics (Nature, 490 (7418):61-70). Building upon these datasets we aim to translate these genomics profiles into diagnostic tools that can be used in every day medical practice. In the first step, we have generated tissue microarrays (TMAs) from FFPE BRCA samples obtained from the patients enrolled into the TCGA project. Next, the TMA slides were used for immunohistochemistry (IHC) using about 100 antibodies specific for major cancer markers, including oncogenic kinases frequently activated or overexpressed in BRCA. The obtained IHC results have been scored and verified independently for each marker by two board-certified pathologists. To identify markers that correlate with or are specific to the molecular subtypes of BRCA, the TMA/IHC read- outs from each tumor sample were correlated with all TCGA genomic data. In addition, we integrate the pathology and genomics results with clinical data obtained from the TCGA-enrolled patients, including up to 4 years follow-up. Finally, we apply system biology tools to integrate the genomic data from TCGA with the proteomic analysis to understand causalities between changes in DNA, transcriptome and signal transduction pathways. Our long-term goal is to identify novel diagnostic biomarkers that will precisely identify each molecular subtype of BRCA and which may be predictive of patient response to therapy thus paving the way for novel personalized therapies for cancer.
Note: This abstract was not presented at the conference.
Citation Format: Jannik Andersen, Parantu Shah, Matthew Ibbs, Violetta Filas, Konstanty Korski, Andrzej Marszalek, Yuanqing Wu, Edward Chang, Carlo Toniatti, Giulio Draetta, Maciej Wiznerowicz. Applying TCGA data for breast cancer diagnostics and pathway analysis. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-56.
Abstract A1-69: Predicting drug response by profiling the epigenome: Super-enhancers as biomarkers
Authors: Orlando David A. , McKeown Michael R. , Chen Mei Wei , Collins Cindy , Guenther Matthew G. , Fritz Christian C. 
Published: November 2015

Abstract
Identifying biomarkers that predict clinical responses to anticancer therapies is an important challenge in oncology. A desirable biomarker is a cell-identifying feature that can distinguish between drug responsive and unresponsive cell types. Most of the best studied biomarkers are gene mutations or copy-number changes, where the genomic alteration is the key defining feature. However, not all differences in clinical responses can be attributed to genetic differences. Therefore, it is critical that we identify other cellular features that can predict response. A recently described epigenomic feature, termed super-enhancer (SE), defines key cell identity and disease genes and SEs are effectors of initiating and maintaining cell type-specific gene expression programs. In this work we generate super-enhancer maps across multiple cancer cell lines and connect those maps to response to different drug treatments. We show that super-enhancers can be used as biomarkers to predict response to drugs across multiple cell-line models of cancer. This work suggests that interrogating epigenomic features, and in particular super-enhancers, can be a powerful biomarker discovery platform and can enable rational patient selection and therapeutic strategies.
Citation Format: David A. Orlando, Michael R. McKeown, Mei Wei Chen, Cindy Collins, Matthew G. Guenther, Christian C. Fritz. Predicting drug response by profiling the epigenome: Super-enhancers as biomarkers. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-69.
Abstract A2-66: Integrative analysis of gene expression and metabolism in isocitrate dehydrogenase 1 mutant acute myeloid leukemia and myeloid knock in mouse model
Authors: Tseng Alan , Li Wanda , Lee Kim-Chung , Yung Karrie , Lam Ching-Wan , Mak Tak 
Published: November 2015

Abstract
Isocitrate dehydrogenase 1 (IDH1) mutations are common drivers of acute myeloid leukemia (AML). While mutant IDH1 is known to disrupt DNA methylation, gene expression, and metabolism in cell lines, it is not known how mutant IDH1 alters global metabolism in vivo. The purpose of this study is to find differential enzymes and metabolites in IDH1 wild type versus mutant AML and knock in mice by integrating differential genes and metabolites in a metabolic network.
Differential genes were found by comparing gene expression and DNA methylation in IDH1 mutant (n = 12) and wild type (n = 89) cytogenetically normal AML patients from The Cancer Genome Atlas. Differential metabolites were found by untargeted liquid chromatography-mass spectrometry of urine, plasma, and bone marrow hematopoietic stem and progenitor cells from unpaired 1-2 year old mixed-sex IDH1 myeloid knock in (n = 13) and control mice (n = 14). Integrative analysis was performed using the MetScape 2 Cytoscape plugin.
71 differential genes and 52 differential metabolites were mapped to the global metabolic network. The set of differential genes and metabolites formed 10 connected subnetworks containing reactions in sphingolipid, phospholipid, and bile acid metabolism. Two enzymes in the subnetworks were differentially expressed in an independent IDH1 mutant versus wild type AML dataset: acyl-CoA thioesterase 4 (ACOT4) and galactosylceramidase (GALC) (P &lt; 0.05, false discovery rate ≤ 0.3). ACOT4 and GALC were significantly upregulated (P &lt; 0.02) in acute promyelocytic leukemia cells treated with all-trans retinoic acid.
In conclusion, we have found novel differential genes and metabolites in IDH1 mutant systems which may be relevant to leukemia differentiation treatment. Integrative analysis of gene expression and metabolite levels could be useful for finding dysregulated metabolic subnetworks in cancer.
Citation Format: Alan Tseng, Wanda Li, Kim-Chung Lee, Karrie Yung, Ching-Wan Lam, Tak Mak. Integrative analysis of gene expression and metabolism in isocitrate dehydrogenase 1 mutant acute myeloid leukemia and myeloid knock in mouse model. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-66.
Abstract IA02: Cancer genomics: Translational challenges
Authors: Griffith Malachi , Griffith Obi , Spies Nicholas , Ainscough Benjamin , Skidmore Zachary , Trani Lee , Ramu Avinash , Krysiak Killanin , Mardis Elaine R. 
Published: November 2015

Abstract
The use of next-generation sequencing and analysis has become the standard approach to studying cancer genomics in the research setting. The knowledge gained has been considerable across multiple tumor types, in many ways changing our perceptions about the genomic basis for cancer. Coincident with this new information is the continuing development and introduction of targeted therapies for cancer treatment. Bringing these two developments together is exciting and challenging for numerous reasons. My talk will focus on some of the challenges that are commonly faced, on the decision-support tools and databases we are creating to help with these challenges, and the ways in which the clinical cancer expert can contribute to these tools.
Citation Format: Malachi Griffith, Obi Griffith, Nicholas Spies, Benjamin Ainscough, Zachary Skidmore, Lee Trani, Avinash Ramu, Killanin Krysiak, Elaine R. Mardis. Cancer genomics: Translational challenges. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr IA02.