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1. Comparison of pregnancy and neonatal outcomes of intracytoplasmic sperm injection performed with frozen versus fresh testicular sperm

Author

Wu, Shenghao, Zhao, Junzhao, Wu, Yanhong, Hu, Yangyang, Fang, Lizi, and Chen, Wu

Subjects
endocrine system, Reproductive Medicine, urogenital system, Urology, Original Article, reproductive and urinary physiology
Abstract

BACKGROUND: It remains controversial whether there is a difference in the prognosis of intracytoplasmic sperm injection (ICSI) using frozen or fresh testicular sperm in patients with obstructive azoospermia (OA). Moreover, in the available studies, few have tracked neonatal outcomes. This study aimed to compare the pregnancy and neonatal outcomes of ICSI using cryopreserved sperm versus fresh sperm collected by testicular sperm aspiration (TESA). METHODS: A total of 317 OA patients treated with ICSI in a university affiliated hospital from January 2016 to December 2020 were included in this study. The participants were divided into two groups according to the type of sperm used for ICSI: frozen sperm group (n=154) and fresh sperm group (n=163). The pregnancy and neonatal outcomes of the two groups were compared. RESULTS: The data produced by this study showed no significant statistical difference in the 2 pronuclei (2PN) fertilization rate, 2PN cleavage rate, high-quality blastocyst rate, and the average number of transferred embryos in the frozen and fresh sperm groups. Similarly, no difference was found in implantation rate, clinical pregnancy rate, multiple pregnancy rate, miscarriage rate, premature delivery rate, live birth rate, and gender ratio at birth (P>0.05). The average newborn birth weight was similar in both groups (2,932.61±728.40 vs. 3,100.32±515.64 g, respectively) (P>0.05). A higher incidence of low birthweight (LBW) newborns was found in the frozen sperm group (20.91% vs. 8.49%) (P0.05). We further analyzed the twin and single pregnancies in the two groups separately, and found that the incidences of LBW were both similar (P>0.05). There was no difference in the Apgar scores at 1 min and 5 min after birth between the two groups (P>0.05). CONCLUSIONS: The use of frozen testicular sperm by TESA was efficient for men with OA. There were similar pregnancy and neonatal outcomes following TESA-ICSI using frozen or fresh sperm in this retrospective study. Prospective investigations are needed for further validation.

Published
2022

2. Enhancing Neural Mathematical Reasoning by Abductive Combination with Symbolic Library

Author

Hu, Yangyang and Yu, Yang

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Machine Learning (cs.LG)
Abstract

Mathematical reasoning recently has been shown as a hard challenge for neural systems. Abilities including expression translation, logical reasoning, and mathematics knowledge acquiring appear to be essential to overcome the challenge. This paper demonstrates that some abilities can be achieved through abductive combination with discrete systems that have been programmed with human knowledge. On a mathematical reasoning dataset, we adopt the recently proposed abductive learning framework, and propose the ABL-Sym algorithm that combines the Transformer neural models with a symbolic mathematics library. ABL-Sym shows 9.73% accuracy improvement on the interpolation tasks and 47.22% accuracy improvement on the extrapolation tasks, over the state-of-the-art approaches. Online demonstration: http://math.polixir.ai, Appeared in ICML 2020 Workshop on Bridge Between Perception and Reasoning: Graph Neural Networks & Beyond, Vienna, Austria, 2020. Code at https://agit.ai/Polixir/ABL-Sym | Video presentation available at https://slideslive.com/s/yangyang-hu-38188 | Online demonstration available at http://math.polixir.ai

Published
2022

3. Antioxidant Activity and Cell Protection of Glycosylated Products in Different Reducing Sugar Duck Liver Protein Systems

Author

Zhan, Feili, Luo, Jiafeng, Sun, Yangying, Hu, Yangyang, Fan, Xiankang, and Pan, Daodong

Subjects
Health (social science), glycosylation, duck liver protein, Plant Science, Health Professions (miscellaneous), Microbiology, antioxidant properties, cellular protection, Food Science
Abstract

Duck liver is an important by-product of duck food. In this study, we investigated the effects of glucose, fructose, and xylose on the antioxidant properties of glycosylated products of duck liver protein and their protective effects on HepG2 cells. The results show that the glycosylation products of the three duck liver proteins (DLP-G, DLP-F, and DLP-X) all exhibit strong antioxidant activity; among three groups, DLP-X shows the strongest ability to scavenge DPPH, ·OH free radicals, and ABTS+ free radicals. The glycosylated products of duck liver protein are not toxic to HepG2 cells and significantly increase the activity of antioxidant enzymes such as SOD, CAT, and GSH-Px in HepG2 cells at the concentration of 2.0 g/L, reducing oxidative stress damage of cells (p < 0.05). DLP-X has a better effect in reducing oxidative damage and increasing cellular activity in HepG2 cells than DLP-G and DLP-F (p < 0.05). In this study, the duck liver protein glycosylated products by glucose, fructose, and xylose were named as DLP-G, DLP-F, and DLP-X, respectively.

Published
2023

4. Analysis on the influence of turbulence model on elbow erosion calculation

Author

Liu Qi, Mao Shaohua, Liu Sheng, Hu Yangyang, Jiang Wei, and Long Xinping

Subjects
lcsh:VM1-989, erosion prediction, Computational Fluid Dynamics(CFD), lcsh:Naval architecture. Shipbuilding. Marine engineering, elbow flow, turbulence model
Abstract

[Objectives] The elbow is a common component of ship piping system. Efforts have been made in order to predict the elbow erosion accurately.[Methods] In this paper,the numerical simulation is conducted to calculate and analyze the flow field inside the pipe. From the two aspects,i.e. the influence of the turbulence model on the prediction of the flow field in the elbow and the influence of turbulence model on elbow erosion calculation,intensive research into the method to predict the erosion of elbow by solid and liquid flows.[Results] The results show that the SST k-ω turbulence model,together with the Oka erosion model,can accurately predict the flow field distribution in the elbow. Turbulence model brings a significant impact on the abrasion calculation,where it affects the calculated value of final erosion and also makes a great difference in the distribution of predicted relative erosion.[Conclusions] The obtained results can provide guidance for future pipe erosion prediction.

Published
2019

6. Microstructure and Texture Evolution of Fe-33Mn-3Si-3Al TWIP Steel on Strain

Author

Jun Li, Su Yu, Cui Haotian, Feng Xiaoxiao, and Hu Yangyang

Subjects
Materials science, Strain (chemistry), Stacking-fault energy, Scanning electron microscope, Twip, General Materials Science, Texture (crystalline), Plasticity, Composite material, Crystal twinning, Microstructure
Abstract

The microstructure and texture evolution of Fe-33Mn-3Si-3Al twinning induced plasticity (TWIP) steel were studied by the scanning electron microscope (SEM) and X-ray diffraction (XRD) at room temperature. After quasi-static tensile, the texture evolution of different strain was observed. It was shown that the Goss and Brass components increased within the strain range of less than 0.6. Whereas, the main components were decreased when the strain levels were greater than 0.6. This behavior was attributed to the low stacking fault energy (SFE) and was related to the strain energy of this high manganese steel. At high strain levels, the high strain energy may contribute to the Brass components transition to the A (rot-Brass) components.

Published
2019

7. Electrostatic self-assembly preparation of reduced graphene oxide-encapsulated alumina nanoparticles with enhanced mechanical properties of alumina nanocomposites

Author

Jingjie Zhang, Zhaoqiang Chen, Chonghai Xu, Hu Yangyang, Mingdong Yi, and Guangchun Xiao

Subjects
Materials science, Nanocomposite, Graphene, Oxide, Nanoparticle, 02 engineering and technology, 010402 general chemistry, 021001 nanoscience & nanotechnology, 01 natural sciences, 0104 chemical sciences, law.invention, chemistry.chemical_compound, Fracture toughness, chemistry, Chemical engineering, Flexural strength, law, Materials Chemistry, Ceramics and Composites, Self-assembly, 0210 nano-technology, Contact area
Abstract

In this study, we employed facile self-assembly methods to synthesize reduced graphene oxide-encapsulated alumina (Al2O3/rGO) nanoparticles. The Al2O3/rGO nanoparticles were subsequently incorporated into an Al2O3 matrix as filler to prepare nanocomposites. The microstructural analysis showed that relatively thin rGO sheets were homogeneously dispersed in the matrix and bonded with the Al2O3 grains forming a three-dimensional rGO network structure. The specific structure caused the rGO sheets to be anchored and bound to the matrix grains, resulting in a high contact area between the rGO sheets and the matrix, whilst the fracture mode alteration, grain refinement and improved interfacial strength of the nanocomposites were related to the unique structure. The results indicated that the nanocomposites with 2.5 vol.% rGO exhibited outstanding mechanical properties, increasing both the flexural strength by 105%, with a maximum value of 636 MPa, and the fracture toughness by 90% (5.9 MPa m1/2) when compared with monolithic Al2O3.

Published
2018

8. Measuring web page complexity by analyzing TCP flows and HTTP headers

Author

Cheng Weiqing, Yin Qiaofeng, Hu Yangyang, and Chen Jiajia

Subjects
Web server, Computer Networks and Communications, Transmission Control Protocol, Computer science, Network packet, 020206 networking & telecommunications, 02 engineering and technology, computer.software_genre, Login, Packet trace, World Wide Web, Server, Signal Processing, Web page, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Internet users, computer, Information Systems
Abstract

To understand website complexity deeply, a web page complexity measurement system is developed. The system measures the complexity of a web page at two levels: transport-level and content-level, using a packet trace-based approach rather than server or client logs. Packet traces surpass others in the amount of information contained. Quantitative analyses show that different categories of web pages have different complexity characteristics. Experimental results show that a news web page usually loads much more elements at more accessing levels from much more web servers within diverse administrative domains over much more concurrent transmission control protocol (TCP) flows. About more than half of education pages each only involve a few logical servers, where most of elements of a web page are fetched only from one or two logical servers. The number of content types for web game traffic after login is usually least. The system can help web page designers to design more efficient web pages, and help researchers or Internet users to know communication details.

Published
2017

9. In vivo inhibitory activity of andrographolide derivative ADN-9 against liver cancer and its mechanisms involved in inhibition of tumor angiogenesis

Author

Zhenji Wang, Zhenwei Wu, Gui-Fu Dai, Yan Guangming, Jiahui Ma, Wei Yang, Wang Yake, Jin Zhao, Yan Zhang, Shang Ning, Jiang Kunkun, Shen Pengpeng, Haiwei Xu, Guan Zhenzhen, Cuiyun Ma, and Hu Yangyang

Subjects
Vascular Endothelial Growth Factor A, 0301 basic medicine, Angiogenesis, Andrographolide, Angiogenesis Inhibitors, Pharmacology, Toxicology, Metastasis, Mice, 03 medical and health sciences, chemistry.chemical_compound, Liver Neoplasms, Experimental, In vivo, Human Umbilical Vein Endothelial Cells, Animals, Humans, Medicine, Neoplasm Invasiveness, Neoplasm Metastasis, Tube formation, Mice, Inbred BALB C, Wound Healing, Neovascularization, Pathologic, business.industry, Akt/PKB signaling pathway, Liver Neoplasms, medicine.disease, Antineoplastic Agents, Phytogenic, Xenograft Model Antitumor Assays, 030104 developmental biology, chemistry, Cancer cell, Andrographis, Diterpenes, business, Liver cancer, Liver Circulation, Signal Transduction
Abstract

It is well known that liver cancer is a highly aggressive malignancy with poor prognosis. Andrographolide (AD), a major bioactive component of Andrographis paniculata (Burm. F.), is a potential anti-cancer pharmacophore and the synthesis of AD derivatives with better cytotoxicity to cancer cells has attracted considerable attentions. In the present study, we evaluated the in vivo inhibitory effects of ADN-9, a 15-benzylidene substituted derivative of AD, on the growth and metastasis of murine hepatoma H22 using an orthotopic xenograft model and a subcutaneous xenograft model, and we further studied the anti-angiogenic action and the related mechanisms of ADN-9 in vivo and in vitro. Importantly, ADN-9 remarkably suppressed the growth and metastasis of both orthotopic and subcutaneous xenograft tumors, and the serum AFP level in orthotopic hepatoma-bearing mice treated with 100mg/kg ADN-9 (ig.) was decreased to the normal level. We also found that ADN-9 showed stronger abilities than AD in shrinking tumors, suppressing the invasion and metastasis of H22 cells, decreasing the MVD and promoting tumor cell apoptosis in subcutaneous xenograft of mice. Additionally, ADN-9 exhibited stronger inhibitory activity than AD against the migration and VEGF-induced capillary-like tube formation in HUVECs, which was further proved to be associated with attenuating VEGF/VEGFR2/AKT signaling pathway. The present research provides the first evidence that a 15-substituted AD derivative is more promising than the parent compound in therapeutic treatment of liver cancer.

Published
2017

10. Comparative experimental study of ethanol-air premixed laminar combustion characteristics by laser induced spark and electric spark ignition

Author

Xiaolu Li, Hu Yangyang, Zhou Xuan, Cangsu Xu, and Anhao Zhong

Subjects
Laminar flame speed, 020209 energy, General Chemical Engineering, Extrapolation, Analytical chemistry, Laminar flow, 02 engineering and technology, General Chemistry, Combustion, Laser, law.invention, Physics::Fluid Dynamics, Ignition system, 020401 chemical engineering, Physics::Plasma Physics, law, Spark (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Electric spark, Physics::Chemical Physics, 0204 chemical engineering
Abstract

An experimental study of laminar combustion characteristics of ethanol-air premixed mixtures was conducted with different ignition methods, including laser induced spark ignition (LISI) and electric spark ignition (SI) at an initial condition of 358 K temperature and 0.1MPa pressure. Flame propagation with the two different ignition methods was analyzed and discussed. The laminar flame speed was extrapolated with a nonlinear extrapolation method. Results indicate that the laminar speed of ethanol-air mixtures with LISI is faster than that with SI at lean mixtures, but slower at stoichiometric and rich mixtures. The peak values of the laminar burning velocity for SI and LISI with nonlinear extrapolation are 50.1 cm/s and 47.6 cm/s at the equivalence ratio of 1.1, respectively. Laser-induced spark ignition is able to ignite leaner ethanol-air mixtures.

Published
2017

11. Fuzzy clustering algorithm for automatically determining the number of clusters

Author

Liu Zengli and Hu Yangyang

Subjects
ComputingMethodologies_PATTERNRECOGNITION, Fuzzy clustering, Computer science, Computer Science::Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Process (computing), Similarity matrix, Firefly algorithm, Image segmentation, Cluster analysis, Algorithm, Image (mathematics)
Abstract

In the process of image segmentation based on clustering, it is necessary to manually determine the number of clusters segmented, which makes the image automation less. A fuzzy clustering algorithm is proposed to automatically determine the number of clusters. First, the similarity matrix is constructed using the features of the extracted image, and the number of clusters is determined using the AP clustering algorithm. Then, the obtained number of clusters is input to the FCM algorithm, and the clustering center is optimized using the firefly algorithm. Finally, a simulation experiment was conducted. The experimental results show that the improved FCM algorithm has a good effect and can realize automatic image segmentation.

Published
2019

12. Porous Se@SiO2 nanocomposite promotes migration and osteogenic differentiation of rat bone marrow mesenchymal stem cell to accelerate bone fracture healing in a rat model

Author

Li,Chunlin, Wang,Qi, Gu,Xiaohua, Kang,Yingjie, Zhang,Yongxing, Hu,Yangyang, Li,Taixi, Jin,Hansong, Deng,Guoying, and Wang,Qiugen

Subjects
International Journal of Nanomedicine
Abstract

Chunlin Li,1,* Qi Wang,1,2,* Xiaohua Gu,3 Yingjie Kang,4 Yongxing Zhang,1 Yangyang Hu,5 Taixi Li,1 Hansong Jin,1 Guoying Deng,1 Qiugen Wang11Trauma Center, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 201620, People’s Republic of China; 2Trauma Center, Shanghai General Hospital of Nanjing Medical University, Shanghai 200080, People’s Republic of China; 3Department of Orthopedics, Shanghai Seventh People’s Hospital, Shanghai, 200137, People’s Republic of China; 4Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, People’s Republic of China; 5Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 201620, People’s Republic of China*These authors contributed equally to thisworkBackground:Delay or failure of bone union is a significant clinical challenge all over the world, and it has been reported that bone marrow mesenchymal stem cells (BMSCs) offer a promising approach to accelerate bone fracture healing. Se can modulate the proliferation and differentiation of BMSCs. Se-treatment enhances the osteoblastic differentiation of BMSCs and inhibiting the differentiation and formation of mature osteoclasts. The purpose of this study was to assess the effects of porous Se@SiO2nanocomposite on bone regeneration and the underlying biological mechanisms.Methods:We oxidized Se2-,to develop Se quantum dots, then we used the Se quantum dots to form a solid Se@SiO2nanocomposite which was then coated with polyvinylpyrrolidone (PVP) and etched in hot water to synthesize porous Se@SiO2nanocomposite. We used XRD pattern to assess the phase structure of the solid Se@SiO2nanocomposite. The morphology of porous Se@SiO2nanocomposite were evaluated by scanning electron microscope (SEM) and the biocompatibility of porous Se@SiO2nanocomposite were investigated by cell counting kit-8 (CCK-8) assays. Then, a release assay was also performed. We used a Transwell assay to determine cell mobility in response to the porous Se@SiO2nanocomposite. For in vitro experiments, BMSCs were divided into four groups to detect reactive oxygen species (ROS)generation, cell apoptosis, alkaline phosphatase activity, calcium deposition, gene activation and protein expression. For in vivo experiments, femur fracture model of rats was constructed to assess the osteogenic effects of porous Se@SiO2nanocomposite.Results:In vitro, intervention with porous Se@SiO2nanocomposite can promote migration and osteogenic differentiation of BMSCs, and protect BMSCs against H2O2-induced inhibition of osteogenic differentiation. In vivo, we demonstrated that the porous Se@SiO2nanocomposite accelerated bone fracture healing using a rat femur fracture model.Conclusion:Porous Se@SiO2nanocomposite promotes migration and osteogenesis differentiation of rat BMSCs andacceleratesbone fracture healing, and porous Se@SiO2nanocomposite may provide clinic benefit for bone tissue engineering.Keywords: bone marrow mesenchymal stem cells, porous Se@SiO2 nanocomposite, antioxidant, migration, osteogenic differentiation

Published
2019

13. Lip analysis in traditional Chinese medicine

Author

Hu Yangyang, Zhou Xiaofang, Zhang Wenqiang, Li Xue, and Li Fufeng

Subjects
0301 basic medicine, Pixel, Computer science, business.industry, Feature extraction, Pattern recognition, 02 engineering and technology, Image segmentation, Mixture model, Secondary color, Support vector machine, 03 medical and health sciences, 030104 developmental biology, Face (geometry), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Segmentation, Artificial intelligence, business
Abstract

Objective: To design the lip-color features and classification model and provide an automatic, quantitative method based on the lip detection in facial image. Methods: In this paper, We adopted the lip segmentation algorithm based on the three-dimensional mixture Skin Gaussian Model and color classification in SVM to solve this problem. Specifically, we used the GMM based iterations to confirm skin-color pixels that belong to the skin area. In the process of optimization, we extracted two-dimensional GMM with five parameters as the characteristic to obtain the main color and secondary color to extract and classify the features of the 877 lip images. Consequence: The accuracy and stability of classification method was successfully proved. Conclusion: Firstly, We proposed a Skin Gaussian mixture Model that can be used for the robust and accurate segmentation of facial skin in a probabilistic manner. Secondly, we extracted five parameters from the two-dimensional GMM of the a, b component in the laboratory space as the characteristic of each picture. Finally, we classified the lips through the SVM classifier. As for the lips with mixed color, the method we proposed can have a good performance in identifying the secondary colors, thereby improving the identification precision of the image segmentation and color classification of the lip.

Published
2017

14. Tongue diagnosis management for mobile health in the wild

Author

Li Fufeng, Zhang Wenqiang, Zhang Weifei, Li Xue, and Hu Yangyang

Subjects
Diagnostic methods, Health management system, Computer science, Quantization (signal processing), Premise, Diagnosis management, Health related, Data science, Digitization, Health data
Abstract

The health management service mode in traditional medicine for the public has attracted more and more attentions in recent years. With the development of large number of health related apps and devices, large quantities of valuable health data can be transmitted and collected through electronic devices such as smart phones. Accordingly, the quantization and digitization of the information in the four diagnostic methods in traditional Chinese medicine have played a more important role at present. In which the inspection diagnosis technology in traditional Chinese medicine can conduct effective diagnosis on health conditions by combining with modern technologies at the premise of reserving the features of traditional medicine. Therefore, the quantization of inspection diagnosis information is especially important. This article mainly introduces the method of tongue diagnosis information quantization in quantization of inspection diagnosis information.

Published
2017

15. A scalable and fast OPTICS for clustering trajectory big data

Author

Du Bo, Xiaohui Huang, Hu Yangyang, Ze Deng, and Mao Zhu

Subjects
DBSCAN, Clustering high-dimensional data, Computer Networks and Communications, Computer science, Correlation clustering, computer.software_genre, Data stream clustering, CURE data clustering algorithm, Consensus clustering, Metric (mathematics), Canopy clustering algorithm, Data mining, Cluster analysis, computer, Software
Abstract

Clustering trajectory data is an important way to mine hidden information behind moving object sampling data, such as understanding trends in movement patterns, gaining high popularity in geographic information and so on. In the era of `Big data', the current approaches for clustering trajectory data generally do not apply for excessive costs in both scalability and computing performance for trajectory big data. Aiming at these problems, this study first proposes a new clustering algorithm for trajectory big data, namely Tra-POPTICS by modifying a scalable clustering algorithm for point data (POPTICS). Tra-POPTICS has employed the spatiotemporal distance function and trajectory indexing to support trajectory data. Tra-POPTICS can process the trajectory big data in a distributed manner to meet a great scalability. Towards providing a fast solution to clustering trajectory big data, this study has explored the feasibility to utilize the contemporary general-purpose computing on the graphics processing unit (GPGPU). The GPGPU-aided clustering approach parallelized the Tra-POPTICS with the Hyper-Q feature of Kelper GPU and massive GPU threads. The experimental results indicate that (1) the Tra-POPTICS algorithm has a comparable clustering quality with T-OPTICS (the state of art work of clustering trajectories in a centralized fashion) and outperforms T-OPTICS by average four times in terms of scalability, and (2) the G-Tra-POPTICS has a comparable clustering quality with T-POPTICS as well and further gains about 30 speedup on average for clustering trajectories comparing to Tra-POPTICS with eight threads. The proposed algorithms exhibit great scalability and computing performance in clustering trajectory big data.

Published
2014

16. Microenvironmental factors that regulate mesenchymal stem cells: lessons learned from the study of heterotopic ossification

Author

Kan, Chen, Chen, Lijun, Hu, Yangyang, Lu, Haimei, Li, Yuyun, Kessler, John A, and Kan, Lixin

Subjects
Cellular Microenvironment, Ossification, Heterotopic, 6 - Ciencias aplicadas::61 - Medicina::616 - Patología. Medicina clínica. Oncología [CDU], Tissue regeneration, Animals, Humans, Regeneration, Mesenchymal stem cell (MSC), Mesenchymal Stem Cells, Microenvironmental Factors, Heterotopic ossification (HO), Article
Abstract

Bone marrow contains a non-hematopoietic, clonogenic, multipotent population of stromal cells that are later called mesenchymal stem cells (MSC). Similar cells that share many common features with MSC are also found in other organs, which are thought to contribute both to normal tissue regeneration and to pathological processes such as heterotopic ossification (HO), the formation of ectopic bone in soft tissue. Understanding the microenvironmental factors that regulate MSC in vivo is essential both for understanding the biology of the stem cells and for effective translational applications of MSC. Unfortunately, this important aspect has been largely underappreciated. This review tries to raise the attention and highlight this critical issue by updating the relevant literature along with discussions of the key issues in the area.

Published
2017

17. 31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016): part two

Author

Ager, Casey, Reilley, Matthew, Nicholas, Courtney, Bartkowiak, Todd, Jaiswal, Ashvin, Curran, Michael, Albershardt, Tina C., Bajaj, Anshika, Archer, Jacob F., Reeves, Rebecca S., Ngo, Lisa Y., Berglund, Peter, ter Meulen, Jan, Denis, Caroline, Ghadially, Hormas, Arnoux, Thomas, Chanuc, Fabien, Fuseri, Nicolas, Wilkinson, Robert W., Wagtmann, Nicolai, Morel, Yannis, Andre, Pascale, Atkins, Michael B., Carlino, Matteo S., Ribas, Antoni, Thompson, John A., Choueiri, Toni K., Hodi, F. Stephen, Hwu, Wen-Jen, McDermott, David F., Atkinson, Victoria, Cebon, Jonathan S., Fitzharris, Bernie, Jameson, Michael B., McNeil, Catriona, Hill, Andrew G., Mangin, Eric, Ahamadi, Malidi, van Vugt, Marianne, van Zutphen, Mariëlle, Ibrahim, Nageatte, Long, Georgina V., Gartrell, Robyn, Blake, Zoe, Simoes, Ines, Fu, Yichun, Saito, Takuro, Qian, Yingzhi, Lu, Yan, Saenger, Yvonne M., Budhu, Sadna, De Henau, Olivier, Zappasodi, Roberta, Schlunegger, Kyle, Freimark, Bruce, Hutchins, Jeff, Barker, Christopher A., Wolchok, Jedd D., Merghoub, Taha, Burova, Elena, Allbritton, Omaira, Hong, Peter, Dai, Jie, Pei, Jerry, Liu, Matt, Kantrowitz, Joel, Lai, Venus, Poueymirou, William, MacDonald, Douglas, Ioffe, Ella, Mohrs, Markus, Olson, William, Thurston, Gavin, Capasso, Cristian, Frascaro, Federica, Carpi, Sara, Tähtinen, Siri, Feola, Sara, Fusciello, Manlio, Peltonen, Karita, Martins, Beatriz, Sjöberg, Madeleine, Pesonen, Sari, Ranki, Tuuli, Kyruk, Lukasz, Ylösmäki, Erkko, Cerullo, Vincenzo, Cerignoli, Fabio, Xi, Biao, Guenther, Garret, Yu, Naichen, Muir, Lincoln, Zhao, Leyna, Abassi, Yama, Cervera-Carrascón, Víctor, Siurala, Mikko, Santos, João, Havunen, Riikka, Parviainen, Suvi, Hemminki, Akseli, Dalgleish, Angus, Mudan, Satvinder, DeBenedette, Mark, Plachco, Ana, Gamble, Alicia, Grogan, Elizabeth W., Krisko, John, Tcherepanova, Irina, Nicolette, Charles, Dhupkar, Pooja, Yu, Ling, Kleinerman, Eugenie S., Gordon, Nancy, Grenga, Italia, Lepone, Lauren, Gameiro, Sofia, Knudson, Karin M., Fantini, Massimo, Tsang, Kwong, Hodge, James, Donahue, Renee, Schlom, Jeffrey, Evans, Elizabeth, Bussler, Holm, Mallow, Crystal, Reilly, Christine, Torno, Sebold, Scrivens, Maria, Foster, Cathie, Howell, Alan, Balch, Leslie, Knapp, Alyssa, Leonard, John E., Paris, Mark, Fisher, Terry, Hu-Lieskovan, Siwen, Smith, Ernest, Zauderer, Maurice, Fogler, William, Franklin, Marilyn, Thayer, Matt, Saims, Dan, Magnani, John L., Gong, Jian, Gray, Michael, Fromm, George, de Silva, Suresh, Giffin, Louise, Xu, Xin, Rose, Jason, Schreiber, Taylor H., Gameiro, Sofia R., Clavijo, Paul E., Allen, Clint T., Hodge, James W., Tsang, Kwong Y., Grogan, Jane, Manieri, Nicholas, Chiang, Eugene, Caplazi, Patrick, Yadav, Mahesh, Hagner, Patrick, Chiu, Hsiling, Waldman, Michelle, Klippel, Anke, Thakurta, Anjan, Pourdehnad, Michael, Gandhi, Anita, Henrich, Ian, Quick, Laura, Young, Rob, Chou, Margaret, Hotson, Andrew, Willingham, Stephen, Ho, Po, Choy, Carmen, Laport, Ginna, McCaffery, Ian, Miller, Richard, Tipton, Kimberly A., Wong, Kenneth R., Singson, Victoria, Wong, Chihunt, Chan, Chanty, Huang, Yuanhiu, Liu, Shouchun, Richardson, Jennifer H., Kavanaugh, W. Michael, West, James, Irving, Bryan A., Jaini, Ritika, Loya, Matthew, Eng, Charis, Johnson, Melissa L., Adjei, Alex A., Opyrchal, Mateusz, Ramalingam, Suresh, Janne, Pasi A., Dominguez, George, Gabrilovich, Dmitry, de Leon, Laura, Hasapidis, Jeannette, Diede, Scott J., Ordentlich, Peter, Cruickshank, Scott, Meyers, Michael L., Hellmann, Matthew D., Kalinski, Pawel, Zureikat, Amer, Edwards, Robert, Muthuswamy, Ravi, Obermajer, Nataša, Urban, Julie, Butterfield, Lisa H., Gooding, William, Zeh, Herbert, Bartlett, David, Zubkova, Olga, Agapova, Larissa, Kapralova, Marina, Krasovskaia, Liudmila, Ovsepyan, Armen, Lykov, Maxim, Eremeev, Artem, Bokovanov, Vladimir, Grigoryeva, Olga, Karpov, Andrey, Ruchko, Sergey, Shuster, Alexandr, Khalil, Danny N., Campesato, Luis Felipe, Li, Yanyun, Lazorchak, Adam S., Patterson, Troy D., Ding, Yueyun, Sasikumar, Pottayil, Sudarshan, Naremaddepalli, Gowda, Nagaraj, Ramachandra, Raghuveer, Samiulla, Dodheri, Giri, Sanjeev, Eswarappa, Rajesh, Ramachandra, Murali, Tuck, David, Wyant, Timothy, Leshem, Jasmin, Liu, Xiu-fen, Bera, Tapan, Terabe, Masaki, Bossenmaier, Birgit, Niederfellner, Gerhard, Reiter, Yoram, Pastan, Ira, Xia, Leiming, Xia, Yang, Hu, Yangyang, Wang, Yi, Bao, Yangyi, Dai, Fu, Huang, Shiang, Hurt, Elaine, Hollingsworth, Robert E., Lum, Lawrence G., Chang, Alfred E., Wicha, Max S., Li, Qiao, Mace, Thomas, Makhijani, Neil, Talbert, Erin, Young, Gregory, Guttridge, Denis, Conwell, Darwin, Lesinski, Gregory B., Gonzales, Rodney JM Macedo, Huffman, Austin P., Wang, Ximi K., Reshef, Ran, MacKinnon, Andy, Chen, Jason, Gross, Matt, Marguier, Gisele, Shwonek, Peter, Sotirovska, Natalija, Steggerda, Susanne, Parlati, Francesco, Makkouk, Amani, Bennett, Mark K., Emberley, Ethan, Huang, Tony, Li, Weiqun, Neou, Silinda, Pan, Alison, Zhang, Jing, Zhang, Winter, Marshall, Netonia, Marron, Thomas U., Agudo, Judith, Brown, Brian, Brody, Joshua, McQuinn, Christopher, Farren, Matthew, Komar, Hannah, Shakya, Reena, Ludwug, Thomas, Morillon, Y. Maurice, Hammond, Scott A., Greiner, John W., Nath, Pulak R., Schwartz, Anthony L., Maric, Dragan, Roberts, David D., Naing, Aung, Papadopoulos, Kyriakos P., Autio, Karen A., Wong, Deborah J., Patel, Manish, Falchook, Gerald, Pant, Shubham, Ott, Patrick A., Whiteside, Melinda, Patnaik, Amita, Mumm, John, Janku, Filip, Chan, Ivan, Bauer, Todd, Colen, Rivka, VanVlasselaer, Peter, Brown, Gail L., Tannir, Nizar M., Oft, Martin, Infante, Jeffrey, Lipson, Evan, Gopal, Ajay, Neelapu, Sattva S., Armand, Philippe, Spurgeon, Stephen, Leonard, John P., Sanborn, Rachel E., Melero, Ignacio, Gajewski, Thomas F., Maurer, Matthew, Perna, Serena, Gutierrez, Andres A., Clynes, Raphael, Mitra, Priyam, Suryawanshi, Satyendra, Gladstone, Douglas, Callahan, Margaret K., Crooks, James, Brown, Sheila, Gauthier, Audrey, de Boisferon, Marc Hillairet, MacDonald, Andrew, Brunet, Laura Rosa, Rothwell, William T., Bell, Peter, Wilson, James M., Sato-Kaneko, Fumi, Yao, Shiyin, Zhang, Shannon S., Carson, Dennis A., Guiducci, Cristina, Coffman, Robert L., Kitaura, Kazutaka, Matsutani, Takaji, Suzuki, Ryuji, Hayashi, Tomoko, Cohen, Ezra E. W., Schaer, David, Li, Yanxia, Dobkin, Julie, Amatulli, Michael, Hall, Gerald, Doman, Thompson, Manro, Jason, Dorsey, Frank Charles, Sams, Lillian, Holmgaard, Rikke, Persaud, Krishnadatt, Ludwig, Dale, Surguladze, David, Kauh, John S., Novosiadly, Ruslan, Kalos, Michael, Driscoll, Kyla, Pandha, Hardev, Ralph, Christy, Harrington, Kevin, Curti, Brendan, Akerley, Wallace, Gupta, Sumati, Melcher, Alan, Mansfield, David, Kaufman, David R., Schmidt, Emmett, Grose, Mark, Davies, Bronwyn, Karpathy, Roberta, Shafren, Darren, Shamalov, Katerina, Cohen, Cyrille, Sharma, Naveen, Allison, James, Shekarian, Tala, Valsesia-Wittmann, Sandrine, Caux, Christophe, Marabelle, Aurelien, Slomovitz, Brian M., Moore, Kathleen M., Youssoufian, Hagop, Posner, Marshall, Tewary, Poonam, Brooks, Alan D., Xu, Ya-Ming, Wijeratne, Kithsiri, Gunatilaka, Leslie A. A., Sayers, Thomas J., Vasilakos, John P., Alston, Tesha, Dovedi, Simon, Elvecrog, James, Grigsby, Iwen, Herbst, Ronald, Johnson, Karen, Moeckly, Craig, Mullins, Stefanie, Siebenaler, Kristen, SternJohn, Julius, Tilahun, Ashenafi, Tomai, Mark A., Vogel, Katharina, Vietsch, Eveline E., Wellstein, Anton, Wythes, Martin, Crosignani, Stefano, Tumang, Joseph, Alekar, Shilpa, Bingham, Patrick, Cauwenberghs, Sandra, Chaplin, Jenny, Dalvie, Deepak, Denies, Sofie, De Maeseneire, Coraline, Feng, JunLi, Frederix, Kim, Greasley, Samantha, Guo, Jie, Hardwick, James, Kaiser, Stephen, Jessen, Katti, Kindt, Erick, Letellier, Marie-Claire, Li, Wenlin, Maegley, Karen, Marillier, Reece, Miller, Nichol, Murray, Brion, Pirson, Romain, Preillon, Julie, Rabolli, Virginie, Ray, Chad, Ryan, Kevin, Scales, Stephanie, Srirangam, Jay, Solowiej, Jim, Stewart, Al, Streiner, Nicole, Torti, Vince, Tsaparikos, Konstantinos, Zheng, Xianxian, Driessens, Gregory, Gomes, Bruno, Kraus, Manfred, Xu, Chunxiao, Zhang, Yanping, Kradjian, Giorgio, Qin, Guozhong, Qi, Jin, Xu, Xiaomei, Marelli, Bo, Yu, Huakui, Guzman, Wilson, Tighe, Rober, Salazar, Rachel, Lo, Kin-Ming, English, Jessie, Radvanyi, Laszlo, Lan, Yan, Postow, Michael, Senbabaoglu, Yasin, Gasmi, Billel, Zhong, Hong, Liu, Cailian, Hirschhorhn-Cymerman, Daniel, Zha, Yuanyuan, Malnassy, Gregory, Fulton, Noreen, Park, Jae-Hyun, Stock, Wendy, Nakamura, Yusuke, Liu, Hongtao, Ju, Xiaoming, Kosoff, Rachelle, Ramos, Kimberly, Coder, Brandon, Petit, Robert, Princiotta, Michael, Perry, Kyle, Zou, Jun, Arina, Ainhoa, Fernandez, Christian, Zheng, Wenxin, Beckett, Michael A., Mauceri, Helena J., Fu, Yang-Xin, Weichselbaum, Ralph R., Lewis, Whitney, Han, Yanyan, Wu, Yeting, Yang, Chou, Huang, Jing, Wu, Dongyun, Li, Jin, Liang, Xiaoling, Zhou, Xiangjun, Hou, Jinlin, Hassan, Raffit, Jahan, Thierry, Antonia, Scott J., Kindler, Hedy L., Alley, Evan W., Honarmand, Somayeh, Liu, Weiqun, Leong, Meredith L., Whiting, Chan C., Nair, Nitya, Enstrom, Amanda, Lemmens, Edward E., Tsujikawa, Takahiro, Kumar, Sushil, Coussens, Lisa M., Murphy, Aimee L., Brockstedt, Dirk G., Koch, Sven D., Sebastian, Martin, Weiss, Christian, Früh, Martin, Pless, Miklos, Cathomas, Richard, Hilbe, Wolfgang, Pall, Georg, Wehler, Thomas, Alt, Jürgen, Bischoff, Helge, Geissler, Michael, Griesinger, Frank, Kollmeier, Jens, Papachristofilou, Alexandros, Doener, Fatma, Fotin-Mleczek, Mariola, Hipp, Madeleine, Hong, Henoch S., Kallen, Karl-Josef, Klinkhardt, Ute, Stosnach, Claudia, Scheel, Birgit, Schroeder, Andreas, Seibel, Tobias, Gnad-Vogt, Ulrike, Zippelius, Alfred, Park, Ha-Ram, Ahn, Yong-Oon, Kim, Tae Min, Kim, Soyeon, Kim, Seulki, Lee, Yu Soo, Keam, Bhumsuk, Kim, Dong-Wan, Heo, Dae Seog, Pilon-Thomas, Shari, Weber, Amy, Morse, Jennifer, Kodumudi, Krithika, Liu, Hao, Mullinax, John, Sarnaik, Amod A., Pike, Luke, Bang, Andrew, Balboni, Tracy, Taylor, Allison, Spektor, Alexander, Wilhite, Tyler, Krishnan, Monica, Cagney, Daniel, Alexander, Brian, Aizer, Ayal, Buchbinder, Elizabeth, Awad, Mark, Ghandi, Leena, Schoenfeld, Jonathan, Lessey-Morillon, Elizabeth, Ridnour, Lisa, Segal, Neil H., Sharma, Manish, Le, Dung T., Ferris, Robert L., Zelenetz, Andrew D., Levy, Ronald, Lossos, Izidore S., Jacobson, Caron, Ramchandren, Radhakrishnan, Godwin, John, Colevas, A. Dimitrios, Meier, Roland, Krishnan, Suba, Gu, Xuemin, Neely, Jaclyn, Timmerman, John, Vanpouille-Box, Claire I., Formenti, Silvia C., Demaria, Sandra, Wennerberg, Erik, Mediero, Aranzazu, Cronstein, Bruce N., Gustafson, Michael P., DiCostanzo, AriCeli, Wheatley, Courtney, Kim, Chul-Ho, Bornschlegl, Svetlana, Gastineau, Dennis A., Johnson, Bruce D., Dietz, Allan B., MacDonald, Cameron, Bucsek, Mark, Qiao, Guanxi, Hylander, Bonnie, Repasky, Elizabeth, Turbitt, William J., Xu, Yitong, Mastro, Andrea, Rogers, Connie J., Withers, Sita, Wang, Ziming, Khuat, Lam T., Dunai, Cordelia, Blazar, Bruce R., Longo, Dan, Rebhun, Robert, Grossenbacher, Steven K., Monjazeb, Arta, Murphy, William J., Rowlinson, Scott, Agnello, Giulia, Alters, Susan, Lowe, David, Scharping, Nicole, Menk, Ashley V., Whetstone, Ryan, Zeng, Xue, Delgoffe, Greg M., Santos, Patricia M., Shi, Jian, Delgoffe, Greg, Nagasaka, Misako, Sukari, Ammar, Byrne-Steele, Miranda, Pan, Wenjing, Hou, Xiaohong, Brown, Brittany, Eisenhower, Mary, Han, Jian, Collins, Natalie, Manguso, Robert, Pope, Hans, Shrestha, Yashaswi, Boehm, Jesse, Haining, W. Nicholas, Cron, Kyle R., Sivan, Ayelet, Aquino-Michaels, Keston, Orecchioni, Marco, Bedognetti, Davide, Hendrickx, Wouter, Fuoco, Claudia, Spada, Filomena, Sgarrella, Francesco, Cesareni, Gianni, Marincola, Francesco, Kostarelos, Kostas, Bianco, Alberto, Delogu, Lucia, Roelands, Jessica, Boughorbel, Sabri, Decock, Julie, Presnell, Scott, Wang, Ena, Marincola, Franco M., Kuppen, Peter, Ceccarelli, Michele, Rinchai, Darawan, Chaussabel, Damien, Miller, Lance, Nguyen, Andrew, Sanborn, J. Zachary, Vaske, Charles, Rabizadeh, Shahrooz, Niazi, Kayvan, Benz, Steven, Patel, Shashank, Restifo, Nicholas, White, James, Angiuoli, Sam, Sausen, Mark, Jones, Sian, Sevdali, Maria, Simmons, John, Velculescu, Victor, Diaz, Luis, Zhang, Theresa, Sims, Jennifer S., Barton, Sunjay M., Kadenhe-Chiweshe, Angela, Dela Cruz, Filemon, Turk, Andrew T., Mazzeo, Christopher F., Kung, Andrew L., Bruce, Jeffrey N., Yamashiro, Darrell J., Connolly, Eileen P., Baird, Jason, Crittenden, Marka, Friedman, David, Xiao, Hong, Leidner, Rom, Bell, Bryan, Young, Kristina, Gough, Michael, Bian, Zhen, Kidder, Koby, Liu, Yuan, Curran, Emily, Chen, Xiufen, Corrales, Leticia P., Kline, Justin, Aguilar, Ethan G., Guerriero, Jennifer, Sotayo, Alaba, Ponichtera, Holly, Pourzia, Alexandra, Schad, Sara, Carrasco, Ruben, Lazo, Suzan, Bronson, Roderick, Letai, Anthony, Kornbluth, Richard S., Gupta, Sachin, Termini, James, Guirado, Elizabeth, Stone, Geoffrey W., Meyer, Christina, Helming, Laura, Wilson, Nicholas, Hofmeister, Robert, Neubert, Natalie J., Tillé, Laure, Barras, David, Soneson, Charlotte, Baumgaertner, Petra, Rimoldi, Donata, Gfeller, David, Delorenzi, Mauro, Fuertes Marraco, Silvia A., Speiser, Daniel E., Abraham, Tara S., Xiang, Bo, Magee, Michael S., Waldman, Scott A., Snook, Adam E., Blogowski, Wojciech, Zuba-Surma, Ewa, Budkowska, Marta, Salata, Daria, Dolegowska, Barbara, Starzynska, Teresa, Chan, Leo, Somanchi, Srinivas, McCulley, Kelsey, Lee, Dean, Buettner, Nico, Shi, Feng, Myers, Paisley T., Curbishley, Stuart, Penny, Sarah A., Steadman, Lora, Millar, David, Speers, Ellen, Ruth, Nicola, Wong, Gabriel, Thimme, Robert, Adams, David, Cobbold, Mark, Thomas, Remy, Al-Muftah, Mariam, Wong, Michael KK, Morse, Michael, Clark, Joseph I., Kaufman, Howard L., Daniels, Gregory A., Hua, Hong, Rao, Tharak, Dutcher, Janice P., Kang, Kai, Saunthararajah, Yogen, Velcheti, Vamsidhar, Kumar, Vikas, Anwar, Firoz, Verma, Amita, Chheda, Zinal, Kohanbash, Gary, Sidney, John, Okada, Kaori, Shrivastav, Shruti, Carrera, Diego A., Liu, Shuming, Jahan, Naznin, Mueller, Sabine, Pollack, Ian F., Carcaboso, Angel M., Sette, Alessandro, Hou, Yafei, Okada, Hideho, Field, Jessica J., Zeng, Weiping, Shih, Vincent FS, Law, Che-Leung, Senter, Peter D., Gardai, Shyra J., Okeley, Nicole M., Abelin, Jennifer G., Saeed, Abu Z., Malaker, Stacy A., Shabanowitz, Jeffrey, Ward, Stephen T., Hunt, Donald F., Profusek, Pam, Wood, Laura, Shepard, Dale, Grivas, Petros, Kapp, Kerstin, Volz, Barbara, Oswald, Detlef, Wittig, Burghardt, Schmidt, Manuel, Sefrin, Julian P., Hillringhaus, Lars, Lifke, Valeria, Lifke, Alexander, Skaletskaya, Anna, Ponte, Jose, Chittenden, Thomas, Setiady, Yulius, Sivado, Eva, Thomas, Vincent, El Alaoui, Meddy, Papot, Sébastien, Dumontet, Charles, Dyson, Mike, McCafferty, John, El Alaoui, Said, Bommareddy, Praveen K., Zloza, Andrew, Kohlhapp, Frederick, Silk, Ann W., Jhawar, Sachin, Paneque, Tomas, Newman, Jenna, Beltran, Pedro, Cao, Felicia, Hong, Bang-Xing, Rodriguez-Cruz, Tania, Song, Xiao-Tong, Gottschalk, Stephen, Calderon, Hugo, Illingworth, Sam, Brown, Alice, Fisher, Kerry, Seymour, Len, Champion, Brian, Eriksson, Emma, Wenthe, Jessica, Hellström, Ann-Charlotte, Paul-Wetterberg, Gabriella, Loskog, Angelica, Milenova, Ioanna, Ståhle, Magnus, Jarblad-Leja, Justyna, Ullenhag, Gustav, Dimberg, Anna, Moreno, Rafael, Alemany, Ramon, Goyal, Sharad, Silk, Ann, Mehnert, Janice, Gabrail, Nashat, Bryan, Jennifer, Medina, Daniel, Mitchell, Leah, Yagiz, Kader, Lopez, Fernando, Mendoza, Daniel, Munday, Anthony, Gruber, Harry, Jolly, Douglas, Fuhrmann, Steven, Radoja, Sasa, Tan, Wei, Pourchet, Aldo, Frey, Alan, Mohr, Ian, Mulvey, Matthew, Andtbacka, Robert H. I., Ross, Merrick, Agarwala, Sanjiv, Grossmann, Kenneth, Taylor, Matthew, Vetto, John, Neves, Rogerio, Daud, Adil, Khong, Hung, Meek, Stephanie M., Ungerleider, Richard, Welden, Scott, Tanaka, Maki, Williams, Matthew, Hallmeyer, Sigrun, Fox, Bernard, Feng, Zipei, Paustian, Christopher, Bifulco, Carlo, Zafar, Sadia, Hemminki, Otto, Bramante, Simona, Vassilev, Lotta, Wang, Hongjie, Lieber, Andre, Hemmi, Silvio, de Gruijl, Tanja, Kanerva, Anna, Ansari, Tameem, Sundararaman, Srividya, Roen, Diana, Lehmann, Paul, Bloom, Anja C., Bender, Lewis H., Walters, Ian B., Berzofsky, Jay A., Chapelin, Fanny, Ahrens, Eric T., DeFalco, Jeff, Harbell, Michael, Manning-Bog, Amy, Scholz, Alexander, Zhang, Danhui, Baia, Gilson, Tan, Yann Chong, Sokolove, Jeremy, Kim, Dongkyoon, Williamson, Kevin, Chen, Xiaomu, Colrain, Jillian, Santo, Gregg Espiritu, Nguyen, Ngan, Volkmuth, Wayne, Greenberg, Norman, Robinson, William, Emerling, Daniel, Drake, Charles G., Petrylak, Daniel P., Antonarakis, Emmanuel S., Kibel, Adam S., Chang, Nancy N., Vu, Tuyen, Campogan, Dwayne, Haynes, Heather, Trager, James B., Sheikh, Nadeem A., Quinn, David I., Kirk, Peter, Addepalli, Murali, Chang, Thomas, Zhang, Ping, Konakova, Marina, Hagihara, Katsunobu, Pai, Steven, VanderVeen, Laurie, Obalapur, Palakshi, Kuo, Peiwen, Quach, Phi, Fong, Lawrence, Charych, Deborah H., Zalevsky, Jonathan, Langowski, John L., Kirksey, Yolanda, Nutakki, Ravi, Kolarkar, Shalini, Pena, Rhoneil, Hoch, Ute, Doberstein, Stephen K., Cha, John, Mallon, Zach, Perez, Myra, McDaniel, Amanda, Anand, Snjezana, Uecker, Darrin, Nuccitelli, Richard, Wieckowski, Eva, Muthuswamy, Ravikumar, Ravindranathan, Roshni, Renrick, Ariana N., Thounaojam, Menaka, Thomas, Portia, Pellom, Samuel, Shanker, Anil, Dudimah, Duafalia, Brooks, Alan, Su, Yu-Lin, Adamus, Tomasz, Zhang, Qifang, Nechaev, Sergey, Kortylewski, Marcin, Wei, Spencer, Anderson, Clark, Tang, Chad, Schoenhals, Jonathan, Tsouko, Efrosini, Heymach, John, de Groot, Patricia, Chang, Joe, Hess, Kenneth R., Diab, Adi, Sharma, Padmanee, Hong, David, Welsh, James, Parsons, Andrea J., Leleux, Jardin, Ascarateil, Stephane, Koziol, Marie Eve, Bai, Dina, Dai, Peihong, Wang, Weiyi, Yang, Ning, Shuman, Stewart, Deng, Liang, Dillon, Patrick, Petroni, Gina, Brenin, David, Bullock, Kim, Olson, Walter, Smolkin, Mark E., Smith, Kelly, Nail, Carmel, Slingluff, Craig L., Sharma, Meenu, Fa’ak, Faisal, Janssen, Louise, Khong, Hiep, Xiao, Zhilan, Hailemichael, Yared, Singh, Manisha, Vianden, Christina, Overwijk, Willem W., Facciabene, Andrea, Stefano, Pierini, Chongyung, Fang, Rafail, Stavros, Nielsen, Michael, Vanderslice, Peter, Woodside, Darren G., Market, Robert V., Biediger, Ronald J., Marathi, Upendra K., Hollevoet, Kevin, Geukens, Nick, Declerck, Paul, Joly, Nathalie, McIntosh, Laura, Paramithiotis, Eustache, Rizell, Magnus, Sternby, Malin, Andersson, Bengt, Karlsson-Parra, Alex, Kuai, Rui, Ochyl, Lukasz, Schwendeman, Anna, Moon, James, Deng, Weiwen, Hudson, Thomas E., Hanson, Bill, Rae, Chris S., Burrill, Joel, Skoble, Justin, Katibah, George, deVries, Michele, Lauer, Peter, Dubensky, Thomas W., Chen, Xin, Zhou, Li, Ren, Xiubao, Aggarwal, Charu, Mangrolia, Drishty, Cohen, Roger, Weinstein, Gregory, Morrow, Matthew, Bauml, Joshua, Kraynyak, Kim, Boyer, Jean, Yan, Jian, Lee, Jessica, Humeau, Laurent, Oyola, Sandra, Duff, Susan, Weiner, David, Yang, Zane, Bagarazzi, Mark, McNeel, Douglas G., Eickhoff, Jens, Jeraj, Robert, Staab, Mary Jane, Straus, Jane, Rekoske, Brian, Liu, Glenn, Melssen, Marit, Grosh, William, Varhegyi, Nikole, Galeassi, Nadejda, Deacon, Donna H., Gaughan, Elizabeth, Ghisoli, Maurizio, Barve, Minal, Mennel, Robert, Wallraven, Gladice, Manning, Luisa, Senzer, Neil, Nemunaitis, John, Ogasawara, Masahiro, Ota, Shuichi, Peace, Kaitlin M., Hale, Diane F., Vreeland, Timothy J., Jackson, Doreen O., Berry, John S., Trappey, Alfred F., Herbert, Garth S., Clifton, Guy T., Hardin, Mark O., Toms, Anne, Qiao, Na, Litton, Jennifer, Peoples, George E., Mittendorf, Elizabeth A., Ghamsari, Lila, Flano, Emilio, Jacques, Judy, Liu, Biao, Havel, Jonathan, Makarov, Vladimir, Chan, Timothy A., Flechtner, Jessica B., Facciponte, John, Ugel, Stefano, De Sanctis, Francesco, Coukos, George, Paris, Sébastien, Pottier, Agnes, Levy, Laurent, Lu, Bo, Cappuccini, Federica, Pollock, Emily, Bryant, Richard, Hamdy, Freddie, Hill, Adrian, Redchenko, Irina, Sultan, Hussein, Kumai, Takumi, Fesenkova, Valentyna, Celis, Esteban, Fernando, Ingrid, Palena, Claudia, David, Justin M., Gabitzsch, Elizabeth, Jones, Frank, Gulley, James L., Herranz, Mireia Uribe, Wada, Hiroshi, Shimizu, Atsushi, Osada, Toshihiro, Fukaya, Satoshi, Sasaki, Eiji, Abolhalaj, Milad, Askmyr, David, Lundberg, Kristina, Albrekt, Ann-Sofie, Greiff, Lennart, Lindstedt, Malin, Flies, Dallas B., Higuchi, Tomoe, Ornatowski, Wojciech, Harris, Jaryse, Adams, Sarah F., Aguilera, Todd, Rafat, Marjan, Castellini, Laura, Shehade, Hussein, Kariolis, Mihalis, Jang, Dadi, vonEbyen, Rie, Graves, Edward, Ellies, Lesley, Rankin, Erinn, Koong, Albert, Giaccia, Amato, Ajina, Reham, Wang, Shangzi, Smith, Jill, Pierobon, Mariaelena, Jablonski, Sandra, Petricoin, Emanuel, Weiner, Louis M., Sherry, Lorcan, Waller, John, Anderson, Mark, Bigley, Alison, Bernatchez, Chantale, Haymaker, Cara, Kluger, Harriet, Tetzlaff, Michael, Jackson, Natalie, Gergel, Ivan, Tagliaferri, Mary, Hwu, Patrick, Snzol, Mario, Hurwitz, Michael, Barberi, Theresa, Martin, Allison, Suresh, Rahul, Barakat, David, Harris-Bookman, Sarah, Drake, Charles, Friedman, Alan, Berkey, Sara, Downs-Canner, Stephanie, Edwards, Robert P., Curiel, Tyler, Odunsi, Kunle, Bruno, Tullia C., Moore, Brandon, Squalls, Olivia, Ebner, Peggy, Waugh, Katherine, Mitchell, John, Franklin, Wilbur, Merrick, Daniel, McCarter, Martin, Palmer, Brent, Kern, Jeffrey, Vignali, Dario, Slansky, Jill, Chan, Anissa S. H., Qiu, Xiaohong, Fraser, Kathryn, Jonas, Adria, Ottoson, Nadine, Gordon, Keith, Kangas, Takashi O., Leonardo, Steven, Ertelt, Kathleen, Walsh, Richard, Uhlik, Mark, Graff, Jeremy, Bose, Nandita, Gupta, Ravi, Mandloi, Nitin, Paul, Kiran, Patil, Ashwini, Sathian, Rekha, Mohan, Aparna, Manoharan, Malini, Chaudhuri, Amitabha, Chen, Yu, Lin, Jing, Ye, Yun-bin, Xu, Chun-wei, Chen, Gang, Guo, Zeng-qing, Komarov, Andrey, Chenchik, Alex, Makhanov, Michael, Frangou, Costa, Zheng, Yi, Coltharp, Carla, Unfricht, Darryn, Dilworth, Ryan, Fridman, Leticia, Liu, Linying, Rajopadhye, Milind, Miller, Peter, Concha-Benavente, Fernando, Bauman, Julie, Trivedi, Sumita, Srivastava, Raghvendra, Ohr, James, Heron, Dwight, Duvvuri, Uma, Kim, Seungwon, Torrey, Heather, Mera, Toshi, Okubo, Yoshiaki, Vanamee, Eva, Foster, Rosemary, Faustman, Denise, Stack, Edward, Izaki, Daisuke, Beck, Kristen, Jia, Dan Tong, Armenta, Paul, White-Stern, Ashley, Marks, Douglas, Taback, Bret, Horst, Basil, Glickman, Laura Hix, Kanne, David B., Gauthier, Kelsey S., Desbien, Anthony L., Francica, Brian, Leong, Justin L., Sung, Leonard, Metchette, Ken, Kasibhatla, Shailaja, Pferdekamper, Anne Marie, Zheng, Lianxing, Cho, Charles, Feng, Yan, McKenna, Jeffery M., Tallarico, John, Bender, Steven, Ndubaku, Chudi, McWhirter, Sarah M., Gugel, Elena Gonzalez, Bell, Charles J. M., Munk, Adiel, Muniz, Luciana, Bhardwaj, Nina, Zhao, Fei, Evans, Kathy, Xiao, Christine, Holtzhausen, Alisha, Hanks, Brent A., Scholler, Nathalie, Yin, Catherine, Van der Meijs, Pien, Prantner, Andrew M., Krejsa, Cecile M., Smith, Leia, Johnson, Brian, Branstetter, Daniel, Stein, Paul L., Jaen, Juan C., Tan, Joanne BL, Chen, Ada, Park, Timothy, Powers, Jay P., Sexton, Holly, Xu, Guifen, Young, Steve W., Schindler, Ulrike, Deng, Wentao, Klinke, David John, Komar, Hannah M., Serpa, Gregory, Elnaggar, Omar, Hart, Philip, Schmidt, Carl, Dillhoff, Mary, Jin, Ming, Ostrowski, Michael C., Koti, Madhuri, Au, Katrina, Peterson, Nichole, Truesdell, Peter, Reid-Schachter, Gillian, Graham, Charles, Craig, Andrew, Francis, Julie-Ann, Kotlan, Beatrix, Balatoni, Timea, Farkas, Emil, Toth, Laszlo, Ujhelyi, Mihaly, Savolt, Akos, Doleschall, Zoltan, Horvath, Szabolcs, Eles, Klara, Olasz, Judit, Csuka, Orsolya, Kasler, Miklos, Liszkay, Gabriella, Barnea, Eytan, Blakely, Collin, Flynn, Patrick, Goodman, Reid, Bueno, Raphael, Sugarbaker, David, Jablons, David, Broaddus, V. Courtney, West, Brian, Kunk, Paul R., Obeid, Joseph M., Winters, Kevin, Pramoonjago, Patcharin, Stelow, Edward B., Bauer, Todd W., Rahma, Osama E., Lamble, Adam, Kosaka, Yoko, Huang, Fei, Saser, Kate A., Adams, Homer, Tognon, Christina E., Laderas, Ted, McWeeney, Shannon, Loriaux, Marc, Tyner, Jeffery W., Druker, Brian J., Lind, Evan F., Liu, Zhuqing, Lu, Shanhong, Kane, Lawrence P., Shayan, Gulidanna, Femel, Julia, Lane, Ryan, Booth, Jamie, Lund, Amanda W., Rodriguez, Anthony, Engelhard, Victor H., Metelli, Alessandra, Wu, Bill X., Fugle, Caroline W., Saleh, Rachidi, Sun, Shaoli, Wu, Jennifer, Liu, Bei, Li, Zihai, Morris, Zachary S., Guy, Emily I., Heinze, Clinton, Kler, Jasdeep, Gressett, Monica M., Werner, Lauryn R., Gillies, Stephen D., Korman, Alan J., Loibner, Hans, Hank, Jacquelyn A., Rakhmilevich, Alexander L., Harari, Paul M., Sondel, Paul M., Huelsmann, Erica, Broucek, Joseph, Brech, Dorothee, Straub, Tobias, Irmler, Martin, Beckers, Johannes, Buettner, Florian, Schaeffeler, Elke, Schwab, Matthias, Noessner, Elfriede, Wolfreys, Alison, Da Costa, Andre, Silva, John, Crosby, Andrea, Staelens, Ludovicus, Craggs, Graham, Cauvin, Annick, Mason, Sean, Paterson, Alison M., Lake, Andrew C., Armet, Caroline M., O’Connor, Rachel W., Hill, Jonathan A., Normant, Emmanuel, Adam, Ammar, Biniszkiewicz, Detlev M., Chappel, Scott C., Palombella, Vito J., Holland, Pamela M., Becker, Annette, Leleti, Manmohan R., Newcomb, Eric, Tan, Joanne B. L., Rapisuwon, Suthee, Radfar, Arash, Gardner, Kellie, Gibney, Geoffrey, Atkins, Michael, Rennier, Keith R., Crowder, Robert, Wang, Ping, Pachynski, Russell K., Carrero, Rosa M. Santana, Rivas, Sarai, Beceren-Braun, Figen, Anthony, Scott, Schluns, Kimberly S., Sawant, Deepali, Chikina, Maria, Yano, Hiroshi, Workman, Creg, Salerno, Elise, Mauldin, Ileana, Deacon, Donna, Shea, Sofia, Pinczewski, Joel, Gajewski, Thomas, Spranger, Stefani, Horton, Brendan, Suzuki, Akiko, Leland, Pamela, Joshi, Bharat H., Puri, Raj K., Sweis, Randy F., Bao, Riyue, Luke, Jason, Theodoraki, Marie-Nicole, Mogundo, Frances-Mary, Won, Haejung, Moreira, Dayson, Gao, Chan, Zhao, Xingli, Duttagupta, Priyanka, Jones, Jeremy, D’Apuzzo, Massimo, and Pal, Sumanta

Subjects
Meeting Abstracts
Published
2016

18. Effects of High Temperature and Water Deficiency during Grain Filling on Activities of Key Starch Synthesis Enzymes and Starch Accumulation in Wheat

Author

Wang Chenyang, Lu Hongfang, Hu Yangyang, Ma Geng, Liu Weixing, Chu Yingying, Li Shasha, and Kang Juan

Subjects
0106 biological sciences, chemistry.chemical_classification, biology, Chemistry, Starch, Crop yield, 04 agricultural and veterinary sciences, Plant Science, 01 natural sciences, Enzyme assay, chemistry.chemical_compound, Enzyme, Amylose, Amylopectin, Soil water, 040103 agronomy & agriculture, biology.protein, 0401 agriculture, forestry, and fisheries, Sucrose synthase, Food science, Agronomy and Crop Science, 010606 plant biology & botany, Biotechnology
Published
2018

19. Laminar flame characteristcs of ethanol-air mixture: Experimental and simulation study

Author

Anhao Zhong, Zhou Xuan, Xu Cangsu, Hu Yangyang, and Xiaolu Li

Subjects
Markstein length, Ethanol, Materials science, Laminar flame speed, Renewable Energy, Sustainability and the Environment, lcsh:Mechanical engineering and machinery, 020209 energy, Laminar flow, 02 engineering and technology, simulation, chemistry.chemical_compound, chemistry, spherical flames, laminar burning velocity, 0202 electrical engineering, electronic engineering, information engineering, lcsh:TJ1-1570, ethanol, Composite material
Abstract

Experimental test for laminar combustion of ethanol-air mixture was investigated in a constant volume combustion bomb. The laminar burning velocity and Markstein length were determined over an extensive range of equivalence ratios from 0.7-1.6 under an initial condition of 0.1 MPa pressure and 358 K temperature, with high-speed Schlieren system. The methods of linear extrapolation and non-linear extrapolation are compared and discussed. Apart from experiments, simulation was carried out in Chemkin by using the Marinov ethanol oxidation mechanism. Results indicate that non-linear extrapolation is more suitable to calculate the laminar burning velocity of ethanol-air mixture. The overall trends of laminar burning velocity vs. equivalence ratio are consistent between the experiment and simulation. The peak values of the laminar burning velocity from present experiment and simulation are 531.2 mm/s and 565.3 mm/s, both appearing at the equivalence ratio of 1.1. Moreover, the Markstein length of ethanol-air mixtures generally decreases with increasing equivalence ratio.

Published
2018

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