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Search Results: 1 - 10 of 21907 matches for " Zhi Yinjie "
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Indicator System for Optimized Clinical TCM Protocols

Xie Yanming,Bai Wenjing,Zhi Yinjie,Weng Weiliang,Wu Changsheng,Yi Danhui,

世界科学技术-中医药现代化 , 2008,
Abstract: This is a study to establish an indicator system for optimized clinical TCM protocols,based on literature consuha- tion,group discussion,and expert consultation.An indicator system containing 7 first class indicators,18 second class indicators,and 39 third class indicators was established.The indicator system has a proven scientific rationality,desir- able to be a framework for optimized clinical TCM protocols.
Hash Function Learning via Codewords
Yinjie Huang,Michael Georgiopoulos,Georgios C. Anagnostopoulos
Computer Science , 2015,
Abstract: In this paper we introduce a novel hash learning framework that has two main distinguishing features, when compared to past approaches. First, it utilizes codewords in the Hamming space as ancillary means to accomplish its hash learning task. These codewords, which are inferred from the data, attempt to capture similarity aspects of the data's hash codes. Secondly and more importantly, the same framework is capable of addressing supervised, unsupervised and, even, semi-supervised hash learning tasks in a natural manner. A series of comparative experiments focused on content-based image retrieval highlights its performance advantages.
Integrating Flux Balance Analysis into Kinetic Models to Decipher the Dynamic Metabolism of Shewanella oneidensis MR-1
Xueyang Feng,You Xu,Yixin Chen,Yinjie J. Tang
PLOS Computational Biology , 2012, DOI: 10.1371/journal.pcbi.1002376
Abstract: Shewanella oneidensis MR-1 sequentially utilizes lactate and its waste products (pyruvate and acetate) during batch culture. To decipher MR-1 metabolism, we integrated genome-scale flux balance analysis (FBA) into a multiple-substrate Monod model to perform the dynamic flux balance analysis (dFBA). The dFBA employed a static optimization approach (SOA) by dividing the batch time into small intervals (i.e., ~400 mini-FBAs), then the Monod model provided time-dependent inflow/outflow fluxes to constrain the mini-FBAs to profile the pseudo-steady-state fluxes in each time interval. The mini-FBAs used a dual-objective function (a weighted combination of “maximizing growth rate” and “minimizing overall flux”) to capture trade-offs between optimal growth and minimal enzyme usage. By fitting the experimental data, a bi-level optimization of dFBA revealed that the optimal weight in the dual-objective function was time-dependent: the objective function was constant in the early growth stage, while the functional weight of minimal enzyme usage increased significantly when lactate became scarce. The dFBA profiled biologically meaningful dynamic MR-1 metabolisms: 1. the oxidative TCA cycle fluxes increased initially and then decreased in the late growth stage; 2. fluxes in the pentose phosphate pathway and gluconeogenesis were stable in the exponential growth period; and 3. the glyoxylate shunt was up-regulated when acetate became the main carbon source for MR-1 growth.
MicrobesFlux: a web platform for drafting metabolic models from the KEGG database
Xueyang Feng, You Xu, Yixin Chen, Yinjie J Tang
BMC Systems Biology , 2012, DOI: 10.1186/1752-0509-6-94
Abstract: We have developed a semi-automatic, web-based platform (MicrobesFlux) for generating and reconstructing metabolic models for annotated microorganisms. MicrobesFlux is able to automatically download the metabolic network (including enzymatic reactions and metabolites) of ~1,200 species from the KEGG database (Kyoto Encyclopedia of Genes and Genomes) and then convert it to a metabolic model draft. The platform also provides diverse customized tools, such as gene knockouts and the introduction of heterologous pathways, for users to reconstruct the model network. The reconstructed metabolic network can be formulated to a constraint-based flux model to predict and analyze the carbon fluxes in microbial metabolisms. The simulation results can be exported in the SBML format (The Systems Biology Markup Language). Furthermore, we also demonstrated the platform functionalities by developing an FBA model (including 229 reactions) for a recent annotated bioethanol producer, Thermoanaerobacter sp. strain X514, to predict its biomass growth and ethanol production.MicrobesFlux is an installation-free and open-source platform that enables biologists without prior programming knowledge to develop metabolic models for annotated microorganisms in the KEGG database. Our system facilitates users to reconstruct metabolic networks of organisms based on experimental information. Through human-computer interaction, MicrobesFlux provides users with reasonable predictions of microbial metabolism via flux balance analysis. This prototype platform can be a springboard for advanced and broad-scope modeling of complex biological systems by integrating other “omics” data or 13?C- metabolic flux analysis results. MicrobesFlux is available at http://tanglab.engineering.wustl.edu/static/MicrobesFlux.html webcite and will be continuously improved based on feedback from users.Arising interests in metabolic engineering have focused on systems analyses of cell metabolisms [1-6]. Metabolic flux analysis i
Statistics-based model for prediction of chemical biosynthesis yield from Saccharomyces cerevisiae
Arul M Varman, Yi Xiao, Effendi Leonard, Yinjie J Tang
Microbial Cell Factories , 2011, DOI: 10.1186/1475-2859-10-45
Abstract: Based on the production data of about 40 chemicals produced from S. cerevisiae, metabolic engineering methods, nutrient supplementation, and fermentation conditions described therein, we generated mathematical models with numerical and categorical variables to predict production yield. Statistically, the models showed that: 1. Chemical production from central metabolic precursors decreased exponentially with increasing number of enzymatic steps for biosynthesis (>30% loss of yield per enzymatic step, P-value = 0); 2. Categorical variables of gene overexpression and knockout improved product yield by 2~4 folds (P-value < 0.1); 3. Addition of notable amount of intermediate precursors or nutrients improved product yield by over five folds (P-value < 0.05); 4. Performing the cultivation in a well-controlled bioreactor enhanced the yield of product by three folds (P-value < 0.05); 5. Contribution of oxygen to product yield was not statistically significant. Yield calculations for various chemicals using the linear model were in fairly good agreement with the experimental values. The model generally underestimated the ethanol production as compared to other chemicals, which supported the notion that the metabolism of Saccharomyces cerevisiae has historically evolved for robust alcohol fermentation.We generated simple mathematical models for first-order approximation of chemical production yield from S. cerevisiae. These linear models provide empirical insights to the effects of strain engineering and cultivation conditions toward biosynthetic efficiency. These models may not only provide guidelines for metabolic engineers to synthesize desired products, but also be useful to compare the biosynthesis performance among different research papers.Producing small-molecule chemicals from microbial biocatalysts offers several advantages. Unlike conventional chemical synthesis which are heavily dependent on petroleum-derived substrates, microbes are able to use renewable material
High-quality reduced graphene oxide-nanocrystalline platinum hybrid materials prepared by simultaneous co-reduction of graphene oxide and chloroplatinic acid
Wang Yinjie,Liu Jincheng,Liu Lei,Sun Darren
Nanoscale Research Letters , 2011,
Abstract: Reduced graphene oxide-nanocrystalline platinum (RGO-Pt) hybrid materials were synthesized by simultaneous co-reduction of graphene oxide (GO) and chloroplatinic acid with sodium citrate in water at 80°C, of pH 7 and 10. The resultant RGO-Pt hybrid materials were characterized using transmission electron microscopy (TEM), powder X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier-transform infrared spectroscopy, and thermogravimetric analysis. Platinum (Pt) nanoparticles were anchored randomly onto the reduced GO (RGO) sheets with average mean diameters of 1.76 (pH 7) and 1.93 nm (pH 10). The significant Pt diffraction peaks and the decreased intensity of (002) peak in the XRD patterns of RGO-Pt hybrid materials confirmed that the Pt nanoparticles were anchored onto the RGO sheets and intercalated into the stacked RGO layers at these two pH values. The Pt loadings for the hybrid materials were determined as 36.83 (pH 7) and 49.18% (pH 10) by mass using XPS analysis. With the assistance of oleylamine, the resultant RGO-Pt hybrid materials were soluble in the nonpolar organic solvents, and the dispersion could remain stable for several months.
Risk Factors of Non-small Cell Lung Cancer with Bone Metastasis after Therapy
Yuanshan YAO, Yinjie ZHOU, Zhenhua YANG, Haibo SHEN
- , 2018, DOI: : 10.3779/j.issn.1009-3419.2018.06.09
Abstract: Background and objective Lung cancer is the leading cause of cancer-related deaths, patients with non-small cell lung cancer (NSCLC) usually have distant metastases, such as bone metastasis, brain metastasis, and lung metastasis. The purpose of this study was to explore the risk factors for bone metastasis in NSCLC patients. Methods A total of 176 cases of NSCLC were selected from May 2009 to May 2011, and patients were divided into two groups, namely the bone metastasis group and non-bone metastasis group. The general clinicopathological data of the two groups and analyzing the independent risk factors of bone metastasis were compared. Results In the general clinicopathological data of NSCLC patients. The thrombus or not and tumor-node-metastasis (TNM) stage were closely related to the occurrence of bone metastasis, and were statistically significant (all P<0.01). Prothrombin time, activated partial thromboplastin time, Fibrinogen, thrombin time, blood platelet, D-Dimer and alkaline phosphatase have significantly difference between the two groups (all P<0.05). Logistic regression analysis showed that fibrinogen, activated partial thromboplast in time, alkaline phosphatase, T4 phase, N3 phase and d-dimer were independent risk factors for bone metastasis in NSCLC patients. Conclusion Fibrinogen, alkaline phosphatase, T3, N2 stage and D-Dimer is the independent risk factors of bone metastases in patients with NSCLC.?
Bridging the Gap between Fluxomics and Industrial Biotechnology
Xueyang Feng,Lawrence Page,Jacob Rubens,Lauren Chircus,Peter Colletti,Himadri B. Pakrasi,Yinjie J. Tang
Journal of Biomedicine and Biotechnology , 2010, DOI: 10.1155/2010/460717
Abstract: Metabolic flux analysis is a vital tool used to determine the ultimate output of cellular metabolism and thus detect biotechnologically relevant bottlenecks in productivity. 13C-based metabolic flux analysis (13C-MFA) and flux balance analysis (FBA) have many potential applications in biotechnology. However, noteworthy hurdles in fluxomics study are still present. First, several technical difficulties in both 13C-MFA and FBA severely limit the scope of fluxomics findings and the applicability of obtained metabolic information. Second, the complexity of metabolic regulation poses a great challenge for precise prediction and analysis of metabolic networks, as there are gaps between fluxomics results and other omics studies. Third, despite identified metabolic bottlenecks or sources of host stress from product synthesis, it remains difficult to overcome inherent metabolic robustness or to efficiently import and express nonnative pathways. Fourth, product yields often decrease as the number of enzymatic steps increases. Such decrease in yield may not be caused by rate-limiting enzymes, but rather is accumulated through each enzymatic reaction. Fifth, a high-throughput fluxomics tool hasnot been developed for characterizing nonmodel microorganisms and maximizing their application in industrial biotechnology. Refining fluxomics tools and understanding these obstacles will improve our ability to engineer highlyefficient metabolic pathways in microbial hosts.
Carbohydrate Metabolism and Carbon Fixation in Roseobacter denitrificans OCh114
Kuo-Hsiang Tang, Xueyang Feng, Yinjie J. Tang, Robert E. Blankenship
PLOS ONE , 2009, DOI: 10.1371/journal.pone.0007233
Abstract: The Roseobacter clade of aerobic marine proteobacteria, which compose 10–25% of the total marine bacterial community, has been reported to fix CO2, although it has not been determined what pathway is involved. In this study, we report the first metabolic studies on carbohydrate utilization, CO2 assimilation, and amino acid biosynthesis in the phototrophic Roseobacter clade bacterium Roseobacter denitrificans OCh114. We develop a new minimal medium containing defined carbon source(s), in which the requirements of yeast extract reported previously for the growth of R. denitrificans can be replaced by vitamin B12 (cyanocobalamin). Tracer experiments were carried out in R. denitrificans grown in a newly developed minimal medium containing isotopically labeled pyruvate, glucose or bicarbonate as a single carbon source or in combination. Through measurements of 13C-isotopomer labeling patterns in protein-derived amino acids, gene expression profiles, and enzymatic activity assays, we report that: (1) R. denitrificans uses the anaplerotic pathways mainly via the malic enzyme to fix 10–15% of protein carbon from CO2; (2) R. denitrificans employs the Entner-Doudoroff (ED) pathway for carbohydrate metabolism and the non-oxidative pentose phosphate pathway for the biosynthesis of histidine, ATP, and coenzymes; (3) the Embden-Meyerhof-Parnas (EMP, glycolysis) pathway is not active and the enzymatic activity of 6-phosphofructokinase (PFK) cannot be detected in R. denitrificans; and (4) isoleucine can be synthesized from both threonine-dependent (20% total flux) and citramalate-dependent (80% total flux) pathways using pyruvate as the sole carbon source.
Inactivation of E. Coli in Water Using Photocatalytic, Nanostructured Films Synthesized by Aerosol Routes
Jinho Park,Eric Kettleson,Woo-Jin An,Yinjie J. Tang,Pratim Biswas
Catalysts , 2013, DOI: 10.3390/catal3010247
Abstract: TiO 2 nanostructured films were synthesized by an aerosol chemical vapor deposition (ACVD) method with different controlled morphologies: columnar, granular, and branched structures for the photocatalytic inactivation of Escherichia coli ( E. coli) in water. Effects of film morphology and external applied voltage on inactivation rate were investigated. As-prepared films were characterized using scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffractometry (XRD), and UV-VIS. Photocatalytic and photoelectrochemical inactivation of E. coli using as-prepared TiO 2 films were performed under irradiation of UVA light (note: UVA has a low efficiency to inactivate E. coli). Inactivation rate constants for each case were obtained from their respective inactivation curve through a 2 h incubation period. Photocatalytic inactivation rate constants of E. coli are 0.02/min (using columnar films), and 0.08/min (using branched films). The inactivation rate constant for the columnar film was enhanced by 330% by applied voltage on the film while that for the branched film was increased only by 30%. Photocatalytic microbial inactivation rate of the columnar and the branched films were also compared taking into account their different surface areas. Since the majority of the UV radiation that reaches the Earth’s surface is UVA, this study provides an opportunity to use sunlight to efficiently decontaminate drinking water.
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