生物统计研究
未名凯拓农业生物技术有限公司
制药/生物工程 制药/生物工程
发布日期： | 2010-12-13 | 工作地点： | 北京-海淀区 |
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工作年限： | 三年以上 | 语言要求： | 英语 熟练 | 学 历： | 博士 |
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1． 职位描述：
凯拓迪恩生物技术研发中心（美国杜邦合资公司）寻求一位生物统计研究员。负责日常植物生物实验数据和种子资源数据的统计分析与管理，并配合公司生物信息管理人员做好数据资源库。
2． 主要工作职责：
1)及时对各类实验数据进行统计分析,对实验设计进行评估,并将结果及时反馈给实验人员,与
实验人员讨论交流分析结果及实验设计中可能存在的问题；
2) 按照操作流程整理并管理实验数据；配合生物信息管理人员，构建相应的网络化管理的数据库；
3)根据主管要求及工作需要,及时查找分析各类数据。
3. 教育背景和工作经验要求：
1)研究方向为统计学、多元统计学、数理统计等相关专业，博士毕业或至少具有3年以上工作经验的硕士；
2)具有扎实的实验设计和线性混合模型统计分析能力及数据处理经验，熟练掌握SAS分析软件;了解统计学的最新进展；
3)为人踏实、工作认真负责,具有团队协作服务精神及敬业精神。
4) 具有熟练的英语听说读写能力和生物学知识者优先考虑。
未名凯拓公司暨国家作物分子设计中心
Introduction of BWK (the National Centre for Molecular Crop Design.)
简 介
2000年9月，在国家科技部、北京市科委、北京大学的共同倡导和支持下，由北京大学、中国科学院遗传与发育生物学研究所、中国农业科学院生物技术研究所、北京市农林科学院等单位共同组建了未名凯拓农业生物技术有限公司暨国家作物分子设计中心。
未名凯拓农业生物技术有限公司以国家发展目标与市场需求为导向，以技术创新为主旨，运用企业化运作机制，建立了科学的研发体系和管理体制，构建了具有国际水平和竞争力的植物基因研究和作物改良技术平台，成为“产、学、研”优势集成的生物技术创新及产业化基地。
为了贯彻“产业报国”avatar的企业理念，加快公司研发成果产业化步伐，2007年5月，北京未名凯拓公司与（首创集团）北京经济发展投资公司共同组建了北京未名凯拓作物设计中心有限责任公司，战略目标是通过战略合作、资源整合、资本运营多种方式实现上市，打造中国农业生物技术旗舰企业。
为了促进中国农业生物技术的发展，使中国现代农业与国际接轨，2007年12月，未名凯拓公司与先锋海外公司（美国杜邦公司的子公司）共同组建了凯拓迪恩生物技术研发中心。
研发中心的战略目标是运用生物技术寻求提高作物产量、品质、抗病性、水资源及营养利用效率的有效途径，为粮食安全和现代农业的可持续发展服务。
Mixture resolution according to the percentage of robusta variety in order to detect adulteration in roasted coffee by near infrared spectroscopy Original Rearch Article
Analytica Chimica Acta
Near infrared spectroscopy (NIRS), combined with multivariate calibration methods, has been ud to quantify the robusta variety content of roasted coffee samples, as a means for controlling and avoiding coffee adulteration, which is a very important issue taking into account the great variability of the final sale price depending on coffee varietal origin. In pursuit of this aim, PLS regression and a wavelet-bad pre-processing method that we h
ave recently developed called OWAVEC were applied, in order to simultaneously operate two crucial pre-processing steps in multivariate calibration: signal correction and data compression. Several pre-processing methods (mean centering, first derivative and two orthogonal signal correction methods, OSC and DOSC) were additionally applied in order to find calibration models with as best a predictive ability as possible and to evaluate the performance of the OWAVEC method, comparing the respective quality of the different regression models constructed. The calibration model developed after pre-processing derivative spectra by OWAVEC provided high quality results (0.79% RMSEP), the percentage of robusta variety being predicted with a reliability notably better than that associated with the models constructed from raw spectra and also from data corrected by other orthogonal signal correction methods, and showing a higher model simplicity.
Article Outline
1. Introduction
2. Materials and methods
2.1. OWAVEC method
2.2. Samples
2.3. Apparatus and software
2.4. Recording of NIR spectra
2.5. Data analysis
3. Results and discussion
3.1. Regression models for quantifying robusta variety content from roasted coffee NIR spectra
ipm3.2. Obrvations on NIR spectra
4. Conclusions
Acknowledgements
References
Bridging metamodels and ontologies in software engineering Original Rearch Article
Journal of Systems and Software
Over the last veral years, metamodels and ontologies have been developed in parallel isolation. Ontological thinking, largely from the rearch field of artificial intelligence, has been increasingly investigated by softwaretom green engineering rearchers, more familiar with the idea of a metamodel. Here, we investigate the literature on both metamodelling and ontologies in order to identify ways in which they can be made compatible and linked in such ajazzilipper way as to benefit both communities and create a contribution to a coherent underpinning theory for software engineering. Analysis of a large number of theoretical and mi-theoretical approaches using as a framework a multi-level modelling construct identifies strengths, weakness, incompatibilities and inconsistencies within the extant literature. A metamodel deals with conceptual definitions while an ontology deals with real-
world descriptors of business entities and is thus better named “domain ontology”. A specific kind of ontology (foundational or high-level) provides “metalevel” concepts for the domain ontologies. In other words, a foundational ontology may be ud at the same abstraction level asviewworld a metamodel and a domain ontology at the same abstraction level as a (design) model, with each pair linked via an appropriate mantic mapping.
Article Outline
1. Introduction
2. Models and metamodels
3. Ontologies
4. Ontologies in software engineering: a bridge between models, metamodels and ontologies
4.1. Domain ontologies and models
4.2. Ontologies and metamodels
4.3. Ambiguities and a possible resolution framework
5. Related work
6. Conclusions and future work
Acknowledgements
References
Vitae
Fast pul detection algorithms for digitized waveforms from scintillators 等位基因频率Original Rearch Article
Computer Physics Communications
Advanced C++ programming methods as well as fast Pul Detection Algorithms (PDA) h
ave been implemented in order to increa the computing speed of a LabVIEW™ data processing software developed for a Digital Pul Shape Discrimination (DPSD) system for liquid scintillators. The newly implemented PDAs are described and compared: the most efficient method has been implemented in the data processing software, which has also been ported into C++. The comparison of the computing speeds of the new and old versions of the PDAs are prented.
Program summary
Program title: DPDS – Digital Pul Detection Software
Catalogue identifier: AEHQ_v1_0
Program summary URL: cpc.cs.qub.ac.uk/summaries/AEHQ_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: Standard CPC licence, cpc.cs.qub.ac.uk/licence/licence.html
No. of lines in distributed program, including test data, etc.: 454 070
dltNo. of bytes in distributed program, including test data, etc.: 20 987 104
Distribution format:
Programming language: C++ (Borland Visual C++)
Computer: IBM PC
相信英文
Operating system: MS Windows 2000 and later…
RAM: <50 Mbytes, highly depends on ttings
Classification: 4.12
External routines: Only standard Borland Visual C++ libraries
Nature of problem: A very slow pul detection algorithm, ud as standard in LABView, is preventing the ability to process achieved data during the pau between plasma disch
arges in modern tokamaks.
