ADaM Terminology

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《神经病学》-痴呆

《神经病学》-痴呆

《神经病学》笔记-痴呆
痴呆dementia:
定义:一种以认知功能障碍为核心症状,后天获得、进行性加重的临床综合症。

老年性痴呆≥65岁。

阿尔兹海默病AD:成年后发病,与弥漫性脑萎缩有关的逐渐进展的智能障碍,是原发性神经变性性痴呆中最常见的一种类型。

特点:隐袭起病,进行性智能衰退,多伴有人格改变。

记忆障碍是其核心症状。

病因:脑内β淀粉样蛋白异常沉积。

病理:老年斑、神经原纤维缠结、广泛神经元缺失、颗粒空泡变性、血管淀粉样变。

表现:①早期(记忆障碍);②中期(计算障碍、精神障碍、人格改变);③晚期(锥体系和锥体外系体征)。

鉴别诊断:①血管性痴呆(急性起病,波动性进展或阶梯型恶化,多见卒中史,影像见血管病变,病理见脑血管病变,多为缺血性);②路易体痴呆(三主征→波动性认知功能障碍、反复发生的视幻觉、自发性锥体外系功能障碍。

病理见神经元胞浆内路易小体形成。


治疗:胆碱酯酶抑制剂、美金刚(中低度亲和、非竞争性NMDA受体拮抗剂)
☞。

使用结合WRN的分子的治疗方法[发明专利]

使用结合WRN的分子的治疗方法[发明专利]

专利名称:使用结合WRN的分子的治疗方法
专利类型:发明专利
发明人:B·A·吉尔克雷斯特,M·S·埃勒,A·N·克勒,O·M·麦菲尔森,C·S·诺伊曼,T·A·路易斯
申请号:CN200780040055.3
申请日:20070829
公开号:CN101528754A
公开日:
20090909
专利内容由知识产权出版社提供
摘要:本发明特别提供了通过向有需要的哺乳动物给药有效量的组合物来治疗哺乳动物的多种疾病和病症的组合物和方法,其中所述的组合物包含可与WRN结合的非DNA小分子,例如螺羟吲哚(SPOX)类别中的成员。

申请人:波士顿大学信托人,哈佛大学校长及研究员协会
地址:美国马萨诸塞州
国籍:US
代理机构:中国专利代理(香港)有限公司
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《孟德尔随机化研究指南》中英文版

《孟德尔随机化研究指南》中英文版

《孟德尔随机化研究指南》中英文版全文共3篇示例,供读者参考篇1Randomized research is a vital component of scientific studies, allowing researchers to investigate causal relationships between variables and make accurate inferences about the effects of interventions. One of the most renowned guides for conducting randomized research is the "Mendel Randomization Research Guide," which provides detailed instructions and best practices for designing and implementing randomized controlled trials.The Mendel Randomization Research Guide offers comprehensive guidance on all aspects of randomized research, from study design and sample selection to data analysis and interpretation of results. It emphasizes the importance of randomization in reducing bias and confounding effects, thus ensuring the validity and reliability of study findings. With clear and practical recommendations, researchers can feel confident in the quality and rigor of their randomized research studies.The guide highlights the key principles of randomization, such as the use of random assignment to treatment groups, blinding of participants and researchers, and intent-to-treat analysis. It also discusses strategies for achieving balance in sample characteristics and minimizing the risk of selection bias. By following these principles and guidelines, researchers can maximize the internal validity of their studies and draw accurate conclusions about the causal effects of interventions.In addition to the technical aspects of randomized research, the Mendel Randomization Research Guide also addresses ethical considerations and practical challenges that researchers may face. It emphasizes the importance of obtaining informed consent from participants, protecting their privacy and confidentiality, and ensuring the safety and well-being of study subjects. The guide also discusses strategies for overcoming common obstacles in randomized research, such as recruitment and retention issues, data collection problems, and statistical challenges.Overall, the Mendel Randomization Research Guide is a valuable resource for researchers looking to improve the quality and validity of their randomized research studies. By following its recommendations and best practices, researchers can conductstudies that produce reliable and actionable findings, advancing scientific knowledge and contributing to evidence-based decision making in various fields.篇2Mendel Randomization Study GuideIntroductionMendel Randomization Study Guide is a comprehensive and informative resource for researchers and students interested in the field of Mendel randomization. This guide provides anin-depth overview of the principles and methods of Mendel randomization, as well as practical advice on how to design and conduct Mendel randomization studies.The guide is divided into several sections, each covering a different aspect of Mendel randomization. The first section provides a brief introduction to the history and background of Mendel randomization, tracing its origins to the work of Gregor Mendel, the father of modern genetics. It also discusses the theoretical foundations of Mendel randomization and its potential applications in causal inference.The second section of the guide focuses on the methods and techniques used in Mendel randomization studies. This includesa detailed explanation of how Mendel randomization works, as well as guidelines on how to select instrumental variables and control for potential confounders. It also discusses the strengths and limitations of Mendel randomization, and provides practical tips on how to deal with common challenges in Mendel randomization studies.The third section of the guide is dedicated to practical considerations in Mendel randomization studies. This includes advice on how to design a Mendel randomization study, collect and analyze data, and interpret the results. It also provides recommendations on how to report Mendel randomization studies and publish research findings in scientific journals.In addition, the guide includes a glossary of key terms and concepts related to Mendel randomization, as well as a list of recommended readings for further study. It also includes case studies and examples of Mendel randomization studies in practice, to illustrate the principles and techniques discussed in the guide.ConclusionIn conclusion, the Mendel Randomization Study Guide is a valuable resource for researchers and students interested in Mendel randomization. It provides a comprehensive overview ofthe principles and methods of Mendel randomization, as well as practical advice on how to design and conduct Mendel randomization studies. Whether you are new to Mendel randomization or looking to deepen your understanding of the field, this guide is an essential reference for anyone interested in causal inference and genetic epidemiology.篇3"Guide to Mendelian Randomization Studies" English VersionIntroductionMendelian randomization (MR) is a method that uses genetic variants to investigate the causal relationship between an exposure and an outcome. It is a powerful tool that can help researchers to better understand the underlying mechanisms of complex traits and diseases. The "Guide to Mendelian Randomization Studies" provides a comprehensive overview of MR studies and offers practical guidance on how to design and carry out these studies effectively.Chapter 1: Introduction to Mendelian RandomizationThis chapter provides an overview of the principles of Mendelian randomization, including the assumptions andlimitations of the method. It explains how genetic variants can be used as instrumental variables to estimate the causal effect of an exposure on an outcome, and outlines the key steps involved in conducting an MR study.Chapter 2: Choosing Genetic InstrumentsIn this chapter, the guide discusses the criteria for selecting appropriate genetic instruments for Mendelian randomization. It covers issues such as the relevance of the genetic variant to the exposure of interest, the strength of the instrument, and the potential for pleiotropy. The chapter also provides practical tips on how to search for suitable genetic variants in public databases.Chapter 3: Data Sources and ValidationThis chapter highlights the importance of using high-quality data sources for Mendelian randomization studies. It discusses the different types of data that can be used, such asgenome-wide association studies and biobanks, and offers advice on how to validate genetic instruments and ensure the reliability of the data.Chapter 4: Statistical MethodsIn this chapter, the guide explains the various statistical methods that can be used to analyze Mendelian randomization data. It covers techniques such as inverse variance weighting, MR-Egger regression, and bi-directional Mendelian randomization, and provides guidance on how to choose the most appropriate method for a given study.Chapter 5: Interpretation and ReportingThe final chapter of the guide focuses on the interpretation and reporting of Mendelian randomization results. It discusses how to assess the strength of causal inference, consider potential biases, and communicate findings effectively in research papers and presentations.ConclusionThe "Guide to Mendelian Randomization Studies" is a valuable resource for researchers who are interested in using genetic data to investigate causal relationships in epidemiological studies. By following the guidance provided in the guide, researchers can enhance the rigor and validity of their Mendelian randomization studies and contribute to a better understanding of the determinants of complex traits and diseases.。

名著中英文对照

名著中英文对照

The Voyage of the Beagle An Essay on the Principle ofPopulation The Interpretation ofDreams The History of the Decline and Fall of the Roman Empire
文学名著
The Iron Heel The People of the Abyss The Sea-Wolf The Son of the Wolf The White Fang Benito Cereno Billy Budd Moby Dick(The Whale) Typee Paradise Lost Paradise Regained A Dream of John Ball and A King's Lesson News from Nowhere Blix McTeague Moran of the Lady Letty The Octopus- A Story ofCalifornia Uncle Tom's Cabin Gulliver's Travels The Battle of the Books and Others Frankenstein Bride ofLammermoor Ivanhoe Rob Roy The Heat ofMid-Lothian The Antiquary The Talisman- A Tale of the Crusaders Waverley A Lover's Complaint A Midsummer Night's Dream All's Well That Ends Well As You Like It Cymbeline King John King Richard II King Richard III Love's Labour's Lost Measure for Measure Much Ado About Nothing Pericles, Prince of Type The Comedy of Errors King Henry the Fourth King Henry the Fifth King Henry the Sixth King Henry the Eighth The History of Troilus and Cressida The Life ofTimon of Athens

ADAMTS―1在各疾病中的表达-5页文档资料

ADAMTS―1在各疾病中的表达-5页文档资料

ADAMTS―1在各疾病中的表达ADAMTS-1(a disintegrin-like and metalloproteinase with thrombospondin type 1motif)是指含I型血小板结合蛋白基序(TSP)的解聚蛋白样金属蛋白酶,是新发现的一类Zn2+依赖的分泌型金属蛋白酶。

该金属蛋白酶是ADAMTS金属蛋白酶家族重要的第一个成员,和该蛋白家族中其它蛋白一样具有血小板反应素基序。

ADAMTS-1是一?N分泌型蛋白,无活性的ADAMTS-l前体在分泌过程中可以经过两个连续的步骤剪切为65kD和87kD两种活性形式。

分泌后的ADAMTS-1大多通过间隔区和C 末端3个TSP重复序列锚定在细胞外基质中。

ADAMTS-1在许多组织高表达,如肝脏、骨骼肌、心脏、肺和肾脏中。

多种因素可影响ADAMTS-1的表达,致炎细胞因子IL-1-B、TNF-d以及内毒素均可诱导其表达;转化生长因子TGF-β则可下调其表达水平。

激素类如雌激素、雄激素、孕酮、甲状旁腺激素、绒毛膜促性腺激素等均可调节其表达情况。

有研究表明血管内皮生长因子(VEGF)可刺激诱导ADAMTS-1的表达,进而对血管生长起到负反馈调节[1]。

ADAMTS-1能够很好的结合到细胞外基质,可以分泌到细胞外基质(extrace l luarm at rix,ECM),并与其发生作用,参与调节ECM 蛋白[2]。

ADAMTS-1 C末端的金属蛋白酶亚结构可与 ECM 结合,降解其聚集蛋白聚糖(aggrecan),蛋白多糖(proteog l ycan)和多能聚糖(versican)。

ADAMTS-1含量在不同疾病中有明显的改变,这可能为开发针对ADAMTS-1的靶向治疗提供新的思路。

1 ADAMTS-1 在病毒性心肌炎及心脏动脉粥样硬化的变化病毒性心肌炎(VMC)是由病毒感染引起的心肌局造性活弥漫性炎症病变。

目前已经发现可诱发VMC的病毒有20余种,其中柯萨奇病毒为其最常见的诱发病毒之一。

薛定谔—麦克斯韦尔方程径向解的存在性和多重性(英文)

薛定谔—麦克斯韦尔方程径向解的存在性和多重性(英文)

In 1887, the German physicist Erwin Schrödinger proposed a radial solution to the Maxwell-Schrödinger equation. This equation describes the behavior of an electron in an atom and is used to calculate its energy levels. The radial solution was found to be valid for all values of angular momentum quantum number l, which means that it can describe any type of atomic orbital.The existence and multiplicity of this radial solution has been studied extensively since then. It has been shown that there are infinitely many solutions for each value of l, with each one corresponding to a different energy level. Furthermore, these solutions can be divided into two categories: bound states and scattering states. Bound states have negative energies and correspond to electrons that are trapped within the atom; scattering states have positive energies and correspond to electrons that escape from the atom after being excited by external radiation or collisions with other particles.The existence and multiplicity of these solutions is important because they provide insight into how atoms interact with their environment through electromagnetic radiation or collisions with other particles. They also help us understand why certain elements form molecules when combined together, as well as why some elements remain stable while others decay over time due to radioactive processes such as alpha decay or beta decay.。

沃尔玛专用汇总Wal-MartTerminology

沃尔玛专用汇总Wal-MartTerminology

目录( 按字母顺序)1. Action Alley 主通道45. Fine Line 细分类2. Assembly 直订46. Flags 旗标3. Associate 员工47. Front End 前台4. Associates Sponsor 员工指导48. EDGM常务副总5. Bailer 打包机49. General Order 总订单6. Basic Merchandise 基本商品50. GO7. BOB 查看购物车底51. Gross Margin 毛利8. Buyers 采购员52. Gross Profit 毛利9. CBL 电脑基础学习53. HBA 美容护肤用品10. CFT 现金转移54. Hardlines 非食品部11. Claim 索赔55. Home Office (HO) 总部12. Clip Strip 挂带56. Home & Seasonal 家用和季节性商品13. Coaching For Success 成功指导57. Inventory 库存14. Code ADAM 亚当代码58. Invoice(INV.) 发票15. Code Black 黑色代码59. ISD 电脑部16. Code Blue 蓝色代码60. Item Number 商品号17. Code Brown 棕色代码61. Label 标签18. Code Red 红色代码62. LISA 防损暗语19. Code White 白色代码63. Loss Prevention 防损部20. COMAC部门经理交流64. Management Trainee Program(MTP) 管理21. COMP人员培训计划22. Competition 竞争对手65. Mark Down(MD) 降价23. Correction Of Errors (COE) 失误纠正66. Mark Up(MU) 提价24. Cost 成本67. Markup Percent 毛利率25. Courtesy Desk 服务台68. Merchandise Transfer Report( MTR) 商26. CSM 顾客服务经理品转运报告27. Customer Service Manager (CSM) 顾客服69. Modular 商品陈列图务经理70. MTR 商品调拨28. Damage 损坏71. 99 Supplies 商场自用品29. Dept 部门72. Not ON File(NOF) 不在档30. Direct 直送73. OH 现货31. Display 陈列74. Open Order 开放式订单32. Distribution 分销75. Out Of Stock 缺货33. Distribution Center 分销中心76. Overstock 库存过剩34. Distribution Turn 到货周期77. Packaging 包装35. District 区域78. Perpetual Inventory 永久库存36. Division 分区79. P&L 盈亏报告37. Division 01 01 分区80. P.L.U 查询码38. DSD 商场直接送货81. PO 订货单39. EDLP 天天平价82. POP 宣传广告牌40. End Cap 货架端( 又称N架) 83. Point Of Sale Replenishment (POS) 补41. Event 活动货系统42. Feature 特卖84. POS 自动补货系统43. Feature Tracking 特卖追踪85. Price Change (Price Adjustment) 价格44. F.I.F.O 先进先出45. Produce 农作物( 果蔬部) 108. Stack Base (SB) 堆头46. Profit Margin 利润率109. Store Manager 商场经理47. Purchase Order 订单110. Store Number 商场号码48. QTY 数量111. Super Center 购物广场49. Rate Of Sale (ROS) 销售率112. SWAS店中店50. Receiving 收货113. TAB 传单广告51. Retail 零售价114. Table 陈列柜52. Return On Investment (ROI) 投资回报率115. Telxon 手提终端53. Riser 加高层116. Top 50 Report 前五十名报告54. Sales Floor 楼面117. Trailer 货柜55. Sam’s Club 山姆会员店118. UPC 条形码56. Seasonal 季节性商品119. Vendor 供应商57. Service Desk 顾客服务台120. Vendor Number 供应商号58. 70-Type Items 70 类商品121. Vendor Pack 供应商包装59. Shrinkage 损耗122. VNDR供货商60. Side Counter 正常货架123. VPI Program 商品促销计划61. Side Kick 边篮124. Walton Institute 沃尔顿学院62. Signs/Signing 标牌125. Warehouse 仓库63. SKU 商品单位126. WH or WHSE 仓库64. Smart System 商场商品零售系统127. WMDC分销中心65. Softlines 服装部128. WTD周至今66. Sponsor 员工指导129. YTD 年至今T&D, HR Page 2 of 21 05/02/13ACTION ALLEY 主通道Sales aisle area of the store which Customers seeimmediately upon entrance from vestibule. Runstoward the back of the store from the front doors.顾客一走进商店的门厅立即看到的,位于货架中间的通道。

具有谷氨酸NMDA活性的新型精神病治疗剂[发明专利]

具有谷氨酸NMDA活性的新型精神病治疗剂[发明专利]

专利名称:具有谷氨酸NMDA活性的新型精神病治疗剂专利类型:发明专利
发明人:莫什·波特诺伊,伊雷特·吉尔-艾德,亚弗拉罕·维兹曼申请号:CN200780048074.0
申请日:20071025
公开号:CN101636182A
公开日:
20100127
专利内容由知识产权出版社提供
摘要:本发明公开了具有抗多巴胺能活性和调节谷氨酸N-甲基-D-天冬氨酸(NMDA)受体活性的能力的中枢神经系统(CNS)活性化合物,例如精神病治疗剂。

这种剂在精神分裂症和双相抑郁症的治疗中是有用的,尤其具有改变精神分裂症的阴性症状的能力。

这种剂对改变其他情绪障碍例如抑郁和焦虑、认知缺陷、运动障碍和药物成瘾的状态也是有用的。

申请人:雷蒙特亚特特拉维夫大学有限公司
地址:以色列特拉维夫
国籍:IL
代理机构:北京安信方达知识产权代理有限公司
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高二英语科学家名称单选题20题

高二英语科学家名称单选题20题

高二英语科学家名称单选题20题1.Who is known for his theory of relativity?A.NewtonB.EinsteinC.DarwinD.Galileo答案:B。

爱因斯坦以相对论闻名于世。

牛顿提出万有引力定律等;达尔文提出进化论;伽利略在天文学和物理学方面有重要贡献。

2.Which scientist is famous for his discovery of penicillin?A.FlemingB.PasteurC.CurieD.Bohr答案:A。

弗莱明因发现青霉素而闻名。

巴斯德在微生物学方面有重大贡献;居里夫人发现镭等放射性元素;玻尔在量子力学方面有重要成就。

3.Who is the scientist associated with the law of universal gravitation?A.EinsteinB.NewtonC.DarwinD.Hawking答案:B。

牛顿与万有引力定律相关。

爱因斯坦以相对论闻名;达尔文提出进化论;霍金在黑洞等领域有重要研究。

4.Which scientist is renowned for his work on evolution?A.NewtonB.DarwinC.EinsteinD.Fleming答案:B。

达尔文因在进化方面的工作而闻名。

牛顿提出万有引力定律等;爱因斯坦以相对论闻名;弗莱明发现青霉素。

5.Who is the scientist known for his research on black holes?A.HawkingB.EinsteinC.NewtonD.Darwin答案:A。

霍金以对黑洞的研究而闻名。

爱因斯坦以相对论闻名;牛顿提出万有引力定律等;达尔文提出进化论。

6.Who is known for making significant contributions to the field of agriculture in Asia?A.Yuan LongpingB.Albert EinsteinC.Thomas EdisonD.Isaac Newton答案:A。

国外 医学预测模型教材

国外 医学预测模型教材

国外医学预测模型教材
在国外,有许多关于医学预测模型的教材可供学习。

以下是一些推荐的教材:
1. 《Predictive Analytics in Healthcare》 by H. Langseth, G. C. Damsgaard, and P. S. Henriksen
这本书介绍了预测分析在医疗领域中的应用,涵盖了各种预测模型和算法,以及如何将它们应用于实际问题中。

2. 《Introduction to Predictive Analytics in Biomedicine》 by D. M. Vining, J. A. MacDiarmid, and N. C. Culverhouse
这本书介绍了生物医学中预测分析的基本概念和方法,包括统计建模、机器学习、数据挖掘等技术,以及如何将它们应用于临床实践和研究中。

3. 《Machine Learning for Healthcare: Prediction and Decision Support》 by S. Chawla, B. Kottegoda, and G. C. Damsgaard
这本书重点介绍了机器学习在医疗领域中的应用,包括分类、聚类、回归等算法,以及如何使用这些算法来构建预测模型和决策支持系统。

4. 《Healthcare Predictive Analytics: Methods and Applications》 by R. K. Jain and P. W. Reilly
这本书涵盖了医疗领域中各种预测模型的算法和应用,包括生存分析、时间序列分析、混合效应模型等,以及如何将它们应用于实际问题和挑战中。

这些教材都是权威性的经典之作,可以帮助您深入了解医学预测模型的基本概念、方法和应用。

成年大脑神经细胞特异性ADAM10基因敲除小鼠模型的建立与鉴定

成年大脑神经细胞特异性ADAM10基因敲除小鼠模型的建立与鉴定

成年大脑神经细胞特异性ADAM10基因敲除小鼠模型的建立与鉴定刘军;周常文;韦秋兰;庄建龙;林熠华;郑杰辉【期刊名称】《遗传》【年(卷),期】2012(34)12【摘要】A disintegrin and metalloproteinase 10 (ADAMlff) is a major sheddase for over 30 different membrane proteins and gets involved in such physiological processes and pathogenesis as embryonic development, cell adhesion, signal transduction, immune reaction, cancer, and Alzheimer's disease. Both ADAM10 knock-out mice and the neural progenitor cell-specific ADAM10 knock-out mice having been reported so far died in the embryonic or perinatal stage, respectively, thus resulting in the failure to investigate ADAM10 function in the adult mouse brain. Through a series of tests, we have succeeded in generating and characterizing the CaMKIIa-Cre/ADAMldcaP/'oxFWee surviving until adulthood by means of crossing ADAMltfoxF/IoxF mice with newly generated CaMKIIa-Cre transgenic mice. PCR analysis of genomic DNAs from different regions of the ADAM10 cKO mouse brain shows that the deleted ADAM10 alleles are mainly found in the cortex and hippocampus. Real-time RT-PCR findings further confirm that ADAM10 mRNAs decrease in the cortex and hippocampus by 55.7% and 60.8%, respectively. Western-blotting analysis demonstrates 63% and 84.8% loss of mature ADAM10proteins from the cortex and hippocampus. Immunohistochemical tests show that there is significantly less ADAM 10-positive staining in the cortical and hippocampal neurons but not gliocytes oiADAMlO cKO mice compared with control mice. In summary, we established the adult neuron-specific ADAM10 knock-out (cKO) mice for the first time, which prevented ADAMKT1' mice from the embryonic and perinatal mortality and laid a firm foundation for the further study of ADAM10 function in the brain of adult mice in vivo.%去整合素和金属蛋白酶10(ADAM10),是一种能够水解30余种跨膜蛋白质的"脱落酶"(sheddase),参与诸多生理过程和致病机制,如胚胎发育、细胞粘附、信号转导、免疫反应、癌症和阿尔茨海默病.迄今,已报道的ADAM10完全基因敲除小鼠和大脑神经前体细胞特异性ADAM10基因敲除小鼠分别于胚胎期或围产期死亡,致使无法研究成年小鼠大脑神经细胞ADAM10基因的功能.文章利用本研究小组建立的CaMKIIα-Cre转基因小鼠与ADAM10loxP/loxP转基因小鼠杂交,获得了CaMKllα-Cre/ADAM10loxP/loxP小鼠,并对其进行鉴定.利用PCR 方法检测成年ADAM10 cKO小鼠大脑基因组DNA表明,ADAM10基因缺失主要发生在前脑皮层和海马中.荧光定量PCR检测结果显示,ADAM10 mRNA的表达水平在前脑皮层和海马中分别降低55.7%和60.8%;使用Western blotting方法研究发现,ADAM10成熟蛋白质的含量在前脑皮层和海马中分别减少63%和84.8%.采用免疫组织化学方法检测表明,成年ADAM10 cKO小鼠与野生型小鼠相比,其大脑皮层和海马神经细胞的ADAM10免疫染色明显减弱,而其它细胞如胶质细胞的免疫染色基本一致.总之,文章成功制备了首个存活至成年的大脑神经细胞特异性ADAM10基因敲除(cKO)小鼠,克服了小鼠因ADAM10缺失在胚胎期或围产期死亡的弊端,为研究成年小鼠大脑神经细胞ADAM10基因的功能奠定了坚实的基础.【总页数】7页(P1570-1576)【作者】刘军;周常文;韦秋兰;庄建龙;林熠华;郑杰辉【作者单位】福建医科大学基础医学院细胞生物学与遗传学系,细胞与发育工程研究中心,干细胞工程与再生医学福建省高校重点实验室,福州,350004;福建医科大学基础医学院细胞生物学与遗传学系,细胞与发育工程研究中心,干细胞工程与再生医学福建省高校重点实验室,福州,350004;福建医科大学基础医学院细胞生物学与遗传学系,细胞与发育工程研究中心,干细胞工程与再生医学福建省高校重点实验室,福州,350004;福建医科大学基础医学院细胞生物学与遗传学系,细胞与发育工程研究中心,干细胞工程与再生医学福建省高校重点实验室,福州,350004;福建医科大学基础医学院细胞生物学与遗传学系,细胞与发育工程研究中心,干细胞工程与再生医学福建省高校重点实验室,福州,350004;福建医科大学基础医学院细胞生物学与遗传学系,细胞与发育工程研究中心,干细胞工程与再生医学福建省高校重点实验室,福州,350004【正文语种】中文【相关文献】1.LRP16 肝特异性基因敲除小鼠模型的构建与鉴定 [J], 安平;王安平;孟秋涛;母义明;2.LRP16肝特异性基因敲除小鼠模型的构建与鉴定 [J], 安平;王安平;孟秋涛;母义明3.海马神经细胞特异性DRD2基因条件性敲除小鼠模型的建立与鉴定 [J], 段朝霞;陈魁君;张东冬;张洁元;王建民4.肝脏特异性Pten基因敲除小鼠模型的制备及鉴定 [J], 刘宏扬;黄丽;张昆丽;翁长江;杨玉莹5.海马神经细胞特异性DRD2基因条件性敲除小鼠模型的建立与鉴定 [J], 段朝霞; 陈魁君; 张东冬; 张洁元; 王建民因版权原因,仅展示原文概要,查看原文内容请购买。

ADAM28反义核酸对人牙髓干细胞生物学特性的影响

ADAM28反义核酸对人牙髓干细胞生物学特性的影响

E eto D f cs f AM2 - DN o ilgc l r p ry fh ma e t l u p tm l H S s A 8 AS O nboo i o e t u nd n a l e c l DP C ) ap o p s e s( Z A h n , n - h n WA iWA n — h n .( si t o tm tl y C ieeP G n r l s i l H O Z e g uU Ho g c e . NG Y, NG Do g s e g I tue fSo aoo . h s n t g n e ea pt , Ho a B in 0 8 3 C ia e ig1 0 5 , h ) j n
AD AM2 反义核酸( — DN)I 8 AS O  ̄ 正义 对照(- N) I S OD 分别转染 HD S s P C ,用半 定量反转 录聚合酶 链反 应( T P R) R—e 和蛋 白印迹法( se bo) 测 AD Wetr lt n 检 AM2 — N 转染 4 h后的封 闭效率 ,应用 四唑盐( T 比色法 、酶 动 8AS OD 8 MT )
【 src] Obet e o iv sg t te e et o iner n tlpoe ae 2 ( D Abtat jci :T n et ae h f c fa ds t i ad me l rti s 8 A AM2 )a tes v i s i gn ao n 8 ni n e s oi do y uloi A — D o rleao , ieet t na dao ts f u ndna p l e cl ( DP C ) l o e xn cet e( SO N) npoirt n df r ii n p poio ma e t u s m el H S s g d f i n ao s h l pt s

cdics标准

cdics标准

cdics标准
CDISC标准涵盖两方面:内容(data,metadata,terminology)和格式(XML-based)标准。

CDISC标准的具体组成部分如下:
1.基础标准(Foundational Standards):这是CDISC标准的核心,包括
SEND、PRM、CDASH、SDTM、ADaM和BRIDG等标准。

这些标准定义了数据表示的模型、域和规范,其中,SEND是非临床数据交换标准,PRM是方案表述模型,CDASH是临床数据获取协调标准,SDTM是研究数据制表模型,ADaM是分析数据模型,BRIDG是领域分析模型。

2.临床试验过程的标准分布:根据一个介绍CDISC标准的图表,各标准在临
床试验过程中的分布如下:
●ODM-XML:用于交换和归档(临床的和转化而来的)研究数据及其相关
元数据、管理数据、参考数据和审计信息。

●SEND:非临床数据交换标准,用于以一致格式收集和呈现非临床数据。

●PRM:方案表述模型,对研究方案的设计提供标准。

●CDASH:临床数据获取协调标准,用于病例报告表中数据收集字段的内容
标准。

●SDTM:研究数据制表模型,用于数据组织和格式化的标准,向监管部门
递交统一标准的数据,提高审评效率。

●ADaM:分析数据模型,用于创建分析数据时所要执行的标准。

●BRIDG:领域分析模型,复合HL7标准的医疗信息系统和复合CDISC标准
的临床研究系统进行信息交换。

CDISC标准可以提高效率、完整性、可追溯性、创新性、数据质量、促进数据共享、降低成本和提高预测性等。

孟德尔遗传定律.pptx

孟德尔遗传定律.pptx

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1.1.2 Concerned concept
杂交(cross): 在遗传分析中有意识地将两个基 因型不同的亲本进行交配称杂交。
F1 (Filial generation) : 由两个基因型不同的亲 本杂交产生的种子及长成的植株。
F2:由F1自交或互交产生的种子及长成的植 株。
隐性基因(recessive gene):在杂合状态下,不表 现其表型效应的基因,一般以小写字母表示。
等位基因(Allele):在同源染色体上占据相同座位 的两个不同形式的基因,一般是由突变所造成的。
A
a
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A stem and pods of the garden pea plant (Pisum sativum)
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1.2.1 Concerned concept
表现型(phenotype ) :生物体某特定基因所表现出 来的性状(可以观察到的各种形态特征、基因的化 学产物、各种行为特征等,如花的颜色、血型、抗 性)。
纯合体(homozygote):基因座上有两个相同的等位 基因,就这个基因座而言,这种个体或细胞称为纯合 体,或称基因的同质结合,如AA、aa。
第四章 孟德尔遗传 定律
Mendel’s principles
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第一节 分离规律
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1.1 Mendel’s Experiments
Mendel, born in 1822, grew up in

adam(adaptive moment estimation)的用处

adam(adaptive moment estimation)的用处

adam(adaptive moment estimation)的用处Adam(Adaptive Moment Estimation)是一种优化算法,广泛应用于深度学习领域。

它结合了动量法和自适应学习率的特点,能够有效地加速模型的训练过程。

在本文中,将介绍Adam算法的原理、优势以及应用场景,并且探讨一些与Adam相关的研究和改进。

首先,介绍一下Adam算法的原理。

Adam算法通过计算梯度的一阶矩估计和二阶矩估计来调整模型的参数。

具体来说,它维护了两个变量:m和v,分别表示梯度的一阶矩估计和二阶矩估计。

这两个变量的初始值都设为0。

在每次迭代中,Adam算法根据当前的梯度更新m和v的值,并且计算出修正后的一阶矩估计m_hat和二阶矩估计v_hat。

然后,利用这些修正后的估计值来更新模型的参数。

具体的更新公式如下:m = beta1 * m + (1 - beta1) * gv = beta2 * v + (1 - beta2) * g^2m_hat = m / (1 - beta1^t)v_hat = v / (1 - beta2^t)theta = theta - alpha * m_hat / (sqrt(v_hat) + epsilon)其中,g表示当前的梯度,beta1和beta2是用来控制更新速度的超参数,一般分别设置为0.9和0.999。

t表示当前的迭代步数,alpha是学习率,epsilon是一个小的常数,用来避免除零错误。

Adam算法的优势主要体现在以下几个方面。

首先,Adam算法具有自适应学习率的特性。

由于v的存在,可以在训练早期自动适应较大的学习率,以便快速收敛;而在训练后期,v_hat的存在可以使学习率变得较小,以稳定模型的训练过程。

这一点与传统的优化算法(如随机梯度下降法)相比,能够更好地平衡模型的学习速度和稳定性。

其次,Adam算法结合了动量法的思想,具有较好的收敛性。

通过引入一阶矩估计m,可以减少参数更新的方差。

admixtools的统计原理

admixtools的统计原理

admixtools的统计原理admixtools是一种用于人群遗传学研究的统计软件工具,它通过对基因组数据进行分析,帮助研究人员了解不同人群之间的遗传差异。

admixtools的统计原理基于贝叶斯方法和最大似然估计,通过模型比较和参数优化,可以推断不同人群之间的遗传混合程度和遗传组成。

在人群遗传学研究中,我们常常面临一个问题:如何准确地分析和解释不同人群之间的遗传差异?admixtools给出了一个解决方案。

它基于贝叶斯方法,通过构建数学模型来描述不同人群之间的遗传关系,并利用统计学方法来推断模型参数。

具体而言,admixtools 使用了一个称为“混合模型”的概率模型,通过比较不同模型的拟合优度,选择最合适的模型并估计其参数。

在admixtools中,一个基本的统计模型是“混合模型”。

这个模型假设每个个体的基因组由多个不同人群的遗传成分组成。

例如,一个个体可能有40%的欧洲人的遗传成分和60%的非洲人的遗传成分。

混合模型的目标是通过分析基因组数据,估计每个个体的遗传组成和各个人群之间的遗传混合程度。

为了达到这个目标,admixtools使用了最大似然估计和贝叶斯方法。

最大似然估计是一种通过最大化观测到的数据的可能性来估计模型参数的方法。

在admixtools中,最大似然估计被用来估计每个个体的遗传组成和各个人群之间的遗传混合程度。

通过最大化基因组数据的可能性,admixtools可以找到最合适的参数值,从而得到准确的估计结果。

贝叶斯方法是一种基于概率论的统计方法,它通过构建先验分布和似然函数来推断模型参数的后验分布。

在admixtools中,贝叶斯方法被用来比较不同模型的拟合优度,并选择最合适的模型。

通过计算每个模型的后验概率,admixtools可以确定最合理的模型,并估计其参数。

除了最大似然估计和贝叶斯方法,admixtools还使用了一些附加的统计技术来提高分析的准确性和稳定性。

例如,它可以通过引入先验信息来约束模型的参数估计,从而减少估计误差。

建立Alzheimer氏病动物模型方法的研究进展

建立Alzheimer氏病动物模型方法的研究进展

建立Alzheimer氏病动物模型方法的研究进展
钱亦华;胡海涛;任惠民
【期刊名称】《解剖科学进展》
【年(卷),期】1999(5)2
【摘要】AD型痴呆占老年性痴呆50%以上,严重危害着老年人的健康。

国内外许多学者进行了大量有关AD的研究,特别是近年关于AD疾病的研究日益受到国内外学者高度重视。

建立一个可靠的模型是研究该病的关键。

迄今为止尚无一个公认理想的AD模型,本文综述了六十年代以来有关AD模型建立方法的研究进展状况,阐明了各种方法的优缺点。

【总页数】5页(P128-132)
【关键词】老年性痴呆;动物模型;方法
【作者】钱亦华;胡海涛;任惠民
【作者单位】西安医科大学解剖学教研室
【正文语种】中文
【中图分类】R749.160.2
【相关文献】
1.帕金森氏病常用实验动物模型建立方法及其分析 [J], 王金润;聂政
2.大鼠帕金森氏病动物模型建立方法的改进 [J], 伦学庆;章翔
3.阿茨海默氏病动物模型的建立方法 [J], 冯颖
4.大鼠帕金森氏病动物模型建立方法的改进 [J], 伦学庆;章翔
5.建立Alzheimer病动物模型的方法 [J], 钱亦华;胡海涛
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Average of Value Derivation Technique Best Case Imputation Technique Baseline Observation Carried Forward Imputation Technique Best Observation Carried Forward Imputation Technique Endpoint Value Derivation Technique
C92225
BC
C81201
BLOCF
C92226Βιβλιοθήκη BOCFC82866
ENDPOINT
C81208
INTERP
Interpolation: A method of imputation involving a missing Interpolation Imputation value that is between known values and is estimated by a Technique function of those known values. Last Observation Carried Forward: A data imputation technique which populates missing values with the subject's previous nonmissing value. Maximum: A data derivation technique which calculates a subject's maximum value over a defined set of records. Minimum: A data derivation technique which calculates a subject's minimum value over a defined set of records. Maximum Likelihood: A data imputation technique which populates missing values with estimates that maximize the probability of observing what has in fact been observed. Mean of Other Group: A data imputation technique which populates missing values with the mean value from a comparator or reference group. Mean Observed Value in a Group: A data imputation technique which populates missing values with the mean value observed in a group of subjects. Last Observation Carried Forward Imputation Technique Maximum Value Derivation Technique Minimum Value Derivation Technique Maximum Likelihood Estimation
Day Imputed: Day is imputed. Month Imputed: Month and day are imputed. Year Imputed: Entire date (year, month and day) is imputed.
Day Imputed Month Day Imputed Year Month Day Imputed
C117752
The analysis is specified in a protocol.
C117753
SPECIFIED IN SAP
The analysis is specified in a statistical analysis plan.
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DATEFL (Date Imputation Flag)
Other key measures that will be used to evaluate the Secondary Outcome intervention(s) or, for observational studies, that are a Measure focus of the study. These are the outcome measures used to assess the secondary objective(s).
Average: A data derivation technique which calculates a subject's average value over a defined set of records. Best Case: A data imputation technique which populates missing values with the best possible outcome. Baseline Observation Carried Forward: A data imputation technique which populates missing values with the subject's nonmissing baseline observation. Best Observation Carried Forward: A data imputation technique which populates missing values with the subject's best-case nonmissing value. Endpoint: A data derivation technique which calculates a subject's analysis end point value.
Subject Trial Status Time Imputation Flag
No No
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ANLPURP (Analysis Purpose)
NCI Code: C117745, Codelist extensible: Yes
C117745 ANLPURP NCI Code C98724 CDISC Submission Value EXPLORATORY OUTCOME MEASURE CDISC Synonym CDISC Definition NCI Preferred Term
NCI Code C117745 CDISC Submission Value ANLPURP Codelist Name CDISC Definition Codelist Extensible Yes
Analysis Purpose
Purpose of a specific analysis result described in ADaM analysis results metadata. Reason for reporting a specific analysis result described in ADaM analysis results metadata. Date Imputation Flag: Indicates the level of imputation reflected in a date value. Derivation Type: Analysis value derivation method. Parameter Type: Indicates whether the parameter is derived as a function of one or more other parameters. Indicates the status of the subject in the trial. Time Imputation Flag: Indicates the level of imputation reflected in a time value.
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DTYPE (Derivation Type)
NCI Code: C81224, Codelist extensible: Yes
C81224 NCI Code C81209 DTYPE CDISC Submission Value AVERAGE CDISC Synonym CDISC Definition NCI Preferred Term
Exploratory Outcome Measure
C98772
PRIMARY OUTCOME MEASURE
Primary Outcome Measure
Primary Outcome Measure
C98781
SECONDARY OUTCOME MEASURE
Secondary Outcome Measure
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ANLREAS (Analysis Reason)
NCI Code: C117744, Codelist extensible: Yes
C117744 ANLREAS NCI Code C117750 C117751 CDISC Submission Value DATA DRIVEN REQUESTED BY REGULATORY AGENCY SPECIFIED IN PROTOCOL CDISC Synonym CDISC Definition NCI Preferred Term
C117744
ANLREAS
Analysis Reason
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