Tài liệu PDF In vivo

Exploration in the World of the Ancients Exploration in the World of the Ancients JOHN S. BOWMAN JOHN S. BOWMAN and MAURICE ISSERMAN General Editors  DISCOVERY EXPLORATION & Exploration in the World of the Ancients Copyright © 2005 by John S. Bowman Maps © 2005 by Facts On File, Inc. All rights reserved. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage or retrieval systems, without permission in writing from the publisher. For information contact: Facts On File, Inc. 132 West 31st Street New York NY 10001 Library of Congress C ataloging-in-Publication Data Bowman, John Stewart, 1931– E xploration in the world of the ancients / John S. Bowman ; Maurice Isserman and John S. Bowman, general editors. p. cm. —(Discovery and exploration) Summary: Discusses the voyages, navigation routes, and watercraft of ex- plorers in the ancient world, from prehistoric times to the beginning of the Middle Ages. Includes bibliographical references (p. ). ISBN 0-8160-5257-3 1. Geography, Ancient—Juvenile literature. 2. Discoveries in geography— Juvenile literature. [1. Geography, Ancient. 2. Discoveries in geography. 3. Explorers.] I. Isserman, Maurice. II. Title. III. Series. G86.B68 2004 910'.9'01—dc22 2003023033 Facts On File books are available at special discounts when purchased in bulk quantities for businesses, associations, institutions, or sales promotions. Please call our Special Sales Department in New York at (212) 967-8800 or (800) 322-8755. You can find Facts On File on the World Wide Web at http://www.factsonfile.com Text design by Erika K. Arroyo Cover design by Kelly Parr Maps by Patricia Meschino and Dale Williams Printed in the United States of America VB FOF 10 9 8 7 6 5 4 3 2 1 This book is printed on acid-free paper. To Francesca who has supported my explorations all these years  Note on Photos  Many of the illustrations and photographs used in this book are old, historical images. The quality of the prints is not always up to current standards, as in some cases the originals are from old or poor quality negatives or are damaged. The content of the illustrations, however, made their inclusion important despite problems in reproduction. Preface xi 1 PYTHEAS VOYAGES NORTH 1 Ancient Navigation 4 Tin Routes, 350 B . C .– A . D . 500 6 Pytheas’s Voyage, ca. 315 B . C .9 The Kyrenia: An Ancient Ship Salvaged 10 2 THE ORIGINAL EXPLORERS 14 Sites Associated with Hominids, 3.5 million–450,000 Years Ago 15 Homo sapiens as Homo explorans 17 Sites Associated with Humans in the Americas before 8000 B . C .21 The First Watercraft 22 Discovering the Pacific Islands 26 3 EARLY ANCIENT EXPLORERS 28 Egyptians and Ships 30 Voyages by Egyptians and Phoenicians, ca. 600–475 B . C . 36 Fictitious Explorations 38 Early Mediterranean and Black Sea Routes, ca. 900–350 B . C .40 4 THE INQUISITIVE GREEKS 44 The Amber Routes 46 Amber Routes from Baltic to Mediterranean Sea, 1500 B . C .– A . D . 500 47 Contents  Lost Atlantis 52 World as Seen by Herodotus, ca. 450 B . C .56 5 ALEXANDER THE GREAT AND THE HELLENISTIC WORLD 59 Journeys of Alexander the Great, 334–325 B . C .65 The Lighthouse and Library of Alexandria 66 Spread of Hellenistic Culture after 323 B . C .69 The Greek Geographers 70 6 THE EXPANSIVE ROMANS 74 All Roads Lead . . . 81 Major Roman Roads In vivo In vivo Bởi: Nguyễn Bá Tiếp In vivo Khái niệm In vivo tiếng Latin có nghĩa trình diễn thể sống Phương pháp in vivo dùng để thí nghiệm dùng mô sống hay toàn thể sống làm đối tượng thử nghiệm Các phương pháp in vivo khác với in vitro (thí nghiệm thể sống, thử nghiệm ống nghiệm) thí nghiệm mô hay thể chết Thông thường, nói đến in vivo, người ta thường nghĩ đến thí nghiệm, thử nghiệm đối tượng sinh vật sống Các thí nghiệm sử dụng động vật hay thử nghiệm lâm sàng người ví dụ nghiên cứu in vivo Christopher Lipinksi Andrew Hopkins cho dù thử nghiệm thể sống để tìm kiếm loại dược phẩm hay có thêm hiểu biết thể sinh vật phải đồng thời xem xét công cụ hóa học chất hóa học đối tượng sinh vật Thực tế cho thấy hợp chất biểu hoạt tính thử nghiệm thể sống (in vitro) khả kết hợp với protein tái tổ hợp, thay đổi trình trao đổi chất tế bào hay chí phá vỡ cấu trúc tế bào phân lập v.v chưa có hoạt tính mong muốn thử nghiệm thể sống Chính thử nghiệm in vivo coi bước thử nghiệm chắn sau phương pháp in vitro tiến hành Vấn đề đạo đức sinh học Phương pháp in vivo sử dụng động vật thí nghiệm gặp phải phản đối cá nhân tổ chức bảo vệ quyền động vật (animal rights) Mặc dù quan khoa học có biện pháp kiểm duyệt chặt chẽ protocol thí nghiệm vấn đề thuộc đạo đức sinh học ngày quan tâm gây nhiều tranh cãi Đôi ta thấy phản đối việc dùng động vật nghiên cứu nước phát triển Hiện tại, chưa có phương pháp thay thế, người ta cân nhắc nguyên tắc 3R (reduce, refinement, replacement) thí nghiệm dùng động vật Trong đó: - Reduce (giảm): Hạn chế số lượng động vật sử dụng thí nghiệm mà thu thông tin cần thiết, 1/3 In vivo - Refinement (đối xử tinh tế): Rèn luyện kỹ thuật tốt ứng dụng kiến thức động vật học nói chung loài vật dùng thí nghiệm cụ thể để tránh gây stress, tránh gây kích thích - Replacement (thay thế): Dùng phương pháp khác (nếu có thể) để tránh dùng động vật cho thí nghiệm Nguyên tắc William Russell (nhà động vật học) Rex Burch (nhà nghiên cứu vi sinh vật) giới thiệu lần vào năm 1959 nguyên ý nghĩa ngày Bước thử nghiệm cuối (thử nghiệm lâm sàng, clinical trials) tiến hành người (đối tượng lựa chọn tùy thuộc vào thử nghiệm) Bước tiến hành phương pháp thí nghiệm trước đó, bản, cung cấp đủ chứng tính an toàn loại chế phẩm nghiên cứu Các giai đoạn quy mô thử nghiệm tùy thuộc vào loại chế phẩm Có thử nghiệm lâm sàng không đem lại kết mong muốn mà đem lại nhiều tranh cãi tác dụng phương pháp điều trị đạo đức y học Thử nghiệm quy mô liệu pháp thay hormone ví dụ Trong sinh học phân tử, thuật ngữ in vivo thường sử dụng (nhưng thực chất không xác) để nghiên cứu tế bào phân lập (isolated cells) Phương pháp gọi với tên thích hợp "ex vivo" Các loại động vật thường sử dụng Thí nghiệm in vivo dùng nghiên cứu di truyền học, sinh học phát triển, nghiên cứu y sinh học, ghép tạng, độc chất học, phát triển dược phẩm, mỹ phẩm v.v Nhiều loại động vật (từ động vật không xương sống đến loài linh trưởng) sử dụng nghiên cứu Người ta ước tính khoảng 50 đến 100 triệu động vật có xương sống dùng cho thí nghiệm hàng năm Các loại động vật có xương sống sử dụng phổ biến loài gặm nhấm (chuột, chuột nhắt, thỏ ), chó, mèo, lợn, loài linh trưởng Tài liệu tham khảo Lipinski C, Hopkins A (2004) "Navigating chemical space for biology and medicine" Nature 432 (7019): 855–61 William Russell and Rex Burch (1959) The Principles of Humane Experimental Technique 2/3 In vivo Michels KB (2006) The women's health initiative curse or blessing? Int J Epidemiol 35(4):814-6 "Vivisection FAQ, British Union for the Abolition of Vivisection; "The Ethics of research involving animals", Nuffield Council on Bioethics, section 1.6 3/3 Thủ thuật Word 2003 Tự in phím tắt trong MS Word  bn  dng nh trong b Microsoft Office mu qu nht,long dn nhng th thut n gin nhng li hiu qu v ng ca 2 ch bin nht trong b  Tun 1 s th nht:  t trong Microsoft Word S dc  x n bn em li hiu qu t nhanh c son tho vn bn nhng bn cha bit nhiu v t  mu  t, tht n gin, chWord s t lit cho bn:   \Macro\Macro (Ho    Xut hin hp thoi Macro: 2. Chn Word Commands trong Macro in bn s p thoi Macros: 3. Ti      c List Commands (hoc nhp ch   Macro name 4. Chc Step Into Xut hin hp thoi List Commands  bn la chn vic linh  tt 5. Chn Current menu and keyboard settings: Linh t  t s dng) trong List Commands Hoc chn All word commands: Lit c nh t ln cht t (bn s  t  6. Ch-> Kt qu s in cho bn mt s dng trong ch Vn ph  m  t c  tt c t c chnh sn tin trong vic s dng. Nu cha bit th thu    tip t Khám phá thanh công cụ MS Word Nhng th thut sau  ng Microsoft Word nhm mc u dng c ng tvic ca bn.  cc nng t x n bn vn tht nhin v  bin nh  khi t thu dng hn na. L nh vic bn th dng mt biu tng (symbol) nht     to ra m  u t  ng. Bn c b i tt hay tho ra m im l c nh khin cho  ca MS Word tr n na.  to ra m       t biu t  n bn m  n b ca s Customize b  n sang mc Commands r    n la chn All             a s lit           n l   p chu   lnh Symbol r  n mum Symbol nm  p thoi Symbol hin ra ba chn mt biu tng mun p OK. Sau khi bn t  m s a biu tng  m tr n hn bt ln na m hp thoi Customize ri nhp chut phm bn va to ra   xem trong menu ng cnh chut phi b i c biu tng cho m  Tng t s d b  c t bc 132 P. Andritsos et al. Formally, let be the attribute of interest, and let denote the set of values of attribute Also let denote the set of attribute values for the remaining attributes. For the example of the movie database, if is the director attribute, with then Let and à be random variables that range over and A respectively, and let denote the distribution that value induces on the values in Ã. For some is the fraction of the tuples in T that contain and also contain value Also, for some is the fraction of tuples in T that contain the value Table 3 shows an example of a table when is the director attribute. For two values we define the distance between and to be the information loss incurred about the variable if we merge values and This is equal to the increase in the uncertainty of predicting the values of variable Ã, when we replace values and with In the movie example, Scorsese and Coppola are the most similar directors. 3 The definition of a distance measure for categorical attribute values is a contribution in itself, since it imposes some structure on an inherently unstructured problem. We can define a distance measure between tuples as the sum of the distances of the individual attributes. Another possible application is to cluster intra-attribute values. For example, in a movie database, we may be interested in discovering clusters of directors or actors, which in turn could help in improving the classification of movie tuples. Given the joint distribution of random variables and à we can apply the LIMBO algorithm for clustering the values of attribute Merging two produces a new value where since and never appear together. Also, The problem of defining a context sensitive distance measure between attribute val- ues is also considered by Das and Mannila [9]. They define an iterative algorithm for computing the interchangeability of two values. We believe that our approach gives a natural quantification of the concept of interchangeability. Furthermore, our approach has the advantage that it allows for the definition of distance between clusters of val- ues, which can be used to perform intra-attribute value clustering. Gibson et al. [12] proposed STIRR, an algorithm that clusters attribute values. STIRR does not define a distance measure between attribute values and, furthermore, produces just two clusters of values. 3 A conclusion that agrees with a well-informed cinematic opinion. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. LIMBO: Scalable Clustering of Categorical Data 133 5 Experimental Evaluation In this section, we perform a comparative evaluation of the LIMBO algorithm on both real and synthetic data sets. with other categorical clustering algorithms, including what we believe to be the only other scalable information-theoretic clustering algorithm COOL- CAT [3,4]. 5.1 Algorithms We compare the clustering quality of LIMBO with the following algorithms. ROCK Algorithm. ROCK [13] assumes a similarity measure between tuples, and de- fines a link between two tuples whose similarity exceeds a threshold The aggregate interconnectivity between two clusters is defined as the sum of links between their tu- ples. ROCK is an agglomerative algorithm, so it is not applicable to large data sets. We use the Jaccard Coefficient for the similarity measure as suggested in the original paper. For data sets that appear in the original ROCK paper, we set the threshold to the value suggested there, otherwise we set to the value that gave us the best results in terms of quality. In our experiments, we use the implementation of Guha et al. [13]. COOLCAT Algorithm. The approach most similar to ours is the COOLCAT algo- rithm [3,4], by Barbará, Couto and Li. The COOLCAT algorithm is a scalable algorithm that optimizes the same objective function as our approach, namely the entropy of the 182 M. Wiesmann and A. Schiper 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. Holliday, J.: Replicated database recovery using multicast communications. In: Proceedings of the Symposium on Network Computing and Applications (NCA’01), Cambridge, MA, USA, IEEE (2001) 104–107 Cheriton, D.R., Skeen, D.: Understanding the limitations of causally and totally ordered communication. In Liskov, B., ed.: Proceedings of the Symposium on Operating Systems Principles. Volume 27., Asheville, North Carolina, ACM Press, New York, NY, USA (1993) 44–57 Keidar, I., Dolev, D.: Totally ordered broadcast in the face of network partitions. In Avresky, D., ed.: Dependable Network Computing. Kluwer Academic Publications (2000) Davidson, S.B., Garcia-Molina, H., Skeen, D.: Consistency in partitioned networks. ACM Computing Surveys 17 (1985) 341–370 Fu, A.W., Cheung, D.W.: A transaction replication scheme for a replicated database with node autonomy. In: Proceedings of the International Conference on Very Large Databases, Santiago, Chile (1994) Kemme, B., Alonso, G.: A suite of database replication protocols based on group commu- nication primitives. In: Proceedings of the International Conference on Distributed Computing Systems (ICDCS’98), Amsterdam, The Netherlands (1998) Kemme, B., Pedone, F, Alonso, G., Schiper, A.: Processing transactions over optimistic atomic broadcast protocols. In: Proceedings of the International Conference on Distributed Computing Systems, Austin, Texas (1999) Holliday, J., Agrawal, D., Abbadi, A.E.: The performance of database replication with group multicast. In: Proceedings of International Symposium on Fault Tolerant Comput- ing (FTCS29), IEEE Computer Society (1999) 158–165 Babao§lu, Ö.,Toueg,S.: Understandingnon-blocking atomic commitement. Technical Report UBLCS-93-2, Laboratory for Computer Science, University of Bologna, 5 Piazza di Porta S. Donato, 40127 Bologna (Italy) (1993) Keidar, I., Dolev, D.: Increasing the resilience of distributed and replicated database systems. Journal of Computer and System Sciences (JCSS) 57 (1998) 309–224 Jiménez-Paris, R., Patiño-Martínez, M., Alonso, G., Aréalo, S.: A low latency non-blocking commit server. In Welch, J., ed.: Proceeedings of the Internationnal Conference on Distributed Computing (DISC 2001). Volume 2180 of lecture notes on computer science., Lisbon, Portugal, Springer Verlag (2001) 93–107 Wiesmann, M., Pedone, F., Schiper, A., Kemme, B., Alonso, G.: Understanding replication in databases and distributed systems. In: Proceedings of International Conference on Distributed Computing Systems (ICDCS’2000), Taipei, Taiwan, R.O.C., IEEE Computer Society (2000) Kemme, B., Bartoli, A., Babao§lu, Ö.: Online reconfiguration in replicated databases based on group communication. In: Proceedings of the Internationnal Conference on Dependable Systems and Networks (DSN2001), Göteborg, Sweden (2001) Amir, Y: Replication using group communication over a partitioned network. PhD thesis, Hebrew University of Jerusalem, Israel (1995) Ezhilchelvan, P.D., Shrivastava, S.K.: Enhancing replica management services to cope with group failures. In Krakowiak, S., Shrivastava, S.K., eds.: Advances in Distributed Systems, Advanced Distributed Computing: From Algorithms to Systems. Volume 1752 of Lecture Notes in Computer Science. Springer (1999) 79–103 Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. A Condensation Approach to Privacy Preserving Data Mining Charu C. Aggarwal and Philip S. Yu IBM T. J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532 {charu,psyu}@us.ibm.com Abstract. In recent years, privacy preserving data mining has become an important problem because of the large amount of personal data which is tracked by many business applications. In many cases, users are unwilling to provide personal Go to View/Master/Slide Master to type in header 1 Propelling Business Growth With A Secure And Continuous Information Infrastructure Jon Murray Go to View/Master/Slide Master to type in header 2 Go to View/Master/Slide Master to type in header 3 Information: Change in Multiple Dimensions information growth information types information uses information regulations more regulations: SEC 17a-4, NASD 3010,Sarbanes-Oxley, Basel II, UK Metadata, eSign Act, 21 CFR Part 11 (more coming!) more growth: information stored on disk arrays growing ~60% in 2006 more uses: ERP, CRM, RFID, collaboration, data mining, discovery … more types: transactions, documents, forms, web, images, voice, messages, reports . Go to View/Master/Slide Master to type in header 4 Customers’ Information Storage and Management Challenges CIO’s Dilemma: How to manage all the information growth with limited resources? Information stored on disk arrays ~ 70% IT budget growth ~ 4 - 5% IT environment getting more complex SLA’s continue to expand and tighten Protection and security increasingly important Go to View/Master/Slide Master to type in header 5 2006 Technology Spending Priorities–CIO Survey Source: Morgan Stanley November CIO Survey, Jan. 3, 2006 1. Security (Software) 2. Security (HW & Services) 3. Storage Area Networks 4. Wireless LAN (access points & routers) 5. Storage Software 6. Portal Software 7. VOIP Infrastructure 8. IT Education and Training 9. Storage Hardware 10. Business Intelligence Software 11.Wireless LAN (clients) 12.New Custom Development 13.Routing 14.Systems Management Software 15.Application Integration Software 16.Notebook PCs 17.Document Management Software 18.Automated Testing Tool 19.Microsoft Office Upgrade 20.Application Software Server 45.Mainframe hardware 46.Printers Go to View/Master/Slide Master to type in header 6 Security Today An Amalgamation of Point Products Across IT Antivirus Anitvirus VPN Encryption Authentication Web Filtering Authentication Threat Detection Change/Patch Management LAN Clients Servers SAN Disk Storage Tape Spyware Firewall Digital Rights Management Encryption Vault Recovery Management Authentication Go to View/Master/Slide Master to type in header 7 Today’s Approach: Secure the Perimeter Go to View/Master/Slide Master to type in header 8 Security Tomorrow: Protect the Information Go to View/Master/Slide Master to type in header 9 Security Must Become Information-Centric  Information-centric Security – An inside-out view of how to secure information  Begins with securing the data itself  Moves out through layers of increasingly intelligent infrastructure  Relies on our ability to leverage the interaction between data and infrastructure Important Technology • Data-level access control • Open policy decision points • Enforcement at point of use Go to View/Master/Slide Master to type in header 10 Information Security (Confidentiality) Information Security Information Security Information Availability Information Availability Information Confidentiality Information Confidentiality Information Integrity Information Integrity EMC’s Heritage P r o t e c t i o n and A Natural and Requested Evolution of EMC’s Data Protection Capabilities [...]... technologies Different processes Pain Points  Move and migrate data  Inconsistent service levels  Restart the enterprise  Gaps in coverage  Protect remote data sites  Growth in complexity and effort  Shorten backup and restore times  Growth in cost and risk to the business  Contain costs  Cannot add resources Continuity Defined: Ensuring applications and data are available during planned and unplanned... – Information that was once offline is now online via archive 17 Go to View/Master/Slide Master to type in header Backup and Archive are Different Backup Archive A secondary copy of information ... loài linh trưởng Tài liệu tham khảo Lipinski C, Hopkins A (2004) "Navigating chemical space for biology and medicine" Nature 432 (7019): 855–61 William Russell and Rex Burch (1959) The Principles... Phương pháp gọi với tên thích hợp "ex vivo" Các loại động vật thường sử dụng Thí nghiệm in vivo dùng nghiên cứu di truyền học, sinh học phát triển, nghiên cứu y sinh học, ghép tạng, độc chất học,... (1959) The Principles of Humane Experimental Technique 2/3 In vivo Michels KB (2006) The women's health initiative curse or blessing? Int J Epidemiol 35(4):814-6 "Vivisection FAQ, British Union