美国东部时间3月30日(北京时间3月31日)消息:某联盟日前对搜索巨人Google公司提出谴责,说它是“对基因机密最大的威胁”,因为它宣布了一项计划并打算创建一个可搜寻的遗传信息数据库。 Google公司被该联盟在本周在巴西的Curitiba市判决为“生物盗窃(Biop
美国东部时间3月30日(北京时间3月31日)消息:某联盟日前对搜索巨人Google公司提出谴责,说它是“对基因机密最大的威胁”,因为它宣布了一项计划并打算创建一个可搜寻的遗传信息数据库。
Google公司被该联盟在本周在巴西的Curitiba市判决为“生物盗窃(Biopiracy)胡克船长”。组织者宣称,Google公司将与J. Craig Venter基因组研究所合作 创建一个可搜寻的网络数据库,其中包括地球上所有基因的资料。这便是其生物盗窃的铁证。
据该联盟组织者称,生物盗窃指的是“独占遗传资源库”。它还可以指公司、大学和政府等各种组织在未经授权的情况下违令使用生物资源。
据该联盟网站的判决称,Google公司犯了生物盗窃罪,因为它打算建立一个可搜寻的数据库,极易导致私人遗传信息被滥用。网站声称:“Google公司与Craig Venter一起,正在推进基因组可搜索化计划。”
ETC Group公司的是这次判决的组织者之一,该公司的Jim Thomas说,Google公司最近围绕储存消费者信息采取了一系列的举动,必将引起各种隐私保护者的抗议。 如果Google公司认为网络私隐是一大罐虫子的话,那么就等着看吧,他们迟早会明白他们将引发一场整个基因机密的大争论。
最早披露出Google公司和Venter之间的这项合作的原始来源是普利策奖得主David Vise写的一篇题为《Google的故事》的文章。但是,Google公司以前就拒绝就此事件发表任何评论,而且Venter也否认了双方合作将进一步发展下去。 Google公司对于记者的采访要求未予回复。
Google公司很想向外界证明它将对许多地区作出技术以外的其他贡献。 Google公司最近任命Larry Brilliant担任了Google.org网站的执行董事,他将与Google公司的联合创办人Larry Page和Sergey Brin一起管理Google公司的慈善捐款和公司在慈善事业方面的策略。
Brilliant是一位内科医生、流行病学家和国际健康卫生专家。他在世界卫生组织发起的根除天花的行动中曾经担任过重要的角色,并且还参与过联合国根除眼盲和骨髓灰质炎疾病的斗争。

Stored procedures are precompiled SQL statements in MySQL for improving performance and simplifying complex operations. 1. Improve performance: After the first compilation, subsequent calls do not need to be recompiled. 2. Improve security: Restrict data table access through permission control. 3. Simplify complex operations: combine multiple SQL statements to simplify application layer logic.

The working principle of MySQL query cache is to store the results of SELECT query, and when the same query is executed again, the cached results are directly returned. 1) Query cache improves database reading performance and finds cached results through hash values. 2) Simple configuration, set query_cache_type and query_cache_size in MySQL configuration file. 3) Use the SQL_NO_CACHE keyword to disable the cache of specific queries. 4) In high-frequency update environments, query cache may cause performance bottlenecks and needs to be optimized for use through monitoring and adjustment of parameters.

The reasons why MySQL is widely used in various projects include: 1. High performance and scalability, supporting multiple storage engines; 2. Easy to use and maintain, simple configuration and rich tools; 3. Rich ecosystem, attracting a large number of community and third-party tool support; 4. Cross-platform support, suitable for multiple operating systems.

The steps for upgrading MySQL database include: 1. Backup the database, 2. Stop the current MySQL service, 3. Install the new version of MySQL, 4. Start the new version of MySQL service, 5. Recover the database. Compatibility issues are required during the upgrade process, and advanced tools such as PerconaToolkit can be used for testing and optimization.

MySQL backup policies include logical backup, physical backup, incremental backup, replication-based backup, and cloud backup. 1. Logical backup uses mysqldump to export database structure and data, which is suitable for small databases and version migrations. 2. Physical backups are fast and comprehensive by copying data files, but require database consistency. 3. Incremental backup uses binary logging to record changes, which is suitable for large databases. 4. Replication-based backup reduces the impact on the production system by backing up from the server. 5. Cloud backups such as AmazonRDS provide automation solutions, but costs and control need to be considered. When selecting a policy, database size, downtime tolerance, recovery time, and recovery point goals should be considered.

MySQLclusteringenhancesdatabaserobustnessandscalabilitybydistributingdataacrossmultiplenodes.ItusestheNDBenginefordatareplicationandfaulttolerance,ensuringhighavailability.Setupinvolvesconfiguringmanagement,data,andSQLnodes,withcarefulmonitoringandpe

Optimizing database schema design in MySQL can improve performance through the following steps: 1. Index optimization: Create indexes on common query columns, balancing the overhead of query and inserting updates. 2. Table structure optimization: Reduce data redundancy through normalization or anti-normalization and improve access efficiency. 3. Data type selection: Use appropriate data types, such as INT instead of VARCHAR, to reduce storage space. 4. Partitioning and sub-table: For large data volumes, use partitioning and sub-table to disperse data to improve query and maintenance efficiency.

TooptimizeMySQLperformance,followthesesteps:1)Implementproperindexingtospeedupqueries,2)UseEXPLAINtoanalyzeandoptimizequeryperformance,3)Adjustserverconfigurationsettingslikeinnodb_buffer_pool_sizeandmax_connections,4)Usepartitioningforlargetablestoi


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