Modality and databases

单词汇总(数据库专业一点的词汇其实主要就是每章后面review items的内容,在这里简单列一下,如果你实在没时间看书,至少这些单词要熟悉.)：1. 数据库系统：database system(DS),database management system(DBMS)2.数据库系统(DS),数据库治理系统(DBMS )3. 关系和关系数据库table= relation , column = attribute 属性,domain, atomic domain, row= tuple ,relational database, relation schema, relation instance, database schema, database instance;4.表=关系,列=属性属性,域,原子域,排二元组,关系型数据库,关系模式,关系实例,数据库模式,数据库实例；1. key 们：super key, candidate key, primary key, foreign key, referencing relation, referenced relation;2.超码,候选码,主码,外码,参照关系,被参照关系5.关系代数(relational algebra): selection, project, natural join, Cartesian product, set operations, union, intersect, set difference( except\minus), Rename, assignment, outer join, grouping, tuple relation calculus6.(关系代数)：选择,工程,自然连接,笛卡尔积,集合运算,集,交集,集合差(除负),重命名,分配,外连接,分组,元组关系演算7.sql组成：DDL ：数据库模式定义语言,关键字：createDML ：数据操纵语言,关键字：Insert > delete、updateDCL ：数据库限制语言,关键字：grant、removeDQL ：数据库查询语言,关键字：select8.3.SQL 语言：DDL , DML , DCL , QL , sql query structure, aggregate functions, nested subqueries, exists(as an operator), unique(as anoperator), scalar subquery, assertion, index(indices), catalogs, authorization, all privileges, granting, revoking , grant option, trigger, stored procedure, stored function4.SQL语言：DDL , DML , DCL , QL , SQL查询结构,聚合函数,嵌套子查询,存在(如运营商),独特的(如运营商),标量子查询,断言指数(指数),目录,授权,所有权限,授予,撤销,GRANT OPTION ,触发器,存储过程,存储函数9. 表结构相关：Integrity constraints, domain constraints, referential integrity constraints10.完整性约束,域名约束,参照完整性约束5.数据库设计(ER 模型)：Entity-Relationship data model, ER diagram, composite attribute, single-valued and multivalued attribute,derived attribute, binary relationship set, degree of relationship set, mapping cardinality, 1-1, 1-m, m-n relationship set (one to one, one to many, many to many), participation, partial or total participation, weak entity sets, discriminator attributes, specialization and generalization6.实体关系数据模型,ER图,复合属性,单值和多值属性,派生属性,二元关系集,关系集,映射基数的程度,1-1, 1-米,MN关系集合(一对一,一对多,多对多),参与局部或全部参与,弱实体集,分辨符属性,特化和概化11. 函数依赖理论：functional dependence, normalization, lossless join (or lossless) decomposition,First Normal Form (1NF), the third normal form (3NF), Boyce-codd normal form (BCNF), R satisfies F, F holds on R, Dependency preservation 保持依赖,Trivial, closure of a set of functional dependencies 函数依赖集的闭包,closure of a set of attributes 属性集闭包,Armstrong 's axioms Armstrong 公理,reflexivity rule 自反律,augmentation rule,增广率, transitivity 传递律,restriction of F to R i F 在Ri 上的限定,canonical cover 正那么覆盖, extraneous attributes 无关属性,decomposition algorithm 分解算法.7.函数依赖,标准化,无损连接〔或无损〕分解,第一范式〔1NF〕,第三范式〔3NF〕 BC范式〔BCNF〕, R满足F, F持有R,依赖保存,平凡,一组函数依赖封闭,一组属性,8. 事务：transition, ACID properties ACID特性,并发限制系统concurrency control system,故障恢复系统recovery system,事务状态transition state,活动的active,局部提交的partiallycommitted,失败的failed,中止的aborted,提交的committed,已结束的terminated,调度schedule,操作冲突conflict of operations, 冲突等价conflict equivalence,冲突可串彳f化conflictserializablity ,可串行化顺序serializablity order,联级回滚cascading rollback,封锁协议lockingprotocol ,共享〔S〕锁shared-mode lock 〔S-lock〕,排他〔X〕锁exclusive -mode lock 〔X-lock〕, 相容卜i compatibility,两阶段封锁协议2-phase locking protocol,意向锁intention lock,时间戳timestamp, 恢复机制recovery scheme,日志log, 基于日志的恢复log-based recovery, 延迟的修改deferredmodification,立即的修改immediate modification,检查点checkpoint.数据库系统DBS Database System数据库系统应用Database system applications文件处理系统file-processing system数据不一致性data inconsistency——致性约束consistency constraint数据抽象Data Abstraction实例instance模式schema物理模式physical schema逻辑模式logical schema物理数据独立性physical data independence数据方^型data model实体-联系模型entity-relationship model 〔E-R〕关系数据模型relational data model基于对象的数据模型object-based data model半结构化数据模型semistructured data model数据库语言database language数据定义语言data-definition language数据操纵语言data-manipulation language查询语言query language元数据metadata应用程序application program标准化normalization数据字典data dictionary存储治理器storage manager查询治理器query processor事务transaction原子性atomicity故障恢复failure recovery并发限制concurrency-control两层和三层数据库体系结构two-tier/three-tier数据才2掘data mining数据库治理员DBA database administrator表table关系relation元组tuple空值null value数据库模式database schema数据库实例database instance关系模式relation schema关系实例relation instance码keys超码super key候选码candidate key主码primary key外码foreign key参照关系referencing relation被参照关系referenced relation属性attribute域domain原子域atomic domain参照完整性约束referential integrity constraint模式图schema diagram查询语言query language过程化语言procedural language非过程化语言nonprocedural language关系运算operations on relations选择元组selection of tuples选择属性selection of attributes自然连接natural join笛卡尔积Cartesian product集合运算set operations关系代数relational algebraSQL 查询语言SQL query structureSelect 字句select clauseFrom 字句from clauseWhere 字句where clause自然连接运算natural join operationAs 字句as clauseOrder by 字句order by clause相关名称 (相关变量,元组变量) correlation name (correlation variable , tuple variable ) 集合运算set operationsUnionInterestExcept空值null values真值"unknown " truth “ unknown 〞聚集函数aggregate functionsavg, min, max, sum, countgroup byhaving嵌套子查询nested subqueries集合比拟set comparisons{ «,? 二 ,〉〉,?=}{some , all}existsuniquelateral 字句lateral clausewith 字句with clause标量子查询scalar subquery数据库彳修改database modification删除deletion插入insertion更新updating参照完整性referential integrity参照完整T约束referential Hntegrity constraint 或子集依赖subset dependency 可延迟的deferrable断言assertion连接类型join types内连接和夕卜连接inner and outer join左外连接、右外连接和全外连接left、right and full outer joinNatural连接条件、using连接条件和on连接条件natural using and so on 视图定义view definition物化视图materialized views视图更新view update事务transactions提交commit work回滚roll back work原子事务atomic transaction完整性约束integrity constraints域约束domain constraints唯——性约束unique constraintCheck 字句check clause参照完整性referential integrity级联删除cascading delete级联更新cascading updates断言assertions日期和时间类型date and time types默认值default values索弓I index大对象large object用户定义类型user-defined types域domains目录catalogs模式schemas授权authorization权卜M privileges选择select插入insert更新update所有权限all privileges授予权卜M granting of privileges收回权卜M revoking of privileges授予权限的权限privileges to privilegesGrant option角色roles视图授权authorization on views执行授权execute authorization调用者权限invoker privileges行级授权row-level authorizationJDBCODBC预备语句prepared statements 访问元数据accessing metadata SQL 注入SQL injection 嵌入式SQL embedded SQL 游标cursors 可更新的游标updatable cursors 动态SQL dynamic SQL SQL 函数SQL functions 存储过程stored procedures 过程化结构procedural constructs夕卜部语言例程external language routines触发器triggerBefore 和after 触发器before and after triggers过渡变量和过渡表transition variables and tables递归查询recursive queries单调查询monotonic queries排名函数ranking functionsRankDense rankPartition by分窗windowing联机分析处理〔OLAP 〕 online analytical processing多维数据multidimensional data度量属性measure attributes维属性dimension attributes转轴pivoting数据立方体data cube切片和切块slicing and dicing上卷和下钻rollup and drill down交叉表cross-tabulation第七章实体-联系数据模型Entity-relationship data model实体和实体集entity and entity set属性attribute域domain简单和复合属T生simple and composite attributes单值和多值属T生single-valued and multivalued attributes空值null value派生属性derived attribute超码、候选码以及主码super key ,candidate key, and primary key联系和联系集relationship and relationship set二元联系集binary relationship set联系集的度degree of relationship set描述性属性descriptive attributes超码、候选码以及主码super key ,candidate key, and primary key角色role自环联系集recursive relationship setE-R 图E-R diagram映射基数mapping cardinality——对——联系one-to-one relationship——对多联系one-to-many relationship多对——联系many-to-one relationship多对多联系many-to-many relationship参与participation全部参与total participation局部参与partial participation弱实体集和强实体集weak entity sets and strong entity sets分辨符属性discriminator attributes标识联系identifying relationship特化和概化specialization and generalization超类和子类superclass and subclass属性继承attribute inheritance单和多继承single and multiple inheritance条件定义的和用户定义的成员资格condition-defined and userdefined membership 不相交概化和重叠概化disjoint and overlapping generalization全部概化和局部概化total and partial generalization聚集aggregationUMLUML 类图UML class diagram第八章E-R 模型和标准化E-R model and normalization分解decomposition函数依赖functional dependencies无损分解lossless decomposition原子域atomic domains第一范式(1NF) first normal form(1NF)合法关系legal relations超码super keyR 满足 F R satisfies FF在R上成立 F holds on RBoyce-Codd 范式BCNF Boyce-Codd normal form(BCNF)保持依赖dependency preservation第三范式(3NF) third normal form(3NF)平凡的函数依赖thivial functional dependencies函数依赖集的闭包closure of a set of functional dependenciesArmstrong 公理Armstrong s axioms属性集闭包closure of attribute setsF 在Ri 上的限定restriction of F to Ri正贝 1 覆盖canonical cover无关属T生extraneous attributesBCNF 分解算法BCNF decomposition algorithm3NF 分解算法3NF decomposition algorithm多值依赖multivalued dependencies第四范式(4NF) fourth normal form(4NF)多值依赖的限定restriction of a multivalued independency投影-连接范式(PJNF) project-join normal form(PJNF)域-码范式(DKNF ) domain-key normal form(DKNF)泛关系universal relation唯一角色假设unique-role assumption 去标准化denormalization。

XploreIntegrated image and data management product to support your drug & biomarker r esearch & discovery programTissue diagnostics and biomarker analytics are the keystones of cancer discovery. However, delivering on the promise of personalized medicine requires multiple data sources to be integrated and analyzed. Management and analysis of large volumes of tissue samples are crucial to realizing the potential of personalized medicine. To accomplish this, imaging and pathology informatics support will be essential to moving forward in an increasingly complex and multifaceted medical research environment.With the power of machine learning and the power of big data management tools, researchers can integrate data from multiple sources, including digital pathology and tissue imaging, enhancing their ability to glean critical insights into disease and identify novel biomarkers.Need for a cutting edge tool that addresses the problems of a modern tissue research laboratoryDigitizing slides will not automatically result in faster and more efficient research and investigative studies. There are several problems that a research product needs to address if digital pathology is to prove an effective tool for drug and biomarker discovery studies.• High volume of images: Virtual Slides – high quality images produced using a scanner – are typically several gigabytes in size. In the past, sharing these image files with colleagues has proven problematic due to their size and the lack of IT infrastructure supporting fast sharing and viewing of these images. Integrating tissue image archives across centers encourages multisite collaboration.• Vendor neutrality: There are few standards in digital pathology imaging, with over a dozen major scanning vendors, each typically using their own proprietary image file format. Each type of scanner usually has its own ‘Viewer’ (digital microscope) to look at these virtual slides. Pathologists therefore may require training with multiple different viewers in order to review all slides.• Multi-modality data management: Collating and organizing data from multiple data sources brings its own challenges. Data may all be stored in different files and across various locations.Spreadsheets, CSV/TSV files, LIMS, and in-house databases and applications are incapable of managing the quantity and variety of data that needs to be captured as part of a typical study.• Image analytics integration: Image analysis tools can quantify and qualify tissue cells and cell structures in a rapid and consistent manner. However, a range of image analysis applications is available– from commercial vendors, as well as in-house and openXploreTechnology backgroundersolutions. Managing slides that are used in multiple applications and the data produced by their algorithms can be difficult, due to limited interoperability.• TMA management: Tissue microarrays (TMAs) provide the means for high-throughput analysis of multiple tissues and cells. The issues involved in collating, organizing and associating data with whole sections of tissue is multiplied with a TMA, as a single TMA slide may contain 200 cores, each potentially representing a different patient. A TMA study of a single 20x10 block, with five stains, three scoring criteria per stain and two reviewing pathologists will result in six thousand ‘scores’. Mapping these scores to different patients, comparing scores and results, and identifying trends across different cohorts will be time-consuming and prone to human error. Data mining tools, detailed in the next section, are required to solve the challenges of conducting large biomarker investigative studies.• Cross study data management: Given the range and volume of datamentioned in the points above, and the high quantity of slides thatmay be part of a typical study, the organizing slides in different ways,and maintaining a link with the aforementioned data, will provechallenging. Slides may need to be included as part of severalstudies. Studies may also be organized in many different ways.• Lab ecosystem: There are many different platforms and applicationsin a lab today and there is a need to ensure flexibility andinteroperability through and image management system that canprovide some context. Labs also have differing views on deploymentmethods for applications.Key design principles that allow a solution to these problemsTo address the problems listed above, Xplore was designed in conjunction with key opinion leaders from across the pathologyspectrum, to impact and accelerate tissue-centric discovery in drug and biomarker research. There are four key design principals that the solution adheres to.An Open solution, allowing institutions to use slides from multiple scanner vendors in a single Viewer, but also launch multiple image analysis vendors.A Flexible solution, providing institutions with tools to manage research the way you want, allowing you to design, organize, manage, search, and interrogate studies in a variety of ways.An Integrated solution, allowing institutions to store image, analytic data with slides both within and across studies, with search and datamining tools to help retrieve important data quickly.A Connected solution, bringing together researchers across an organization, across multiple sites and across geographies, amplifying the expertise of all those involved anywhere.Let us explore in detail how some of the issues highlighted earlier in the biomarker research process can be addressed through an effective tissue research product.View virtual slides in a single digital viewerOur Solution – the Xplore ViewerXplore supports all major scanning vendors in a single web-based viewer, thereby reducing training needs across multiple platforms. Both bright field and fluorescent slides are supported, in addition to z-stacking and multi-regions.Ve n dor Exte n sion Philips .isyntax / .tiffHamamatsu .ndpi / .ndpisLeica / Ariol .scnPerkin Elmer .qptiffAperio .svsVentana .bif / .tif / .svsZeiss .cziOlympus .vsiOmnyx .rtsSakura .svs3DHistec h .mrsxHuron .tif Mikroscan.svsTrestle .tifVendor Neutrality - Support for all major scanning vendorsXplore offers a range of tools that you would expect in a modern digital pathology viewer, including the following features,extremely important in a research setting, Annotation & Measuring tools, Split screen (Sync up to four whole slide images or TMA Cores), Fluorescent multi-channel image adjustments, Counting tools to assist validating image analysis algorithms, Screenshots and Z-Stack (switch between different planes).Manage and organize images and data in ascalable user centric mannerOur Solution – Flexible Folder, Study & Slide Management The Philips Xplore product can ingest thousands of images in seconds, provides a database for easily organizing Whole Slide Images (WSI’s), documents, image analysis and slide associated metadata within aflexible hierarchical structure. Users are assigned as owners of a particular study with permissions to share slides and data within and across teams of researchers.Xplore’s configurable study-based folder hierarchy makes a very flexible product for research. Xplore has no fixed study hierarchy,Xplore - Key design principlesallowing multi-disciplinary research.Slides can be included as part of multiple studies, without duplicating/multiplying the large file sizes on disk. Slides can also be added to a hierarchy of the researcher’s choosing, for example stain, body site, sample ID or case ID or principal investigator. Slides can therefore be better organized to meet the context/purpose of the study.Maintain metadata and third party dataOur Solution – Datasets, import, barcodes and documentsDatasets can be created with custom fields and metadata. Image analysis data can be uploaded into Xplore and easily searched or interrogated for biomarker evaluation, case selection and correlations.1D, 2D, Datamatrix support, allows metadata on the slide to be automatically added to the Xplore database on slide acquisition. Xplore also has an import facility, to associate additional information not included in the barcode with slides and studies using CSV or TSV files. Data can be uploaded against the slide name or by TMA core position/ID, or by information collected on the barcode, for example Sample ID.The document management system allows supporting research material, journals, publications and presentations to be stored alongside studies.Manage high volumes of slides, identify trends and outliers and create cohortsOur Solution – Precision SearchXplore’s Search engine allows user to identify cohorts, based on searching a range of data across folders and slides, or studies, slides, cores and scoring data in the case of TMA’s.Search across system generated fields, barcode metadata, clinical/image analysis data uploaded via CSV, and manual scoring data, to perform a deep and detailed analysis of any study managed in Xplore, and get a better understanding of how biomarkers are expressing themselves on different TMA cores, whole slides and therefore patients.Use the results of Search to create additional research studies; create charts from Search results to more easily spot trends and outliers; save for future use; or export data for use in other 3rd partyapplications.Conduct large biomarker investigative and evaluation studiesOur Solution – Comprehensive TMA moduleXplore’s TMA (Tissue Microarray) module is designed to speed up research studies that require manual scores of TMA cores, thereby helping evaluate new tissue biomarkers in TMAs quickly and ing scoring templates, map templates, and an automated TMA de-arrayer, TMA cores can be segmented, identified, and sent out for scoring. Patient information (managed through datasets), scoring criteria and the TMA Core tissue are available through a single interface. The TMA Core tissue is locked to the question(s) being asked upon it, so the user is unable to answer scoring questions on a TMA Core that is not visible on screen.Virtual TMAThe Virtual TMA module in Xplore allows selected cores from multiple recipient blocks to be combined in a single, score-able Virtual TMA Study.Given there are discrepancies or anomalies in the data that has been captured through image analysis or manual scoring,individual TMA Cores from one or more TMA Slides or TMA StudiesCross study data management - Example Folder & Study structures in XploreThe Xplore Search interface can build complex queries across multi-modality dataTMA Management - TMA Scoring interface in Xplorecan be segmented into a new, composite, ‘Virtual TMA’ study. This study can then be sent out again for scoring, but only providing the researcher with the TMA Cores that need to be scored, rather than the several hundred that may be on a single slide. This greatly increases the efficiency of getting selected TMA Cores re-scored.Easily identify trends and spot outliersOur solution – ChartsThe Charts tools in Xplore will help researchers and pathologists more easily quantify data, and help spot trends and outliers in Xplore. The results of a study or advanced search can be opened in a variety of charts. Tools for the researcher allow him or her to directly open the relevant data point to obtain a full breakdown of the data that has been captured against a whole slide or TMA core.By providing a charts and graphing facility within Xplore, the link with the virtual slide and therefore tissue is maintained. The process of matching a data point in a spreadsheet or other application with the original tissue can be extremely difficult, but Xplore allows the researcher to click a data point and open the tissue in the viewer within seconds.Integrate with multiple imageanalysis vendorsOur solution – Image Analysis agnosticXplore’s open product structure offers interoperability with offerings from third party image analysis vendors, such as Visiopharm OncoTopix. Xplore’s advanced search engine provides tools for cohort and training set selection, allowing researchers to launch slides and associated annotations in third party applications.Image analysis results can then be imported back into Xplore, providing a central repository for virtual slides, clinical, genomic and molecular data and image analysis results. As before, any data captured alongside the slide can be searched upon, allowing both image analysis and manual scoring data to be queried alongside patient information.Embed in the lab eco systemOur solution Flexible platform that integrates and brings together expertise Xplore embeds easily with a lab ecosystem through the use of current lab credential systems with a single sign on capability. Xplore supports both cloud and on premise deployments.Connect your team to a shared archiveOur solution – Web-based technology and advancedsystem architectureXplore enables knowledge sharing and expertise across multiple research studies and centres by providing tools for sharing studies with colleagues.Entire studies, or parts of a study, can be shared with one or more individuals. Different access levels can be provided, to allow for review of study results, or collaborative studies, in which multiple pathologists can score and annotate slides.What Next?Digitizing slides without providing tools to manage the images and associated data will not automatically lead to efficiencies in pathological research studies. Xplore sits at the center of a research pathology workflow. Xplore will continue to develop with the vision of becoming the centerpiece for pathology informatics and data integration across the spectrum of tissue-centricdiscovery.© 2020 Koninklijke Philips N.V.All rights are reserved. Reproduction or transmission in whole or in part, in any form or by any means, electronic, mechanical or otherwise, is prohibited without the prior written consent of the copyright owner.4522 207 41601 - January 2020Visit us on: /computationalpathology or /digitalpathologyScatter Chart in Xplore with clickable data-points linking to the Viewer to view the tissue in more detail。

Contents（times new roman 三号）Abstract in Chinese (I)A b s t r a c t i n English (II)Introduction…………………………………………………………….……. 1 C h a p e r1.F u n c t i o n a lg r a m m a r (4)1.1E x p e r i e n t i a l m e t a f u n c t i o n (5)1.2I n t e r p e r s o n a l m e t a f u n c t i o n (5)1.3 Textual metafuncton (6)Chapter2. The experiential metafunction of company o v e r v i e w (7)2.1 Analyze company overview from the perspective of t r a n s i t i v i t y (7)2.1.1 Transitivity (7)2.1.2 A statistic analysis of the processes used in the companyoverviews………………………………………………………92.1. 3 S u m m a r y (13)2.2 Analyze company overview from the perspective of v o i c e….……1 62.2.1 Voice (16)2.2.2 A statistic analysis of voice used in the company o v e r v i e w s…1 62.2.3 Summary (19)Chaper3. The interpersonal metafunction of compa ny o v e r v i e w (20)3.1 Analyze company overview from the perspective of m o o d (20)3.1. 1 M o o d (20)3.1.2 A statistic analysis of voice used in the company o v e r v i e w s (20)3.1. 3 S u m m a r y (24)3. 2 Modality (26)C h a p e r4.T h e t e x t u a l m e t a f u n c t i o n o f c o m p a n y o v e r v i e w (27)4.1 Analyze company overview from the perspective of t h e m e (27)4.1. 1 Th e me (27)4.1.2 A statistic analysis of theme-rheme patterns used in thecompanyoverviews………………...………………………………….....304.1. 3 Summary (34)4.2 Analyze company overview from the perspective ofc o h e s i o n………3 44.2. 1 Cohesion (35)4.2.2 A statistic analysis of cohesive devices used in thecompanyoverviews………………………………………………………364.2.3 Summary………………………………………..………………38 Conclusion……………………………………………….….………………39 References (42)Ap pe n di x (43)Acknowledgements…………………………………….…………………4 5（times new roman 小四）说明：目录中每一级子目录都应较其上一级总目录缩进2个字母的位置，以第二章目录为例。

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GE HealthcareWomen’s HealthcareSenographePristinaGE imagination at workThe Senographe* Pristina is a full field digital mammography system designed to offer an extensive breast care solution with screening and diagnostic capabilities, focused on an ergonomic design for the technologist and patient comfort. Senographe Pristina features a 24 x 29 cm detector, designed to offer full breast coverage in a single image. Smaller breasts can also be imaged in any view with paddles that can slide to both sides of the detector.The Senographe Pristina does not require daily calibration. Ergonomics for technologists•Re-imagined user interface• Park Positioning during patient positioning• One touch access to preset rotation for positioning• Variable speed motorized gantry movements• Sliding compression paddles can move to the side of the detector for compressionErgonomics and design for patient comfort•Designed for Patient comfort•Wheelchair access, MITA compliant•Thinner Bucky than previous platform•Rounded edges detector for patient comfortImage quality• Automatic Optimization of Parameters (AOP), selects all exposure parameters based on breast radiological properties • Three AOP modes + 1 Automatic mode for implants• eContrast is an image processing feature that makes automatic adjustments of brightness and contrast• DQE at IEC 62220-2-3 equivalent spectrum, at 75µGy: 70% (+/-3) at 0.5lp/mm and 64% (+/-3) at 2lp/mm Smooth digital workflow connectivity• Automated Quality Control• Integrated Repeat and Reject AnalysisTechnical SpecificationsDetector• Detector ready to use right after system boot• Detector size: 24 x 29 cm• Pixel size (pitch): 100 μm• Acquisition dynamic range: 14 bits• Bucky front cover thickness: 49mm• Optimized room for positioning due to the bucky depth: 470mm • Image size:– LFOV image size - approx. 13 MB per image– Regular image size - approx. 9 MB per image• Patented needle structure CsI scintillator, single piece construction• Breast support with rounded edge• Air coolingTube technology• X-Ray tube type: Artemis• Anode target materials - Dual track: Molybdenum (Mo) enriched with Vanadium, and Rhodium (Rh)• Four focal spots: 0.1 and 0.3 IEC on each target• Target angle: 0 degree• Maximal high voltage: 49 kV• Tube current:– Molybdenum target:• 100 mA from 25 to 30 kV on large focal spot• 40 mA from 25 to 30 kV on small focal spot– Rhodium target:• 62 mA from 25 to 30 kV on large focal spot• 35 mA from 25 to 30 kV on small focal spot• Anode size (tracks diameter): 100 mm• Anode heat storage capacity: 250kJ (340 kHU)• Anode maximum dissipation: 500 W (40 kHU/min)• Max casing con tinuous dissipation:150 W (12 kHU/min) at 40 °C• Permanent filtration: 0.69 mm Beryllium• Weight: 7 kg• X-ray tube assembly: self-encased X-ray tube, oil-free, lead-free, air-cooled head• Tube protection: software monitoring of tube loadGrid/breast support•Universal grid compatible with 2D Conventional Mammography and DBT•Ergonomic breast support designed for patient comfort and cleanability• Motorized lock of the grid and breast support• Breast support material: carbon fiber composite• Optimized grid motion ensuring no grid structure visible in the image• Detector to breast support edge-to-edge distance ≤ 5 mm• It is always possible for the technologist to takecompression control even if the patient has started self-compression• PAC is inhibited during acquisition, the patient cannot interfere with the examinationPositioner• Isocentric arm with motorized rotation and vertical movement• Source to image receptor distance: 660 mm• Floor to image receptor distance: from 65 cm to 150 cm • Rotation angle: -180/+180 degrees• Ergonomic hand -rest: one at each side of the tube arm and two additional behindSafety features• Gantry motions locked when compression force appliedUser interface• Four sets of single speed switches for rotation , angulation and lift movements, with an accelerating speed profile• Four sets of preset position switches for positioning in CC and MLO• Automatic sto p at +/- 90 degrees for lateral positions • Collimation buttons on the tube head for field of view size and location• Parameters display– Tube arm support rotation angle– Compressed breast thickness (in mm) – Compression force (in daN)• Ergonomic control console– Controls exposure– Provides information on system status– Gives access to advanced parameters for system set-up• Patented automatic view names marking based on breast laterality• View name can be edited while the exam is performedAcquisition workstation• Time to display processed image (average): 10 seconds • Time between exposures (typical): 12 seconds• Dose calculated and displayed on the image after every exposure (Entrance Skin Dose and Average Glandular Dose) • Quad core Intel i5 workstation:– Memory: 32GB– Hard disk: 1 internal 250GB disk for the system – Hard disk: 1TB for image storage – Ports: 4 Gigabit Ethernet port – DVI Display and port connector• 2 types of display available – 1MP LCD Monitor• 48 cm (19”) medical grade • 1280 x 1024 pixels (landscape) • High luminance - up to 300 Cd/m2 • Contrast ratio: 2000:1• Viewing angle: 170 degrees• Mounted on a rotating arm for in -room accessAutomatic exposureAutomatic Optimization of Parameters (AOP) Fully automatic mode• AOP is an automatic exposure system that selects all exposure parameters based on radiological density of the breast: - track (Mo or Rh) - filter (Mo or Ag) - kV - mAs• The system identifies the densest part of the breast to select the appropriate exposure parameters • Three AOP modes are available:– "Standard + ”: dose to patient comparable to screen/film Mammography– “Dose -”: priority is given to dose reduction– “Standard”: balances low noise and dose reduction • Automatic acquisition mode for implants Manual mode• Manual selection of all parameters: track, filter, kV and mAsCollimator• Filters: Molybdenum: 0.030 mm; Silver: 0.030 mm • Field of View (FOV) in detector plane, in cm:– For standard contact views: 24 x 29 maximum FOV or 19 x 23 regular FOV, automatic adjustment depending on paddle used, breast support and gantry rotation angle• Field of View (FOV) selection: automatic and manual• FOV size: selected automatically based on the paddle or geometric magnification platform used, can be modified manually by using the collimation size switch on the tube head • FOV location (left, right, center): selected automatically based on the tube arm angle, can be modified manually by using the collimation position switch on the tube head • Compression and exposure are prevented if the FOV and compression paddle sizes or locations are not consistent • Light centering device: a light automatically switches on when a preset position is reached, at compression start or at paddle insertion; can be turned on with the collimation switches buttons located on the tube head or on the acquisition consoleCompression• Compression modes:– Motor driven compression up to 20 daN – Manual compression up to 27 daN• Dual foot-pedals for column height and compression adjustments• User defined motorized compression force limit: 4 to 20 daN • Min force for AOP: 3 daN• Compression speed: 3 speed levels• Selectable automatic decompression after exposure, to minimize patient time under compressionPatient Assisted Compression (PAC)*Commercialized as Pristina Dueta in some countries• Wireless and ergonomic -designed device that allows the patient to continue the compression after the technologist has positioned correctly and reached a threshold of compression• Designed to minimize patients' perceived pain and discomfort• Intended to be available for every patient positioning• PAC’s speed profile is similar to the technologist-controlled one-Nio Color 3MP (MDNC-3421) – Barco:• High performance color IPS-TFT Color LCD• 54cm (21.3”)• 2048 x 1536 pixels (landscape)• Brightness: 900 cd/m2• Contrast ratio: 1400:1• Viewing angle: 178°• Mounted on a rotating arm for in-room access • Image PresentationeContrast allows you to choose among 6 levels to better adapt to breast morphology and radiologist display preferences:–eContrast 1 provides a “film-like” aspect withimproved visibility of the skin line– eContrast 2 to 4 provide increasing steps of imagesharpness and contrast– eContrast 5 provides a high level of sharpnessand contrast, with a very high level of tissuepenetration– eContrast 6 is adapted to very dense breast orimplants– Automatic windowing (window level and windowwidth)– Other features: zoom, roaming, inversion, flip,rotation of images, window width and level setting,annotations and measurements• In case of power failure, an Uninterruptible Power Supp ly (UPS) allows to close the examination without loss of informationConnectivity• DICOM** 3.0 platform:– Modality Worklist User– Storage Provider– Storage Commitment User– Query/Retrieve User– Basic Grayscale Print User– Verification Provider– DICOM-compliant CD, DVD-R/-RW and USB DataInterchange• Connectivity features: customizable Autopush to multiple DICOM databases, Autoprint, Autodelete based on Storage Commitment• Modality Perform Procedure Step User• Connectivity to GE Service for remote diagnostic capability • IHE Profiles: Scheduled workflow, Mammography image, Tomosynthesis profile, Portable data for imaging, Consistent time integrationQuality assurance• Complete quality control program• Automation of quality control tests: Flat Field, MTF, AOP, SNR• Test history and results can be reviewed• Data can be exported for data tracking• Automated Repeat and Reject Analysis Radiation shield• Choice between two radiation shields:– Integrated to the control console– StandaloneHigh voltage generator• Generator Integrated into the gantry for room saving • Generator type: high frequency single-phase power supply• Ripple: < 4% from peak to peak• Power: 5 kW max• Generator max rating:• 2 to 600 mAs (depending on track, filter and kV)•22 to 49 kV, in 1 kV steps depending on track • Generator protection: software monitoring tube load Standard configuration• Motorized isocentric gantry• X-ray tube with rotating Mo/Rh anode• 24 x 29 cm flat panel detector• Acquisition workstation– CD,DVD-R/-RW– 1MP or 3MP display– Control console– UPS• Pair of dual foot-pedals• Standard Face shield• 24 x 29 cm bucky with grid• 24 x 29 cm paddle• Quality control toolkit• User manual and technical documentationOptions• 1.5 and 1.8 magnification stands• Additional 24 x 29 cm paddle• 19 x 23 cm sliding paddle• 24 x 29 cm Flexible compression paddle• 19 x 23 cm Flexible & sliding compression paddle• 10x23 Sliding Implant/Small breast compression paddle • Square spot sliding compression paddle• Round spot sliding paddle• 2D Localization 19x23 Swiss Cheese sliding compression paddle• 2D Localization 19x23 sliding standard compressionpaddle•2D crosshair device• X-Ray protective shield• Bar code reader• Printers compatibility: AGFA DRYSTAR AXYS• Upgradable to Senographe Pristina 3D and/or SenoBright HD• X-ray remote control hand switch• X-ray footswitchSenographe Pristina 3DSenographe Pristina 3D is a three-dimensional imaging technology that uses a low dose short X-ray sweep around a compressed breast. The acquired projection images are processed electronically in order to reconstruct a 3D representation of the entire breast. This imaging technique is designed to separate the tissues and to reduce the overlapping of structures, which represents a limiting factor in standard 2D mammography.The 3D option is available for the Senographe Pristina platform that generates 3D and 2D images. Senographe Pristina 3D Technology• Sweep angle is 25° with 9 projections at any rotation angle between -160°/+160°• The “Step and Shoot” tube motion stops for each exposure to avoid image blur• Mo and Rh tube tracks create narrow x-ray spectra,exactly where the dose efficiency is for thin (Mo) andmedium and thick breasts (Rh)• Detector: 100 microns with no binning, high DQE in 3Dmode (IEC 62220-2-3, equivalent spectrum at 5µGy):65% (+/-2) at 0.5lp/mm and 57% (+/-2) at 2lp/mm• Automatic reconstruction of the images by using ASIR DBT iterative algorithms• The dose of a DBT (Digital Breast Tomosynthesis) view isdesigned to be equivalent to the dose of a 2D standardacquisition of the same view• Capability to reconstruct 0.5mm or 1mm distancebetween tomo-planes• 3D+2D mode allows the user to acquire in a single action a 3D sequence followed by 2D image for a given view,without releasing the compressionSenoBright HDThe SenoBright HD (Contrast Enhanced Spectral Mammography CESM) application shall enable contrast heightened breast imaging using a dual energy technique. This imaging technique can be used as an adjunct following mammography and ultrasound exams to localize a known or suspected lesion.Patient Comfort• As with previous generation GE mammography systems, patients lying in a recumbent position can be examined with SenoBright HDErgonomics designed for technologist• User can switch between standard mammography and Spectral Mammography mode during the same exam session• SenoBright HD provides a timer function to both monitor and record time after injection, which is displayed as an annotated field in the images• SenoBright HD offers Automatic Optimization of Parameters (AOP) and manual exposure modes for the dual-energy exam• S enoBright HD will automatically acquire the Spectral Mammography images for each view with a single action of the x-ray exposure control TechnologySenoBright HD chooses filtering materials depending on the operating mode and the exposure levels necessary. For the high-energy acquisition, a proprietary multi-layer filter is used to shape the resulting energies of the x-ray spectrum to those required to best highlight iodine.Energy LevelsThe energy levels may vary depending on breast thickness • 26-34 KVp for lower energy acquisition• 49 KVp for higher energy acquisition.System Power supply• Input frequency: 50Hz/60Hz• Input voltage: single-phase 200-240 V~• EATON UPS 5P650 650VASystem Weight• Gantry: 420 kg• Control Station without monitors: 160 kg Environmental conditions• Temperature range: 15° to 30°C• Humidity range: 10% to 80%• Atmospheric pressure range: 70 kPa to 106kPa(0 to 3000m altitude)Screening ProtocolFor reference, in the US a DBT screening examination may consist of one of the following combinations (CC: craniocaudal, MLO: mediolateral oblique):- a 2D CC view and a 3D DBT MLO view, or- a 3D DBT image set consisting of CC and MLO views, and a 2D synthesized image set consisting of CC and MLO V-Preview images.V-Preview is the 2D synthesized image generated by GE SenoIris mammography software from GE DBT images.Note: Breast cancer screening may be regulated by country specific rules. Please refer to competent Healthcare Authorities for guidanceWireless Footswitch OptionThe wireless footswitch is available on the Senographe Pristina platform. The footswitch includes pedals to activate lift up, lift down, compression and decompression functionalities. The communication range between the footswitch and the receiver shall be located within 1.5m radius from the gantry.Mobile OptionA Mobile Mounting Device is available for Senographe Pristina 2D and 3D to allow its installation and transportation in a mobile unitWorkflow OptionsThe Senographe Pristina is compatible with iCAD Second Look (2D CAD), iCAD Tomo Detection 1.0 (3D CAD) and iCAD ProFound AI for Tomo (3D CAD)Senographe PristinaNOTE:- Weights and dimensions may vary slightly depending on equipment configuration.Senographe Pristina, PAC, Wireless footswitch, Mobiles and iCAD are not available in all countries. Please refer to your GE Healthcare sales representative.1885m775368Maximum HeightGantry BasePlateM ax imM ax im uData subject to change.Marketing Communications GE Medical SystemsSociété en Commandite Simple au capital de 85.418.040 Euros 283, rue de la Minière, 78530 Buc France RCS Versailles B 315 013 359A General Electric company, doing business as GE HealthcareUK: 0800 0329201Spain: 0900 993620 Germany************France: 0800 908719Austria: 0800 291888 SwitzerlandItaly: 0800 786947 German: 0800 837279French: 0800 837279GE, the GE Monogram, and imagination at work are trademarks of the General Electric Company*Trademark of General Electric Company** DICOM is a trademark of National Electrical Manufacturers Association.All other trademarks, service marks, company names and product names are the property of their respective owners© 2016-2019 Copyright GE Healthcare。

2025年软件资格考试数据库系统工程师(基础知识、应用技术)合卷(中级)模拟试卷(答案在后面)一、基础知识（客观选择题，75题，每题1分，共75分）1、数据库系统工程师在数据库设计过程中，以下哪个阶段是确定数据库中数据模型和概念模型的阶段？A、需求分析阶段B、概念结构设计阶段C、逻辑结构设计阶段D、物理结构设计阶段2、在关系数据库中，以下哪种数据类型可以存储固定长度的字符串？A、VARCHARB、CHARC、TEXTD、BLOB3、在数据库系统中，为了确保数据的一致性，在执行事务时必须遵循ACID属性。

以下哪个选项不是ACID属性的一部分？A. 原子性B. 一致性C. 隔离性D. 可用性4、下列关于关系数据库规范化理论的描述中，哪一项是不正确的？A. 第一范式要求每个属性都应该是不可再分的基本项。

B. 满足第二范式的前提是先满足第一范式，并且所有非主属性完全依赖于整个候选键。

C. 第三范式消除了传递依赖。

D. BCNF（Boyce-Codd范式）比第三范式更严格，它不允许任何属性部分依赖或传递依赖于候选键。

5、在数据库系统中，以下哪一项不是关系模型的三要素？A. 属性B. 关系C. 范式D. 约束6、在SQL语言中，用于删除表的命令是：A. DROP TABLEB. DELETE FROMC. TRUNCATE TABLED. DELETE7、在数据库系统中，什么是数据模型？请简述其作用。

8、什么是数据库规范化理论？请简述其目的。

（1）第一范式（1NF）：要求每个属性都是不可分割的最小数据单位。

（2）第二范式（2NF）：在满足1NF的基础上，要求非主属性完全依赖于主键。

（3）第三范式（3NF）：在满足2NF的基础上，要求非主属性不传递依赖于主键。

（4）巴斯-科德范式（BCNF）：在满足3NF的基础上，要求每个非平凡函数依赖都由主键决定。

通过规范化理论，可以优化数据库设计，提高数据库的质量和性能。

参考文献(人工智能)曹晖目的：对参考文献整理（包括摘要、读书笔记等），方便以后的使用。

分类：粗分为论文（paper）、教程（tutorial）和文摘（digest）。

0介绍 (1)1系统与综述 (1)2神经网络 (2)3机器学习 (2)3.1联合训练的有效性和可用性分析 (2)3.2文本学习工作的引导 (2)3.3★采用机器学习技术来构造受限领域搜索引擎 (3)3.4联合训练来合并标识数据与未标识数据 (5)3.5在超文本学习中应用统计和关系方法 (5)3.6在关系领域发现测试集合规律性 (6)3.7网页挖掘的一阶学习 (6)3.8从多语种文本数据库中学习单语种语言模型 (6)3.9从因特网中学习以构造知识库 (7)3.10未标识数据在有指导学习中的角色 (8)3.11使用增强学习来有效爬行网页 (8)3.12★文本学习和相关智能A GENTS：综述 (9)3.13★新事件检测和跟踪的学习方法 (15)3.14★信息检索中的机器学习——神经网络，符号学习和遗传算法 (15)3.15用NLP来对用户特征进行机器学习 (15)4模式识别 (16)4.1JA VA中的模式处理 (16)0介绍1系统与综述2神经网络3机器学习3.1 联合训练的有效性和可用性分析标题：Analyzing the Effectiveness and Applicability of Co-training链接：Papers 论文集\AI 人工智能\Machine Learning 机器学习\Analyzing the Effectiveness and Applicability of Co-training.ps作者：Kamal Nigam, Rayid Ghani备注：Kamal Nigam (School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, knigam@)Rayid Ghani (School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213 rayid@)摘要：Recently there has been significant interest in supervised learning algorithms that combine labeled and unlabeled data for text learning tasks. The co-training setting [1] applies todatasets that have a natural separation of their features into two disjoint sets. We demonstrate that when learning from labeled and unlabeled data, algorithms explicitly leveraging a natural independent split of the features outperform algorithms that do not. When a natural split does not exist, co-training algorithms that manufacture a feature split may out-perform algorithms not using a split. These results help explain why co-training algorithms are both discriminativein nature and robust to the assumptions of their embedded classifiers.3.2 文本学习工作的引导标题：Bootstrapping for Text Learning Tasks链接：Papers 论文集\AI 人工智能\Machine Learning 机器学习\Bootstrap for Text Learning Tasks.ps作者：Rosie Jones, Andrew McCallum, Kamal Nigam, Ellen Riloff备注：Rosie Jones (rosie@, 1 School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213)Andrew McCallum (mccallum@, 2 Just Research, 4616 Henry Street, Pittsburgh, PA 15213)Kamal Nigam (knigam@)Ellen Riloff (riloff@, Department of Computer Science, University of Utah, Salt Lake City, UT 84112)摘要：When applying text learning algorithms to complex tasks, it is tedious and expensive to hand-label the large amounts of training data necessary for good performance. This paper presents bootstrapping as an alternative approach to learning from large sets of labeled data. Instead of a large quantity of labeled data, this paper advocates using a small amount of seed information and alarge collection of easily-obtained unlabeled data. Bootstrapping initializes a learner with the seed information; it then iterates, applying the learner to calculate labels for the unlabeled data, and incorporating some of these labels into the training input for the learner. Two case studies of this approach are presented. Bootstrapping for information extraction provides 76% precision for a 250-word dictionary for extracting locations from web pages, when starting with just a few seed locations. Bootstrapping a text classifier from a few keywords per class and a class hierarchy provides accuracy of 66%, a level close to human agreement, when placing computer science research papers into a topic hierarchy. The success of these two examples argues for the strength of the general bootstrapping approach for text learning tasks.3.3 ★采用机器学习技术来构造受限领域搜索引擎标题：Building Domain-specific Search Engines with Machine Learning Techniques链接：Papers 论文集\AI 人工智能\Machine Learning 机器学习\Building Domain-Specific Search Engines with Machine Learning Techniques.ps作者：Andrew McCallum, Kamal Nigam, Jason Rennie, Kristie Seymore备注：Andrew McCallum (mccallum@ , Just Research, 4616 Henry Street Pittsburgh, PA 15213)Kamal Nigam (knigam@ , School of Computer Science, Carnegie Mellon University Pittsburgh, PA 15213)Jason Rennie (jr6b@)Kristie Seymore (kseymore@)摘要：Domain-specific search engines are growing in popularity because they offer increased accuracy and extra functionality not possible with the general, Web-wide search engines. For example, allows complex queries by age-group, size, location and cost over summer camps. Unfortunately these domain-specific search engines are difficult and time-consuming to maintain. This paper proposes the use of machine learning techniques to greatly automate the creation and maintenance of domain-specific search engines. We describe new research in reinforcement learning, information extraction and text classification that enables efficient spidering, identifying informative text segments, and populating topic hierarchies. Using these techniques, we have built a demonstration system: a search engine forcomputer science research papers. It already contains over 50,000 papers and is publicly available at ....采用多项Naive Bayes 文本分类模型。

primary outcome was the rate of revascularization following stenting.Results26patients met the inclusion criteria with a mean age of56.5years old,34.6%of whom were female.On presenta-tion the median National Institute of Health Stroke Scale (NIHSS)was11and media Alberta Stroke Program Early CT Score(ASPECTS)was9.MT was performed using A Direct Aspiration First Pass T echnique(ADAPT)in all patients.Fol-lowing Neuroform Atlas stent placement,3patients(11.5%) had moderate in stent stenosis while severe stenosis was encountered in4patients(15.4%).The rate of successful revascularization(TICI IIB-III)was identified in92.3%of the patients.On follow up vascular images,re-occlusion occurred in2patients(7.7%)and symptomatic hemorrhage was encountered in3patients(11.5%).Excellent outcome at90 days(mRS0–2)was achieved in13/26(50%)of patients. Conclusions Our series provides preliminary safety and efficacy data regarding the use of the Neuroform Atlas stent as a res-cue modality in ICAS-ELVO cases.Disclosures jthia:None. E.Almallouhi:None.K.Kicie-linski:None.J.Lena:None. A.Spiotta:None.S.Al Kasab: None.E-066ROBOTIC ARM USE IN NEUROENDOVASCULARPROCEDURES:SYSTEMATIC REVIEW OF LITERATUREE Almallouhi,S Al Kasab,E Brennan,A Spiotta.Medical University of South Carolina, Charleston,SC10.1136/neurintsurg-2022-SNIS.177Background The use of robotic arm in neuroendovascular pro-cedures has gained increasing popularity in the last few years. The theoretical benefits of using a robotic arm include more accurate deployment of stents and coils,and the ability to per-form procedures remotely.However,limited evidence is cur-rently available to support the use of robotic arm in routine neuroendovascular practice.Methods Databases searched include PubMed,CINAHL Com-plete,and Scopus from database date of inception through February,11th2022.We included all human studies that reported the procedural and clinical outcomes of using a robotic arm as a primary approach to perform neuroendovas-cular procedures.The search strategies used a combination of subject headings and keywords for the following two concepts: robotic arm,and neuroendovascular procedures.Results A total of11studies were identified including10case reports/case series and1comparative study.Overall,65proce-dures were performed using the CorPath(Corindus Inc.) robotic arm including28diagnostic cerebral angiograms,28 cervical carotid stents,8cerebral aneurysm embolization,and one selective spinal angiogram.No complications were reported and only one case(1.5%)required conversion to manual approach.In the single comparative study that com-pared manual vs.robotic carotid stenting,there was no differ-ence in the procedural and clinical outcomes in both groups. Conclusion The use of robotic arm is a promising new tech-nology in the neuroendovascular field.Future comparative studies are needed to confirm efficacy and safety of using the robotic arm.Disclosures E.Almallouhi:None.S.Al Kasab:None. E. Brennan:None. A.Spiotta:1;C;Stryker,Penumbra,and Medtronic.E-067VERAPAMIL AND ITS ASSOCIATION WITH HIVES INRADIAL ARTERY CATHETERIZATION FORNEUROENDOVASCULAR DIAGNOSTIC ANGIOGRAMS1D Romeo,2M Salem,2J Burkhardt,2B Jankowitz.1Perelman School of Medicine,Universityof Pennsylvania,Philadelphia,PA;2Department of Neurosurgery,University of Pennsylvania, Philadelphia,PA10.1136/neurintsurg-2022-SNIS.178Introduction Different combinations of medications are utilizedduring radial artery(RA)catheterization for neuroendovascular procedures to preclude RA spasm.These combinations com-monly include verapamil,a calcium channel blocker amongthe‘radial cocktail’,without established benefit in mitigatingthe risk of RA spasm.However,the negative effects of verapa-mil such as the acute development of hives are sparsely reported in the context of RA neuro-angiography.We soughtto assess their association using a case-control cohort of oursingle center experience.Methods A series of consecutive patients undergoing diagnosticradial angiograms were reviewed and analyzed.Patients whohad verapamil in their medication cocktail were classified as‘cases’and patients without verapamil were‘controls’.We lim-ited the analysis to diagnostic angiograms,excluding treatment procedures to avoid potential confounding factors such as the complexity of a given treatment.The primary outcomes assessed were the presence of hives and radial artery spasm.Results215radial diagnostic neuro-angiography procedureswere included in our analysis.137patients underwent RA catherization with verapamil and84underwent RA catheriza-tion without verapamil.Of the137patients that received verapamil,4(2.9%)developed hives during the RA catheriza-tion procedure and were subsequently treated with Benadryl(25mg)and0(0.0%)experienced RA vasospasm.Of the78who did not receive verapamil,there were0(0.0%)cases ofhives and1(1.3%)case of RA spasm.Conclusion The administration of verapamil for those under-going RA vasospasm appears to be associated with the devel-opment of hives without significant reduction in the RAspasm risk.More data is needed to better understand the util-ity and safety of verapamil in RA catherization procedures. Disclosures D.Romeo:None.M.Salem:None.J.Burkhardt: None.B.Jankowitz:None.E-068A SINGLE-INSTITUTION RETROSPECTIVE ANALYSIS OFPEDIATRIC CEREBRAL VENOUS SINUS THROMBOSISM Brown.Interventional and Diagnostic Radiology,Aurora St.Luke’s Medical Center, Milwaukee,WI10.1136/neurintsurg-2022-SNIS.179Objective Pediatric cerebral venous sinus(sinovenous)throm-bosis(CSVT)is a rare complication with limited data on pre-sentation,treatment,and prognosis.Most literature consists ofcase reports and small case series that demonstrate variable morbidity and mortality outcomes.The purpose of this studywas to retrospectively analyze the risk of CSVT in a single-institution pediatric population,to identify correlating factors,and to evaluate clinical features,diagnosis,management,and prognosis.Methods A comprehensive retrospective review of electronic medical records was performed for9149hospitalizations in 6374unique patients evaluated by the pediatric neurosurgery on December 24, 2023 by guest. Protected by copyright./ J NeuroIntervent Surg: first published as 10.1136/neurintsurg-2022-SNIS.179 on 23 July 2022. Downloaded fromservice at Riley Hospital for Children(Indianapolis,IN)from 1990–2020.All patients18years of age or younger who were either admitted to the neurosurgical service or evaluated in consultation by neurosurgery were included.Results A total of36(0.56%)patients were found to have CSVT,19of which were males and17were females.Patients aged from3months to16years,with an average age of6.3 +/-6.1years and median of5years.Eleven(30.6%)patients presented with traumatic brain injury,7(19.4%)were found to have systemic infections,and17(47.2%)had some form of hypercoagulable state.At the time of CSVT diagnosis,no patient had a history of cancer.One(2.8%)patient was found to have upper extremity deep venous thrombosis.Thirteen (36.1%)patients were subsequently treated with anticoagulation.Conclusion This study is one of the largest single institution retrospective review of CSVT to date.Children admitted for non-operative traumatic brain injuries exhibited a statistically significant increased risk of CSVT.Death was associated with coma at presentation.Anticoagulation and thrombolytic proto-cols were not associated with improved morbidity and mortal-ity.Establishment of treatment guidelines,screening protocols, and prospective studies may contribute to more successful out-comes.(Study is ongoing and pending6additional cases,pos-sibly more,with additional chart review focusing on any interventions performed.)Disclosures M.Brown:None.E-069TREATMENT OF INTRACRANIAL INTERNAL CAROTID PSEUDOANEURYSM AND ANEURYSMALSUBARACHNOID HEMORRHAGE WITH PIPELINE SHIELDFLOW DIVERTER1M Abouelleil,2L Lyons,2K Aljaboori,3J Singer.1Spectrum Health/Michigan State,Grand Rapids,MI;2Spectrum Health/Michigan State,Grand Rapids,MI;3Neurosurgery,Spectrum Health/Michigan State,Grand Rapids,MI10.1136/neurintsurg-2022-SNIS.180Summary We describe the successful management of aneurys-mal subarachnoid hemorrhage(SAH)in two patients using Pipeline shield embolization and single antiplatelet therapy. The first patient,a43-year-old male,developed an iatrogenic supraclinoid internal carotid artery(ICA)pseudoaneurysm after pituitary tumor resection.The second patient had a ruptured left posterior inferior cerebellar artery(PICA)aneurysm.Both aneurysms were successfully treated with Pipeline flex shield embolization device with no intraoperative or perioperative complications.Stent patency was also confirmed6months after placement without intra-stent thrombus formation.Our successful experience with the Pipeline flex shield demon-strates the possibility of reducing dual antiplatelet therapy to a single agent in the setting of SAH.Background The commonly used treatment approaches for ruptured intracranial aneurysms are either endovascular coiling or surgical clipping.1The complexity of a considerable popu-lation of aneurysms,which can be dictated by shape,size,or location,makes them unamenable to either treatment approach.These aneurysms are usually managed with either stent assisted coiling or flow diverting stents.2,3Despite their promising results,thromboembolism and ischemia are known complications.4Although the use of antiplatelet therapy can reduce thrombogenicity,it increases the risk of bleeding,espe-cially in the setting of aneurysmal subarachnoid hemorrhage.Shielded Pipeline stents are flow diverting stents that are coated with phosphorylcholine,an antithrombotic agent that inhibits platelet aggregation and activation.The advent of this technology reduced the risk of thrombotic events compared to‘unshielded’stents.5Conclusion Our presented cases demonstrate the efficacy andsafety of Pipeline shield stents,combined with single antiplate-let therapy for6months,in the management of aneurysmalSAH.Disclosures M.Abouelleil:None.L.Lyons:None.K.Alja-boori:None.J.Singer:None.E-070THE USE OF INPATIENT VIRTUAL SHARED MEDICALAPPOINTMENTS FOR PATIENTS WITH ACUTECEREBROVASCULAR PATHOLOGIES AND THEIRCAREGIVERS:A PILOT STUDY TO IMPROVE HEALTHLITERACY,PATIENT SELF-EFFICACY AND REDUCEPROVIDER BURNOUT1U Mahajan,1N Sharma,1M Maynard,1L Kang,2C Labak,2M Sajatovic,2A Hoffer,2B Shammassian,2J Wright,2X Zhou,2C Wright.1Case Western Reserve University, Cleveland,OH;2University Hospitals Cleveland Medical Center,Cleveland,OH10.1136/neurintsurg-2022-SNIS.181Objective Admission to the hospital for an acute cerebrovascu-lar condition such as stroke or brain hemorrhage can be a traumatic and disorienting experience for patients and theirfamily members.The COVID-19pandemic has further intensi-fied this experience in addition to exacerbating clinician and resident burnout.T o ameliorate some of these concerns,ateam of resident and medical student trainees implemented avirtual shared medical appointment(vSMA)program for inpa-tients with acute cerebrovascular disorders and their caregivers.This study hypothesized that an early intervention vSMA improves patient and caregiver health literacy and prepared-ness,simultaneously educates trainees on effective communica-tion skills,and reduces clinician burnout.Methods Patients and caregivers of admitted patients were screened through the neurosurgery,neurocritical care,and neurology electronic medical record census.A weekly60-minute secure virtual session consisted of introductions,a10-minute standardized presentation on cerebrovascular disease management,followed by participant-guided discussion.Partici-pants completed pre-and post-surveys.We report data on this feasibility study and present challenges,both expected and unforeseen.Results A total of170patients were screened;13patients and26caregivers participated in at least1session.A total of6 different healthcare providers facilitated sessions.The vSMA program received overwhelmingly positive feedback from care-givers.Surveys demonstrated96.4%of caregivers and75%of patients were satisfied with the session.96.4%of caregiversand87.5%of patients would recommend this type of appointment to a friend or family member.88.8%of pro-viders felt validated by conducting the session.The participantgroup had a20%greater percentage of patients dischargedhome without home needs compared to the non-participant group.The primary obstacle encountered included technologi-cal frustrations with the consent process and the sessions themselves.Conclusions Implementation of a vSMA program at a tertiarycare center during a pandemic was feasible.Themes caregivers expressed on the post-survey included better understanding of on December 24, 2023 by guest. Protected by copyright./ J NeuroIntervent Surg: first published as 10.1136/neurintsurg-2022-SNIS.179 on 23 July 2022. Downloaded from。