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美国国立医学图书馆生物技术信息中心可编程开发工具简介美国国立图书馆下属的生物技术信息中心，为生物医学研究者提供了庞大的信息资源和强大可靠的检索工具。
可编程开发工具就是NCBI所开发的功能强大的检索编程工具接口，通过它可以自动化的大批量的从Entrez数据库检索数据，从而为科研人员了解本专业动态提供材料，并为未来研究提供方向指导。
标签：E-utilities；Entrez数据库；生物技术信息中心；数据管道自2003年，美国国立医学图书馆下属的生物技术信息中心发布第一版NLM 归档和交换标记套件以来[1]，基于NCBI可编程开发工开发的数据挖掘的产品便大量问世。
如由陈朝美开发的可视化文献引文分析工具CiteSpace[2]，也有多个针对某一特定领域的数据挖掘工具[3]。
1 应用程序编程接口API是提供应用程序与开发人员基于某软件或硬件得以访问的能力，而又无需访问源码，或理解内部工作机制的细节。
一些桌面操作系统如Windows、Linux，移动端操作系统Android、IOS等都提供有相应的API于开发人员，以便开发人员开发用户需要的软件。
E-utilities便是NCBI提供给开发人员使用的结构化接口--API接口。
2 E-utilities组成E-utilities是一组9个服务器端程序组成的，包括：①EInfo：提供在给定数据库的每个字段索引记录的数量；数据库的最后更新日期；从数据库中可用的链接到其他Entrez数据库；②ESearch：在给定的数据库中查询匹配的唯一标识符列表的文本查询的响应；查询的术语翻译；③EPost：从指定数据库中接受UIDs 列表，在历史服务器上存储该套内容；响应查询和网络环境，上传数据集；④ESummary：给定的数据库通过UIDs列表，相应的文档摘要反馈；⑤EFetch：给定的数据库通过UIDs列表，相应数据记录的以指定的格式反馈；⑥ELink：给定的数据库响应UIDs列表，既有相同数据库相关的UIDs列表，又有其他Entrez 数据库中的UIDs列表；从一个或者多个UIDs中检查指定链接的存在；通过原LinkOut提供的一个创建特殊UID和数据库或者LinkOut URLs和多个UIDs属性创建超链接；⑦EGQuery：在每个Entrez数据库中，反馈一个应用大量数据匹配的文本查询；⑧Espell：给定的数据库查询用的一个文本拼写的建议；⑨EcitMatch：检索PMID相关的一组输入引用字符串。
MagMAX ™ FFPE DNA/RNA Ultra KitAutomated or manual isolation of total nucleic acid (TNA) from FFPE samples using AutoLys tubesCatalog Number A31881Pub. No. MAN0017538 Rev.A.0WARNING! Read the Safety Data Sheets (SDSs) and follow thehandling instructions. Wear appropriate protective eyewear,clothing, and gloves. Safety Data Sheets (SDSs) are available from /support .Product descriptionThe Applied Biosystems ™ MagMAX ™ FFPE DNA/RNA Ultra Kit is designed to isolate both DNA and RNA from the same section offormaldehyde- or paraformaldehyde-fixed, paraffin-embedded (FFPE)tissues. The kit also allows for flexibility to isolate DNA only, RNA only or total nucleic acid (TNA). The kit uses MagMAX ™ magnetic-bead technology, ensuring reproducible recovery of high-quality nucleic acid through manual or automated processing. The isolated nucleic acid is appropriate for use with a broad range of downstream assays, such as quantitative real-time RT-PCR and next-generation sequencing.In addition to the use of traditional solvents, the kit is compatible with Autolys M tubes that enable a faster and more convenient means of deparaffinizing FFPE samples by eliminating the need for organic solvents such as xylene or CitriSolv and ethanol. Samples are put into the tubes for protease digestion, tubes are lifted with the Auto-pliers or Auto-Lifter and then samples are spun down. The wax and debris are contained in the upper chamber while the lysate is passed through.Afterwards, the clarified lysate can be directly purified with the MagMAX ™ FFPE DNA/RNA Ultra Kit.For guides without using AutoLys M tubes for sequential DNA and RNA isolation, or DNA isolation, or RNA isolation only, seeMagMAX ™ FFPE DNA/RNA Ultra Kit User Guide (sequential DNA/RNA isolation) (Pub. No. MAN0015877), or MagMAX ™ FFPE DNA/RNA Ultra Kit User Guide (DNA isolation only) (Pub. No. MAN0015905), or MagMAX ™ FFPE DNA/RNA Ultra Kit User Guide (RNA isolation only)(Pub. No. MAN0015906), respectively.This guide describes isolation of TNA from FFPE tissue blocks or FFPE slides using AutoLys M tubes. Three optimized methods for sections or curls both up to 40 µm using AutoLys M tubes are included:•Manual sample processing.•KingFisher ™ Flex Magnetic Particle Processor with 96 Deep-Well Head (DW96; 96-well deep well setting).•KingFisher ™ Duo Prime Magnetic Particle Processor (12-well deep well setting).For sequential DNA and RNA isolation, or DNA isolation, or RNA isolation only, see MagMAX ™ FFPE DNA/RNA Ultra Kit User Guide (sequential DNA/RNA isolation) (Pub. No. MAN0017541), MagMAX ™FFPE DNA/RNA Ultra Kit User Guide (DNA isolation only)(Pub. No. MAN0017539), or MagMAX ™ FFPE DNA/RNA Ultra Kit User Guide (RNA isolation only) (Pub. No. MAN0017540), respectively.Contents and storageReagents provided in the kit are sufficient for 48 TNA isolations from sections up to 40 µm with the AutoLys workflow.Table 1 MagMAX ™ FFPE DNA/RNA Ultra Kit (Cat. No. A31881)Additional reagents are available separately; Protease Digestion Buffer, Binding Solution, and DNA Wash Buffer are also available as Cat. No. A32796. [2]Shipped at room temperature. [3]Not used in TNA workflow[4]Final volume; see “Isolate TNA“ on page 4.Required materials not suppliedUnless otherwise indicated, all materials are available through . MLS: Fisher Scientific ( ) or other major laboratory supplier.Table 2 Materials required for nucleic acid isolation (all methods)Table 3 Additional materials required for manual isolationTable 4 Additional materials required for automated isolationIf needed, download the KingFisher ™ Duo Prime or Flex programThe programs required for this protocol are not pre-installed on theKingFisher ™Duo Prime Magnetic Particle Processor or on theKingFisher ™Flex Magnetic Particle Processor 96DW.1.On the MagMAX ™ FFPE DNA/RNA Ultra Kit product web page,scroll down to the Product Literature section.2.Right-click on the appropriate program file(s) for your samplesize to download the program to your computer:3.4.Refer to the manufacturer's documentation for instructions forinstalling the program on the instrument.Procedural guidelines•Perform all steps at room temperature (20–25°C) unless otherwise noted.•When mixing samples by pipetting up and down, avoid creating bubbles.•When working with RNA:–Wear clean gloves and a clean lab coat.–Change gloves whenever you suspect that they are contaminated.–Open and close all sample tubes carefully. Avoid splashing or spraying samples.–Use a positive-displacement pipettor and RNase-free pipette tips.–Clean lab benches and equipment periodically with an RNase decontamination solution, such as RNase Zap ™ Solution (Cat.No. AM9780).•Incubation at 60°C can be extended overnight to increase DNA yields, followed by incubation at 90°C for 1 hour.•Volumes for reagent mixes are given per sample. We recommend that you prepare master mixes for larger sample numbers. To calculate volumes for master mixes, refer to the per-well volume and add 5–10% overage.Before you beginBefore first use of the kit•Prepare the Wash Solutions from the concentrates:–Add 46 mL of isopropanol to RNA Wash Buffer Concentrate ,mix, and store at room temperature.–Add 168 mL of ethanol to Wash Solution 2 Concentrate , mix,and store at room temperature.Before each use of the kit•Equilibrate the Nucleic Acid Binding Beads to room temperature.•Pre-heat the incubators or ovens to 60°C and 90°C.•Prepare the following solutions according to the following tables.Table 5 Protease solutionTable 6 TNA Binding BufferPrepare the FFPE samples•For curls from FFPE tissue blocks: proceed to “Prepare the curls from FFPE tissue blocks“ on page 3.•For FFPE slide-mounted sections: proceed to “Prepare samples from FFPE slides“ on page 3.Prepare the curls from FFPE tissue blocksa.Cut sections from FFPE tissue blocks using a microtome.Note: For miRNA extraction, we recommend using sections of 10 µm or thicker.b.Collect each section in an AutoLys M tube.1Section FFPE tissue blocks a.Add 235 µL of the Protease Solution (see Table 5).Note: If working with curls, they might stick straight up so make sure to submerge samples in theProtease Solution with a tip or a 1 mL syringe plunger or do a quick spin down at 3000 rpm for 1 minute prior to the addition of buffer to collapse the curl. Time may be extended.b.Incubate at 60°C for 1 hour or longer.Note: Use the AutoLys racks and place in an incubator or oven.c.Incubate at 90°C for 1 hour.Note: For automated isolation, set up the processing plates during the incubation.·For isolation using KingFisher ™ Duo Prime Magnetic Particle Processor, proceed to “Set up the processing plate“ on page 4.·For isolation using KingFisher ™ Flex Magnetic Particle Processor 96DW, proceed to “Set up the TNA processing plates“ on page 5.2Digest with Protease a.Allow samples to cool down for 3–5 minutes before proceeding to lift the tubes.e the Auto-plier for individual tube lifting or the Auto-lifter for multiple tube lifting of up to 24 tubes.c.Lock the tubes in position by hand or use the locking lid.d.Centrifuge at 2000 × g for 10 minutes in a benchtop centrifuge with plate adapters.e.Unlock the tubes by hand or remove the locking lid.e the Auto-plier or Auto-lifer to lift the inner tube for sample access.g.Proceed to purification. See “Isolate TNA“ on page 43Lift the tubesPrepare samples from FFPE slidesa.Pipet 2–4 µL of Protease Digestion Buffer depending on the tissue size evenly across the FFPE tissuesection on the slide to pre-wet the section.Note: You can adjust the volume of Protease Digestion Buffer if the tissue is smaller or larger.b.Scrape the tissue sections in a single direction with a clean razor blade or scalpel, then collect the tissueon the slide into a cohesive mass.c.Transfer the tissue mass into an AutoLys M tube with the scalpel or a pipette tip.d.Add 235 µL of the Protease Solution (see Table 5).Note: Be sure to submerge samples in the Protease Solution with a tip or a 1 mL syringe plunger e.Incubate at 60°C for 1 hour or longer.Note: Use the AutoLys racks and place in an incubator or oven.f.Incubate at 90°C for 1 hour.Note: For automated isolation, set up the processing plates during the incubation.·For isolation using KingFisher ™ Duo Prime Magnetic Particle Processor, proceed to “Set up the processing plate“ on page 4.·For isolation using KingFisher ™ Flex Magnetic Particle Processor 96DW, proceed to “Set up the TNA processing plates“ on page 5.4Scrape the samples and digest with Proteasea.Allow samples to cool down for 3–5 minutes before proceeding to lift the tubes.e the Auto-plier for individual tube lifting or the Auto-lifter for multiple tube lifting of up to 24 tubes.c.Lock the tubes in position by hand or use the locking lid.d.Centrifuge at 2000 × g for 10 minutes in a benchtop centrifuge with plate adapters.e.Unlock the tubes by hand or remove the locking lid.e the Auto-plier or Auto-lifer to lift the inner tube for sample access.g.Proceed to purification. See “Isolate TNA“ on page 45Lift the tubesIsolate TNA•To isolate TNA manually, proceed to “Isolate TNA manually“ on page 4.•To isolate TNA using the KingFisher ™Duo Prime Magnetic Particle Processor, proceed to “Isolate TNA using KingFisher ™ Duo Prime Magnetic Particle Processor“ on page 4.•To isolate TNA using the KingFisher ™Flex Magnetic Particle Processor 96DW, proceed to “Isolate TNA using KingFisher ™ Flex Magnetic Particle Processor 96DW“ on page 5.Isolate TNA manuallyUse microcentrifuge tubes to perform manual TNA isolations.a.After the Protease digestion is complete, add 20 µL of Nucleic Acid Binding Beads to the samples.b.Add 900 µL of TNA Binding Buffer (see Table 6) to the sample.c.Shake for 5 minutes at speed 10 or 1150 rpm.d.Place the sample on the magnetic stand for 2 minutes or until the solution clears and the beads are pelleted against the magnet.e.Carefully discard the supernatant with a pipette.1Bind the TNA to beadsa.Wash the beads with 500 µL of RNA Wash Buffer.b.Shake for 1 minute at speed 10 or 1150 rpm until the mixture is thoroughly chocolate brown in color.c.Place the sample on the magnetic stand for 2 minutes or until the solution clears and the beads arepelleted against the magnet.d.Carefully discard the supernatant with a pipette.e.Repeat steps a-d.f.Wash the beads with 500 µL of Wash Solution 2.g.Shake for 1 minute at speed 10 or 1150 rpm until the mixture is thoroughly chocolate brown in color.h.Place the sample on the magnetic stand for 2 minutes or until the solution clears and the beads arepelleted against the magnet.i.Carefully discard the supernatant with a pipette.j.Repeat steps f-i.k.Shake for 1–2 minutes at speed 10 or 1150 rpm to dry the beads.Do not over-dry the beads. Over-dried beads results in low TNA recovery yields.2Wash TNA on the beadsa.Add 50 µL of Elution Solution to the beads.b.Shake for 5 minutes at speed 10 or 1150 rpm and at 55°C until the mixture is thoroughly chocolatebrown in color.c.Place the sample on the magnetic stand for 2 minutes or until the solution clears and the beads arepelleted against the magnet.The supernatant contains the purified TNAThe purified TNA is ready for immediate use. Store at –20°C or –80°C for long-term storage.3Elute the TNAIsolate TNA using KingFisher ™ Duo Prime Magnetic Particle ProcessorDuring the protease incubation, add processing reagents to the wells of a MagMAX ™ Express-96 Deep WellPlate as indicated in the following table.Table 7 TNA plate setupRow on the MagMAX ™ Express-96 Deep Well Plate.4Set up the processing plate a.Ensure that the instrument is set up for processing with the deep well 96–well plates and select theappropriate program A31881_DUO_large_vol_TNA on the instrument.b.At the end of the protease incubation, add 200 µL of sample to each well in Row G of the TNA plate.c.Add 20 µL of Nucleic Acid Binding Beads to each sample well in Row G.d.Start the run and load the prepared processing plates when prompted by the instrument.5Bind, wash, rebind, and elute the TNA5Bind, wash, rebind,and elute the TNA(continued)e.At the end of the run, remove the Elution Plate from the instrument and transfer the eluted TNA(Row A of TNA plate) to a new plate and seal immediately with a new MicroAmp ™ Clear Adhesive Film.IMPORTANT! Do not allow the purified samples to sit uncovered at room temperature for more than 10minutes, to prevent evaporation and contamination.The purified TNA is ready for immediate use. Store at –20°C or –80°C for long-term storage.Isolate TNA using KingFisher ™ Flex Magnetic Particle Processor 96DWDuring the protease incubation, add processing reagents to the wells of MagMAX ™ Express-96 Plates asindicated in the following table.Table 8 TNA plates setupPosition on the instrument6Set up the TNA processing platesa.Ensure that the instrument is set up for processing with the deep well magnetic head and select theA31881_FLEX_large_vol_TNA program on the instrument.b.At the end of the protease incubation, add 200 µL of sample to each well in Plate 1.c.Add 20 µL of Nucleic Acid Binding Beads to each sample well in Plate 1.d.Start the run and load the prepared processing plates in their positions when prompted by theinstrument (see “Set up the TNA processing plates“ on page 5).e.At the end of the run, remove the Elution Plate from the instrument and seal immediately with a newMicroAmp ™ Clear Adhesive Film.IMPORTANT! Do not allow the purified samples to sit uncovered at room temperature for more than 10minutes, to prevent evaporation and contamination.The purified TNA is ready for immediate use. Store at –20°C or –80°C for long-term storage. .7Bind, wash, rebind, and elute the TNALimited product warrantyLife Technologies Corporation and/or its affiliate(s) warrant their products as set forth in the Life Technologies' General Terms and Conditions of Sale found on Life Technologies' website at /us/en/home/global/terms-and-conditions.html . If you have any questions,please contact Life Technologies at /support .Manufacturer: Life Technologies Corporation | 2130 Woodward Street | Austin, TX 78744The information in this guide is subject to change without notice.DISCLAIMER : TO THE EXTENT ALLOWED BY LAW, LIFE TECHNOLOGIES AND/OR ITS AFFILIATE(S) WILL NOT BE LIABLE FOR SPECIAL, INCIDENTAL, INDIRECT, PUNITIVE,MULTIPLE, OR CONSEQUENTIAL DAMAGES IN CONNECTION WITH OR ARISING FROM THIS DOCUMENT, INCLUDING YOUR USE OF IT.Important Licensing Information : This product may be covered by one or more Limited Use Label Licenses. By use of this product, you accept the terms and conditions of all applicable Limited Use Label Licenses.©2018 Thermo Fisher Scientific Inc. All rights reserved. All trademarks are the property of Thermo Fisher Scientific and its subsidiaries unless otherwise specified./support | /askaquestion 。
碧云天生物技术/Beyotime Biotechnology订货热线：400-168-3301或800-8283301订货e-mail：******************技术咨询：*****************网址：碧云天网站微信公众号生物素标记EMSA探针－β-Catenin/TCF (0.2μM)产品编号产品名称包装GS018B 生物素标记EMSA探针－β-Catenin/TCF (0.2µM) 200µl产品简介：生物素标记EMSA探针－β-Catenin/TCF是用于EMSA(也称gel shift)研究的生物素(Biotin)标记的β-Catenin/TCF consensus oligonucleotide。
这个生物素标记的双链寡核苷酸含有公认的β-Catenin/TCF结合位点，可以用作EMSA研究时的探针。
β-Catenin/TCF consensus oligo的序列如下：5'-CCC TTT GAT CTT ACC-3'3'-GGG AAA CTA GAA TGG-5'本生物素标记EMSA探针已经过纯化，可以直接用于EMSA结合反应。
本生物素标记EMSA探针可以和碧云天的化学发光法EMSA试剂盒(GS009)配套使用。
一个包装的生物素标记探针可以进行约200-400个样品的EMSA检测。
包装清单：产品编号产品名称包装GS018B 生物素标记EMSA探针－β-Catenin/TCF (0.2µM) 200µl—说明书1份保存条件：-20ºC保存，一年有效。
注意事项：避免加热到40ºC以上，温度过高会导致双链DNA探针解聚成单链。
而单链无法用于EMSA研究。
对于基于生物素标记的EMSA检测的详细操作可以参考碧云天的化学发光法EMSA试剂盒(GS009)的使用说明。
本产品仅限于专业人员的科学研究用，不得用于临床诊断或治疗，不得用于食品或药品，不得存放于普通住宅内。
生物信息学英文介绍Introduction to Bioinformatics.Bioinformatics is an interdisciplinary field that combines biology, computer science, mathematics, statistics, and other disciplines to analyze and interpret biological data. At its core, bioinformatics leverages computational tools and algorithms to process, manage, and minebiological information, enabling a deeper understanding of the molecular basis of life and its diverse phenomena.The field of bioinformatics has exploded in recent years, driven by the exponential growth of biological data generated by high-throughput sequencing technologies, proteomics, genomics, and other omics approaches. This data deluge has presented both challenges and opportunities for researchers. On one hand, the sheer volume and complexityof the data require sophisticated computational methods for analysis. On the other hand, the wealth of information contained within these data holds the promise oftransformative insights into the functions, interactions, and evolution of biological systems.The core tasks of bioinformatics encompass genome annotation, sequence alignment and comparison, gene expression analysis, protein structure prediction and function annotation, and the integration of multi-omic data. These tasks require a range of computational tools and algorithms, often developed by bioinformatics experts in collaboration with biologists and other researchers.Genome annotation, for example, involves the identification of genes and other genetic elements within a genome and the prediction of their functions. This process involves the use of bioinformatics algorithms to identify protein-coding genes, non-coding RNAs, and regulatory elements based on sequence patterns and other features. The resulting annotations provide a foundation forunderstanding the genetic basis of traits and diseases.Sequence alignment and comparison are crucial for understanding the evolutionary relationships betweenspecies and for identifying conserved regions within genomes. Bioinformatics algorithms, such as BLAST and multiple sequence alignment tools, are widely used for these purposes. These algorithms enable researchers to compare sequences quickly and accurately, revealing patterns of conservation and divergence that inform our understanding of biological diversity and function.Gene expression analysis is another key area of bioinformatics. It involves the quantification of thelevels of mRNAs, proteins, and other molecules within cells and tissues, and the interpretation of these data to understand the regulation of gene expression and its impact on cellular phenotypes. Bioinformatics tools and algorithms are essential for processing and analyzing the vast amounts of data generated by high-throughput sequencing and other experimental techniques.Protein structure prediction and function annotation are also important areas of bioinformatics. The structure of a protein determines its function, and bioinformatics methods can help predict the three-dimensional structure ofa protein based on its amino acid sequence. These predictions can then be used to infer the protein'sfunction and to understand how it interacts with other molecules within the cell.The integration of multi-omic data is a rapidly emerging area of bioinformatics. It involves theintegration and analysis of data from different omics platforms, such as genomics, transcriptomics, proteomics, and metabolomics. This approach enables researchers to understand the interconnectedness of different biological processes and to identify complex relationships between genes, proteins, and metabolites.In addition to these core tasks, bioinformatics also plays a crucial role in translational research and personalized medicine. It enables the identification of disease-associated genes and the development of targeted therapeutics. By analyzing genetic and other biological data from patients, bioinformatics can help predict disease outcomes and guide treatment decisions.The future of bioinformatics is bright. With the continued development of high-throughput sequencing technologies and other omics approaches, the amount of biological data available for analysis will continue to grow. This will drive the need for more sophisticated computational methods and algorithms to process and interpret these data. At the same time, the integration of bioinformatics with other disciplines, such as artificial intelligence and machine learning, will open up new possibilities for understanding the complex systems that underlie life.In conclusion, bioinformatics is an essential field for understanding the molecular basis of life and its diverse phenomena. It leverages computational tools and algorithms to process, manage, and mine biological information, enabling a deeper understanding of the functions, interactions, and evolution of biological systems. As the amount of biological data continues to grow, the role of bioinformatics in research and medicine will become increasingly important.。
Hamster™ IVSecuGen Hamsters are the industry’s most accurate, rugged, and affordable USB fingerprint readers. T hese h igh-performance, v ersatile r eaders f eature a dvanced o ptical s ensors e ngineered with patented SEIR™ technology to give you the highest quality in fingerprint biometrics.Features• Accurate, patented optical USB fingerprint sensor with 508 DPI resolution• Auto-On TM to automatically detect the presence of a finger when placed on the reader• Smart Capture TM for reliable scanning of dry, moist, aged, scarred and difficult-to-scan fingers • Hardened optical glass platen that is scratch, impact, corrosion and electrostatic shock resistant • Compact and ergonomic design works with any finger or thumb • Integrated finger guide and removable, weighted stand • Supports BioAPI, INCITS 378-2004 and ISO 19794• Supports SecuGen proprietary, 3rd party and MINEX Certified Template Extractor and Matcher algorithms • Supports FIPS 201, FBI Certified as “PIV Single Finger Capture Device” and STQC Certified for UIDBenefits of Using SecuGen ReadersReduced password reset requests, decreased risk of security breaches, and improved accountability, all in a convenient and intuitive method for almost any user. Easy to install and use with software applications requiring fingerprint authentication that lets your fingerprints act like digital passwords that cannot be lost, forgotten or stolen.Consistent performance and security for a growing number of applications, with support for a wide range of operating systems, computing environments, and U.S. and international biometric standards.Backed by the industry’s best quality guarantee and over a decade of field use and proven performance under extreme conditions. This helps avoid the cost and inconvenience of replacing damaged units made by other sensor manufacturers.STQC CertifiedFBI CertifiedSpecificationsWhat’s Inside a Hamster?At the heart of the Hamster readers are SecuGen’s next-generation fingerprint sensors with advanced features including:• Auto-On™ that automatically detects the presence of a finger when placed on the reader for quick and easy authentication 1• Smart Capture ™ that captures high qualityfingerprints from dry, moist, aged, scarred and difficult-to-scan fingers for greater accuracy and reliability 1• High durability and ruggedness with provenresistance to electrostatic shock, impact, drops, scratches, extreme temperatures, humidity, and contaminants such as sweat, dirt, and oil• Patented optical technologies for clear, accurate image quality with high contrast, high signal-to-noise ratio, and practically no distortion using Surface Enhanced Irregular Reflection (SEIR™)• Reliable performance even under challenging conditions and tough environments• Rejection of false fingerprints such as latent prints and 2-D fingerprint images such as photocopies 2• Fingerprint data protection with templates that cannot be used to reconstruct fingerprint images 2• Greater sensor-to-sensor consistency for high accuracy when matching fingerprints enrolled with different fingerprint readers at different locations • Ease of integration into many types of applications using a wide offering of free Software Developer Kits • Multi-Device Connectivity™ that allows multiple fingerprint readers to be connected to one computer at the same time 2• Device Recognition™ that uses a programmable internal serial number to identify and differentiate multiple fingerprint readers 31 Auto-On and Smart Capture are built into the sensor but need to be enabled by the software application that calls for a fingerprint scan.2 Feature dependent upon software application used 3Option available with SecuGen SDKsProduct Name Model NumberFingerprint Sensor Optic Module Image Resolution Image Size Platen SizeEffective Sensing Area Image GrayscaleLight Source / Typical Lifetime Fingerprint Capture Speed Electrical / Communication InterfaceSupply VoltageCurrent Consumption (max)PhysicalDimensions (W x L x H) WeightCable LengthOperating Temperature Operating Humidity Other WarrantySupported Standards ComplianceSupported Operating SystemsHamster IV HSDU04P , HFDU04SDU04P , FDU04508 DPI258 x 336 pixels 16.1 mm x 18.2 mm 12.9 mm x 16.8 mm 256 levels (8-bit)LED / 60,000 hours0.2 ~ 0.8 sec with Smart Capture (HSDU04P)0.4 ~ 1.2 sec with Smart Capture (HFDU04)USB 1.1 Full-Speed, USB 2.0 Hi-Speed 5 V DC (via USB)150 mA27 x 40 x 73 mm (without stand)53 x 73 x 84 mm (with stand)100 g (without stand)165 g (with stand)1500 mm -20o ~ 65o C90% or less RH, noncondensing One year limitedANSI INCITS 378, BioAPI, FIPS 201, ISO/IEC 19794-2, ISO/IEC 19794-4FCC, CE, RoHS, FBI PIV Single Finger, STQC Windows 7, Vista, XP , 2000, 9x Windows Server 2008 R2, 2003Android (HSDU04P), Java, Linux© 2013 SecuGen Corp. All rights reserved. SecuGen, SecuGen Hamster, Auto-On, Device Recognition, HSDU04P , Multi-Device Connectivity, Open the World with Your Fingertip!, SDU04P , SEIR and Smart Capture are trademarks or registered trademarks of SecuGen Corporation in the United States and/or other countries. Other company, product, and service names may be trademarks or service marks of others. Product specifications and availability are subject to change without notice. (03/13)Open the World with Your Fingertip!®。
附录：序列分析软件目录*此目录包括了一些通用序列分析软件，部分专业性较强的软件及网站可参阅书中相应章节(一)商品化的序列分析软件名称 用途 出处Ball&Stick 有关Macintosh微机的分子图像显示，打印及其他操作;通过可演示相关信息 Cherwell Scientific Publishing 27 Park End Street Oxford，OX1 1HU，UKChemDraw Chem 3D 二维和三维化学结构的排版，对于杂志上的制图及分子结构的图示特别有用Cambridge ScientificComputing 875 MassachusettsAve.，Suite 61 Cambridge，MA02139DNA Strider 一套简单有用的Macintosh序列分析程序，可进行限制酶作图，或根据DNA序列作环状质粒图 Christian MarckService de Biochimie—Bat 142 Center d'Etudes Nucleaires de Saclay 91191 Gif-sur-Yvette Cedex FranceEUGENE&SAM 用于Sun Microsystems系统SPARC工作站的多用途核酸蛋白质分析软件包，包括用于序列检索的FASTA，还可对DNA和蛋白质数据库快速进行关键词检索 Lark Sequeneing Technologies 9545 Katy Freeway，Suite 200 Houston，TX 77024GCG 包括核酸蛋白质序列分析、测序计划管理、数据库检索、RNA折叠、蛋白质二级结构预测、序列基序的推出及检索等程序的软件包，用于VMS及UNIX多用户系统 Genetics Computer Group University Research Park 575 Science Drive，Suite B Madison，WI 53711Gene Construction Kit DNA Inspector 与大多数分析软件不同，它管理产显示克隆策略中的分子构建过程，包括分子构建，电泳条带。
高三英语植物遗传修饰单选题50题1. The process of plant genetic modification often involves ____ genes from one organism to another.A. transferringB. transformingC. transmittingD. transplanting答案：A。
解析：本题考查与植物遗传修饰相关的动词辨析。
A选项transferring有转移、传递（ 尤指将某物从一个地方、人或事物转移到另一个地方、人或事物）的意思，在植物遗传修饰中，经常涉及将基因从一个生物体转移到另一个生物体，符合概念。
B选项transforming主要表示改变、转变，强调的是形态、性质等方面的彻底改变，而不是基因的转移这个概念。
C选项transmitting侧重于传播、传送（ 信号、信息等），不太适用于基因的操作。
D选项transplanting 主要指移植 器官、植物等），通常是比较宏观的物体，与基因的操作不符。
2. In plant genetic modification, a ____ is a small circular piece of DNA that can be used to carry new genes into a plant cell.A. plasmidB. plastidC. plasmodiumD. plasma答案：A。
解析：A选项plasmid（质粒）在植物遗传修饰中是一种小的环状DNA，可以用来携带新基因进入植物细胞，这是植物遗传修饰中重要的工具。
B选项plastid（质体）是植物细胞中的一种细胞器，与携带基因进入细胞的概念不同。
C选项plasmodium（ 疟原虫）与植物遗传修饰毫无关系。
D选项plasma（血浆、等离子体）也与植物遗传修饰概念不相关。
3. Which of the following is a common method for plant genetic modification?A. Cross - breedingB. Mutation breedingC. Gene editingD. All of the above答案：D。
TECHNOLOGY非编码RNA组科学数据库：NONCODE任菲1,2 何顺民1 刘长宁1 赵屹11. 中国科学院计算技术研究所前瞻实验室，北京 1001902. 中南大学，湖南 410083NONCODE科学数据库是中国科学院计算技术研究所生物信息学研究组和中国科学院生物物理研究所生物信息学实验室共同开发和维护的一个提供给科学研究人员分析非编码RNA基因的综合数据平台。
自从其2005年发布以来，非编码RNA基因的数量飞速增长[1-3]，而且人们也逐步认识到非编码RNA基因在大多数物种中都发挥着重要的调控作用[4]。
《Science》杂志在2005年1月的期刊中曾给予NONCODE数据库较高的评价和推荐。
2006年，ISI Web of Knowledge 邀请收录NONCODE科学数据库；2007年，中国国家医药卫生科学数据共享平台收录了NONCODE科学数据库。
目前在NONCODE 2.0数据库中，非编码RNA基因的数量大约为20多万条目，其中包括了microRNA，Piwi-interacting RNA和mRNA-like ncRNA等。
同时，在NONCODE中的非编码RNA基因数据分析平台中，还为研究人员提供了BLAST序列比对服务，非编码RNA基因在基因组中定位以及它们的上下游相关注释信息的浏览服务。
研究人员可以通过/ 或者 / 网站来访问该数据平台。
非编码RNA；科学数据库；RNA组学摘 要：关键词：本页已使用福昕阅读器进行编辑。
福昕软件（Ｃ）２００５－２００７，版权所有，仅供试用。
TECHNOLOGYN o n -c o d i n g R N A S c i e n t i f i c D a t a b a s e :NONCODERen Fei 1, He Shunmin 1,2, Liu Changning 1, Zhao Yi 11. Center for Advanced Computing Research, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190,China2. Central South University, Changsha, Hunan Province 410083, ChinaThe NONCODE database is an integrated knowledge database designed for theanalysis of noncoding RNAs (ncRNAs). Since NONCODE was firstly released in 2005, the number of known ncRNAs has grown rapidly, and there is a growing acknowledgement that ncRNAs play important regulatory roles in most organisms. In the NONCODE database, the number of collected ncRNAs has reached 206226, including a wide range of microRNAs, Piwi-interacting RNAs and mRNA-like ncRNAs. The improvements of the database include not only new and updated ncRNA data sets, but also an incorporation of BLAST alignment search service and access through our custom UCSC Genome Browser. NONCODE can be accessed through or .Non-coding RNA; Scientific database; Rnomics本页已使用福昕阅读器进行编辑。
第一类：基于引物设计功能的软件。
此类软件主要是针对重亚硫酸盐序列进行甲基化特异性PCR（methylation-specific PCR, MS-PCR or MSP）和重亚硫酸盐测序（bisulfite sequencing, BS）引物的设计。
由于重亚酸盐修饰的特殊性，使常规的分子生物学软件，如Primer Premier、Oligo、DNASis、DNA Max、Vecter NTI Suit等均无法适用于MSP和BS引物的设计。
国外的研究者根据MSP和BS的特殊性，分别提供了多种免费或付费的专业软件，其中，某些软件，如MethPrimer已被国内外研究者广泛使用。
软件：MethPrimer主页：/methprimer简介：设计甲基化特异性PCR（methylation-specific PCR, MS-PCR or MSP）和重亚硫酸盐测序（bisulfite sequencing）引物的最早和最经典的软件，也是一款免费软件，它广受表观遗传学研究者的喜爱，也是DNA甲基化研究者使用最频繁的一款表观遗传学DNA甲基化研究工具软件。
就本人及同事的使用经验而言，该软件所设计的引物扩增效率和特异性均非常好，不过，在实际应用中，如果能配合Primer Premier软件对引物的Tm值进行适当的调整或修改，可进一步提高PCR的扩增效率。
此外，此款软件在进行引物设计的过程中，会自动对待分析序列的CpG岛进行分析，它所得到的结果与其它几款CpG岛专业分析软件（见下）一致。
当然，如果用户只想对CpG岛的分析和密度进行分析时，可选择其它CpG 岛专业分析软件。
文献：Li LC, Dahiya R. MethPrimer: designing primers for methylation PCRs. Bioinformatics. 2002 Nov;18(11):1427-31.评级：★★★★★软件：BiSearch主页：http://bisearch.enzim.hu/简介：与MethPrimer类似的专用于重亚硫酸盐修饰序列相关PCR或测序引物设计的。
常见的大数据术语表_光环大数据数据分析培训大数据的出现带来了许多新的术语，但这些术语往往比较难以理解。
因此，我们通过本文给出一个常用的大数据术语表，抛砖引玉，供大家深入了解。
其中部分定义参考了相应的博客文章。
当然，这份术语表并没有100%包含所有的术语，如果你认为有任何遗漏之处，请告之我们。
A聚合(Aggregation)–搜索、合并、显示数据的过程算法(Algorithms)–可以完成某种数据分析的数学公式分析法(Analytics)–用于发现数据的内在涵义异常检测(Anomalydetection)–在数据集中搜索与预期模式或行为不匹配的数据项。
除了“Anomalies”,用来表示异常的词有以下几种：outliers,exceptions,surprises,contaminants.他们通常可提供关键的可执行信息匿名化(Anonymization)–使数据匿名，即移除所有与个人隐私相关的数据应用(Application)–实现某种特定功能的计算机软件人工智能(ArtificialIntelligence)–研发智能机器和智能软件，这些智能设备能够感知周遭的环境，并根据要求作出相应的反应，甚至能自我学习B行为分析法(BehaviouralAnalytics)–这种分析法是根据用户的行为如“怎么做”，“为什么这么做”，以及“做了什么”来得出结论，而不是仅仅针对人物和时间的一门分析学科，它着眼于数据中的人性化模式大数据科学家(BigDataScientist)–能够设计大数据算法使得大数据变得有用的人大数据创业公司(Bigdatastartup)–指研发最新大数据技术的新兴公司生物测定术(Biometrics)–根据个人的特征进行身份识别B字节(BB:Brontobytes)–约等于1000YB(Yottabytes)，相当于未来数字化宇宙的大小。
1B字节包含了27个0！商业智能(BusinessIntelligence)–是一系列理论、方法学和过程，使得数据更容易被理解C分类分析(Classificationanalysis)–从数据中获得重要的相关性信息的系统化过程;这类数据也被称为元数据(metadata),是描述数据的数据云计算(Cloudcomputing)–构建在网络上的分布式计算系统，数据是存储于机房外的（即云端）聚类分析(Clusteringanalysis)–它是将相似的对象聚合在一起，每类相似的对象组合成一个聚类(也叫作簇)的过程。
Biometrics:A Tool for Information Security Anil K.Jain,Fellow,IEEE,Arun Ross,Member,IEEE,and Sharath Pankanti,Senior Member,IEEEAbstract—Establishing identity is becoming critical in our vastly interconnected society.Questions such as“Is she really who she claims to be?,”“Is this person authorized to use this facility?,”or “Is he in the watchlist posted by the government?”are routinely being posed in a variety of scenarios ranging from issuing a driver’s license to gaining entry into a country.The need for reliable user authentication techniques has increased in the wake of heightened concerns about security and rapid advancements in networking, communication,and mobility.Biometrics,described as the science of recognizing an individual based on his or her physical or behav-ioral traits,is beginning to gain acceptance as a legitimate method for determining an individual’s identity.Biometric systems have now been deployed in various commercial,civilian,and forensic applications as a means of establishing identity.In this paper,we provide an overview of biometrics and discuss some of the salient research issues that need to be addressed for making biometric technology an effective tool for providing information security.The primary contribution of this overview includes:1)examining ap-plications where biometrics can solve issues pertaining to informa-tion security;2)enumerating the fundamental challenges encoun-tered by biometric systems in real-world applications;and3)dis-cussing solutions to address the problems of scalability and security in large-scale authentication systems.Index Terms—Biometrics,cryptosystems,digital rights manage-ment,grand challenge,information security,multibiometrics.I.I NTRODUCTIONT HE PROBLEM of information security entails the pro-tection of information elements(e.g.,multimedia data) thereby ensuring that only authorized users are able to access the contents available in digital media.Content owners,such as authors and authorized distributors,are losing billions of dol-lars annually in revenue due to the illegal copying and sharing of digital media.In order to address this growing problem, digital rights management(DRM)systems are being deployed to regulate the duplication and dissemination of digital content [68].The critical component of a DRM system is user authenti-cation which determines whether a certain individual is indeed authorized to access the content available in a particular digital medium.In a generic cryptographic system,the user authen-tication method is possession based.That is,the possession of the decrypting key is sufficient to establish the authenticityManuscript received May1,2005;revised March2,2006.The associate ed-itor coordinating the review of this manuscript and approving it for publication was Prof.Pierre Moulin.A.K.Jain is with the Department of Computer Science and Engineering, Michigan State University,East Lansing,MI48824USA(e-mail:jain@cse. ).A.Ross is with the Lane Department of Computer Science and Electrical Engineering,West Virginia University,Morgantown,WV26506USA(e-mail: arun.ross@).S.Pankanti is with the Exploratory Computer Vision Group,IBM T. J.Watson Research Center,Yorktown Heights,NY10598USA(e-mail: sharat@).Digital Object Identifier10.1109/TIFS.2006.873653of the user.Since cryptographic keys are long and random(e.g.,128bits for the advanced encryption standard(AES)[1],[2]),they are difficult to memorize.As a result,these keys are stored somewhere(for example,on a computer or a smart card)and released based on some alternative authentication mechanism(e.g.,password).Most passwords are so simple, that they can be easily guessed(especially based on social engineering methods)or broken by simple dictionary attacks [3].It is not surprising that the most commonly used password is the word“password.”Thus,multimedia data protected by a cryptographic algorithm are only as secure as the password (weakest link)used to release the correct decrypting key(s) that can be used for establishing user authenticity.Simple passwords are easy to guess and,thus,compromise security; complex passwords are difficult to remember and,thus,are expensive to maintain.1Some users tend to“store”complex passwords at easily accessible locations.Furthermore,most people use the same password across different applications;an impostor upon determining a single password can now access multiple applications.Finally,in a multiuser account scenario, passwords are unable to provide nonrepudiation(i.e.,when a password is divulged to a friend,it is impossible to determine who the actual user is:this may eliminate the feasibility of countermeasures such as holding conniving legitimate users accountable in a court of law).Many of these limitations associated with the use of pass-words can be ameliorated by the incorporation of better methods for user authentication.Biometric authentication or, simply biometrics[5],[6],[69],refers to establishing identity based on the physical and behavioral characteristics(also known as traits or identifiers)of an individual such as face,fingerprint,hand geometry,iris,keystroke,signature,voice, etc.Biometric systems offer several advantages over traditional authentication schemes.They are inherently more reliable than password-based authentication as biometric traits cannot be lost or forgotten(passwords can be lost or forgotten);biometric traits are difficult to copy,share,and distribute(passwords can be announced in hacker websites);and they require the person being authenticated to be present at the time and point of au-thentication(conniving users can deny that they have shared the password).It is difficult to forge biometrics(it requires more time,money,experience,access privileges)and it is unlikely for a user to repudiate having accessed the digital content using biometrics.Thus,a biometrics-based authentication scheme is a powerful alternative to traditional authentication schemes. In some instances,biometrics can be used in conjunction with passwords(or tokens)to enhance the security offered by the authentication system.In the context of a DRM system, 1For example,anywhere between25%and50%of helpdesk calls relate to password resets;these calls cost as much as U.S.$30per end user,with the helpdesk receiving at leastfive calls per end user every year[4].1556-6013/$20.00©2006IEEEFig.1.Examples of biometric characteristics:(a)face,(b)fingerprint,(c)hand geometry,(d)iris,(e)keystroke,(f)signature,and(g)voice.biometrics can be used1)to facilitate the entire authentication mechanism,or2)secure the cryptographic keys that protect a specific multimediafile.A number of biometric characteristics have been in use for different applications[79].Each biometric trait has its strengths and weaknesses,and the choice depends on the application.No single biometric is expected to effectively meet all of the re-quirements(e.g.,accuracy,practicality,and cost)of all applica-tions(e.g.,DRM,access control,and welfare distribution).In other words,no biometric is“optimal”although a number of them are“admissible.”The suitability of a specific biometric for a particular application is determined depending upon the requirements of the application and the properties of the bio-metric characteristic.It must be noted that traits,such as voice and keystroke,lend themselves more easily to a challenge-re-sponse mechanism that may be necessary in some applications (e.g.,telebanking).A brief description of the commonly used biometrics is given below(Fig.1).1)Face:Face recognition is a nonintrusive method,and fa-cial images are probably the most common biometric character-istic used by humans to make personal recognition.The applica-tions of facial recognition range from a static,controlled“mug-shot”authentication to a dynamic,uncontrolled face identifica-tion in a cluttered background.The most popular approaches to face recognition[7]are based on either:1)the location and shape of facial attributes,such as the eyes,eyebrows,nose,lips, and chin and their spatial relationships or2)the overall(global) analysis of the face image that represents a face as a weighted combination of a number of canonical faces.While the authen-tication performance of the face recognition systems that are commercially available is reasonable[8],they impose a number of restrictions on how the facial images are obtained,often re-quiring afixed and simple background or special illumination. These systems also have difficulty in matching face images cap-tured from two drastically different views and under different illumination conditions(i.e.,varying temporal contexts).It is questionable whether the face itself,without any contextual in-formation,is a sufficient basis for recognizing a person from a large number of identities with an extremely high level of con-fidence.In order that a facial recognition system works well in practice,it should automatically:1)detect whether a face is present in the acquired image;2)locate the face if there is one;3)recognize the face from a general viewpoint(i.e.,from any pose).2)Fingerprint:Humans have usedfingerprints for per-sonal identification for many decades and the matching(i.e., identification)accuracy usingfingerprints has been shown to be very high[9].Afingerprint is the pattern of ridges and valleys on the surface of afingertip,the formation of which is determined during thefirst seven months of fetal development. Fingerprints of identical twins are different and so are the prints on eachfinger of the same person.Today,afingerprint scanner costs about U.S.$20when ordered in large quantities and the marginal cost of embedding afingerprint-based biometric in a system(e.g.,laptop computer)has become affordable in a large number of applications.The accuracy of the currently available fingerprint recognition systems is adequate for authentication systems involving a few hundred users.Multiplefingerprints of a person provide additional information to allow for large-scale identification involving millions of identities.One problem with the currentfingerprint recognition systems is that they require a large amount of computational resources,especially when operating in the identification mode.Finally,fingerprints of a small fraction of the population may be unsuitable for the automatic identification because of genetic factors,aging,envi-ronmental,or occupational reasons(e.g.,manual workers may have a large number of cuts and bruises on theirfingerprints that keep changing).3)Hand Geometry:Hand geometry recognition systems are based on a number of measurements taken from the human hand,including its shape,size of palm,and lengths and widths of thefingers[10].Commercial hand geometry-based authentica-tion systems have been installed in hundreds of locations around the world.The technique is very simple,relatively easy to use, and inexpensive.Environmental factors,such as dry weather or individual anomalies such as dry skin,do not appear to have any negative effects on the authentication accuracy of hand geom-etry-based systems.The geometry of the hand is not known toJAIN et al.:BIOMETRICS:A TOOL FOR INFORMATION SECURITY127be very distinctive and hand geometry-based recognition sys-tems cannot be scaled up for systems requiring identification of an individual from a large population.Further,hand geom-etry information may not be invariant during the growth period of children.In addition,an individual’s jewelry(e.g.,rings)or limitations in dexterity(e.g.,from arthritis),may pose further challenges in extracting the correct hand geometry information. The physical size of a hand geometry-based system is large,and it cannot be embedded in certain devices such as laptops.There are authentication systems available that are based on measure-ments of only a fewfingers(typically,index and middle)instead of the entire hand.These devices are smaller than those used for hand geometry,but are still much larger than those used in some other biometrics(e.g.,fingerprint,face,and voice).4)Iris:The iris is the annular region of the eye bounded by the pupil and the sclera(white of the eye)on either side. The visual texture of the iris is formed during fetal development and stabilizes during thefirst two years of life.The complex iris texture carries very distinctive information useful for personal recognition[11],[71],[72].The accuracy and speed of currently deployed iris-based recognition systems is promising and points to the feasibility of large-scale identification systems based on iris information.Each iris is believed to be distinctive and,like fingerprints,even the irises of identical twins are expected to be different.It is extremely difficult to surgically tamper the tex-ture of the iris.Further,the ability to detect artificial irises(e.g., designer contact lenses)has been demonstrated in the literature. Although the early iris-based recognition systems required con-siderable user participation and were expensive,the newer sys-tems have become more user friendly and cost-effective.While iris systems have a very low false accept rate(FAR)compared to other biometric traits,the false reject rate(FRR)of these sys-tems can be high[75].5)Keystroke:It is hypothesized that each person types on a keyboard in a characteristic way.This behavioral biometric is not expected to be unique to each individual but it is expected to offer sufficient discriminatory information that permits identity verification[12].Keystroke dynamics is a behavioral biometric; for some individuals,one may expect to observe large variations in typical typing patterns.Further,the keystrokes of a person using a system could be monitored unobtrusively as that person is keying in information.However,this biometric permits“con-tinuous verification”of an individual over a period of time. 6)Signature:The way a person signs his or her name is known to be a characteristic of that individual[13].Although signatures require contact with the writing instrument and an effort on the part of the user,they have been accepted in govern-ment,legal,and commercial transactions as a method of authen-tication.Signatures are a behavioral biometric that change over a period of time and are influenced by physical and emotional conditions of the signatories.Signatures of some people vary substantially:even successive impressions of their signature are significantly different.Further,professional forgers may be able to reproduce signatures that fool the system.7)Voice:V oice is a combination of physical and behavioral biometrics.The features of an individual’s voice are based on the shape and size of the appendages(e.g.,vocal tracts,mouth, nasal cavities,and lips)that are used in the synthesis of theTABLE IE XAMPLES OF C OMMONLY U SED R EPRESENTATION AND M ATCHING S CHEMES FOR F IVE D IFFERENT B IOMETRIC T RAITS.A DV ANCEMENTS IN S TATISTICAL P ATTERN R ECOGNITION,S IGNAL P ROCESSING,AND C OMPUTER V ISIONH A VE R ESULTED IN O THER S OPHISTICATED S CHEMES NOTI NDICATED HERETABLE IIC OMPARISON OF V ARIOUS B IOMETRIC T ECHNOLOGIES B ASED ON THEP ERCEPTION OF THE A UTHORS.H IGH,M EDIUM,AND L OW A RE D ENOTED BY H,M,AND L,R ESPECTIVELY.U NIVERSALITY(D O A LL P EOPLE H A VE IT?),D ISTINCTIVENESS(C AN P EOPLE B E D ISTINGUISHED B ASED ON ANI DENTIFIER?),P ERMANENCE(H OW P ERMANENT A RE THE I DENTIFIERS?), AND C OLLECTABLE(H OW W ELL C AN THE I DENTIFIERS B E C APTURED AND Q UANTIFIED?)A RE P ROPERTIES OF B IOMETRIC IDENTIFIERS.P ERFORMANCE (M ATCHING S PEED AND A CCURACY),A CCEPTABILITY(W ILLINGNESS OFP EOPLE TO A CCEPT),AND C IRCUMVENTION(F OOLPROOF)A RE A TTRIBUTESOF B IOMETRIC S YSTEMS[18]sound[70].These physical characteristics of human speech are invariant for an individual,but the behavioral part of the speech of a person changes over time due to age,medical conditions (such as common cold),emotional state,etc.V oice is also not very distinctive and may not be appropriate for large-scale iden-tification.A text-dependent voice recognition system is based on the utterance of afixed predetermined phrase.A text-inde-pendent voice recognition system recognizes the speaker inde-pendent of what he or she speaks.A text-independent system is more difficult to design than a text-dependent system but offers more protection against fraud.A disadvantage of voice-based recognition is that speech features are sensitive to a number of factors such as background noise.Speaker recognition is most appropriate in phone-based applications but the voice signal over phone is typically degraded in quality by the communi-cation channel.Table I lists some of the commonly used representation and matching schemes for a few biometric traits.Table II compares various biometric traits based on seven different factors.II.B IOMETRIC V ARIANCEPassword-based authentication systems do not involve any complex pattern recognition techniques(passwords have to match exactly)and,hence,they almost always perform ac-curately as intended by their system designers.On the other128IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,VOL.1,NO.2,JUNE2006Fig.2.Variations in a biometric signal:(a)inconsistent presentation:change in facial pose with respect to the camera[76];(b)irreproducible presentation: temporary change infingerprint due to the wear and tear of ridges.hand,biometric signals and their representations(e.g.,facial image and eigen-coefficients of facial image)of a person vary dramatically depending on the acquisition method,acquisition environment,user’s interaction with the acquisition device,and (in some cases)variation in the traits due to various patho-phys-iological phenomena.Below,we present some of the common reasons for biometric signal/representation variations.1)Inconsistent Presentation:The signal captured by the sensor from a biometric identifier depends upon both the intrinsic biometric identifier characteristic as well as the way the biometric identifier was presented.Thus,an acquired bio-metric signal is a nondeterministic composition of a physical biometric trait,the user characteristic behavior,and the user interaction facilitated by the acquisition interface.For example, the three-dimensional(3-D)shape of thefinger gets mapped onto the two-dimensional(2-D)surface of the sensor surface. As thefinger is not a rigid object and since the process of projecting thefinger surface onto the sensor surface is not precisely controlled,different impressions of afinger are re-lated to each other by various transformations.Further,each impression of afinger may possibly depict a different portion of its surface.In case of face acquisition,different acquisitions may represent different poses of the face[Fig.2(a)].Hand geometry measurements may be based on different projections of hand on a planar surface.Different iris/retina acquisitions may correspond to different nonfrontal projections of iris/retina on to the image planes.2)Irreproducible Presentation:Unlike the synthetic iden-tifiers[e.g.,radio-frequency identification(RFID)],biometric identifiers represent measurements of a biological trait or be-havior.These identifiers are prone to wear-and-tear,accidental injuries,malfunctions,and pathophysiological development. Manual work,accidents,etc.,inflict injuries to thefinger, thereby changing the ridge structure of thefinger either perma-nently or semipermanently[Fig.2(b)].Wearing different kinds of jewelry(e.g.,rings)may affect hand geometry measurements in an irreproducible way.Facial hair growth(e.g.,sideburns and mustache),accidents(e.g.,broken nose),attachments Fig.3.Imperfect acquisition:three different impressions of a subject’sfinger exhibiting poor quality ridges possibly due to extremefinger dryness.(e.g.,eyeglasses and jewelry),makeup,swellings,cyst growth, and different hairstyles may all correspond to irreproducible face depictions.Retinal measurements can change in some pathological developments(e.g.,diabetic retinopathy).Inebri-ation results in erratic signatures.The common cold changes a person’s voice.All of these phenomena contribute to dramatic variations in the biometric identifier signal captured at different acquisitions.3)Imperfect Signal/Representational Acquisition:The signal acquisition conditions in practical situations are not per-fect and cause extraneous variations in the acquired biometric signal.For example,nonuniform contact results in poor quality fingerprint acquisition.That is,the ridge structure of afinger would be completely captured only if ridges belonging to the part of thefinger being imaged are in complete physical/optical contact with the image acquisition surface and the valleys do not make any contact with the image acquisition surface. However,the dryness of the skin,shallow/worn-out ridges(due to aging/genetics),skin disease,sweat,dirt,and humidity in the air all confound the situation resulting in a nonideal contact situation(Fig.3).In the case of inkedfingerprints,inappro-priate inking of thefinger often results in“noisy”low contrast (poor quality)images,which lead to either spurious or missing fingerprint features(i.e.,minutiae).Different illuminations cause conspicuous differences in the facial appearance.Backlit illumination may render image acquisition virtually useless in many applications.Depending upon ergonomic conditions, the signature may vary significantly.The channel bandwidth characteristics affect the voice signal.Further,the feature extraction algorithm is imperfect and introduces measurement errors.Various image processing op-erations might introduce inconsistent biases to perturb feature localization.A particular biometric identifier of two different people can be very similar because of the inherent lack of distinctive information in it or because of the inadequate repre-sentation used for the identifier.As a result of these complex variations in the biometric signal/representations,determining whether two presentations of a biometric identifier are the same typically involves complex pattern recognition and decision making.III.O PERATION OF A B IOMETRIC S YSTEMA biometric system may be viewed as a signal detection system with a pattern recognition architecture that senses a raw biometric signal,processes this signal to extract a salient set of features,compares these features against the feature sets residing in the database,and either validates a claimed identityJAIN et al.:BIOMETRICS:A TOOL FOR INFORMATION SECURITY129Fig.4.Fingerprint minutiae:(a)The common fingerprint minutiae types:(b)ridge ending:(x ;y )are the minutia coordinates, is the angle that the minutia tangent forms with the horizontal axis:(c)ridge bifurcation.or determines the identity associated with the signal.Biometric systems attempt to elicit repeatable and distinctive human presentations,and consist (in theory,if not in actual practice)of user-friendly,intuitive interfaces for guiding the user in presenting the necessary traits.In the context of biometric systems,sensing consists of a biometric sensor (e.g.,fingerprint sensor or charge-coupled device (CCD)camera),which scans the biometric characteristic of an individual to produce a digital representation of the characteristic.A quality check is generally performed to ensure that the acquired sample can be reliably processed by successive stages.In order to facilitate matching,the input digital representation is usually further processed by a feature extractor to generate a compact but expressive representation called a feature set which can be stored as a template for future comparison.The feature extraction stage discards the unnecessary and extraneous information from the sensed measurements and gleans useful information necessary for matching.Let us consider the example of fingerprint matching to illus-trate how a biometric matcher operates.The most widely used local features are based on minute details (minutiae)of the fin-gerprint ridges [Fig.4(a)].The pattern of the minutiae of a fin-gerprint forms a valid representation of the fingerprint.This rep-resentation is compact and captures a signi ficant component of information in fingerprints;compared to other representations,minutiae extraction is relatively more robust to various sources of fingerprint degradation.Most types of minutiae in fingerprint images are not stable and cannot be reliably identi fied by auto-matic image processing methods.The most widely used features are based on:1)ridge ending;and 2)ridge bifurcation,whichare represented in terms oftriplets,where [x,y]repre-sents the spatial coordinates in a fixed image-centric coordinate systemand represents orientation of the ridge at that minutia [Fig.4(b)and (c)].Typically,in a live-scan fingerprint image of good quality,there are about 20–70minutiae.How are two biometric measurements matched?Typically,a biometric matcher undoes some of the intraclass variations in the biometric measurements to be matched by aligning them with respect to each other.Once the two representations are aligned,an assessment of their similarity is measured.The sim-ilarity between the two representations is typically quanti fied in terms of a matching score;the higher the matchingscore,Fig.5.Fingerprint matching.Here,matching consists of feature (minutiae)ex-traction followed by alignment and determination of corresponding minutiae (highlighted using filled circles).(a)Matching two impressions of the same fin-gers;(b)matching impressions from different fingers.the more similar are the representations.For example,given two (query and template)fingerprint feature representations,the matching module determines whether the prints are impres-sions of the same finger by a comparison of the query and tem-plate features.Only in highly constrained fingerprint systems can one assume that the query and template fingerprints depict the same portion of the finger and are aligned (in terms of dis-placement from the origin of the imaging coordinate system and of their orientations)with each other.So,in typical situ-ations,one needs to (either implicitly or explicitly)align (or register)the fingerprints (or their representations)before de-ciding whether the prints are mated pairs.After aligning the fin-gerprints,the number of matching (or corresponding)features is determined and a fingerprint similarity is de fined in terms of the number of corresponding minutiae.Fig.5illustrates the matching process.Even in the best practical situations,all minu-tiae in query and template prints are rarely matched due to spu-rious minutiae introduced by dirt/leftover smudges,variations in the area of finger being imaged,and displacement of the minutia owing to distortion of the print (Fig.6)from pressing the finger130IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,VOL.1,NO.2,JUNE2006Fig.6.Two(good quality)fingerprint impressions of the samefinger exhibiting nonlinear elastic deformation.Afingerprint matching algorithm that assumes a rigid transformation between the twofingerprint representations cannot success-fully match the minutiae points present in the two prints.whose surface is deformable against theflat surface of the ac-quisition device[55];the matcher uses a system parameter—the threshold value—to decide whether a given pair of prints be-longs to the samefinger(mated pair)or not.IV.F UNCTIONALITIES OF A B IOMETRIC S YSTEM Biometrics is not only a fascinating pattern recognition re-search problem but,if carefully used,could also be an enabling technology with the potential to make our society safer,reduce fraud,and lead to user convenience(user friendly man-machine interface)by broadly providing the following three functionali-ties.1)Verification(“Is this person truly John Doe?”):Bio-metrics can verify with high certainty the authenticityof a claimed enrollment based on the input biometricsample.For example,a person claims that he or she isknown as John Doe within the authentication systemand offers his or herfingerprint;the system then ei-ther accepts or rejects the claim based on a comparisonperformed between the offered pattern and the enrolledpattern associated with the claimed mer-cial applications,such as computer network logon,elec-tronic data security,ATMs,credit-card purchases,phys-ical access control,cellular phones,personal digital as-sistants(PDAs),medical records management,and dis-tance learning are sample authentication applications.Authentication applications are typically cost sensitivewith a strong incentive for being user friendly.2)Identification(“Is this person in the database?”):Given an input biometric sample,an identificationdetermines if the input biometric sample is associ-ated with any of a large number(e.g.,millions)ofenrolled identities.Typical identification applicationsinclude welfare-disbursement,national ID cards,bordercontrol,voter ID cards,driver’s license,criminal investi-gation,corpse identification,parenthood determination,missing children identification,etc.These identificationapplications require a large sustainable throughput withas little human supervision as possible.3)Screening(“Is this a wanted person?”):Screeningapplications determine whether a person belongs toa watchlist of identities.Examples of screening ap-plications could include airport security,security atpublic events,and other surveillance applications.Thescreening watchlist consists of a moderate(e.g.,a fewhundred)number of identities.By their very nature,thescreening applications:1)do not have a well-defined“user”enrollment phase;2)can expect only minimalcontrol over their subjects and imaging conditions;3)require large sustainable throughput with as little humansupervision as possible.Screening cannot be accom-plished without biometrics(e.g.,by using token-basedor knowledge-based identification).Biometric systems are being increasingly deployed incivilian applications that have several thousand enrolledusers.The Schiphol Privium scheme at the Amsterdamairport,for example,employs iris scan cards to speedup the passport and visa control procedures.2Passengersenrolled in this scheme insert their card at the gate andlook into a camera;the camera acquires the eye image ofthe traveler,processes it to locate the iris,and computesthe IrisCode[11];the computed IrisCode is comparedwith the data residing in the card to complete user ver-ification.A similar scheme is also being used to verifythe identity of Schiphol airport employees working inhigh-security areas.Thus,biometric systems can be usedto enhance user convenience while improving security.A.Matcher Accuracy and Template CapacityUnlike password or token-based system,a practical bio-metric system does not make perfect match decisions and can make two basic types of errors:1)False Match:the biometric system incorrectly declares a successful match between the input pattern and a nonmatching pattern in the database(in the case of identification/screening)or the pattern associated with an incorrectly claimed identity(in the case of verification).2)False Nonmatch:the biometric system incorrectly declares failure of match between the input pattern and a matching pattern in the database(identification/screening)or the pattern associated with the correctly claimed identity(verification). Besides the above two error rates,the failure to capture(FTC) rate and the failure to enroll(FTE)rate are also necessary to summarize the accuracy of a biometric system.The FTC rate is only applicable when the biometric device supports automatic capture functionality,and denotes the percentage of times the biometric device fails to capture a sample when the biometric characteristic is presented to it.This type of error typically occurs when the device is not able to locate a biometric signal of sufficient quality(e.g.,an extremely faintfingerprint or an occluded face).The FTE rate,on the other hand,denotes the percentage of times users are not able to enroll in the recognition system.There is a tradeoff between the FTE rate and the perceived system accuracy(FMR and FNMR).FTE errors typically occur when the system rejects poor quality inputs during enrollment.Consequently,the database contains only good quality templates and the perceived system accuracy improves.Because of the interdependence among the failure rates and error rates,all of these rates(i.e.,FTE,FTC,FNMR, and FMR)constitute important accuracy specifications of a 2“Schiphol backs eye scan security,”(CNN World News,March27,2002, Available at /2002/WORLD/europe/03/27/schiphol.secu-rity/).。
