The document provides an overview of unit 2.4 which introduces students to basic concepts in bioinformatics and databases. The objectives are to understand relational databases, major online biological databases, and how to extract data from databases. It also discusses challenges with large genomic data sets and how bioinformatics can help make sense of such data through databases, algorithms, and computational approaches.
The document discusses the history of database management and database models through 6 generations from 1900 to present. It describes the evolution from early manual record keeping systems to current big data technologies. Key database models discussed include hierarchical, network, relational, object-oriented, and dimensional models. The document also covers topics like data warehousing and data mining.
eScience: A Transformed Scientific MethodDuncan Hull
The document discusses the concept of eScience, which involves synthesizing information technology and science. It explains how science is becoming more data-driven and computational, requiring new tools to manage large amounts of data. It recommends that organizations foster the development of tools to help with data capture, analysis, publication, and access across various scientific disciplines.
The document provides information about a database course including:
1) An overview of the course content which covers database fundamentals, the relational model, normalization, conceptual modeling, query languages, and advanced SQL topics.
2) Details about the lecturer including their academic background and publications.
3) Assessment details for the course including exams, labs, and project work accounting for 100% of the grade.
The document provides an overview of information systems and databases as covered in the HSC course. It discusses different types of information systems and focuses on organizing, storing, and retrieving data with database systems. It describes skills needed to analyze database information systems and provides examples to practice these skills. Finally, it covers topics like database design, data storage and retrieval methods, and some social and ethical issues related to information systems.
History of database processing module 1 (2)chottu89
The document discusses the history and evolution of database management systems from the 1960s to present. It covers early stages like organizational databases in the 1960s, the introduction of the relational model in the 1970s, object-oriented databases in the 1980s, client-server applications in the 1990s, and internet-based databases in the 2000s. It also describes some common database components, models, and relationships.
This document provides an introduction and overview of an IS220 Database Systems course. It outlines that the course will cover topics like database design, file organization, indexing and hashing, query processing and optimization, transactions, object-oriented and XML databases. It notes that the class will be 70% theory and 30% hands-on assignments completed in pairs. Assessment will include group work, tests, and a final exam. Class rules require punctuality, use of English, dressing professionally, and minimum 80% attendance.
1. The document introduces databases and their history, from early data storage and retrieval to modern database management systems.
2. It discusses Edgar Codd's invention of the relational database model in 1970 which changed the field by separating data from application code for easier modification and generalization.
3. The document outlines what a database management system does, including managing large amounts of data, supporting efficient and concurrent access, and providing security.
This document provides information about a database management systems (DBMS) course offered by the Department of Computer Science & Engineering at Cambridge University. The course objectives are to provide a strong foundation in database concepts, practice SQL programming, demonstrate transactions and concurrency, and design database applications. Course outcomes include identifying and defining database objects, using SQL, designing simple databases, and developing applications. The course modules cover topics such as conceptual modeling, the relational model, SQL, normalization, transactions, and recovery protocols. Required textbooks are also listed.
The document discusses the history of database management and database models through 6 generations from 1900 to present. It describes the evolution from early manual record keeping systems to current big data technologies. Key database models discussed include hierarchical, network, relational, object-oriented, and dimensional models. The document also covers topics like data warehousing and data mining.
eScience: A Transformed Scientific MethodDuncan Hull
The document discusses the concept of eScience, which involves synthesizing information technology and science. It explains how science is becoming more data-driven and computational, requiring new tools to manage large amounts of data. It recommends that organizations foster the development of tools to help with data capture, analysis, publication, and access across various scientific disciplines.
The document provides information about a database course including:
1) An overview of the course content which covers database fundamentals, the relational model, normalization, conceptual modeling, query languages, and advanced SQL topics.
2) Details about the lecturer including their academic background and publications.
3) Assessment details for the course including exams, labs, and project work accounting for 100% of the grade.
The document provides an overview of information systems and databases as covered in the HSC course. It discusses different types of information systems and focuses on organizing, storing, and retrieving data with database systems. It describes skills needed to analyze database information systems and provides examples to practice these skills. Finally, it covers topics like database design, data storage and retrieval methods, and some social and ethical issues related to information systems.
History of database processing module 1 (2)chottu89
The document discusses the history and evolution of database management systems from the 1960s to present. It covers early stages like organizational databases in the 1960s, the introduction of the relational model in the 1970s, object-oriented databases in the 1980s, client-server applications in the 1990s, and internet-based databases in the 2000s. It also describes some common database components, models, and relationships.
This document provides an introduction and overview of an IS220 Database Systems course. It outlines that the course will cover topics like database design, file organization, indexing and hashing, query processing and optimization, transactions, object-oriented and XML databases. It notes that the class will be 70% theory and 30% hands-on assignments completed in pairs. Assessment will include group work, tests, and a final exam. Class rules require punctuality, use of English, dressing professionally, and minimum 80% attendance.
1. The document introduces databases and their history, from early data storage and retrieval to modern database management systems.
2. It discusses Edgar Codd's invention of the relational database model in 1970 which changed the field by separating data from application code for easier modification and generalization.
3. The document outlines what a database management system does, including managing large amounts of data, supporting efficient and concurrent access, and providing security.
This document provides information about a database management systems (DBMS) course offered by the Department of Computer Science & Engineering at Cambridge University. The course objectives are to provide a strong foundation in database concepts, practice SQL programming, demonstrate transactions and concurrency, and design database applications. Course outcomes include identifying and defining database objects, using SQL, designing simple databases, and developing applications. The course modules cover topics such as conceptual modeling, the relational model, SQL, normalization, transactions, and recovery protocols. Required textbooks are also listed.
Memory efficient java tutorial practices and challengesmustafa sarac
This document summarizes challenges in building memory-efficient Java applications and common patterns of memory usage. It discusses how object representation and collection choices can significantly impact memory usage, with overhead sometimes accounting for 50-90% of memory consumption. The document provides examples of how data type modeling decisions, such as high levels of delegation, large base classes, and unnecessary fields, can lead to high memory overhead. It emphasizes measuring and understanding memory usage at the data type and collection level in order to make informed design tradeoffs.
Is one enough? Data warehousing for biomedical researchGreg Landrum
The document discusses challenges in storing and managing real-world biomedical data from multiple sources for analysis. It describes three different data warehouse case studies used at Novartis - Avalon, MAGMA, and the Entity Warehouse. The Entity Warehouse takes a novel approach of modeling data as entities that can be linked together, with results stored in tables by type. It is designed to integrate both internal and external data while allowing broad access. However, the document concludes that no single warehouse fits all needs, and multiple solutions may be required to fully enable data analysis.
This document discusses data intensive computing and its relationship to data curation and preservation. It defines data intensive computing as I/O-bound computations that require large volumes of data that is too big to fit in memory. The role of data infrastructures is described, including bringing compute to archived data through queries, scripts, or APIs. Approaches like MapReduce, Hadoop, and Storm are presented for making best use of resources for data intensive workloads. The conclusion is that data intensive computing requires new ways of parallel computing due to huge data volumes and offers new opportunities for data reuse and reduction.
This document provides an introduction to computer technology and databases. It defines computers as machines that can accept data as input, process it logically and arithmetically, and produce outputs. Data is defined as unorganized facts like words, numbers, images and sounds, while information is organized data that has meaning and is useful. The document also defines databases as collections of logically related data designed to meet the information needs of multiple users. Database management systems (DBMS) are programs that provide database management, control access to data, and contain query languages to retrieve information easily from the database. Proper database design is also discussed as important to structure data for easy access and updating without redundancy.
The document discusses different types of databases including relational databases, analytical databases, operational databases, and object-oriented databases. It describes key characteristics of each type of database such as how they model and store data. Relational databases use tables to store data and link tables using relationships while analytical databases store archived data for analysis and operational databases manage dynamic data. Object-oriented databases integrate object-oriented programming with databases.
The document provides an overview of key concepts in database systems including:
1) It defines data, databases, DBMS and typical database system components.
2) It describes different data management approaches including manual, file-based and database approaches.
3) It outlines the functions of a DBMS including data storage, security, and integrity management.
This document provides an introduction to database systems. It discusses what a database is and the functions of a database management system (DBMS). It outlines three approaches to data management - manual, file-based, and database-based. The database approach centralizes data storage and provides tools to ensure data integrity and security. A DBMS performs functions like data storage management, security management, and backup/recovery to maintain the database. The document compares the advantages of database systems like data sharing and improved accessibility over file-based systems.
The document discusses practical computing issues that arise when working with large datasets. It begins by noting that many statistical analyses can be done on a single laptop. It then discusses storing very large datasets, which may require terabytes of storage. The document outlines some basic computing concepts for working with big data, including software engineering practices, databases, and distributed computing.
The document provides an overview of databases and database management systems. It defines what a database is and provides examples. It discusses the objectives and purpose of databases, including controlling redundancy, ease of use, data independence, accuracy, recovery from failure, privacy and security. Key terms related to database design and structure are explained, such as tables, rows, indexes, primary keys and foreign keys. The document also covers data definition language, data manipulation language, SQL, users and types of databases. Factors to consider when selecting a database management system are outlined.
Organizing Data in a Traditional File Environment
File organization Term and Concepts
Computer system organizes data in a hierarchy
Bit: Smallest unit of data; binary digit (0,1)
Byte: Group of bits that represents a single character
Field: Group of characters as word(s) or number
Record: Group of related fields
File: Group of records of same type
Open Source Database Management Software available on the NetDlis Mu
This document discusses open source database management software available online. It provides an introduction to online databases and database management systems. It then covers the history of database systems from the 1940s to current web databases. It also discusses the structure of databases and different types including bibliographic, full-text, numeric, image, audio/video, and mixed databases.
The document discusses several aspects of database design including:
- Logical design which involves deciding on the database schema and relation schemas.
- Physical design which involves deciding on the physical layout of the database.
- Entity-relationship modeling which involves modeling an enterprise as entities and relationships.
- Extensions to the relational model to include object orientation and complex data types.
Design and implementation of Clinical Databases using openEHRPablo Pazos
This document provides an overview of designing and implementing clinical databases using openEHR. It discusses clinical information requirements, organization, and database technologies. OpenEHR's goals are to create flexible, interoperable EHRs through archetypes and templates that define clinical concepts. For database design, archetype IDs, paths, and node IDs are important for querying openEHR data. Relational databases can be used through object-relational mapping, mapping classes to tables, relationships, and inheritance.
Types of database processing,OLTP VS Data Warehouses(OLAP), Subject-oriented
Integrated
Time-variant
Non-volatile,
Functionalities of Data Warehouse,Roll-Up(Consolidation),
Drill-down,
Slicing,
Dicing,
Pivot,
KDD Process,Application of Data Mining
Relational databases have pretty much ruled over the IT world for the last 30 years. However, Web 2.0 and the incipient Internet of Things (IoT) are some of the sources of a data explosion that has proved to exceed the limits of what modern relational databases can handle in a growing number of cases. As a result, new technologies had to be developed to handle these new use cases. We generally group these technologies under the umbrella of Big Data. In this two part presentation, we will start by understanding how relational databases have evolved to become the powerhouses they are today. In part 2 we will look at how non SQL databases are tackling the big data problem to scale beyond what relational databases can provide us today.
Object databases store objects rather than data types like numbers and strings. Objects have attributes that define their characteristics and methods that define their behaviors. Relational databases store data in normalized tables with rows and columns. Object databases are suited for complex data and relationships, while relational databases work better for large volumes of simple transactional data.
The Entity-Attribute-Value model is a semi-structured data model where each attribute-value pair describing an entity is stored as a single row. This flexible model allows for an unlimited number of attributes per entity.
Lec20.pptx introduction to data bases and information systemssamiullahamjad06
The document provides an overview of databases and information systems. It defines what a database is, how data is organized in a hierarchy from bits to files, and the different types of database models including hierarchical, network, and relational. It also discusses how structured query language and query by example are used to retrieve data in relational databases. Finally, it outlines different types of computer-based information systems used in organizations like transaction processing systems, management information systems, and decision support systems.
Semi-automated Exploration and Extraction of Data in Scientific TablesElsevier
Ron Daniel and Corey Harper of Elsevier Labs present at the Columbia University Data Science Institute: https://www.elsevier.com/connect/join-us-as-elsevier-data-scientists-present-at-columbia-university
This document provides an overview of data warehousing and related concepts. It defines a data warehouse as a centralized database for analysis and reporting that stores current and historical data from multiple sources. The document describes key elements of data warehousing including Extract-Transform-Load (ETL) processes, multidimensional data models, online analytical processing (OLAP), and data marts. It also outlines advantages such as enhanced access and consistency, and disadvantages like time required for data extraction and loading.
Genetic engineering techniques allow scientists to modify the DNA of living organisms. This includes selective breeding, cloning, and gene splicing. Selective breeding involves choosing which organisms to mate to produce offspring with desired traits, but does not allow control over specific gene transfer. Cloning creates an exact genetic copy of an organism. Gene splicing cuts DNA from one organism and inserts it into another, transferring traits between them. These techniques have led to genetically modified organisms that can benefit agriculture and medicine by increasing crop yields, producing human proteins like insulin in other organisms, and potentially curing genetic diseases.
Memory efficient java tutorial practices and challengesmustafa sarac
This document summarizes challenges in building memory-efficient Java applications and common patterns of memory usage. It discusses how object representation and collection choices can significantly impact memory usage, with overhead sometimes accounting for 50-90% of memory consumption. The document provides examples of how data type modeling decisions, such as high levels of delegation, large base classes, and unnecessary fields, can lead to high memory overhead. It emphasizes measuring and understanding memory usage at the data type and collection level in order to make informed design tradeoffs.
Is one enough? Data warehousing for biomedical researchGreg Landrum
The document discusses challenges in storing and managing real-world biomedical data from multiple sources for analysis. It describes three different data warehouse case studies used at Novartis - Avalon, MAGMA, and the Entity Warehouse. The Entity Warehouse takes a novel approach of modeling data as entities that can be linked together, with results stored in tables by type. It is designed to integrate both internal and external data while allowing broad access. However, the document concludes that no single warehouse fits all needs, and multiple solutions may be required to fully enable data analysis.
This document discusses data intensive computing and its relationship to data curation and preservation. It defines data intensive computing as I/O-bound computations that require large volumes of data that is too big to fit in memory. The role of data infrastructures is described, including bringing compute to archived data through queries, scripts, or APIs. Approaches like MapReduce, Hadoop, and Storm are presented for making best use of resources for data intensive workloads. The conclusion is that data intensive computing requires new ways of parallel computing due to huge data volumes and offers new opportunities for data reuse and reduction.
This document provides an introduction to computer technology and databases. It defines computers as machines that can accept data as input, process it logically and arithmetically, and produce outputs. Data is defined as unorganized facts like words, numbers, images and sounds, while information is organized data that has meaning and is useful. The document also defines databases as collections of logically related data designed to meet the information needs of multiple users. Database management systems (DBMS) are programs that provide database management, control access to data, and contain query languages to retrieve information easily from the database. Proper database design is also discussed as important to structure data for easy access and updating without redundancy.
The document discusses different types of databases including relational databases, analytical databases, operational databases, and object-oriented databases. It describes key characteristics of each type of database such as how they model and store data. Relational databases use tables to store data and link tables using relationships while analytical databases store archived data for analysis and operational databases manage dynamic data. Object-oriented databases integrate object-oriented programming with databases.
The document provides an overview of key concepts in database systems including:
1) It defines data, databases, DBMS and typical database system components.
2) It describes different data management approaches including manual, file-based and database approaches.
3) It outlines the functions of a DBMS including data storage, security, and integrity management.
This document provides an introduction to database systems. It discusses what a database is and the functions of a database management system (DBMS). It outlines three approaches to data management - manual, file-based, and database-based. The database approach centralizes data storage and provides tools to ensure data integrity and security. A DBMS performs functions like data storage management, security management, and backup/recovery to maintain the database. The document compares the advantages of database systems like data sharing and improved accessibility over file-based systems.
The document discusses practical computing issues that arise when working with large datasets. It begins by noting that many statistical analyses can be done on a single laptop. It then discusses storing very large datasets, which may require terabytes of storage. The document outlines some basic computing concepts for working with big data, including software engineering practices, databases, and distributed computing.
The document provides an overview of databases and database management systems. It defines what a database is and provides examples. It discusses the objectives and purpose of databases, including controlling redundancy, ease of use, data independence, accuracy, recovery from failure, privacy and security. Key terms related to database design and structure are explained, such as tables, rows, indexes, primary keys and foreign keys. The document also covers data definition language, data manipulation language, SQL, users and types of databases. Factors to consider when selecting a database management system are outlined.
Organizing Data in a Traditional File Environment
File organization Term and Concepts
Computer system organizes data in a hierarchy
Bit: Smallest unit of data; binary digit (0,1)
Byte: Group of bits that represents a single character
Field: Group of characters as word(s) or number
Record: Group of related fields
File: Group of records of same type
Open Source Database Management Software available on the NetDlis Mu
This document discusses open source database management software available online. It provides an introduction to online databases and database management systems. It then covers the history of database systems from the 1940s to current web databases. It also discusses the structure of databases and different types including bibliographic, full-text, numeric, image, audio/video, and mixed databases.
The document discusses several aspects of database design including:
- Logical design which involves deciding on the database schema and relation schemas.
- Physical design which involves deciding on the physical layout of the database.
- Entity-relationship modeling which involves modeling an enterprise as entities and relationships.
- Extensions to the relational model to include object orientation and complex data types.
Design and implementation of Clinical Databases using openEHRPablo Pazos
This document provides an overview of designing and implementing clinical databases using openEHR. It discusses clinical information requirements, organization, and database technologies. OpenEHR's goals are to create flexible, interoperable EHRs through archetypes and templates that define clinical concepts. For database design, archetype IDs, paths, and node IDs are important for querying openEHR data. Relational databases can be used through object-relational mapping, mapping classes to tables, relationships, and inheritance.
Types of database processing,OLTP VS Data Warehouses(OLAP), Subject-oriented
Integrated
Time-variant
Non-volatile,
Functionalities of Data Warehouse,Roll-Up(Consolidation),
Drill-down,
Slicing,
Dicing,
Pivot,
KDD Process,Application of Data Mining
Relational databases have pretty much ruled over the IT world for the last 30 years. However, Web 2.0 and the incipient Internet of Things (IoT) are some of the sources of a data explosion that has proved to exceed the limits of what modern relational databases can handle in a growing number of cases. As a result, new technologies had to be developed to handle these new use cases. We generally group these technologies under the umbrella of Big Data. In this two part presentation, we will start by understanding how relational databases have evolved to become the powerhouses they are today. In part 2 we will look at how non SQL databases are tackling the big data problem to scale beyond what relational databases can provide us today.
Object databases store objects rather than data types like numbers and strings. Objects have attributes that define their characteristics and methods that define their behaviors. Relational databases store data in normalized tables with rows and columns. Object databases are suited for complex data and relationships, while relational databases work better for large volumes of simple transactional data.
The Entity-Attribute-Value model is a semi-structured data model where each attribute-value pair describing an entity is stored as a single row. This flexible model allows for an unlimited number of attributes per entity.
Lec20.pptx introduction to data bases and information systemssamiullahamjad06
The document provides an overview of databases and information systems. It defines what a database is, how data is organized in a hierarchy from bits to files, and the different types of database models including hierarchical, network, and relational. It also discusses how structured query language and query by example are used to retrieve data in relational databases. Finally, it outlines different types of computer-based information systems used in organizations like transaction processing systems, management information systems, and decision support systems.
Semi-automated Exploration and Extraction of Data in Scientific TablesElsevier
Ron Daniel and Corey Harper of Elsevier Labs present at the Columbia University Data Science Institute: https://www.elsevier.com/connect/join-us-as-elsevier-data-scientists-present-at-columbia-university
This document provides an overview of data warehousing and related concepts. It defines a data warehouse as a centralized database for analysis and reporting that stores current and historical data from multiple sources. The document describes key elements of data warehousing including Extract-Transform-Load (ETL) processes, multidimensional data models, online analytical processing (OLAP), and data marts. It also outlines advantages such as enhanced access and consistency, and disadvantages like time required for data extraction and loading.
Genetic engineering techniques allow scientists to modify the DNA of living organisms. This includes selective breeding, cloning, and gene splicing. Selective breeding involves choosing which organisms to mate to produce offspring with desired traits, but does not allow control over specific gene transfer. Cloning creates an exact genetic copy of an organism. Gene splicing cuts DNA from one organism and inserts it into another, transferring traits between them. These techniques have led to genetically modified organisms that can benefit agriculture and medicine by increasing crop yields, producing human proteins like insulin in other organisms, and potentially curing genetic diseases.
Epigenetics refers to heritable changes in gene expression that occur without changes to DNA sequences. This chapter discusses several molecular processes that lead to epigenetic changes, including DNA methylation, histone modifications, and RNA molecules. These epigenetic processes produce diverse effects, such as paramutation, behavioral influences, environmental impacts, and cell differentiation. The epigenome represents the overall pattern of chromatin modifications in an organism and can be detected using techniques like bisulfate sequencing and ChIP.
Epigenetic phenomena involve changes in gene expression and chromatin configuration that are independent of DNA sequence. Epigenetics includes DNA methylation and histone post-translational modifications. While monozygotic twins share an identical genotype, there can be significant phenotypic discordance due to epigenetic differences. The epigenome is influenced by environmental factors and changes with age, leading to epigenetic differences even between identical twins. Epigenetic mechanisms stably maintain gene expression states and are essential for cell differentiation.
Nucleic acid hybridization uses labeled probes to identify related DNA or RNA molecules in a complex mixture. It relies on base complementarity between the probe and target molecules to form double-stranded hybrids. Probes can be radioactively or nonradioactively labeled. Hybridization is affected by factors like temperature, salt concentration, and mismatches. Southern blotting uses hybridization to detect specific DNA sequences separated by gel electrophoresis and transferred to membranes.
C.H. Waddington coined the term "epigenetics" to describe mechanisms above genetics that explain cell differentiation. Epigenetics refers to non-sequence dependent inheritance, such as how stem cells determine cell fate and how identical twins can have different traits despite identical DNA. DNA methylation and histone modifications form an epigenetic code that regulates gene expression and chromatin structure in a heritable, but potentially reversible manner independent of DNA sequence.
The document describes the bacteriophage lambda and its ability to enter either the lytic or lysogenic life cycle in an infected bacterial cell. It discusses how the phage regulatory proteins CI (repressor) and Cro control transcription and determine whether the phage follows the lytic pathway of viral replication and host cell lysis, or the lysogenic pathway of integrating into the host genome. The repressor binds DNA operators and represses transcription from lytic promoters, while Cro competes with repressor binding to instead activate lytic transcription and inhibit lysogeny.
Regulation of gene expression allows organisms to benefit from efficiency, conserving energy and cell size. In prokaryotes, operons regulate groups of genes, turned on or off by repressors, activators, or inducers. Eukaryotes separate transcription and translation, introducing many regulatory mechanisms. These include epigenetic modifications, transcription factors, RNA processing, stability, and translation factors. Cancer arises from dysregulation of genes controlling cell growth, especially tumor suppressors and oncogenes.
Agarose gel electrophoresis is a technique used to separate DNA fragments by size. It involves pouring agarose gel containing DNA samples into a chamber, applying an electric current which causes the negatively charged DNA to migrate through the gel at rates depending on their size. Larger DNA fragments move slower through the gel matrix than smaller fragments. Restriction enzymes are used to cut DNA into fragments at specific recognition sites. The fragments can then be visualized on an agarose gel to produce a restriction map. Polymerase chain reaction (PCR) is used to amplify specific DNA regions using primers and repeated heating/cooling cycles.
1) Primary databases contain original experimental data directly submitted by researchers, such as sequence data in GenBank, EMBL, and DDBJ.
2) Secondary databases contain derived or analyzed data from primary databases to make the information more useful, such as protein family databases like PROSITE and BLOCKS.
3) Biological databases serve important purposes like organizing and providing computational support for analyzing biological data, enabling researchers to retrieve information through various search criteria.
Presentation A - Using Restriction Enzymes.pptxBlackHunt1
This document provides information about using restriction enzymes to analyze DNA through gel electrophoresis. It describes how restriction enzymes cut DNA at specific recognition sequences, producing restriction fragments that can be separated by size using gel electrophoresis. The document explains that restriction enzymes are used in research and medicine to create recombinant DNA by producing complementary sticky ends on DNA fragments, and in DNA profiling for various applications. It provides examples of specific restriction enzymes and their recognition sequences.
This document discusses the properties and production of recombinant vaccines. It notes that recombinant vaccines are produced using recombinant DNA technology by inserting DNA encoding an antigen into cells to express and purify the antigen. The first approved recombinant vaccine was for hepatitis B. The document outlines the types of recombinant vaccines and the process of recombinant DNA technology, including isolating genetic material, amplifying genes of interest, and inserting recombinant DNA into host cells to produce the foreign gene product. It lists some advantages as being able to more quickly produce recombinant vaccines in larger quantities without infectious particles, making them safer for immunosuppressed individuals.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Communicating effectively and consistently with students can help them feel at ease during their learning experience and provide the instructor with a communication trail to track the course's progress. This workshop will take you through constructing an engaging course container to facilitate effective communication.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
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Bioinformatics&Databases.ppt
1. Unit 2.4: Bioinformatics and Databases
Objectives: At the end of this unit, students will
-have been introduced to ome basic concepts and considerations
in bioinformatics and computational biology
-know what a relational database is
-understand why databases are useful for dealing with large
amounts of data
-have been introduced to some of the major online biological
databases and their features
-have gained experience in extracting data from online
biological databases
Reading:
Stein, L.D. 2003. Integrating biological databases. Nat Rev
Genet 4: 337-345.
2. Assignments:
Read the excerpts from Current Protocols in
Bioinformatics on Entrez and the UCSC Browser.
Follow along with the examples in Protocol 1 of each
section.
3. “Genomic research makes it possible to look at biological
phenomena on a scale not previously possible: all genes in a
genome, all transcripts in a cell, all metabolic processes in a
tissue. One feature that all of these approaches share is the
production of massive quantities of data. GenBank, for example,
now accommodates >1010 nucleotides of nucleic acid sequence
data and continues to more than double in size every year. New
technologies for assaying gene expression patterns, protein
structure, protein-protein interactions, etc., will provide even
more data. How to handle these data, make sense of them, and
render them accessible to biologists working on a wide variety of
problems is the challenge facing bioinformatics—an emerging
field that seeks to integrate computer science with applications
derived from molecular biology. We are swimming in a rapidly
rising sea of data. . . how do we keep from drowning?”
—Roos (2001). Science. 291:1260
4. Bioinformatics is one solution to this problem—a way of coping
with large data sets and making sense of genomic-scale data. But
like with most approaches, it is important to have a sense of what
types of things are possible or not possible to achieve using
bioinformatics approaches.
Learn to know the difference—Bioinformatics is:
• sometimes a time-saver: you can automate common and/or
repetative tasks, and parse large files
• sometimes essential: how else would you analyze results from a
25,000 gene microarray experiment
• sometimes not helpful/not useful/unimportant: it can be easier
and more straightforward to do a simple wet-lab experiment than
to devise an elaborate computational approach
• sometimes not possible: computers can’t do everything!
5. It’s also important to have an understanding of the underlying concepts
and algorithms in bioinformatics, just as it’s important to understand the
basic concepts and chemical basis of molecular biology, or genetics, or
biochemistry, if you’re going to do wet-lab experiments.
“Many biologists are comfortable using algorithms like BLAST or
GenScan without really understanding how the underlying algorithm
works. . . . BLAST solves a particular problem only approximately and it
has certain systematic weaknesses. . . . Users that do not know how
BLAST works might misapply the algorithm or misinterpret the results it
returns.” [Pevzner (2004). Bioinformatics 20(14): 2159-2161.]
9. Algorithms
• An algorithm is a sequence of instructions that
one must perform in order to solve a well-
formulated problem
• First you must identify exactly what the problem
is!
• A problem describes a class of computational
tasks. A problem instance is one particular input
from that task
• In general, you should design your algorithms to
work for any instance of a problem (although
there are cases in which this is not possible)
10. Computer technology: memory, CPU speed, cost
• Dramatic improvements on yearly basis
• We do a lot of our work using desktop Macs out of the box
- 2 quad core 2.8 GHz processors, 500 GB disk space, 4 GB RAM for
~$3000
- 2 quad core 3.0 GHz processors, 2.5 TB disk space, 8 GB RAM for
~$6000
• CPU speed vs. memory: which is more important?
- for protein structure, might need many calculations but limited
memory
- for genome searches, might have few calculations but huge amounts
to store in memory
• Reading from memory is several orders of magnitude faster
than reading from disk
11. Databases
• What is a database?
– A collection of related data elements
• tables
• columns (fields)
• rows (records)
– Records retrieved using a query language
– Database technology is well established
12. Tables (entitites)
•basic elements of information to track, e.g., gene, organism,
sequence, citation
Columns (fields)
•attributes of tables, e.g. for citation table, title, journal,
volume, author
Rows (records)
•actual data
•whereas fields describe what data is stored, the rows of a table
are where the actual data is stored
Databases
13. A very simple form of (non-electronic) database is a filing
cabinet. In the filing cabinet, you can store many different records
(sheets of paper), each containing mulitple data elements.
Example: a filing cabinet of invoices
•the filing cabinet is a table
•the columns are the fields of data on the individual
invoices (customer, product, price, quantity)
•the rows (records) are the individual invoices
The biggest problem with a filing cabinet is that you can only
store your data one way (e.g., in alphabetical order of the
customer’s last name), and there’s no good way of searching your
files based on any other criteria (say, by product ordered).
Databases
14. Example: a filing cabinet of invoices
•the filing cabinet is a table
•the columns are the fields of data on the individual invoices
(customer, product, price, quantity)
•the rows (records) are the individual invoices
Databases
A flat-file database—a spreadsheet—is the electronic
analogue to the filing cabinet:
This is more easily searchable than a paper file cabinet, but
is still very unwieldly, especially for large amounts of data.
15. Databases
Suppose you now want to be able to send an advertisement to
every customer who bought the Acme Snow Machine. You could
add a column to your table that includes the address for each
customer, but this is very inefficient—you will keep repeating
information for customers (like Elmer) who make multiple
purchases. Plus, as the number of rows and columns grows,
searching a flat file becomes more and more time consuming.
Also, it is difficult to construct complex queries (e.g., customers
who bought the Snow Machine and who like opera or live in the
Southwest desert)
16. Relational Databases
The solution is the relational database. A relational database contains multiple
tables and defines the relationships between them. Thus you might also have a
customer table and a product table, like this:
18. Relational Databases
Now only three items need to be filled in for an invoice: a customer, a
product, and a quantity. The price and total fields can be filled in
automatically: price from a product_table “lookup” and total by “calculation”
(price * qty).
19. Relational Databases
Now we can send our advertisement to every customer who bought the Acme
Snow Machine by getting their addresses from the customer_table table.
To do this, we use Structured Query Language (SQL):
SELECT customer_table.name, customer_table.address
FROM customer_table, invoice
WHERE invoice.product = “Acme Snow Machine”
AND invoice.customer = customer_table.name
20. Relational Databases
We can also make our complex query
“customers who bought the Snow Machine and who like opera or live in the
Southwest desert)”:
SELECT customer_table.name
FROM customer_table, invoice
WHERE invoice.product = “Snow Machine”
AND invoice.customer = customer_table.name
AND (customer_table.notes LIKE %opera% OR
cutomer_table.address = “Southwest desert”)
21. Online Databases
When you query an online database, your query is translated
into SQL, the database is interrogated, and the answer displayed
on your web browser.
Your computer and
browser (the “client”)
Software to receive
and translate the
instructions you enter
into your browser (on
the “server”)
The database itself
Image source: David Lane and Hugh E. Williams. Web Database Applications with PHP & MySQL. O’Reilly (2002).
22. Biological Databases
•Over 1000 biological databases
•Vary in size, quality, coverage, level of interest
•Many of the major ones covered in the annual
Database Issue of Nucleic Acids Research
•What makes a good database?
•comprehensiveness
•accuracy
•is up-to-date
•good interface
•batch search/download
•API (web services, DAS, etc.)
23. “The Ten Commandments When Using
Servers”
•Remember the server, the database, and the program version used
•Write down sequence identification numbers
•Write down the program parameters
•Save your internet results the right way
(use screenshots or PDFs if necessary)
•Databases are not like good wine
(use up-to-date builds)
•Use local installs when it becomes necessary
Source: Bioinformatics for Dummies
24. “Ten Important Bioinformatics Databases”
GenBank www.ncbi.nlm.nih.gov nucleotide sequences
Ensembl www.ensembl.org human/mouse genome (and others)
PubMed www.ncbi.nlm.nih.gov literature references
NR www.ncbi.nlm.nih.gov protein sequences
SWISS-PROT www.expasy.ch protein sequences
InterPro www.ebi.ac.uk protein domains
OMIM www.ncbi.nlm.nih.gov genetic diseases
Enzymes www.chem.qmul.ac.uk enzymes
PDB www.rcsb.org/pdb/ protein structures
KEGG www.genome.ad.jp metabolic pathways
Source: Bioinformatics for Dummies
25. NCBI (National Center for Biotechnology
Information)
• over 30 databases including
GenBank, PubMed, OMIM, and
GEO
• Access all NCBI resources via
Entrez
(www.ncbi.nlm.nih.gov/Entrez/)
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38. GenBank® is the NIH genetic
sequence database, an annotated
collection of all publicly available
DNA sequences. There are
approximately 65,369,091,950
bases in 61,132,599 sequence
records in the traditional GenBank
divisions and 80,369,977,826
bases in 17,960,667 sequence
records in the WGS division as of
August 2006.
www.ncbi.nlm.nih.gov/GenBank
40. The Reference Sequence (RefSeq) database is
a non-redundant collection of richly annotated
DNA, RNA, and protein sequences from diverse
taxa. Each RefSeq represents a single, naturally
occurring molecule from one organism. The goal
is to provide a comprehensive, standard dataset
that represents sequence information for a
species. It should be noted, though, that RefSeq
has been built using data from public archival
databases only.
RefSeq biological sequences (also known as
RefSeqs) are derived from GenBank records
but differ in that each RefSeq is a synthesis of
information, not an archived unit of primary
research data. Similar to a review article in the
literature, a RefSeq represents the consolidation
of information by a particular group at a
particular time.
45. The MOD squad
•Most model organism communities have established organism-
specific Model Organism Databases (MODs)
•Many of these databases have different schemas and implementations,
although there is movement toward harmonizing many features via the
Generic Model Organism Database project.
46. The MOD squad
SGD: yeast (www.yeastgenome.org)
Wormbase: C. elegans (www.wormbase.org)
FlyBase: Drosophila (flybase.bio.indiana.edu)
Zfin: zebrafish (zfin.org)
and many others (Xenopus, Dictyostelium,
Arabisdopsis…)
47. The MOD squad: what about Homo sapiens?
There is not a true “model organism” database for Human.
The two main sources of genome information that have
evolved are the UCSC Genome Browser and Ensembl.
EnsEMBL www.ensembl.org
UCSC genome.ucsc.edu