Information and media literacy (IML) is a combination of information literacy and media literacy. It enables people to show and make informed judgments as users of information and media, as well as to become skillful creators and producers of information and media messages. The transformative nature of IML includes creative works and creating new knowledge; to publish and collaborate responsibly requires ethical, cultural and social understanding. IML is also known as media and information literacy (MIL). UNESCO first adopted the term MIL in 2008 as a "composite concept" combining the competencies of information literacy and media literacy. UNESCO emphasizes the importance of global education in media and information literacy, and in 2013 defined Media and Information Literacy (MIL) as the ability to access, evaluate, use, and create information and media content in critical and ethical ways. Prior to the 1990s, the primary focus of information literacy was research skills. Media literacy, a study that emerged around the 1970s, traditionally focuses on the analysis and the delivery of information through various forms of media. Information literacy, as a skill proposed as early as 1974, centers on an individual's ability to recognize information needs and effectively locate, evaluate, and use information. These days, the study of information literacy has been extended to include the study of media literacy in many countries like the UK, Australia and New Zealand. It is also referred to as information and communication technologies (ICT) in the United States. Educators such as Gregory Ulmer have also defined the field as electracy.Media literacy is the ability to actively inquire into and think critically about information. It includes the ability to understand, evaluate, and create media content, and is an essential skill in today's information society. Livingstone, Van Couvering, and Thumim (2008) described the distinction between media literacy and information literacy: "Media literacy views media as lenses or windows for observing the world and expressing the self, whereas information literacy sees information as a tool for taking action in the world." == Integration of media and information literacy == Historically, the fields of information and media literacy have been separate, but over the course of the 21st century there have been calls to integrate both fields. Most definitions of information and media literacy include not only the abilities to locate, access, and analyze information but also the ability to create information. Only by integrating media literacy with information literacy can students better understand the sources of information and how it is used. Media education has primarily taken place in educational institutions, while information education has primarily occurred in libraries. Discussions surrounding the overlap of information literacy and media literacy came to fruition in the mid-to-late 2000s and 2010s as noted by Marcus Leaning. == In the digital age == The definition of literacy is "the ability to read and write". In practice many more skills are needed to locate, critically assess and make effective use of information. By extension, literacy now also includes the ability to manage and interact with digital information and media, in personal, shared and public domains. Historically, "information literacy" has largely been seen from the relatively top-down, organisational viewpoint of library and information sciences. However the same term is also used to describe a generic "information literacy" skill. The modern digital age has led to the proliferation of information spread across the Internet. Individuals must be able to recognize whether information is true or false and better yet know how to locate, evaluate, use, and communicate information in various formats; this is called information literacy. Towards the end of the 20th century, literacy was redefined to include "new literacies" relating to the new skills needed in everyday experience. "Multiliteracies" recognised the multiplicity of literacies, which were often used in combination. "21st century skills" frameworks link new literacies to wider life skills such as creativity, critical thinking, accountability. What these approaches have in common is a focus on the multiple skills needed by individuals to navigate changing personal, professional and public "information landscapes". As the conventional definition of literacy itself continues to evolve among practitioners, so too has the definition of information literacies. Noteworthy definitions include: Zurkowski defined information literacy as "the ability to find known or knowable content on any subject." CILIP, the Chartered Institute of Library and Information Practitioners, defines information literacy as "the ability to think critically and make balanced judgements about any information we find and use". In the United States, the definition proposed by the Association of College and Research Libraries (ACRL) is the most widely recognized. It defines information literacy as "a set of abilities requiring individuals to recognize when information is needed and to locate, evaluate, and use the needed information effectively." JISC, the Joint Information Systems Committee, refers to information literacy as one of six "digital capabilities", seen as an interconnected group of elements centered on "ICT literacy". Mozilla groups digital and other literacies as "21st century skills", a "broad set of knowledge, skills, habits and traits that are important to succeed in today's world". UNESCO, the United Nations Educational, Scientific and Cultural Organization, recognizing the necessity of teaching and learning both traditional and new types of information, the global importance of education was emphasized in 2008 through the "Teacher Media and Information Literacy (MIL) Curriculum". It defines MIL as a set of competencies that enable citizens to access, retrieve, understand, evaluate, use, create, and share information and media content in all formats through various tools in a critical, ethical, and effective manner, so as to participate in and carry out personal, professional, and social activities. Besides this, UNESCO also asserts information literacy as a "universal human right". == 21st-century students == In modern society, although the overall level of education has improved, the channels for knowledge production and dissemination have become increasingly diverse and commercialized, and traditional authoritative institutions no longer hold a monopoly over knowledge validation. While digital platforms have broadened access to information, they have also weakened trust mechanisms and evaluation standards, making epistemological skepticism a norm. Moreover, with the rise and spread of social media, misinformation and disinformation can be just as easily accessed in both densely and sparsely populated areas. These factors further underscore the importance of information literacy education. The IML learning capacities prepare students to be 21st century literate. According to Jeff Wilhelm (2000), "technology has everything to do with literacy. And being able to use the latest electronic technologies has everything to do with being literate." He supports his argument with J. David Bolter's statement that "if our students are not reading and composing with various electronic technologies, then they are illiterate. They are not just unprepared for the future; they are illiterate right now, in our current time and context". In a broader sense, developing this advanced competency of media and information literacy is essential, as it is crucial for students to exercise their freedom of expression in the 21st century. Wilhelm's statement is supported by the 2005 Wired World Phase II (YCWW II) survey conducted by the Media Awareness Network of Canada on 5000 Grade 4 – 11 students. The key findings of the survey were: 62% of Grade 4 students prefer the Internet. 38% of Grade 4 students prefer the library. 91% of Grade 11 students prefer the Internet. 9% of Grade 11 students prefer the library. Marc Prensky (2001) uses the term "digital native" to describe people who have been brought up in a digital world. The Internet has been a pervasive element of young people's home lives. 94% of kids reported that they had Internet access at home, and a significant majority (61%) had a high-speed connection. By the time kids reach Grade 11, half of them (51 percent) have their own Internet-connected computer, separate and apart from the family computer. The survey also showed that young Canadians are now among the most wired in the world. Contrary to the earlier stereotype of the isolated and awkward computer nerd, today's wired kid is a social kid. In general, many students are better networked through the use of technology than most teachers and parents, who may not understand the abilities of technology.
Gooch shading
Gooch shading is a non-photorealistic rendering technique for shading objects. It is also known as "cool to warm" shading, and is widely used in technical illustration. == History == Gooch shading was developed by Amy Gooch et al. at the University of Utah School of Computing and first presented at the 1998 SIGGRAPH conference. It has since been implemented in shader libraries, software, and games released by Autodesk, Nvidia, and Valve. == Process == Gooch shading defines an additional two colors in conjunction with the original model color: a warm color (such as yellow) and a cool color (such as blue). The warm color indicates surfaces that are facing toward the light source while the cool color indicates surfaces facing away. This allows shading to occur only in mid-tones so that edge lines and highlights remain visually prominent. The Gooch shader is typically implemented in two passes: all objects in the scene are first drawn with the "cool to warm" shading, and in the second pass the object's edges are rendered in black.
VMDS
VMDS abbreviates the relational database technology called Version Managed Data Store provided by GE Energy as part of its Smallworld technology platform and was designed from the outset to store and analyse the highly complex spatial and topological networks typically used by enterprise utilities such as power distribution and telecommunications. VMDS was originally introduced in 1990 as has been improved and updated over the years. Its current version is 6.0. VMDS has been designed as a spatial database. This gives VMDS a number of distinctive characteristics when compared to conventional attribute only relational databases. == Distributed server processing == VMDS is composed of two parts: a simple, highly scalable data block server called SWMFS (Smallworld Master File Server) and an intelligent client API written in C and Magik. Spatial and attribute data are stored in data blocks that reside in special files called data store files on the server. When the client application requests data it has sufficient intelligence to work out the optimum set of data blocks that are required. This request is then made to SWMFS which returns the data to the client via the network for processing. This approach is particularly efficient and scalable when dealing with spatial and topological data which tends to flow in larger volumes and require more processing then plain attribute data (for example during a map redraw operation). This approach makes VMDS well suited to enterprise deployment that might involve hundreds or even thousands of concurrent clients. == Support for long transactions == Relational databases support short transactions in which changes to data are relatively small and are brief in terms in duration (the maximum period between the start and the end of a transaction is typically a few seconds or less). VMDS supports long transactions in which the volume of data involved in the transaction can be substantial and the duration of the transaction can be significant (days, weeks or even months). These types of transaction are common in advanced network applications used by, for example, power distribution utilities. Due to the time span of a long transaction in this context the amount of change can be significant (not only within the scope of the transaction, but also within the context of the database as a whole). Accordingly, it is likely that the same record might be changed more than once. To cope with this scenario VMDS has inbuilt support for automatically managing such conflicts and allows applications to review changes and accept only those edits that are correct. == Spatial and topological capabilities == As well as conventional relational database features such as attribute querying, join fields, triggers and calculated fields, VMDS has numerous spatial and topological capabilities. This allows spatial data such as points, texts, polylines, polygons and raster data to be stored and analysed. Spatial functions include: find all features within a polygon, calculate the Voronoi polygons of a set of sites and perform a cluster analysis on a set of points. Vector spatial data such as points, polylines and polygons can be given topological attributes that allow complex networks to be modelled. Network analysis engines are provided to answer questions such as find the shortest path between two nodes or how to optimize a delivery route (the travelling salesman problem). A topology engine can be configured with a set of rules that define how topological entities interact with each other when new data is added or existing data edited. == Data abstraction == In VMDS all data is presented to the application as objects. This is different from many relational databases that present the data as rows from a table or query result using say JDBC. VMDS provides a data modelling tool and underlying infrastructure as part of the Smallworld technology platform that allows administrators to associate a table in the database with a Magik exemplar (or class). Magik get and set methods for the Magik exemplar can be automatically generated that expose a table's field (or column). Each VMDS row manifests itself to the application as an instance of a Magik object and is known as an RWO (or real world object). Tables are known as collections in Smallworld parlance. # all_rwos hold all the rwos in the database and is heterogeneous all_rwos << my_application.rwo_set() # valve_collection holds the valve collection valves << all_rwos.select(:collection, {:valve}) number_of_valves << valves.size Queries are built up using predicate objects: # find 'open' valves. open_valves << valves.select(predicate.eq(:operating_status, "open")) number_of_open_valves << open_valves.size _for valve _over open_valves.elements() _loop write(valve.id) _endloop Joins are implemented as methods on the parent RWO. For example, a manager might have several employees who report to him: # get the employee collection. employees << my_application.database.collection(:gis, :employees) # find a manager called 'Steve' and get the first matching element steve << employees.select(predicate.eq(:name, "Steve").and(predicate.eq(:role, "manager")).an_element() # display the names of his direct reports. name is a field (or column) # on the employee collection (or table) _for employee _over steve.direct_reports.elements() _loop write(employee.name) _endloop Performing a transaction: # each key in the hash table corresponds to the name of the field (or column) in # the collection (or table) valve_data << hash_table.new_with( :asset_id, 57648576, :material, "Iron") # get the valve collection directly valve_collection << my_application.database.collection(:gis, :valve) # create an insert transaction to insert a new valve record into the collection a # comment can be provide that describes the transaction transaction << record_transaction.new_insert(valve_collection, valve_data, "Inserted a new valve") transaction.run()
Friendly artificial intelligence
Friendly artificial intelligence (friendly AI or FAI) is hypothetical artificial general intelligence (AGI) that would have a positive (benign) effect on humanity or at least align with human interests such as fostering the improvement of the human species. It is a part of the ethics of artificial intelligence and is closely related to machine ethics. While machine ethics is concerned with how an artificially intelligent agent should behave, friendly artificial intelligence research is focused on how to practically bring about this behavior and ensuring it is adequately constrained. == Etymology and usage == The term was coined by Eliezer Yudkowsky, who is best known for popularizing the idea, to discuss superintelligent artificial agents that reliably implement human values. Stuart J. Russell and Peter Norvig's leading artificial intelligence textbook, Artificial Intelligence: A Modern Approach, describes the idea: Yudkowsky (2008) goes into more detail about how to design a Friendly AI. He asserts that friendliness (a desire not to harm humans) should be designed in from the start, but that the designers should recognize both that their own designs may be flawed, and that the robot will learn and evolve over time. Thus the challenge is one of mechanism design—to define a mechanism for evolving AI systems under a system of checks and balances, and to give the systems utility functions that will remain friendly in the face of such changes. "Friendly" is used in this context as technical terminology, and picks out agents that are safe and useful, not necessarily ones that are "friendly" in the colloquial sense. The concept is primarily invoked in the context of discussions of recursively self-improving artificial agents that rapidly explode in intelligence, on the grounds that this hypothetical technology would have a large, rapid, and difficult-to-control impact on human society. == Risks of unfriendly AI == The roots of concern about artificial intelligence are very old. Kevin LaGrandeur showed that the dangers specific to AI can be seen in ancient literature concerning artificial humanoid servants such as the golem, or the proto-robots of Gerbert of Aurillac and Roger Bacon. In those stories, the extreme intelligence and power of these humanoid creations clash with their status as slaves (which by nature are seen as sub-human), and cause disastrous conflict. By 1942 these themes prompted Isaac Asimov to create the "Three Laws of Robotics"—principles hard-wired into all the robots in his fiction, intended to prevent them from turning on their creators, or allowing them to come to harm. In modern times as the prospect of superintelligent AI looms nearer, philosopher Nick Bostrom has said that superintelligent AI systems with goals that are not aligned with human ethics are intrinsically dangerous unless extreme measures are taken to ensure the safety of humanity. He put it this way: Basically we should assume that a 'superintelligence' would be able to achieve whatever goals it has. Therefore, it is extremely important that the goals we endow it with, and its entire motivation system, is 'human friendly.' In 2008, Eliezer Yudkowsky called for the creation of "friendly AI" to mitigate existential risk from advanced artificial intelligence. He explains: "The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else." Steve Omohundro says that a sufficiently advanced AI system will, unless explicitly counteracted, exhibit a number of basic "drives", such as resource acquisition, self-preservation, and continuous self-improvement, because of the intrinsic nature of any goal-driven systems and that these drives will, "without special precautions", cause the AI to exhibit undesired behavior. Alexander Wissner-Gross says that AIs driven to maximize their future freedom of action (or causal path entropy) might be considered friendly if their planning horizon is longer than a certain threshold, and unfriendly if their planning horizon is shorter than that threshold. Luke Muehlhauser, writing for the Machine Intelligence Research Institute, recommends that machine ethics researchers adopt what Bruce Schneier has called the "security mindset": Rather than thinking about how a system will work, imagine how it could fail. For instance, he suggests even an AI that only makes accurate predictions and communicates via a text interface might cause unintended harm. In 2014, Luke Muehlhauser and Nick Bostrom underlined the need for 'friendly AI'; nonetheless, the difficulties in designing a 'friendly' superintelligence, for instance via programming counterfactual moral thinking, are considerable. == Coherent extrapolated volition == Yudkowsky advances the Coherent Extrapolated Volition (CEV) model. According to him, our coherent extrapolated volition is "our wish if we knew more, thought faster, were more the people we wished we were, had grown up farther together; where the extrapolation converges rather than diverges, where our wishes cohere rather than interfere; extrapolated as we wish that extrapolated, interpreted as we wish that interpreted". Rather than a Friendly AI being designed directly by human programmers, it is to be designed by a "seed AI" programmed to first study human nature and then produce the AI that humanity would want, given sufficient time and insight, to arrive at a satisfactory answer. The appeal to an objective through contingent human nature (perhaps expressed, for mathematical purposes, in the form of a utility function or other decision-theoretic formalism), as providing the ultimate criterion of "Friendliness", is an answer to the meta-ethical problem of defining an objective morality; extrapolated volition is intended to be what humanity objectively would want, all things considered, but it can only be defined relative to the psychological and cognitive qualities of present-day, unextrapolated humanity. == Other approaches == Steve Omohundro has proposed a "scaffolding" approach to AI safety, in which one provably safe AI generation helps build the next provably safe generation. Seth Baum argues that the development of safe, socially beneficial artificial intelligence or artificial general intelligence is a function of the social psychology of AI research communities and so can be constrained by extrinsic measures and motivated by intrinsic measures. Intrinsic motivations can be strengthened when messages resonate with AI developers; Baum argues that, in contrast, "existing messages about beneficial AI are not always framed well". Baum advocates for "cooperative relationships, and positive framing of AI researchers" and cautions against characterizing AI researchers as "not want(ing) to pursue beneficial designs". In his book Human Compatible, AI researcher Stuart J. Russell lists three principles to guide the development of beneficial machines. He emphasizes that these principles are not meant to be explicitly coded into the machines; rather, they are intended for the human developers. The principles are as follows: The machine's only objective is to maximize the realization of human preferences. The machine is initially uncertain about what those preferences are. The ultimate source of information about human preferences is human behavior. The "preferences" Russell refers to "are all-encompassing; they cover everything you might care about, arbitrarily far into the future." Similarly, "behavior" includes any choice between options, and the uncertainty is such that some probability, which may be quite small, must be assigned to every logically possible human preference. == Public policy == James Barrat, author of Our Final Invention, suggested that "a public-private partnership has to be created to bring A.I.-makers together to share ideas about security—something like the International Atomic Energy Agency, but in partnership with corporations." He urges AI researchers to convene a meeting similar to the Asilomar Conference on Recombinant DNA, which discussed risks of biotechnology. John McGinnis encourages governments to accelerate friendly AI research. Because the goalposts of friendly AI are not necessarily eminent, he suggests a model similar to the National Institutes of Health, where "Peer review panels of computer and cognitive scientists would sift through projects and choose those that are designed both to advance AI and assure that such advances would be accompanied by appropriate safeguards." McGinnis feels that peer review is better "than regulation to address technical issues that are not possible to capture through bureaucratic mandates". McGinnis notes that his proposal stands in contrast to that of the Machine Intelligence Research Institute, which generally aims to avoid government involvement in friendly AI. == Criticism == Some critics believe that both human-level AI and superintelligence are unlikely and that, therefore, friendly AI is unlik
Automated journalism
Automated journalism, also known as algorithmic journalism or robot journalism, is a term that attempts to describe modern technological processes that are now in use in the journalistic profession, such as news articles and videos generated by computer programs. There are four main fields of application for automated journalism, namely automated content production, data mining, news dissemination and content optimization. Through generative artificial intelligence, stories are produced automatically by computers rather than human reporters. In the 2020s, generative pre-trained transformers have enabled the generation of articles, simply by providing prompts. Automated journalism is sometimes seen as an opportunity to free journalists from routine reporting, providing them with more time for complex tasks. It also allows efficiency and cost-cutting, alleviating some financial burden that many news organizations face. However, automated journalism is also perceived as a threat to the authorship and quality of news and a threat to the livelihoods of human journalists. == History == Historically, the process involved an algorithm that scanned large amounts of provided data, selected from an assortment of pre-programmed article structures, ordered key points, and inserted details such as names, places, amounts, rankings, statistics, and other figures. These programs interpret, organize, and present data in human-readable ways. The output can also be customized to fit a certain voice, tone, or style. Early implementations were mainly used for stories based on statistics and numerical figures. Common topics include sports recaps, weather, financial reports, real estate analysis, and earnings reviews. Data science and AI companies such as Automated Insights, Narrative Science, United Robots and Monok develop and provide these algorithms to news outlets. In 2016, early adopters included news providers such as the Associated Press, Forbes, ProPublica, and the Los Angeles Times. StatSheet, an online platform covering college basketball, runs entirely on an automated program. In 2006, Thomson Reuters announced their switch to automation to generate financial news stories on its online news platform. Reuters used a tool called Tracer. An algorithm called Quakebot published a story about a 2014 California earthquake on The Los Angeles Times website within three minutes after the shaking had stopped. The Associated Press began using automation to cover 10,000 minor baseball leagues games annually, using a program from Automated Insights and statistics from MLB Advanced Media. Outside of sports, the Associated Press also uses automation to produce stories on corporate earnings. Since 2014, Associated Press has been publishing quarterly financial stories with help from Automated Insights. In May 2020, Microsoft announced that a number of its MSN contract journalists would be replaced by robot journalism. On 8 September 2020, The Guardian published an article entirely written by the neural network GPT-3, although the published fragments were manually picked by a human editor. Agentic Tribune produces all of its news articles automatically using AI. News broadcasters in Kuwait, Greece, South Korea, India, China and Taiwan have presented news with anchors based on generative AI models, prompting concerns about job losses for human anchors and audience trust in news that has historically been influenced by parasocial relationships with broadcasters, content creators or social media influencers. Algorithmically generated anchors have also been used by allies of ISIS for their broadcasts. In 2023, Google reportedly pitched a tool to news outlets that claimed to "produce news stories" based on input data provided, such as "details of current events". Some news company executives who viewed the pitch described it as "[taking] for granted the effort that went into producing accurate and artful news stories." In February 2024, Google launched a program to pay small publishers to write three articles per day using a beta generative AI model. The program does not require the knowledge or consent of the websites that the publishers are using as sources, nor does it require the published articles to be labeled as being created or assisted by these models. Meta AI, a chatbot based on Llama 3 which summarizes news stories, was noted by The Washington Post to copy sentences from those stories without direct attribution and to potentially further decrease the traffic of online news outlets. == Benefits == === Speed === Robot reporters are built to produce large quantities of information at quicker speeds. The Associated Press announced that their use of automation has increased the volume of earnings reports from customers by more than ten times. With software from Automated Insights and data from other companies, they can produce 150 to 300-word articles in the same time it takes journalists to crunch numbers and prepare information. By automating routine stories and tasks, journalists are promised more time for complex jobs such as investigative reporting and in-depth analysis of events. Francesco Marconi of the Associated Press stated that, through automation, the news agency freed up 20 percent of reporters’ time to focus on higher-impact projects. This has also been stated by a spokesperson at Gannett, who stated "By leveraging AI, we are able to expand coverage and enable our journalists to focus on more in-depth reporting." GBH reports that AI tools help increase the reach of news publishers. Mike Carragi, a product manager at Patch, stated that they were able to increase their reach from 1200 communities to 7000 communities in just a few months without the need for new employees solely through the adoption of generative AI. In fact, many communities are served solely by AI generated content, which creates summaries of existing information within the community. === Cost === Automated journalism is cheaper because more content can be produced within less time. It also lowers labour costs for news organizations. Reduced human input means less expenses on wages or salaries, paid leaves, vacations, and employment insurance. Automation serves as a cost-cutting tool for news outlets struggling with tight budgets but still wish to maintain the scope and quality of their coverage. == Concerns == === Authorship === In an automated story, there is often confusion about who should be credited as the author. Several participants of a study on algorithmic authorship attributed the credit to the programmer; others perceived the news organization as the author, emphasizing the collaborative nature of the work. There is also no way for the reader to verify whether an article was written by a robot or human, which raises issues of transparency although such issues also arise with respect to authorship attribution between human authors too. === Credibility and quality === Concerns about the perceived credibility of automated news is similar to concerns about the perceived credibility of news in general. Critics doubt if algorithms are "fair and accurate, free from subjectivity, error, or attempted influence." Again, these issues about fairness, accuracy, subjectivity, error, and attempts at influence or propaganda has also been present in articles written by humans over thousands of years. A common criticism is that machines do not replace human capabilities such as creativity, humour, and critical-thinking. However, as the technology evolves, the aim is to mimic human characteristics. When the UK's Guardian newspaper used an AI to write an entire article in September 2020, commentators pointed out that the AI still relied on human editorial content. Austin Tanney, the head of AI at Kainos said: "The Guardian got three or four different articles and spliced them together. They also gave it the opening paragraph. It doesn’t belittle what it is. It was written by AI, but there was human editorial on that." The largest single study of readers' evaluations of news articles produced with and without the help of automation exposed 3,135 online news consumers to 24 articles. It found articles that had been automated were significantly less comprehensible, in part because they were considered to contain too many numbers. However, the automated articles were evaluated equally on other criteria including tone, narrative flow, and narrative structure. Beyond human evaluation, there are now numerous algorithmic methods to identify machine written articles although some articles may still contain errors that are obvious for a human to identify, they can at times score better with these automatic identifiers than human-written articles. A 2017 Nieman Reports article by Nicola Bruno discusses whether or not machines will replace journalists and addresses concerns around the concept of automated journalism practices. Ultimately, Bruno came to the conclusion that AI would assist journalist
Outline of brain mapping
The following outline is provided as an overview of and topical guide to brain mapping: Brain mapping – set of neuroscience techniques predicated on the mapping of (biological) quantities or properties onto spatial representations of the (human or non-human) brain resulting in maps. Brain mapping is further defined as the study of the anatomy and function of the brain and spinal cord through the use of imaging (including intra-operative, microscopic, endoscopic and multi-modality imaging), immunohistochemistry, molecular and optogenetics, stem cell and cellular biology, engineering (material, electrical and biomedical), neurophysiology and nanotechnology. == Broad scope == History of neuroscience History of neurology Brain mapping Human brain Neuroscience Nervous system. === The neuron doctrine === Neuron doctrine – A set of carefully constructed elementary set of observations regarding neurons. For more granularity, more current, and more advanced topics, see the cellular level section Asserts that neurons fall under the broader cell theory, which postulates: All living organisms are composed of one or more cells. The cell is the basic unit of structure, function, and organization in all organisms. All cells come from preexisting, living cells. The Neuron doctrine postulates several elementary aspects of neurons: The brain is made up of individual cells (neurons) that contain specialized features such as dendrites, a cell body, and an axon. Neurons are cells differentiable from other tissues in the body. Neurons differ in size, shape, and structure according to their location or functional specialization. Every neuron has a nucleus, which is the trophic center of the cell (The part which must have access to nutrition). If the cell is divided, only the portion containing the nucleus will survive. Nerve fibers are the result of cell processes and the outgrowths of nerve cells. (Several axons are bound together to form one nerve fibril. See also: Neurofilament. Several nerve fibrils then form one large nerve fiber. Myelin, an electrical insulator, forms around selected axons. Neurons are generated by cell division. Neurons are connected by sites of contact and not via cytoplasmic continuity. (A cell membrane isolates the inside of the cell from its environment. Neurons do not communicate via direct cytoplasm to cytoplasm contact.) Law of dynamic polarization. Although the axon can conduct in both directions, in tissue there is a preferred direction of transmission from cell to cell. Elements added later to the initial Neuron doctrine A barrier to transmission exists at the site of contact between two neurons that may permit transmission. (Synapse) Unity of transmission. If a contact is made between two cells, then that contact can be either excitatory or inhibitory, but will always be of the same type. Dale's law, each nerve terminal releases a single type of neurotransmitter. Some of the basic postulates in the Neuron doctrine have been subsequently questioned, refuted, or updated. See the cellular level section topics for additional information. === Map, atlas, and database projects === Brain Activity Map Project – 2013 NIH $3 billion project to map every neuron in the human brain in ten years, based upon the Human Genome Project. NIH Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative [1] Community outreach site for above where the public may comment [2] Human Brain Project (EU) – 1 billion euro, 10-year project to simulate the human brain with supercomputers. BigBrain A high-resolution 3D atlas of the human brain created as part of the HBP. Human Connectome Project – 2009 NIH $30 million project to build a network map of the human brain, including structural (anatomical) and functional elements. Emphasis included research into dyslexia, autism, Alzheimer's disease, and schizophrenia. See also Connectome a, comprehensive map of neural connections in the brain. Allen Brain Atlas – 2003 $100 million project funded by Paul Allen (Microsoft) BrainMaps – National Institute of Health (NIH) database including 60 terabytes of image scans of primate and non-primates, integrated with information covering structure and function. NeuroNames – Defines the brain in terms of about 550 primary structures (about 850 unique structures) to which all other structures, names, and synonyms are related. About 15,000 neuroanatomical terms are cross indexed, including many synonyms in seven languages. Coverage includes the brain and spinal cord of the four species most frequently studied by neuroscientists: human, macaque (monkey), rat and mouse. The controlled, standardized vocabulary for each structure is located in an unambiguous, strict physical hierarchy, and these terms are selected based on ease of pronunciation, mnemonic value, and frequency of use in recent neuroscientific publications. Relation of each structure to its superstructures and substructures is included. The controlled vocabulary is suitable for uniquely indexing neuroanatomical information in digital databases. Decade of the Brain 1990–1999 promotion by NIH and the Library of Congress "to enhance public awareness of the benefits to be derived from brain research". Communications targeted Members of Congress, staffs, and the general public to promote funding. Talairach Atlas see Jean Talairach Harvard Whole Brain Atlas see Human brain MNI Template see Medical image computing Blue Brain Project and Artificial brain International Consortium for Brain Mapping see Brain Mapping List of neuroscience databases NIH Toolbox National Institute of Health (USA) toolbox for the assessment of neurological and behavioral function Organization for Human Brain Mapping The Organization for Human Brain Mapping (OHBM) is an international society dedicated to using neuroimaging to discover the organization of the human brain. == Imaging and recording systems == This section covers imaging and recording systems. The general section covers history, neuroimaging, and techniques for mapping specific neural connections. The specific systems section covers the various specific technologies, including experimental and widely deployed imaging and recording systems. === General === Most imaging work to date on individual neurons has been conducted outside the brain, typically on large neurons, and has been most frequently destructive. New techniques are however rapidly emerging. Search on "Single neuron imaging" and see related topics: Biological neuron model, Single-unit recording, Neural oscillation, Computational neuroscience. dMRI (above) is also promising in non-destructive imaging of single neurons inside the brain. History of neuroimaging (redirects from Brain scanner) Neuroimaging (redirects from Brain function map) Connectomics – mapping technique showing neural connections in a nervous system. === Specific systems === Cortical stimulation mapping Diffusion MRI (dMRI) – includes diffusion tensor imaging (DTI) and diffusion functional MRI (DfMRI). dMRI is a recent breakthrough in brain mapping allowing the visualization of cross connections between different anatomical parts of the brain. It allows noninvasive imaging of white matter fiber structure and in addition to mapping can be useful in clinical observations of abnormalities, including damage from stroke. Electroencephalography (EEG) – uses electrodes on the scalp and other techniques to detect the electrical flow of currents. Electrocorticography – intracranial EEG, the practice of using electrodes placed directly on the exposed surface of the brain to record electrical activity from the cerebral cortex. Electrophysiological techniques for clinical diagnosis Functional magnetic resonance imaging (fMRI) Medical image computing (brain research of leads medical and surgical uses of mapping technology) Neurostimulation (in research stimulation is frequently used in conjunction with imaging) Positron emission tomography (PET) – a nuclear medical imaging technique that produces a three-dimensional image or picture of functional processes in the body. The system detects pairs of gamma rays emitted indirectly by a positron-emitting radionuclide (tracer), which is introduced into the body on a biologically active molecule. Three-dimensional images of tracer concentration within the body are then constructed by computer analysis. In modern scanners, three dimensional imaging is often accomplished with the aid of a CT X-ray scan performed on the patient during the same session, in the same machine. === Imaging and recording componentry === ==== Electrochemical ==== Haemodynamic response – the rapid delivery of blood to active neuronal tissues. Blood Oxygenation Level Dependent signal (BOLD), corresponds to the concentration of deoxyhemoglobin. The BOLD effect is based on the fact that when neuronal activity is increased in one part of the brain, there is also an increased amount of cerebral blood flow to that area. Functional m
KiSAO
The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. KiSAO is part of the BioModels.net project and of the COMBINE initiative. == Structure == KiSAO consists of three main branches: simulation algorithm simulation algorithm characteristic simulation algorithm parameter The elements of each algorithm branch are linked to characteristic and parameter branches using has characteristic and has parameter relationships accordingly. The algorithm branch itself is hierarchically structured using relationships which denote that the descendant algorithms were derived from, or specify, more general ancestors.