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Stem cells: Research in the human stem cell field grew out of findings by Canadian scientists Ernest A. McCulloch and James E. Till in the 1960s. 2000s - Several reports of adult stem cell plasticity are published. Stem cells have the remarkable potential to develop into many different cell types in the body. Serving as a sort of repair system for the body, they can theoretically divide without limit to replenish other cells as long as the person or animal is still alive. When a stem cell divides, each new cell has the potential to either remain a stem cell or become another type of cell with a more specialized function, such as a muscle cell, a red blood cell, or a brain cell. Stem cells have two important characteristics that distinguish them from other types of cells. First, they are unspecialized cells that renew themselves for long periods through cell division. The second is that under certain physiologic or experimental conditions, they can be induced to become cells with special functions such as the beating cells of the heart muscle or the insulin-producing cells of the pancreas. Scientists primarily work with two kinds of stem cells from animals and humans: embryonic stem cells and adult stem cells, which have different functions and characteristics (more than 9 weeks) Human embryonic and adult stem cells each have advantages and disadvantages regarding potential use for cell-based regenerative therapies. Of course, adult and embryonic stem cells differ in the number and type of differentiated cells types they can become. Embryonic stem cells can become all cell types of the body because they are pluripotent. Adult stem cells are generally limited to differentiating into different cell types of their tissue of origin. However, some evidence suggests that adult stem cell plasticity may exist, increasing the number of cell types a given adult stem cell can become. Large numbers of embryonic stem cells can be relatively easily grown in culture, while adult stem cells are rare in mature tissues and methods for expanding their numbers in cell culture have not yet been worked out. This is an important distinction, as large numbers of cells are needed for stem cell replacement therapies. A potential advantage of using stem cells from an adult is that the patient's own cells could be expanded in culture and then reintroduced into the patient. The use of the patient's own adult stem cells would mean that the cells would not be rejected by the immune system. This represents a significant advantage as immune rejection is a difficult problem that can only be circumvented with immunosuppressive drugs. Embryonic stem cells from a donor introduced into a patient could cause transplant rejection. However, whether the recipient would reject donor embryonic stem cells has not been determined in human experiments. Blood Stem Cell: Whereas other types of cells in the body have a limited lifespan and die after dividing their endowed number of times, a stem cell can reproduce forever. The stem cell is immortal (in cellular terms). A stem cell can forgo immortality and turn into an ordinary blood cell, a red blood cell (an erythrocyte), a white blood cell (a leukocyte), or a large cell (a megakaryocyte) that fragments into the platelets needed for blood to clot. Cardiac muscle contractions do not require nerve stimulation. The cells are specialized to contract rhythmically on their own. The internal control system in the heart serves to coordinate the muscle cell contractions to produce an organized productive heart beat. The external nerve supply to the heart is autonomic. It serves to modify cardiac contractions to meet changing body needs. cardiac muscle (microscopic) Visceral smooth muscle is found primarily in the walls of hollow abdominal organs such as the intestine, urinary bladder and uterus. The cells are linked together in large sheets of cells that contract together - no fine movements are possible. Visceral smooth muscle does not require external nerve stimulation for contraction, but external autonomic nerves serve to modify contractions. Multi-unit smooth muscle occurs in small individual units - the cells are not linked into large sheets. This type of smooth muscle requires an external nerve supply to initiate its contractions. It is found where small fine contractions are needed, such as the iris and cilia body of the eye. smooth muscle (whole mount) Synapse Output Neuron (a) Newborn (b) 6 Months Nerve Cell (c) 24 Months Transport Selection by Neural net Distance Money in Purse (code) Near(0) Little Far (0.5) Walk Walk Far(1) Walk Bus Bus Bus Input Money in Purse Input Money in Purse Distance Taxi Output Distance Walk Bus Output Walk Bus Taxi Taxi Trip Plan by Neural Net Days Budget 200$ less 500$ less 1000$ less 2000$ less 4 days 6 days Domestic Domestic Input 10 days Domestic Hawaii Hawaii Hawaii Europe Europe Input Budget 8 days Output Days Domestic Hawaii Output Domestic Trip Budget Hawaii Days Europe Trip Europe A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms (also known as evolutionary computation) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination). Genetic algorithms in particular became popular through the work of John Holland in the early 1970s, and particularly his 1975 book. His work originated with studies of cellular automata, conducted by Holland and his students at the University of Michigan. Try 3 kinds of change by some probability until total N unit pieces pieces pieces crossover N A N A B B mutation copy G times Crossover: A: 01001 11010 → 01001 01011 B: 10101 01011 → 10101 11010 Solution: Selection of the highest adaptation unit after G times repetition Reproduction: Main articles: crossover (genetic algorithm) and mutation (genetic algorithm) The next step is to generate a second generation population of solutions from those selected through genetic operators: crossover (also called recombination), and/or mutation. For each new solution to be produced, a pair of "parent" solutions is selected for breeding from the pool selected previously. By producing a "child" solution using the above methods of crossover and mutation, a new solution is created which typically shares many of the characteristics of its "parents". New parents are selected for each child, and the process continues until a new population of solutions of appropriate size is generated. These processes ultimately result in the next generation population of chromosomes that is different from the initial generation. Generally the average fitness will have increased by this procedure for the population, since only the best organisms from the first generation are selected for breeding, along with a small proportion of less fit solutions, for reasons already mentioned above. A cellular automaton (plural: cellular automata) is a discrete model studied in computability theory, mathematics, and theoretical biology. It consists of a regular grid of cells, each in one of a finite number of states. The grid can be in any finite number of dimensions. Time is also discrete, and the state of a cell at time t is a function of the states of a finite number of cells (called its neighborhood) at time t − 1. These neighbors are a selection of cells relative to the specified cell, and do not change (though the cell itself may be in its neighborhood, it is not usually considered a neighbor). Every cell has the same rule for updating, based on the values in this neighbourhood. Each time the rules are applied to the whole grid a new generation is created. a cellular automata pattern on its shell Cephalopods Neural networks can be used as cellular automata, too. The complex moving wave patterns on the skin of cephalopods are a good display of corresponding activation patterns in the animals' brain. Cellular Automaton Rules: 0 1 0 1 1 0 1 Start Fractal 0 Start Rules: 3.ARIMA The ARIMA procedure analyzes and forecasts equally spaced univariate time series data, transfer function data, and intervention data using the Auto Regressive Integrated Moving-Average (ARIMA) or autoregressive movingaverage (ARMA) model. An ARIMA model predicts a value in a response time series as a linear combination of its own past values, past errors (also called shocks or innovations), and current and past values of other time series. The ARIMA approach was first popularized by Box and Jenkins, and ARIMA models are often referred to as Box-Jenkins models. The general transfer function model employed by the ARIMA procedure was discussed by Box and Tiao (1975). When an ARIMA model includes other time series as input variables, the model is sometimes referred to as an ARIMAX model. Pankratz (1991) refers to the ARIMAX model as dynamic regression. The ARIMA procedure provides a comprehensive set of tools for univariate time series model identification, parameter estimation, and forecasting, and it offers great flexibility in the kinds of ARIMA or ARIMAX models that can be analyzed. The ARIMA procedure supports seasonal, subset, and factored ARIMA models; intervention or interrupted time series models; multiple regression analysis with ARMA errors; and rational transfer function models of any complexity. average yearly temperature in the north grove compared with year 1880 Year Internet Intrusion Detection in 2003 (IP Address) 11 1 10 1 USA 2 China 3 Korea 4 Holland 5 Japan 6 England 7 Brazil 8 Czech 9 Canada 10 Australia 11 Others 9 8 7 6 5 4 3 2 Forecast by ARIMA Model Intrusion Times Forecast Real Value 1 10 11 20 21 Date in Dec, 2003 31 4.Frame Problem (McCarthy and Hayes 1969 ) To most AI researchers, the frame problem is the challenge of representing the effects of action in logic without having to represent explicitly a large number of intuitively obvious non-effects. To many philosophers, the AI researchers' frame problem is suggestive of a wider epistemological issue, namely whether it is possible, in principle, to limit the scope of the reasoning required to derive the consequences of an action. The frame problem is the problem of how a rational agent bounds the set of beliefs to change when an action is performed. This problem originates from artificial intelligence, where it is formulated as the problem of avoiding to specify all conditions that are not affected by actions, in the context of representing dynamical domains in a formal logic. Bring a Picture No.1 Robot Condition of thinking: Robot Picture Car Bomb Condition of other thing: Ceiling Wall Floor Door Electricity : : <Infinite condition> 1. Bring a picture with bomb No.2 Robot 2. explosion during thinking Many Condition Solution Action Grouping, Selection, Neglect, etc 5.Perceptron The perceptron is a type of artificial neural network invented in 1957 at the Cornell Aeronautical Laboratory by Frank Rosenblatt. It can be seen as the simplest kind of feedforward neural network: a linear classifier. A perceptron is a connected network that simulates an associative memory. The most basic perceptron is composed of an input layer and output layer of nodes, each of which are fully connected to the other. Assigned to each connection is a weight which can be adjusted so that, given a set of inputs to the network, the associated connections will produce a desired output. The adjusting of weights to produce a particular output is called the "training" of the network which is the mechanism that allows the network to learn. Perceptrons are among the earliest and most basic models of artificial neural networks, yet they are at work in many of todayís complex neural net applications The perceptron is a kind of binary classifier that maps its input x (a binary vector) to an output value f(x) (a single binary value) calculated as where w is a vector of real-valued weights and is the dot product (which computes a weighted sum). b is the 'bias', a constant term that does not depend on any input value. The value of f(x) (0 or 1) is used to classify x as either a positive or a negative instance, in the case of a binary classification problem. The bias can be thought of as offsetting the activation function, or giving the output neuron a "base" level of activity. If b is negative, then the weighted combination of inputs must produce a positive value greater than − b in order to push the classifier neuron over the 0 threshold. Spatially, the bias alters the position (though not the orientation) of the decision boundary. Since the inputs are fed directly 6.Data Mining Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Huge Data Base Knowledge Presentation Collection Processing Output Useful Knowledge Useful Data Reverse Data Mining = Data Base Discovery For example: In case of the getting of foreign language knowledge, we need some database for it. we have to discover the database. And also it can be used for Humanoid Robot. Huge Data Base Knowledge Presentation Collection Processing Input Target Knowledge TRIZ (pronounced /triːz/) is a Russian acronym for "Teoriya Resheniya Izobretatelskikh Zadatch" (Теория решения изобретательских задач), Theory of solving inventive problems or Theory of inventive problem solving. It was developed by Genrich Altshuller and his colleagues starting in 1946. TRIZ is a methodology, tool set, knowledge base, and model-based technology for generating innovative ideas and solutions for problem solving. TRIZ provides tools and methods for use in problem formulation, system analysis, failure analysis, and patterns of system evolution (both 'as-is' and 'could be'). TRIZ, in contrast to techniques such as brainstorming (which is based on random idea generation), aims to create an algorithmic approach to the invention of new systems, and the refinement of old systems. Find out the Method of Invention Essence of TRIZ: For creative problem solving, TRIZ provides a dialectic way of thinking, to understand the problem as a system, to image the ideal solution first, and to solve contradictions. TRIZ Technique: 1. Problem → Think for System 2. Imagination of Ideal Solution 3. Solution of Contradiction 7. Pattern recognition In computer science, the imposition of identity on input data, such as speech, images, or a stream of text, by the recognition and delineation of patterns it contains and their relationships. Stages in pattern recognition may involve measurement of the object to identify distinguishing attributes, extraction of features for the defining attributes, and comparison with known patterns to determine a match or mismatch. Pattern recognition has extensive application in astronomy, medicine, robotics, and remote sensing by satellites. Check Needful Technologies: -Neural Network -Generic Algorithm -Signal Processing -AI -Data Mining -Fuzzy Recognition Mechanism by Human New Recognition System Development 8.Fuzzy Fuzzy logic can be used to control household appliances such as washing machines (which sense load size and detergent concentration and adjust their wash cycles accordingly) and refrigerators. A basic application might characterize subranges of a continuous variable. For instance, a temperature measurement for anti-lock brakes might have several separate membership functions defining particular temperature ranges needed to control the brakes properly. Each function maps the same temperature value to a truth value in the 0 to 1 range. These truth values can then be used to determine how the brakes should be controlled. Consider the linguistic variable, age. Suppoe it takes on values, young, middle-aged, old. Zadeh represents the three values as three fuzzy sets, over the value age_in_years. Each set has its unique possibility function. So a person aged 45 would certainly be condiered middle aged. That person's possibility value would be 1.0. On the other hand, a person aged 60, might have a possibility of 0.4 of beiing middle aged, and a possibility of 0.3 of being in the old set. 9. Expert System An expert system, also known as a knowledge based system, is a computer program that contains some of the subject-specific knowledge, and contains the knowledge and analytical skills of one or more human experts. This class of program was first developed by researchers in artificial intelligence during the 1960s and 1970s and applied commercially throughout the 1980s. It can be used for action control, medical care, computer game, legal adviser and information analysis for our experience. It also is used fuzzy technique. Knowledge Base Expert Q Inference A Expert System Ontology, Metadata Intellectual Function of Human Figure, Table, Letter, Voice, Picture, Compare, Calculation, Memory, Learning, Meaning, Pattern Recognition, Inference, Intuition, Imagination, Judgment, Arts, Idea, Creation Bottle Neck = Knowledge Acquisition How, What, Who, Which ? Why expert system does not success ? = Need Perfect Function for Diagnosis or Judgment = No Self-Learning System = Not Easy Handling = Not Friendly communication with human 10. Regression Analysis In statistics, regression analysis examines the relation of a dependent variable (response variable) to specified independent variables (explanatory variables). The mathematical model of their relationship is the regression equation. The dependent variable is modeled as a random variable because of uncertainty as to its value, given only the value of each independent variable. A regression equation contains estimates of one or more hypothesized regression parameters ("constants"). These estimates are constructed using data for the variables, such as from a sample. The estimates measure the relationship between the dependent variable and each of the independent variables. They also allow estimating the value of the dependent variable for a given value of each respective independent variable. Uses of regression include curve fitting, prediction (including forecasting of timeseries data), modeling of causal relationships, and testing scientific hypotheses about relationships between variables. C= Consumption E= Earnings C C= a1 + a2 x E 20 19 C= 3 + 0.4 x E 10 E=40, C=19 3 0 10 20 30 E 40 Multiple Regression Analysis : The general purpose of multiple regression (the term was first used by Pearson, 1908) is to learn more about the relationship between several independent or predictor variables and a dependent or criterion variable. For example, a real estate agent might record for each listing the size of the house (in square feet), the number of bedrooms, the average income in the respective neighborhood according to census data, and a subjective rating of appeal of the house. Once this information has been compiled for various houses it would be interesting to see whether and how these measures relate to the price for which a house is sold. For example, one might learn that the number of bedrooms is a better predictor of the price for which a house sells in a particular neighborhood than how "pretty" the house is (subjective rating). One may also detect "outliers," that is, houses that should really sell for more, given their location and characteristics. Personnel professionals customarily use multiple regression procedures to determine equitable compensation. One can determine a number of factors or dimensions such as "amount of responsibility" (Resp) or "number of people to supervise" (No_Super) that one believes to contribute to the value of a job. The personnel analyst then usually conducts a salary survey among comparable companies in the market, recording the salaries and respective characteristics (i.e., values on dimensions) for different positions. This information can be used in a multiple regression analysis to build a regression equation of the form: Salary = 0.5Resp + 0.8No_Super Once this so-called regression line has been determined, the analyst can now easily construct a graph of the expected (predicted) salaries and the actual salaries of job incumbents in his or her company. Thus, the analyst is able to determine which position is underpaid (below the regression line) or overpaid (above the regression line), or paid equitably. In the social and natural sciences multiple regression procedures are very widely used in research. In general, multiple regression allows the researcher to ask (and hopefully answer) the general question "what is the best predictor of ...". For example, educational researchers might want to learn what are the best predictors of success in high-school. Psychologists may want to determine which personality variable best predicts social adjustment. Sociologists may want to find out which of the multiple social indicators best predict whether or not a new immigrant group will adapt and be absorbed into society. V: No. of understand English Vocabulary X: No. of studied English Vocabulary at Elementary School Y: No. of studied English Vocabulary at Junior High School a,b,c : No. of English Vocabulary V = aX + bY + c For Example: a= 5, b=3 , c=100 V = 5X + 3Y + 100 V 10,000 Y 6,000 3,000 2,000 1,000 2,000 X 100 0 1,000 2,000 3,000 11. Bioinformatics Bioinformatics and computational biology involve the use of techniques including applied mathematics, informatics, statistics, computer science, artificial intelligence, chemistry, and biochemistry to solve biological problems usually on the molecular level. Research in computational biology often overlaps with systems biology. Major research efforts in the field include sequence alignment, gene finding, genome assembly, protein structure alignment, protein structure prediction, prediction of gene expression and protein-protein interactions, and the modeling of evolution. Living Body Phenomenon - Alignment information of genetic map and amino-acid - Architecture analysis of living body - Interaction between protein Living Body = Genetic network = Protein network Information Flow Phenomenon Information Analysis Method Computer 12. Artificial Life Artificial intelligence has traditionally used a top down approach while alife generally works from the bottom up. Artificial Life, (commonly Alife or alife) is a field of study and associated art form which examine systems related to life, its processes and its evolution through simulations using computer models, robotics, and biochemistry. There are three main kinds of alife: soft from software, hard from hardware, and wet from biochemistry approaches respectively. Artificial life imitates traditional biology by trying to recreate biological phenomena. The term "Artificial Life" is often used to specifically refer to soft alife. Artificial life is one of the hottest research fields which is related to studies on so-called complex systems. As its name suggests, artificial life is a study on life which is realized using computers. In this study area, people are trying to understand the origin of life, the various systems in living organisms, or the mechanisms of evolution by means of making models for them and implementing experimental systems as software. Some people even attempt to create software that can itself be regarded as “life”. What is life? Vast question that computer scientists have taken their turn trying to answer by grabbing onto two key concepts: 1) life reproduces itself, and 2) life evolves. In the 1970s, an artificial life computer program travelled around the world: the ‘game of life.’ In it, ‘ cells ’ (actually, black dots on the computer screen) appear, move, and die according to a set of simple rules. From an initial population distributed on the screen at random, stable structures emerge, some moving, some immobile, that resemble, in circumstance, what might have been the first living organisms. Today, artificial life calls on increasingly complex ideas, such as emergence, and touches more and more the fields of robotics and bionics . 13.Tierra (Computer Simulation for Artificial Life) Tierra is a computer simulation developed by ecologist Thomas S. Ray in the early 1990s in which computer programs compete for central processing unit (CPU) time and access to main memory. The computer programs in Tierra are evolvable and can mutate, self-replicate and recombine. Tierra is a frequently cited example of an artificial life model; in the metaphor of the Tierra, the evolvable computer programs can be considered as digital organisms which compete for energy (CPU time) and resources (main memory). Tierra Computer Simulation System Tierra Start Artificial Living Thing 1 Resister Pointer ALT1 Artificial Living Thing 2 Memory Area Reservation Resister Pointer ALT2 ALT (i) start artificial living in their memory. Artificial Living Thing 3 Resister Pointer ALT3 Memory Processor Byte Code Gene Machine Language Bit Change Reproduction Memory acquisition Processor Time Acquisition Execution