Pattern Identification
Pattern Identification - Related Videos
14:27
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Cited by 73 •
2013
Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.
14:12
Rodent Identification II
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2015
Source: Kay Stewart, RVT, RLATG, CMAR; Valerie A. Schroeder, RVT, RLATG. University of Notre Dame, IN Animal records must be accurately maintained to ensure that data collection is correct. Records range from maintaining information on cage cards to having a detailed database with all of the relevant information on each animal. The primary component of recordkeeping is the individual identification of research animals. There are a variety of methods suitable for identifying mice and rats. This...
11:09
Rodent Identification I
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2015
Source: Kay Stewart, RVT, RLATG, CMAR; Valerie A. Schroeder, RVT, RLATG. University of Notre Dame, IN A fundamental requirement of biomedical research is the proper identification of research animals. It is essential that the right animal is utilized for procedures and data collection. Laboratory mice and rats can be identified with the following permanent methods: ear tags, ear punch codes, microchip implantation, tail tattoos for adult mice, and toe tattoos for neonates. Temporary methods of...
12:37
Pyrosequencing for Microbial Identification and Characterization
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Cited by 20 •
2013
Pyrosequencing is a versatile technique that facilitates microbial genome sequencing that can be used to identify bacterial species, discriminate bacterial strains, and detect genetic mutations that confer resistance to anti-microbial agents. In this video, the procedure for microbial amplicon generation, amplicon pyrosequencing, and DNA sequence analysis will be demonstrated.
11:46
Decoding Auditory Imagery with Multivoxel Pattern Analysis
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California Imagine the sound of a bell ringing. What is happening in the brain when we conjure up a sound like this in the "mind's ear?" There is growing evidence that the brain uses the same mechanisms for imagination that it uses for perception.1 For example, when imagining visual images, the visual cortex becomes activated, and when imagining sounds, the auditory cortex is engaged. However, to what extent are...
FAQs
The comparison focuses on how successive elements change. A constant difference suggests an additive relationship, while a consistent multiplicative relationship points to scaling from one element to the next. A repeating cycle is recognized when elements or changes recur in a regular order. Testing each proposed rule against known and new cases helps determine which relationship best fits the pattern.
Symmetry and repetition provide structural clues that may not appear through numerical differences alone. In shapes, symbols, or other organized elements, matching arrangements can reveal a transformation or recurring configuration. Recognizing these features supports classification and helps connect concrete observations with broader mathematical rules, especially when the relevant relationship is spatial or symbolic rather than purely numerical.
A rule that matches a few observations may not adequately describe the full pattern. Checking it against known cases confirms consistency, while applying it to new cases examines whether it supports reliable prediction. This testing process reduces unsupported generalization and can provide a foundation for moving from observed regularity toward algebraic reasoning or proof.
The same underlying regularity may appear differently in numbers, shapes, symbols, or data. Comparing successive elements is especially useful for numerical sequences, whereas symmetry or repeated structure may be more informative for visual arrangements. Selecting a representation that makes relationships visible can improve classification, clarify the proposed rule, and support more effective generalization.
Begin by organizing the observations in an order that makes comparison possible. Examine changes between successive elements, look for multiplication, repetition, or symmetry, and propose a rule that explains the observed structure. Then test the rule against the original examples and new cases. A successful result can be expressed as a generalization, prediction, or candidate proof.
It is useful when observed examples suggest a relationship that can be extended to cases not yet given. After a consistent rule has been identified and tested, that rule can support predictions and be expressed algebraically. This process helps learners move from concrete examples to abstract relationships, while also providing a basis for classification and mathematical proof.
In research, regularities within complex datasets can reveal structure that is not immediately obvious from individual observations. Those findings may guide model selection by indicating which kind of relationship deserves further examination. Pattern identification therefore serves not only classroom reasoning but also the organization and interpretation of data in applied and research mathematics.