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Related Concept Videos

Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Structural Joints: Fibrous Joints01:03

Structural Joints: Fibrous Joints

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Fibrous joints are a type of joint where the bones are connected by fibrous connective tissue. These joints provide stability and minimal to no movement between the articulating bones. There are three types of fibrous joints.
Suture
All the bones of the skull, except for the mandible, are joined to each other by a fibrous joint called a suture. The fibrous connective tissue found at a suture strongly unites the adjacent skull bones and thus helps to protect the brain and form the face. In...
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Internal Loadings in Structural Members: Problem Solving01:28

Internal Loadings in Structural Members: Problem Solving

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When designing or analyzing a structural member, it is important to consider the internal loadings developed within the member. These internal loadings include normal force, shear force, and bending moment. Engineers can ensure that the structural member can support the applied external forces by calculating these internal loadings.
To illustrate this, let's consider a beam OC of 5 kN, inclined at an angle of 53.13° with the horizontal and supported at both ends. Determine the internal...
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Stability of structures01:14

Stability of structures

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In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
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Structural Joints: Cartilaginous Joints01:17

Structural Joints: Cartilaginous Joints

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As the name indicates, at a cartilaginous joint, the adjacent bones are united by cartilage, a tough but flexible type of connective tissue. Unlike synovial joints, these types of joints lack a joint cavity and involve bones joined together by either hyaline cartilage or fibrocartilage.
There are two types of cartilaginous joints:
Synchondrosis
A synchondrosis ("joined by cartilage") is a cartilaginous joint where bones are connected by hyaline cartilage. Synchondrosis may be temporary...
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Structural Joints: Synovial Joints01:16

Structural Joints: Synovial Joints

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Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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RNA structural alignments, part II: non-Sankoff approaches for structural alignments.

Kiyoshi Asai1, Michiaki Hamada

  • 1Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-ku, Tokyo, Japan.

Methods in Molecular Biology (Clifton, N.J.)
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Summary

This study presents a novel approach for RNA structural alignment, reducing computational complexity. It separates structure and alignment prediction, improving efficiency for longer RNA sequences.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • RNA Structure Prediction

Background:

  • The Sankoff algorithm for simultaneous RNA structural alignment is computationally intensive (O(L^6)).
  • High computational cost limits its application to long RNA sequences.
  • Efficient algorithms are crucial for analyzing large RNA datasets.

Purpose of the Study:

  • To introduce methods for predicting RNA structures and alignments separately, bypassing Sankoff's high computational demands.
  • To develop an integrated approach where structure and alignment predictions inform each other iteratively.
  • To improve the efficiency and scalability of RNA structural alignment.

Main Methods:

  • Separating RNA structure and alignment prediction into distinct but interdependent processes.
  • Utilizing base-pairing probabilities (BPPs) or stem candidates from structure prediction for alignment.
  • Employing probabilistic consistency transformation (PCT) to integrate alignment information back into structure prediction.
  • Applying the maximum expected accuracy (MEA) principle for evaluating structural alignment using sum-of-pairs (SPS) scores.

Main Results:

  • Demonstrated a significant reduction in computational complexity compared to the Sankoff algorithm.
  • Developed an iterative approach enhancing the accuracy of both structure and alignment predictions.
  • Showcased the effectiveness of integrating information between the two prediction processes.

Conclusions:

  • Separate prediction of RNA structure and alignment offers a computationally feasible alternative to the Sankoff algorithm for long sequences.
  • Iterative refinement, where structure and alignment predictions influence each other, improves overall accuracy.
  • The proposed methods provide a scalable and efficient solution for RNA structural alignment.