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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...
Structural Classification of Joints01:20

Structural Classification of Joints

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...
Joints01:26

Joints

Joints, also called articulations or articular surfaces, are points at which ligaments or other tissues connect adjacent bones. Joints permit movement and stability, and can be classified based on their structure or function.
Structural joint classifications are based on the material that makes up the joint as well as whether or not the joint contains a space between the bones. Joints are structurally classified as fibrous, cartilaginous, or synovial.
Fibrous Joints Are Immovable
The bones of a...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

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Related Experiment Video

Updated: May 20, 2026

Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics
08:48

Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics

Published on: January 9, 2016

Combining joint models for biomedical event extraction.

David McClosky1, Sebastian Riedel, Mihai Surdeanu

  • 1Department of Computer Science, Stanford University, Stanford, CA, USA. mcclosky@stanford.edu

BMC Bioinformatics
|July 5, 2012
PubMed
Summary
This summary is machine-generated.

Stacking biomedical event extraction models significantly boosts performance, outperforming simpler combination methods. Removing low-precision novel events further refines results for state-of-the-art accuracy.

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Automated Joint Space Detection Improves Bone Segmentation Accuracy
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Automated Joint Space Detection Improves Bone Segmentation Accuracy

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Last Updated: May 20, 2026

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08:48

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Published on: January 9, 2016

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06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

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Area of Science:

  • Biomedical Natural Language Processing
  • Computational Linguistics
  • Machine Learning for Bioinformatics

Background:

  • Biomedical event extraction is crucial for understanding complex biological processes.
  • Existing systems like UMass and Stanford use structured prediction, dual decomposition, and parsing algorithms.
  • Model combination techniques are explored to enhance extraction accuracy.

Purpose of the Study:

  • To investigate and compare model combination techniques for biomedical event extraction.
  • To evaluate the effectiveness of stacking versus simpler methods like intersection and union.
  • To analyze the impact of event structure graph properties on system combination.

Main Methods:

  • Focus on stacking, using Stanford system predictions as features for the UMass system.
  • Comparison with direct combination methods: intersection and union.
  • Analysis of event structure graph properties and their influence on combined system performance.
  • Tracing event origins to understand individual system contributions.

Main Results:

  • Stacking significantly improved performance, while intersection and union showed no significant benefits.
  • The stacked system (FAUST) achieved state-of-the-art F1 score of 56.6% on the Genia (Task 1) test set after removing low-precision novel events.
  • FAUST secured 1st place in three BioNLP 2011 shared tasks (Genia Task 1 & 2, Infectious Diseases) and 2nd place in another.

Conclusions:

  • Stacking is a superior model combination technique for biomedical event extraction compared to intersection and union.
  • Complementary data views from sub-systems are essential for effective model combination.
  • Removing low-precision novel events generated by stacking further enhances overall system performance.