The authors would like to acknowledge the Charles Burstone Foundation Award for supporting the project.

An institutional review board exemption was obtained for evaluating CBCT volumes archived in the Division of Oral and Maxillofacial Radiology (IRB No. 17-071S-2).
1. Volume selection and criteria
<ol>
	<li>Acquire a CBCT image of the head and face16.</li>
	<li>Examine the image for tooth alignment, missing teeth, voxel size, field of view, and overall quality of the image.</li>
	<li>Make sure the voxel size is not larger than 350 &#181;m (0.35 mm).</li>
</ol>
2. Segmentation of the teeth and bone
<ol>
	<li>Load the raw DICOM files of the CBCT image into Mimics software for segmentation (Figure 2). Click Image &#62; Crop Project. Crop the image to include only the maxilla and maxillary teeth. 
	NOTE: The field of view should be large enough to capture the maxilla and maxillary teeth. Make sure the image includes the tooth crowns, the hard palate up to the nasal floor, maxillary sinuses, facial surfaces of the maxillary teeth, and the posterior extent of the hard palate and maxillary tuberosity.</li>
	<li>Right click on the tab for Mask and create a New Mask for the image. Rename the mask as UL1, UL2, &#8230;, UL7 for the left side and UR1, UR2, &#8230;, UR7 for the right side, based on the tooth of interest.</li>
	<li>Identify the tooth of interest on the masked CBCT image (see views). Use the Clear Mask tool to erase the mask. The software might be unable to distinguish between the teeth and bone because the gray values of the two are similar. 
	NOTE: The thresholding tool in Mimics is unable to segment the teeth and bone separately. Therefore, a different method for segmentation is required.</li>
	<li>Click on the Multiple Slice Edit tool (Ctrl + M). Select the view (Axial, Coronal, or Sagittal). Manually highlight (i.e., draw) some of the slices as deemed necessary. 
	NOTE: Highlighting more slices adds more detail to the structure.</li>
	<li>Click the Interpolate tool to fill up the volume for the skipped slices and apply.</li>
	<li>Generate the 3D volume for the tooth by right-clicking on the mask and selecting the option to calculate the 3D volume.</li>
	<li>Repeat steps 2.2-2.6 for each tooth of the maxillary arch.</li>
	<li>Select all the 3D maxillary teeth, UL7-UR7. Right-click to select Smoothing. Set the smoothing factor to 0.4 and iterations to 4.</li>
	<li>To segment the maxillary bones right-click on the tab for Mask. Create a new mask for the image.</li>
	<li>From the drop-down menu for predefined threshold sets, select Custom. Adjust the threshold value to include the complete maxillary bone. Be sure to check the Fill Holes box before applying the threshold. 
	NOTE: Small holes of &#8804;1 mm in the cortical bone are acceptable, because they can be removed easily in later stages.</li>
	<li>Click on the Dynamic Region Growing Tool to fill the large holes visible in the mask. Select the maxillary bone mask as the target for the tool in addition to selecting the Multiple Layer box. Use 50 for the Min and 150 for Max values. Hold down the Control key while clicking on the areas of cortical bone that were not highlighted in the mask.</li>
	<li>Right-click on the maxillary bone mask for the Smooth Mask function. Repeat this step 3x for best results.</li>
	<li>Generate the 3D volume for the maxilla by right-clicking on the mask and selecting the option to calculate the 3D volume.</li>
	<li>Select the 3D maxillary bone. Right-click to select smoothing. Set the smoothing factor to ~0.4 and iterations to 4.</li>
	<li>Select the 3D maxillary bone and right-click to select Wrap. Set 0.2 mm for the smallest detail and 1 mm for the gap closing distance. Check the Protect Thin Walls option. Press Ok.</li>
	<li>Rename the 3D maxillary bone &#34;Maxilla&#34;.</li>
</ol>
3. Cleaning and meshing
<ol>
	<li>Select the 3D objects and copy (Ctrl + C).</li>
	<li>Open the 3matic software, and paste (Ctrl + V) the selected 3D objects. They will appear in the object tree and work area of 3matic as a 3D structure (Figure 3).</li>
	<li>Click the Fix tab from the tool bar and use the Smooth option. Under the Operations box select the desired 3D object(s) or entities and Apply the default parameters.</li>
	<li>Click the Finish tab from the tool bar and use the Local Smoothing option. Under the Operations Box select the desired 3D object(s) or entities. Use the cursor to manually smoothen out the desired regions.</li>
	<li>Duplicate the teeth. On the object tree select all teeth, right-click, and select Duplicate.</li>
	<li>Select All Duplicated Teeth, group, and name the folder &#34;group 1&#34;. The original set will serve as the final teeth for analysis.</li>
	<li>For the duplicated teeth in group 1, click the Curve Module and the Create Curve option. Manually draw a curve around the cementoenamel junction (CEJ) for all duplicated teeth.</li>
	<li>Select the Curve, Contour, and Border entities under the Smooth Curve option.</li>
	<li>Separate the crown and root surfaces into their own parts by selecting the Split Surfaces by Curves option and left-clicking on the 3D object to select.</li>
	<li>Generate the PDL from the root structure of the tooth by splitting the tooth into root and crown at the CEJ.
	<ol>
		<li>Duplicate the 3D objects from group 1 (generated in step 3.6) as group 2. For group 2, in the object tree box, click on the Object. From the surface list delete the crown surface. Perform this step for all objects in group 2.</li>
		<li>For group 2, click on Design Module &#62; Hollow. Apply the desired parameters (Table 1).</li>
		<li>Click on the Fix Module &#62; Fix Wizard. Click on the individual parts, update, and follow the given directions.</li>
		<li>Repeat step 3.10.3 for all parts. Rename all parts in the group 2 as &#34;UL1_PDL&#34; to &#34;UL7_PDL&#34; and &#34;UR1_PDL&#34; to &#34;UR7_PDL&#34;.</li>
	</ol>
	</li>
	<li>In group 1, from the object tree box, click on Object. From the surface list delete the root surface.</li>
	<li>Select Fill Hole Normal option and select the contour. Click on Bad Contour and Apply. The entire space will be filled.</li>
	<li>Select the Design Module &#62; Local Offset and select the entire crown surface. Check the following options: Direction (select external), Offset Distance (select 0.5), and Diminishing Distance (select 2.0). Apply.</li>
	<li>Repeat step 3.13.</li>
	<li>Repeat steps 3.11-3.14 for each tooth of the maxillary arch.</li>
	<li>Remesh (Figure 3)
	<ol>
		<li>Click the Remesh Module &#62; Create Non-Manifold Assembly &#62; Main Entity &#62; Maxilla from the object tree. Select intersecting entity for all objects from 3.4 (original teeth) and select Apply.</li>
		<li>Click the Remesh Module. Split the non-manifold assembly.</li>
		<li>Repeat steps 3.16.1-3.16.2 using an intersecting entity as all objects from group 1 and Apply.</li>
		<li>As an optional step, only if required, select the Finish Module &#62; Trim &#62; Entity &#62; Maxilla. Select the excess structure (i.e., noise) and Apply.</li>
		<li>Click the Fix Module &#62; Fix Wizard &#62; Maxilla &#62; Update. Follow the directions given.</li>
		<li>Repeat step 3.16.1 using an intersecting entity as all objects from group 2 and Apply.</li>
		<li>Click the Remesh Module &#62; Adaptive Remesh. Select all intersecting entities from 3.16.6 and Apply.</li>
		<li>Click the Remesh Module &#62; Split Non-manifold Assembly.</li>
		<li>Click the Remesh Module &#62; Create Non-manifold Assembly &#62; Main Entity &#62; Individual Object (PDL) from group 2 from the object tree. Select Intersecting Entity &#62; Select Respective Object from step 3.4 (corresponding to that tooth type) and Apply.</li>
		<li>Click Remesh Module &#62; Adaptive Remesh. Select the intersecting entity from 3.16.9 and Apply.</li>
		<li>Click Remesh Module &#62; Split Non-manifold Assembly.</li>
		<li>Repeat steps 3.16.9-3.16.11 for each tooth.</li>
	</ol>
	</li>
	<li>Click the Remesh Module &#62; Quality Preserving Reduce Triangles. In the object tree select all entities (i.e., teeth, PDLs, and Maxilla) and Apply.</li>
	<li>Click Remesh Module &#62; Create Volume Mesh &#62; Select Entity. Choose Mesh Parameters.</li>
	<li>Repeat step 3.18 for all entities (i.e., teeth, PDLs, and Maxilla).</li>
	<li>Manually export the input(.inp) files from 3Matic to Abaqus (Figure 4).</li>
</ol>
4. Finite element analysis
NOTE: All custom Python scripts can be found in the supplemental attachments. They have been generated using the macro manager function in Abaqus.
<ol>
	<li>Preprocessing setup
	<ol>
		<li>Open Abaqus and select Standard Model. Click File &#62; Set the Work Directory &#62; Select Location for File Storage.</li>
		<li>Click File &#62; Run Script and select Model_setup_Part1.py</li>
		<li>In the Model Directory specify the file path to load .inp files on Abaqus.</li>
		<li>Click on Models &#62; Simulation &#62; Parts &#62; Maxilla &#62; Surfaces.</li>
		<li>Name the surface in the dialog box &#34;UL1 _socket&#34;.</li>
		<li>Under Select the Region of the Surface choose By Angle. Add &#34;15&#34; as the angle.</li>
		<li>Make sure all areas of the socket are selected. Press Done when completed.</li>
		<li>Repeat steps 4.1.4-4.1.7 for the individual sockets.</li>
		<li>Click on Models &#62; Simulation &#62; Parts. Then select UL1 &#62; Surfaces. Name the surface &#34;UL1&#34;.</li>
		<li>Under Select the Region of the Surface opt for &#34;Individually&#34;. Select the tooth on the screen and press Done.</li>
		<li>Repeat steps 4.1.9-4.1.10 for all teeth.</li>
		<li>Click on Models &#62; Simulation &#62; Parts. Then select UL1_PDL &#62; Surfaces. Name the surface &#34;UL1_PDL_inner&#34;.</li>
		<li>Under Select the Region of the Surface choose By Angle. Add &#34;15&#34; as the angle. 
		NOTE: If an error is found during the final simulation, reduce the angle and reselect the surface.</li>
		<li>Make sure the entire inner surface area of the PDL is selected. Press Done when completed.</li>
		<li>Select UL1_PDL &#62; Surfaces. Name the surface &#34;UL1_PDL_outer&#34;.</li>
		<li>Under Select the Region of the Surface choose By Angle. Add &#34;15&#34; as the angle. 
		NOTE: If an error is found during the final simulation, reduce the angle and reselect the surface.</li>
		<li>Make sure the entire outer surface area of the PDL is selected. Press Done when completed.</li>
		<li>Repeat steps 4.1.13-4.1.19 for all PDLs.</li>
		<li>Click on File &#62; Run Script and select Model_setup_Part2.py</li>
		<li>Click on Models &#62; Simulation &#62; BCs. Name BC_all, then select Step as Initial. Under category, select &#34;Mechanical&#34;, and under &#34;Types of Selected Step&#34; select &#34;Displacement/Rotation&#34;. Press Continue.</li>
		<li>Under Select Regions for the Boundary Condition select By Angle. Add &#34;15&#34; as the angle. Check Create Set. Select individual sockets for the 14 teeth. Press Done. 
		NOTE: This helped simulate instantaneous tooth movement.</li>
		<li>Click on Models &#62; Simulation &#62; Assembly &#62; Sets &#62; Create Set. Name the set &#34;U1_y_force&#34;.</li>
		<li>In Select the Nodes for the Set choose Individually. 
		NOTE: A one Newton concentrated force was applied on a randomly selected tooth node in either the positive Y direction (simulating a distalization force) or the positive Z direction (simulating an intrusive force).</li>
		<li>Select a node at the center of the crown on the buccal surface of the upper central incisor (U1) and press Done.</li>
		<li>Click Sets &#62; Create Set. Name the set &#34;U1_z_force&#34;.</li>
		<li>Repeat steps 4.1.23-4.1.24.</li>
		<li>Repeat steps 4.1.22-4.1.26 for all teeth. 
		NOTE: Before a set is generated for a particular tooth as in 4.1.25, go to Instance &#62; Resume for that tooth.</li>
	</ol>
	</li>
	<li>Model set up
	<ol>
		<li>Click on Models &#62; Simulation &#62; Assembly &#62; Instances. Select All Instances and click Resume.</li>
		<li>Click on Tools &#62; Query &#62; Point/Node. Select a node at the center of a randomly selected central incisor and press Done.</li>
		<li>Under the command center at the bottom of the page, copy the X, Y, and Z coordinates of the node selected in step 4.2.2.</li>
		<li>Under the vertical tool bar select Translate Instance and select the entire assembly (i.e., all instances) on the screen. Press Done.</li>
		<li>In the Select a Start Point for the Translation Vector box, paste the copied coordinates in step 4.2.3 or enter the X, Y, and Z values. Click Enter.</li>
		<li>Under Select an End Point for the Translation Vector or enter X,Y,Z: enter the coordinates &#34;0.0&#34;, &#34;0.0&#34;, and &#34;0.0&#34;. Click Enter.</li>
		<li>For Position of Instance, press Ok.</li>
		<li>Click on Tools &#62; Query &#62; Point/Node and select a node directly above the midline of the center incisors. Enter Done.</li>
		<li>Under the command center at the bottom of the page, copy the X, Y, and Z coordinates of the node selected in step 4.2.8.</li>
		<li>Under the vertical tool bar select Translate Instance and select the entire assembly (i.e., all instances) on the screen. Enter Done.</li>
		<li>Paste the copied coordinates into the Select a Start Point for the Translation Vector - or Enter X,Y,Z box. Click Enter.</li>
		<li>Under Select an End Point for the Translation Vector - or enter X,Y,Z: insert the coordinates as copied in step 4.2.9. Change the X coordinate to 0.0. Click Enter.</li>
		<li>For Position of Instance, press Ok.</li>
		<li>Click on File &#62; Run Script and select Model_setup_Part3.py. Insert or change material properties.</li>
		<li>Click on Models &#62; Simulation &#62; Materials and click Bone/PDL/Tooth. Insert tissue specific properties.</li>
		<li>Click on File &#62; Run Script and select Functions.py.</li>
	</ol>
	</li>
	<li>Processing the model
	<ol>
		<li>Click on File &#62; Run Script and select Job_submission.py. 
		NOTE: The job module is where the user sets up one or more actions on the model, and job manager is where model analysis is started, progress is shown, and completion is noted.</li>
		<li>In the dialog box titled Suppress All, enter the sides (L or R) of the teeth based on constraints (Under Models &#62; Simulation &#62; Constraints). Press Ok.</li>
		<li>In the dialog box titled Job Submission enter &#34;Y&#34; to run the analysis for the specified tooth/teeth. Press Ok.</li>
		<li>In the dialog box titled Directions for Analysis enter &#34;Y&#34; to specify force application. Press Ok.</li>
	</ol>
	</li>
	<li>Post processing for C RES estimation
	<ol>
		<li>Choose File &#62; Run Script &#62; Bulk_process.py.</li>
		<li>In the dialog box titled Analyze Multiple Jobs enter &#34;Y&#34; for the specified tooth/teeth. Press Ok.</li>
		<li>In the dialog box titled Directions for Analysis enter &#34;Y&#34; for specifying force application. Press Ok.</li>
		<li>In the dialog box titled Get Input enter Specific Tooth Number as outlined named Instances (e.g., UL1 or UL5, etc.). Press Ok.</li>
		<li>Check coordinates for the Force About Point and Estimated Location in the command box. If they are not similar, then repeat steps 4.3.1-4.4.4. 
		NOTE: After the jobs for each step were run, a user-defined algorithm created in Python was run within the Abaqus interface to analyze the reaction force system and subsequent moments created as a result of load application. The algorithm automatically suggests a new node location to apply the load such that a moment of near-zero magnitude is created within the force system. This proceeds in an iterative process, until the node location that creates a moment closest to zero when a force is applied through it is found or estimated. The algorithm is described in detail in the Discussion section.</li>
	</ol>
	</li>
</ol>

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J., Gr&#246;ning, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Biomechanical+Function+of+Periodontal+Ligament+Fibres+in+Orthodontic+Tooth+Movement.">The Biomechanical Function of Periodontal Ligament Fibres in Orthodontic Tooth Movement.</a> PLoS One. 9 (7), e102387 (2014).</li><li>Huang, H., Tang, W., Yan, B., Wu, B., Cao, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mechanical+responses+of+the+periodontal+ligament+based+on+an+exponential+hyperelastic+model:+a+combined+experimental+and+finite+element+method.">Mechanical responses of the periodontal ligament based on an exponential hyperelastic model: a combined experimental and finite element method.</a> Computer Methods in Biomechanics and Biomedical Engineering. 19 (2), 188-198 (2016).</li><li>Yang, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+new+device+for+measuring+density+of+jaw+bones.">A new device for measuring density of jaw bones.</a> Dentomaxillofacial Radiology. 31 (5), 313-316 (2002).</li><li>Gradl, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass+density+measurement+of+mineralized+tissue+with+grating-based+X-ray+phase+tomography.">Mass density measurement of mineralized tissue with grating-based X-ray phase tomography.</a> PLoS One. 11 (12), e01677979 (2016).</li><li>Jiang, F., Kula, K., Chen, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimating+the+location+of+the+center+of+resistance+of+canines.">Estimating the location of the center of resistance of canines.</a> Angle Orthodontics. 86 (3), 365-371 (2016).</li><li>Nyashin, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Center+of+resistance+and+center+of+rotation+of+a+tooth:+experimental+determination,+computer+simulation+and+the+effect+of+tissue+nonlinearity.">Center of resistance and center of rotation of a tooth: experimental determination, computer simulation and the effect of tissue nonlinearity.</a> Computer Methods in Biomechanics and Biomedical Engineering. 19, 229-239 (2016).</li><li>Toms, S. R., Eberhardt, A. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+nonlinear+finite+element+analysis+of+the+periodontal+ligament+under+orthodontic+tooth+loading.">A nonlinear finite element analysis of the periodontal ligament under orthodontic tooth loading.</a> American Journal of Orthodontics and Dentofacial Orthopedics. 123 (6), 657-665 (2003).</li><li>Osipenko, M. A., Nyashin, M. Y., Nyashin, Y. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Centre+of+resistance+and+centre+of+rotation+of+a+tooth:+the+definitions,+conditions+of+existence,+properties.">Centre of resistance and centre of rotation of a tooth: the definitions, conditions of existence, properties.</a> Russian Journal of Biomechanics. 3 (1), 5-15 (1999).</li><li>Dathe, H., N&#228;gerl, H., Dietmar, K. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+caveat+concerning+center+of+resistance.">A caveat concerning center of resistance.</a> Journal of Dental Biomechanics. 4, 1758736013499770 (2013).</li><li>Hohmann, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+different+modeling+strategies+for+the+periodontal+ligament+on+finite+element+simulation+results.">Influence of different modeling strategies for the periodontal ligament on finite element simulation results.</a> American Journal of Orthodontics and Dentofacial Orthopedics. 139 (6), 775-783 (2011).</li></ol>

The authors have nothing to disclose.

This study shows a set of tools to establish a consistent workflow for finite element analysis (FEA) of models of maxillary teeth derived from CBCT images of patients to determine their CRES. For the clinician, a clear and straightforward map of the CRES of the maxillary teeth would be an invaluable clinical tool to plan tooth movements and predict side effects. The finite element method (FEM) was introduced in dental biomechanical research in 197317, and since then has been ...

The center of resistance (CRES) of a tooth or segment of teeth is analogous to the center of mass of a free body. It is a term borrowed from the field of mechanics of rigid bodies. When a single force is applied at the CRES, translation of the tooth in the direction of the line of action of the force occurs1,2. The position of the CRES depends not only on the tooth&#39;s anatomy and properties but also on its environment (e.g., periodontal ligament, surrounding bone, adjacent teeth). The tooth is a restrained body, making its CRES similar to the center of mass of a free body. In the manipulation of appliances, most orthodontists consider the relationship of the force vector to the CRES of a tooth or a group of teeth. Indeed, whether an object will display tipping or bodily movement when submitted to a single force is mainly determined by the location of the CRES of the object and the distance between the force vector and the CRES. If this can be accurately predicted, treatment results will be greatly improved. Thus, an accurate estimation of CRES can greatly enhance the efficiency of orthodontic tooth movement.
For decades, the orthodontic field has been revisiting research regarding the location of the CRES of a given tooth, segment, or arch1,2,3,4,5,6,7,8,9,10,11,12. However, these studies have been limited in their approach in many ways. Most studies have determined the CRES for only a few teeth, leaving out the majority. For example, the maxillary central incisor and the maxillary incisor segment have been evaluated quite extensively. On the other hand, there are only a few studies on the maxillary canine and first molar and none for the remaining teeth. Also, many of these studies have determined the location of the CRES based on generic anatomical data for teeth, measurements from two-dimensional (2D) radiographs, and calculations on 2D drawings8. In addition, some of the current literature uses generic models or three-dimensional (3D) scans of dentiform models rather than human data4,8. As orthodontics shifts into 3D technology for planning tooth movement, it is crucial to revisit this concept to develop a 3D, scientific understanding of tooth movement.
With technological advancements resulting in increased computational power and modeling capabilities, the ability to create and study more complex models has increased. The introduction of computed tomography scanning and cone-beam computed tomography (CBCT) scanning has thrust models and calculations from the 2D world into 3D. Simultaneous increases in computing power and software complexity have allowed researchers to use 3D radiographs to extract accurate anatomical models for use in advanced software to segment the teeth, bone, periodontal ligament (PDL), and various other structures7,8,9,10,13,14,15. These segmented structures can be converted into a virtual mesh for use in engineering software to calculate the response of a system when a given force or displacement is applied to it.
This study proposes a specific, replicable methodology that can be utilized to examine hypothetical orthodontic force systems applied on models derived from CBCT images of live patients. In utilizing this methodology, investigators can then estimate the CRES of various teeth and take into consideration the biological morphology of dental structures, such as tooth anatomy, number of roots and their orientation in 3D space, mass distribution, and structure of periodontal attachments. A general outline of this process is shown in Figure 1. This is to orient the reader to the logical process involved in generation of 3D tooth models for locating the CRES.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>3-matic software</td>
			<td>Materialise, Leuven, Belgium.</td>
			<td></td>
			<td>Cleaning and meshing</td>
		</tr>
		<tr>
			<td>Abaqus/CAE software, version 2017</td>
			<td>Dassault Syst&#232;mes Simulia Corp., Johnston, RI, USA.</td>
			<td></td>
			<td>Finite Element Analysis</td>
		</tr>
		<tr>
			<td>Mimics software, version 17.0</td>
			<td>Materialise, Leuven, Belgium.</td>
			<td></td>
			<td>Segmentation of teeth and bone</td>
		</tr>
	</tbody>
</table>

a finite element approach for locating the center of resistance of maxillary teeth

The center of resistance (CRES) is regarded as the fundamental reference point for predictable tooth movement. The methods used to estimate the CRES of teeth range from traditional radiographic and physical measurements to in vitro analysis on models or cadaver specimens. Techniques involving finite element analysis of high-dose micro-CT scans of models and single teeth have shown a lot of promise, but little has been done with newer, low-dose, and low resolution cone beam computed tomography (CBCT) images. Also, the CRES for only a few select teeth (i.e., maxillary central incisor, canine, and first molar) have been described; the rest have been largely ignored. There is also a need to describe the methodology of determining the CRES in detail, so that it becomes easy to replicate and build upon.
This study used routine CBCT patient images for developing tools and a workflow to obtain finite element models for locating the CRES of maxillary teeth. The CBCT volume images were manipulated to extract three-dimensional (3D) biological structures relevant in determining the CRES of the maxillary teeth by segmentation. The segmented objects were cleaned and converted into a virtual mesh made up tetrahedral (tet4) triangles having a maximum edge length of 1 mm with 3matic software. The models were further converted into a solid volumetric mesh of tetrahedrons with a maximum edge length of 1 mm for use in finite element analysis. The engineering software, Abaqus, was used to preprocess the models to create an assembly and set material properties, interaction conditions, boundary conditions, and load applications. The loads, when analyzed, simulated the stresses and strains on the system, aiding in locating the CRES. This study is the first step in accurate prediction of tooth movement.

In order to verify segmentation and manual outlining as described in the Procedures section (step 2), a maxillary first molar was extracted from a dry skull, and a CBCT image was taken. The image processing and editing software Mimics was used for manually outlining the tooth as described in step 2. Subsequently, meshing was performed, the segmented models were cleaned with 3matic software, and they were imported to Abaqus for analysis. We did not find any significant difference in the li...

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A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth

This study outlines the necessary tools for utilizing low-dose three-dimensional cone beam-based patient images of the maxilla and maxillary teeth to obtain finite element models. These patient models are then used to accurately locate the CRES of all the maxillary teeth.

The center of resistance (CRES) is regarded as the fundamental reference point for predictable tooth movement. The methods used to estimate the CRES of ...

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9.3K Views.  University of Connecticut Health. This study outlines the necessary tools for utilizing low-dose three-dimensional cone beam-based patient images of the maxilla and maxillary teeth to obtain finite element models. These patient models are then used to accurately locate the CRES of all the maxillary teeth.

Video: A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth

Choice and No-Choice Assays for Testing the Resistance of A. thaliana to Chewing Insects

Larvae of the small white cabbage butterfly are a pest in agricultural settings. This caterpillar species feeds from plants in the cabbage family, which include many crops such as cabbage, broccoli, Brussel sprouts etc. Rearing of the insects takes place on cabbage plants in the greenhouse. At least two cages are needed for the rearing of Pieris rapae. One for the larvae and the other to contain the adults, the butterflies. In order to investigate the role of plant hormones and toxic plant chemicals in resistance to this insect pest, we demonstrate two experiments. First, determination of the role of jasmonic acid (JA - a plant hormone often indicated in resistance to insects) in resistance to the chewing insect Pieris rapae. Caterpillar growth can be compared on wild-type and mutant plants impaired in production of JA. This experiment is considered "No Choice", because larvae are forced to subsist on a single plant which synthesizes or is deficient in JA. Second, we demonstrate an experiment that investigates the role of glucosinolates, which are used as oviposition (egg-laying) signals. Here, we use WT and mutant Arabidopsis impaired in glucosinolate production in a "Choice" experiment in which female butterflies are allowed to choose to lay their eggs on plants of either genotype. This video demonstrates the experimental setup for both assays as well as representative results.

Plant resistance to chewing insect herbivores can be tested in several ways. Here, we demonstrate how to set-up a choice and a no-choice experiment with the model plant Arabidopsis thaliana to identify resistance against the pest species Pieris rapae.

Larvae of the small white cabbage butterfly are a pest in agricultural settings. This caterpillar species feeds from plants in the cabbage family, which ...

Methods for the Study of the Zebrafish Maxillary Barbel

Barbels are skin sensory appendages found in fishes, reptiles and amphibians. The zebrafish, Danio rerio, develops two pairs of barbels- a short nasal pair and a longer maxillary pair. Barbel tissue contains cells of ectodermal, mesodermal and neural crest origin, including skin cells, glands, taste buds, melanocytes, circulatory vessels and sensory nerves. Unlike most adult tissue, the maxillary barbel is optically clear, allowing us to visualize the development and maintenance of these tissue types throughout the life cycle. 
This video shows early development of the maxillary barbel (beginning approximately one month post-fertilization) and demonstrates a surgical protocol to induce regeneration in the adult appendage (&gt;3 months post-fertilization). Briefly, the left maxillary barbel of an anesthetized fish is elevated with sterile forceps just distal to the caudal edge of the maxilla. A fine, sterile spring scissors is positioned against the forceps to cut the barbel shaft at this level, establishing an anatomical landmark for the amputation plane. Regenerative growth can be measured with respect to this plane, and in comparison to the contralateral barbel. Barbel tissue regenerates rapidly, reaching maximal regrowth within 2 weeks of injury.
Techniques for analyzing the regenerated barbel include dissecting and embedding matched pairs of barbels (regenerate and control) in the wells of a standard DNA electrophoresis gel. Embedded specimens are conveniently photographed under a stereomicroscope for gross morphology and morphometry, and can be stored for weeks prior to downstream applications such as paraffin histology, cryosectioning, and/or whole mount immunohistochemistry. These methods establish the maxillary barbel as a novel in vivo tissue system for studying the regenerative capacity of multiple cell types within the genetic context of zebrafish.

The zebrafish maxillary barbel is an integumentary sense organ containing ectodermal, mesodermal and neural crest derivatives. Importantly, the adult barbel can regenerate after proximal amputation. This video introduces maxillary barbel development and demonstrates a surgical protocol to induce regeneration, followed by collection, embedding and downstream imaging of barbel specimens.

Barbels are skin sensory appendages found in fishes, reptiles and amphibians.  The zebrafish, Danio rerio, develops two pairs of barbels- a short nasal ...

Bioassays for Monitoring Insecticide Resistance

Pest resistance to pesticides is an increasing problem because pesticides are an integral part of high-yielding production agriculture. When few products are labeled for an individual pest within a particular crop system, chemical control options are limited. Therefore, the same product(s) are used repeatedly and continual selection pressure is placed on the target pest. There are both financial and environmental costs associated with the development of resistant populations. The cost of pesticide resistance has been estimated at approximately $ 1.5 billion annually in the United States. This paper will describe protocols, currently used to monitor arthropod (specifically insects) populations for the development of resistance. The adult vial test is used to measure the toxicity to contact insecticides and a modification of this test is used for plant-systemic insecticides. In these bioassays, insects are exposed to technical grade insecticide and responses (mortality) recorded at a specific post-exposure interval. The mortality data are subjected to Log Dose probit analysis to generate estimates of a lethal concentration that provides mortality to 50% (LC50) of the target populations and a series of confidence limits (CL's) as estimates of data variability. When these data are collected for a range of insecticide-susceptible populations, the LC50 can be used as baseline data for future monitoring purposes. After populations have been exposed to products, the results can be compared to a previously determined LC50 using the same methodology.

This manuscript demonstrates and discusses techniques used to survey pesticide susceptibility and detect resistance to contact and systemic pesticides in arthropod pests.

Pest resistance to pesticides is an increasing problem because pesticides are an integral part of high-yielding production agriculture. When few products ...

Expression, Detergent Solubilization, and Purification of a Membrane Transporter, the MexB Multidrug Resistance Protein

Multidrug resistance (MDR), the ability of a cancer cell or pathogen to be resistant to a wide range of structurally and functionally unrelated anti-cancer drugs or antibiotics, is a current serious problem in public health. This multidrug resistance is largely due to energy-dependent drug efflux pumps. The pumps expel anti-cancer drugs or antibiotics into the external medium, lowering their intracellular concentration below a toxic threshold. We are studying multidrug resistance in Pseudomonas aeruginosa, an opportunistic bacterial pathogen that causes infections in patients with many types of injuries or illness, for example, burns or cystic fibrosis, and also in immuno-compromised cancer, dialysis, and transplantation patients. The major MDR efflux pumps in P. aeruginosa are tripartite complexes comprised of an inner membrane proton-drug antiporter (RND), an outer membrane channel (OMF), and a periplasmic linker protein (MFP) 1-8. The RND and OMF proteins are transmembrane proteins. Transmembrane proteins make up more than 30% of all proteins and are 65% of current drug targets. The hydrophobic transmembrane domains make the proteins insoluble in aqueous buffer. Before a transmembrane protein can be purified, it is necessary to find buffer conditions containing a mild detergent that enable the protein to be solubilized as a protein detergent complex (PDC) 9-11. In this example, we use an RND protein, the P. aeruginosa MexB transmembrane transporter, to demonstrate how to express a recombinant form of a transmembrane protein, solubilize it using detergents, and then purify the protein detergent complexes. This general method can be applied to the expression, purification, and solubilization of many other recombinantly expressed membrane proteins. The protein detergent complexes can later be used for biochemical or biophysical characterization including X-ray crystal structure determination or crosslinking studies.

In this protocol we demonstrate the expression, solubilization, and purification of a recombinantly expressed membrane protein, MexB, as a soluble protein detergent complex. MexB is a multidrug resistance membrane transporter from the opportunistic bacterial pathogen Pseudomonas aeruginosa.

Multidrug resistance (MDR), the ability of a cancer cell or pathogen to be resistant to a wide range of structurally and functionally unrelated ...

Characterizing Herbivore Resistance Mechanisms: Spittlebugs on Brachiaria spp. as an Example

Plants can resist herbivore damage through three broad mechanisms: antixenosis, antibiosis and tolerance1. Antixenosis is the degree to which the plant is avoided when the herbivore is able to select other plants2. Antibiosis is the degree to which the plant affects the fitness of the herbivore feeding on it1.Tolerance is the degree to which the plant can withstand or repair damage caused by the herbivore, without compromising the herbivore's growth and reproduction1. The durability of herbivore resistance in an agricultural setting depends to a great extent on the resistance mechanism favored during crop breeding efforts3.
We demonstrate a no-choice experiment designed to estimate the relative contributions of antibiosis and tolerance to spittlebug resistance in Brachiaria spp. Several species of African grasses of the genus Brachiaria are valuable forage and pasture plants in the Neotropics, but they can be severely challenged by several native species of spittlebugs (Hemiptera: Cercopidae)4.To assess their resistance to spittlebugs, plants are vegetatively-propagated by stem cuttings and allowed to grow for approximately one month, allowing the growth of superficial roots on which spittlebugs can feed. At that point, each test plant is individually challenged with six spittlebug eggs near hatching. Infestations are allowed to progress for one month before evaluating plant damage and insect survival. Scoring plant damage provides an estimate of tolerance while scoring insect survival provides an estimate of antibiosis. This protocol has facilitated our plant breeding objective to enhance spittlebug resistance in commercial brachiariagrases5.

Characterizing Herbivore Resistance Mechanisms: Spittlebugs on Brachiaria spp. as an Example

This video explains mechanisms of host plant resistance to herbivory and demonstrates a no-choice test that estimates the relative contributions of antibiosis and tolerance to spittlebug resistance in Brachiaria spp.

Plants can resist herbivore damage through three broad mechanisms: antixenosis, antibiosis and tolerance1. Antixenosis is the degree to which the plant is ...

Quantitative Comparison of cis-Regulatory Element (CRE) Activities in Transgenic Drosophila melanogaster

Gene expression patterns are specified by cis-regulatory element (CRE) sequences, which are also called enhancers or cis-regulatory modules. A typical CRE possesses an arrangement of binding sites for several transcription factor proteins that confer a regulatory logic specifying when, where, and at what level the regulated gene(s) is expressed. The full set of CREs within an animal genome encodes the organism&prime;s program for development1, and empirical as well as theoretical studies indicate that mutations in CREs played a prominent role in morphological evolution2-4. Moreover, human genome wide association studies indicate that genetic variation in CREs contribute substantially to phenotypic variation5,6. Thus, understanding regulatory logic and how mutations affect such logic is a central goal of genetics. 
Reporter transgenes provide a powerful method to study the in vivo function of CREs. Here a known or suspected CRE sequence is coupled to heterologous promoter and coding sequences for a reporter gene encoding an easily observable protein product. When a reporter transgene is inserted into a host organism, the CRE&prime;s activity becomes visible in the form of the encoded reporter protein. P-element mediated transgenesis in the fruit fly species Drosophila (D.) melanogaster7 has been used for decades to introduce reporter transgenes into this model organism, though the genomic placement of transgenes is random. Hence, reporter gene activity is strongly influenced by the local chromatin and gene environment, limiting CRE comparisons to being qualitative. In recent years, the phiC31 based integration system was adapted for use in D. melanogaster to insert transgenes into specific genome landing sites8-10. This capability has made the quantitative measurement of gene and, relevant here, CRE activity11-13 feasible. The production of transgenic fruit flies can be outsourced, including phiC31-based integration, eliminating the need to purchase expensive equipment and/or have proficiency at specialized transgene injection protocols. 
Here, we present a general protocol to quantitatively evaluate a CRE&prime;s activity, and show how this approach can be used to measure the effects of an introduced mutation on a CRE&prime;s activity and to compare the activities of orthologous CREs. Although the examples given are for a CRE active during fruit fly metamorphosis, the approach can be applied to other developmental stages, fruit fly species, or model organisms. Ultimately, a more widespread use of this approach to study CREs should advance an understanding of regulatory logic and how logic can vary and evolve.

Quantitative Comparison of cis-Regulatory Element CRE Activities in Transgenic Drosophila melanogaster

Phenotypic variation for traits can result from mutations in cis-regulatory element (CRE) sequences that control gene expression patterns. Methods derived for use in Drosophila melanogaster can quantitatively compare the levels of spatial and temporal patterns of gene expression mediated by modified or naturally occurring CRE variants.

Gene expression patterns are specified by cis-regulatory element (CRE) sequences, which are also called enhancers or cis-regulatory modules. A typical CRE ...

Assessing Murine Resistance Artery Function Using Pressure Myography

Pressure myograph systems are exquisitely useful in the functional assessment of small arteries, pressurized to a suitable transmural pressure. The near physiological condition achieved in pressure myography permits in-depth characterization of intrinsic responses to pharmacological and physiological stimuli, which can be extrapolated to the in vivo behavior of the vascular bed. Pressure myograph has several advantages over conventional wire myographs. For example, smaller resistance vessels can be studied at tightly controlled and physiologically relevant intraluminal pressures. Here, we study the ability of 3rd order mesenteric arteries (3-4 mm long), preconstricted with phenylephrine, to vaso-relax in response to acetylcholine. Mesenteric arteries are mounted on two cannulas connected to a pressurized and sealed system that is maintained at constant pressure of 60 mmHg. The lumen and outer diameter of the vessel are continuously recorded using a video camera, allowing real time quantification of the vasoconstriction and vasorelaxation in response to phenylephrine and acetylcholine, respectively.
	To demonstrate the applicability of pressure myography to study the etiology of cardiovascular disease, we assessed endothelium-dependent vascular function in a murine model of systemic hypertension. Mice deficient in the &alpha;1 subunit of soluble guanylate cyclase (sGC&alpha;1-/-) are hypertensive when on a 129S6 (S6) background (sGC&alpha;1-/-S6) but not when on a C57BL/6 (B6) background (sGC&alpha;1-/-B6). Using pressure myography, we demonstrate that sGC&alpha;1-deficiency results in impaired endothelium-dependent vasorelaxation. The vascular dysfunction is more pronounced in sGC&alpha;1-/-S6 than in sGC&alpha;1-/-B6 mice, likely contributing to the higher blood pressure in sGC&alpha;1-/-S6 than in sGC&alpha;1-/-B6 mice. 
	Pressure myography is a relatively simple, but sensitive and mechanistically useful technique that can be used to assess the effect of various stimuli on vascular contraction and relaxation, thereby augmenting our insight into the mechanisms underlying cardiovascular disease.

In pressure myography, an intact small segment of a vessel is mounted onto two small cannulas and pressurized to a suitable luminal pressure. Here, we describe the method to measure vasorelaxation response of the mouse 3rd order mesenteric arteries in c57 and sGC&alpha;1-/- mice using pressure myography.

Pressure myograph systems are  exquisitely useful in the functional assessment of small arteries, pressurized  to a suitable transmural pressure. The  ...

Isolation of Microvascular Endothelial Tubes from Mouse Resistance Arteries

The control of blood flow by the resistance vasculature regulates the supply of oxygen and nutrients concomitant with the removal of metabolic by-products, as exemplified by exercising skeletal muscle. Endothelial cells (ECs) line the intima of all resistance vessels and serve a key role in controlling diameter (e.g.&#160;endothelium-dependent vasodilation) and, thereby, the magnitude and distribution of tissue blood flow. The regulation of vascular resistance by ECs is effected by intracellular Ca2+ signaling, which leads to production of diffusible autacoids (e.g.&#160;nitric oxide and arachidonic acid metabolites)1-3 and hyperpolarization4,5 that elicit smooth muscle cell relaxation. Thus understanding the dynamics of endothelial Ca2+ signaling is a key step towards understanding mechanisms governing blood flow control. Isolating endothelial tubes eliminates confounding variables associated with blood in the vessel lumen and with surrounding smooth muscle cells and perivascular nerves, which otherwise influence EC structure and function. Here we present the isolation of endothelial tubes from the superior epigastric artery (SEA) using a protocol optimized for this vessel.
To isolate endothelial tubes from an anesthetized mouse, the SEA is ligated in situ to maintain blood within the vessel lumen (to facilitate visualizing it during dissection), and the entire sheet of abdominal muscle is excised. The SEA is dissected free from surrounding skeletal muscle fibers and connective tissue, blood is flushed from the lumen, and mild enzymatic digestion is performed to enable removal of adventitia, nerves and smooth muscle cells using gentle trituration. These freshly-isolated preparations of intact endothelium retain their native morphology, with individual ECs remaining functionally coupled to one another, able to transfer chemical and electrical signals intercellularly through gap junctions6,7. In addition to providing new insight into calcium signaling and membrane biophysics, these preparations enable molecular studies of gene expression and protein localization within native microvascular endothelium.

We present a preparation for visualizing and manipulating calcium signaling in native, intact microvascular endothelium. Endothelial tubes freshly isolated from mouse resistance arteries supplying skeletal muscle retain in vivo morphology and dynamic signaling within and between neighboring cells. Endothelial tubes can be prepared from microvessels of other tissues and organs.

The control of blood flow by the resistance vasculature regulates the supply of oxygen and nutrients concomitant with the removal of metabolic ...

Autonomously Bioluminescent Mammalian Cells for Continuous and Real-time Monitoring of Cytotoxicity

Mammalian cell-based in vitro assays have been widely employed as alternatives to animal testing for toxicological studies but have been limited due to the high monetary and time costs of parallel sample preparation that are necessitated due to the destructive nature of firefly luciferase-based screening methods. This video describes the utilization of autonomously bioluminescent mammalian cells, which do not require the destructive addition of a luciferin substrate, as an inexpensive and facile method for monitoring the cytotoxic effects of a compound of interest. Mammalian cells stably expressing the full bacterial bioluminescence (luxCDABEfrp) gene cassette autonomously produce an optical signal that peaks at 490 nm without the addition of an expensive and possibly interfering luciferin substrate, excitation by an external energy source, or destruction of the sample that is traditionally performed during optical imaging procedures. This independence from external stimulation places the burden for maintaining the bioluminescent reaction solely on the cell, meaning that the resultant signal is only detected during active metabolism. This characteristic makes the lux-expressing cell line an excellent candidate for use as a biosentinel against cytotoxic effects because changes in bioluminescent production are indicative of adverse effects on cellular growth and metabolism. Similarly, the autonomous nature and lack of required sample destruction permits repeated imaging of the same sample in real-time throughout the period of toxicant exposure and can be performed across multiple samples using existing imaging equipment in an automated fashion.

Mammalian cells expressing the bacterial bioluminescence gene cassette (lux) produce light autonomously. The resulting bioluminescent dynamics upon chemical exposure have been demonstrated to reflect the treatment effects on cellular growth and metabolism, making these cells an inexpensive, continuous, real-time toxicity screening tool that can easily be adapted for high-throughput automation.

Mammalian  cell-based in vitro assays have been  widely employed as alternatives to animal testing for toxicological studies but  have been limited due to ...

Quantitative Localization of a Golgi Protein by Imaging Its Center of Fluorescence Mass

The Golgi complex consists of serially stacked membrane cisternae which can be further categorized into sub-Golgi regions, including the cis-Golgi, medial-Golgi, trans-Golgi and trans-Golgi network. Cellular functions of the Golgi are determined by the characteristic distribution of its resident proteins. The spatial resolution of conventional light microscopy is too low to resolve sub-Golgi structure or cisternae. Thus, the immuno-gold electron microscopy is a method of choice to localize a protein at the sub-Golgi level. However, the technique and instrument are beyond the capability of most cell biology labs. We describe here our recently developed super-resolution method called Golgi protein localization by imaging centers of mass (GLIM) to systematically and quantitatively localize a Golgi protein. GLIM is based on standard fluorescence labeling protocols and conventional wide-field or confocal microscopes. It involves the calibration of chromatic-shift aberration of the microscopic system, the image acquisition and the post-acquisition analysis. The sub-Golgi localization of a test protein is quantitatively expressed as the localization quotient. There are four main advantages of GLIM; it is rapid, based on conventional methods and tools, the localization result is quantitative, and it affords ~ 30 nm practical resolution along the Golgi axis. Here we describe the detailed protocol of GLIM to localize a test Golgi protein.

The precise localization of Golgi residents is essential for understanding the cellular functions of the Golgi. However, conventional optical microscopy is unable to resolve the sub-Golgi structure. Here we describe the protocol for a conventional microscopy based super-resolution method to quantitatively determine the sub-Golgi localization of a protein.

The Golgi complex consists of serially stacked membrane cisternae which can be further categorized into sub-Golgi regions, including the cis-Golgi, ...