Journal: Nature Communications

Article Title: NEMO reshapes the α-Synuclein aggregate interface and acts as an autophagy adapter by co-condensation with p62

doi: 10.1038/s41467-023-44033-0

Figure Lengend Snippet: a, b, c Colocalization of p62 and aSyn aggregates is decreased in the Q330X NEMO patient brain despite increased p62 expression. a Paraffin-embedded brain sections from control, DLBL (Dementia with Lewy Bodies) or the Q330X NEMO patient brain were analyzed by immunohistochemistry and fluorescence SR-SIM using antibodies against aSyn and p62. Scale bar, 200 µm. b Foci staining positive for both aSyn and p62 were quantified in 28-30 fields of view per brain section. Data are shown as mean ± SEM, n = 28/30 individual cells. c Foci staining positive for p62 only were quantified in 10 fields of view per brain section. Data are displayed as mean ± SEM from 10 fields of view. Statistics: Kruskal–Wallis test followed by Tuckey’s multiple comparison test. *** p ≤ 0.001. d, e Foci-like concentration of p62 and NBR1 at aSyn aggregates is reduced in NEMO-deficient cells. CRISPR/Cas9 NEMO KO or wildtype (WT) SH-SY5Y cells were transiently transfected with aSyn A53T-GFP. One day after transfection, the cells were treated with aSyn A53T seeds, fixed 48 h after seeding, and analyzed by immunohistochemistry and fluorescence SR-SIM using antibodies against p62 (d) and NBR1 (e) . 3D-reconstructions were performed using the surface module of Imaris 10.0.1 image analysis software. Scale bars, D/E: 1 µm. f, g The abundance of LC3 and LAMP2 at aSyn aggregates is decreased in NEMO-deficient cells. CRISPR/Cas9 NEMO KO or WT SH-SY5Y cells were transiently transfected with aSyn A53T-GFP. One day after transfection, the cells were treated with aSyn A53T seeds, fixed 48 h after seeding, and analyzed by immunohistochemistry and fluorescence SR-SIM using antibodies against LC3 (f) and LAMP2 (g) . 3D-reconstructions were performed using the surface module of Imaris 10.0.1 image analysis software. Coverage of aSyn-GFP aggregates by LC3 and LAMP2 was analyzed using the Imaris 10.0.1 surface modules and a surface-to-surface MatLab Plugin. The coverage of the reconstructed aSyn-GFP surface was quantified and plotted as percentage of the total aSyn-GFP aggregate surface for LC3 (f) and LAMP2 (g) . Data are displayed as mean ± SEM, n = 11/20 individual cells for LC3 (* p = 0.0422) and n = 14/13 individual cells for LAMP2 (* p = 0.0143). Statistics: two-tailed student´s t -test. * p ≤ 0.05. Scale bars, f 2 µm (left panel), 1 µm (right panel), g 0.5 µm (left panel), 1 µm (right panel).

Article Snippet: Surface-to-surface contacts sites were analyzed using a MatLab Plugin (Imaris website modules).

Techniques: Expressing, Control, Immunohistochemistry, Fluorescence, Staining, Comparison, Concentration Assay, CRISPR, Transfection, Software, Two Tailed Test

Journal: IEEE sensors journal

Article Title: Wearable Inertial Sensors for Range of Motion Assessment

doi: 10.1109/JSEN.2019.2960320

Figure Lengend Snippet: WIS module pictorially represented on a human model in T-pose

Article Snippet: The results indicate that the EGM approach, requiring four quaternion products, is computationally efficient compared to the EG approach, requiring eight quaternion products. table ft1 table-wrap mode="anchored" t5 TABLE III caption a7 Technique Computation time for one WIS module ( μ s) Computation time for five WIS modules ( μ s) EG approach 65.01 325.05 EGM approach 18.50 92.50 Open in a separate window C omputation T ime for D ifferent M isalignment T echniques A MATLAB routine was developed to obtain the positive and negative peaks of the time series WIS module data using findpeaks command.

Techniques:

Journal: IEEE sensors journal

Article Title: Wearable Inertial Sensors for Range of Motion Assessment

doi: 10.1109/JSEN.2019.2960320

Figure Lengend Snippet: C omputation T ime for D ifferent M isalignment T echniques

Article Snippet: The results indicate that the EGM approach, requiring four quaternion products, is computationally efficient compared to the EG approach, requiring eight quaternion products. table ft1 table-wrap mode="anchored" t5 TABLE III caption a7 Technique Computation time for one WIS module ( μ s) Computation time for five WIS modules ( μ s) EG approach 65.01 325.05 EGM approach 18.50 92.50 Open in a separate window C omputation T ime for D ifferent M isalignment T echniques A MATLAB routine was developed to obtain the positive and negative peaks of the time series WIS module data using findpeaks command.

Techniques:

Journal: Bioinspiration & biomimetics

Article Title: A robotic leg inspired from an insect leg.

doi: 10.1088/1748-3190/ac78b5

Figure Lengend Snippet: Figure 3. Setup for the experiment to track the trajectories of the beetle legs and body. (A) 3D motion-capturing system consists of (i) six T40 VICON cameras (Vicon Motion Systems, Oxford, UK), each with an equal resolution of 4 megapixels (2336 × 1728) for tracking the position (100 fps), (ii) the VICON server for recording, storing and showing the 3D position collected by the cameras and (iii) MATLAB software (MathWorks, Natick, MA, USA) for analyzing the angular displacement and the distance among positions. (B) Three reflective markers were stuck on each front leg to represent the tarsus and tibia segments. The L-shape structure with three reflective markers was attached to the beetle’s body, allowing the reference plane to be made. (C) Diagram showing how angular displacement was calculated. Angle between claw and tibia is used to describe the bending ability of the tarsus. (D) These markers were displayed by the points on the Nexus software. To present the tibia and tarsus segments of each front leg and the beetle’s body, we linked the three points on the left and right front leg and three points on the body. We investigated the movement of the front legs of the beetle when walking on the plate and mesh substrates by measuring the displacement D of the claws. (E) Setup used to measure the horizontal force (hooking force) and vertical force of the artificial tarsus when it switched to rigid condition includes (i) the digital force gauge (SAUTER FH100) to measure force values and transfer data to the PC, (ii) the linear guide slide to move the tarsus during the hooking force measurement and (iii) actuator AX12A for pulling the string to bend the tarsus.

Article Snippet: The Denavit–Hartenberg (DH) parameters of the robotic leg were then configured in the MATLAB kinematics module (Robotics System Toolbox) to translate the scaled trajectory into the motors’ joint angles (figure 10(B)).

Techniques: Software

Journal: Bioinspiration & biomimetics

Article Title: A robotic leg inspired from an insect leg.

doi: 10.1088/1748-3190/ac78b5

Figure Lengend Snippet: Figure 10. Demonstration with the 3D printed tarsus. Visual deformation of the artificial tarsus. (A) Inverse kinematics method was applied to control the movement of the robot leg so that the robot tibia can generate a similar trajectory to the beetle’s tibia. (B) DH parameters for the configuration of the robotic leg: segments 1, 2, 3, and 4 represent coxa, trochanter, femur and tibia respectively. (C) Simulating the movement of the robot leg on the mesh substrate. When the motor ‘contracts’, which is denoted as state 1, the tarsus is bent so that the claws attach to the mesh. Moreover, the claws still hook onto the mesh when the leg switches to the swing phase, while the height of the mesh follows that of the claws closely, indicating attachment. When the leg returns and the motor ‘relaxes’, which is denoted as state 2, the tarsus switches to flexible condition. Then, the claws can come out of the mesh, and the height of the mesh does not rise past its resting height, indicating release.

Article Snippet: The Denavit–Hartenberg (DH) parameters of the robotic leg were then configured in the MATLAB kinematics module (Robotics System Toolbox) to translate the scaled trajectory into the motors’ joint angles (figure 10(B)).

Techniques: Control

Journal: Tomography

Article Title: ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging

doi: 10.18383/j.tom.2018.00055

Figure Lengend Snippet: T1 perfusion map generated by ePAD plugin derived from the algorithm in Jarrett et al.'s study , with the map overlaid on magnetic resonance (MR) image using a color lookup table.

Article Snippet: To support interoperability of image annotations, ePAD writes all image annotations and results of quantitative imaging analyses in standardized file formats, and it supports migration of annotations from various propriety formats. ePAD also provides a plugin architecture supporting MATLAB server-side modules in addition to client-side plugins, permitting the community to extend the ePAD platform in various ways for new cancer use cases.

Techniques: Generated, Derivative Assay

Journal: Tomography

Article Title: ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging

doi: 10.18383/j.tom.2018.00055

Figure Lengend Snippet: QF Explore Plugin Suite: gray-level co-occurrence matrix feature extraction and comparison chart. The user can compare the feature values for various regions of interest (ROIs). GLCM contrast and correlation is higher for vascular ROIs .

Article Snippet: To support interoperability of image annotations, ePAD writes all image annotations and results of quantitative imaging analyses in standardized file formats, and it supports migration of annotations from various propriety formats. ePAD also provides a plugin architecture supporting MATLAB server-side modules in addition to client-side plugins, permitting the community to extend the ePAD platform in various ways for new cancer use cases.

Techniques: Extraction, Comparison