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"Chen, Yuan-Liu"
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Full Cross-Sectional Profile Measurement of a High-Aspect-Ratio Micro-Groove Using a Deflection Probe Measuring System
For the full cross-sectional profile measurement of high-aspect-ratio micro-grooves, traditional measurement methods have blind measurement areas in the vertical sidewall and its intersection area with the bottom. This paper proposes a deflection-based scanning method that utilizes a large length-to-diameter ratio probe to achieve a full cross-sectional profile measurement of micro-grooves. Blind measurement areas were eliminated by a deflection-based scanning method. The complete groove profile was obtained by stitching the positive and reversal deflection-based measurement results. The optimal deflection angle of the probe was calculated by considering the profile-stitching setting and the principle of minimizing the probe deformation during the measurement process. A four-axis measurement system was established to measure high-aspect-ratio micro-grooves, which incorporated a force feedback mechanism to maintain a constant contact force during the measurement and an integrated error separation module to modify the measurement results. The measurement method and system were experimentally validated to achieve a full cross-sectional profile measurement of micro-grooves with a width of 50 μm and an aspect ratio of no less than 3. The standard deviation of the measurement results was 82 nm, and the expanded uncertainty was 108 nm.
Journal Article
A Fiber-Based Chromatic Dispersion Probe for Simultaneous Measurement of X-Axis and Z-Axis Displacements with Nanometric Resolutions
In this paper, a fiber-based chromatic dispersion probe for simultaneous measurement of X-axis and Z-axis displacements with nanometric resolutions by using the full width at half maxima (FWHM) of the detected spectral signal has been proposed and demonstrated. For X-axis, FWHM is employed for indicating the X-axis displacement based on the fact that the FWHM remains almost constant with the varying Z-axis displacement of the fiber detector and shows a linear relationship with the X-axis displacement within a specific Z-axis displacement range. For the Z-axis, the linear relationship between the centroid wavelength λ of the detected spectral signal and the Z-axis displacement is employed for indicating the Z-axis displacement based on the fact that the sensitivity (slope of the λ-Z curve) is also linear with X-axis displacement within a certain X-axis displacement range. Theoretical and experimental investigations have verified the feasibility of the proposed chromatic dispersion probe, which yields X- and Z-axis measurement ranges of 2.3 μm and 15 μm and X- and Z-axis measurement resolutions of better than 25 nm and 50 nm, respectively. Experiments were further performed to evaluate the basic performance of the prototype probe and the maximum measurement errors were less than 10 nm and 60 nm for X- and Z-axis displacements, respectively.
Journal Article
A Fiber-Based Chromatic Dispersion Probe for Simultaneous Measurement of Dual-Axis Absolute and Relative Displacement
This paper presents a fiber-based chromatic dispersion probe for the simultaneous measurement of dual-axis absolute and relative displacement with nanometric resolutions. The proposed chromatic dispersion probe is based on optical dispersion. In the probe, the employed light beam is split into two sub-beams, and then the two sub-beams are made to pass through two optical paths with different optical settings where two identical single-mode fiber detectors are located at different defocused positions of the respective dispersive lenses. In this way, two spectral signals can be obtained to indicate the absolute displacement of each of the dual-axes. A signal processing algorithm is proposed to generate a normalized output wavelength that indicates the relative displacement of the dual-axis. With the proposed chromatic dispersion probe, the absolute and relative displacement measurements of the dual-axis can be realized simultaneously. Theoretical and experimental investigations reveal that the developed chromatic dispersion probe realizes an absolute measurement range and a measurement resolution of approximately 180 μm and 50 nm, respectively, for each axis. Moreover, a relative displacement measurement range and a measurement resolution of about 240 μm and 100 nm, respectively, are achieved for the dual-axis.
Journal Article
A novel Oprm1-Cre mouse maintains endogenous expression, function and enables detailed molecular characterization of μ-opioid receptor cells
Key targets of both the therapeutic and abused properties of opioids are μ-opioid receptors (MORs). Despite years of research investigating the biochemistry and signal transduction pathways associated with MOR activation, we do not fully understand the cellular mechanisms underlying opioid addiction. Given that addictive opioids such as morphine, oxycodone, heroin, and fentanyl all activate MORs, and current therapies such as naloxone and buprenorphine block this activation, the availability of tools to mechanistically investigate opioid-mediated cellular and behavioral phenotypes are necessary. Therefore, we derived, validated, and applied a novel MOR-specific Cre mouse line, inserting a T2A cleavable peptide sequence and the Cre coding sequence into the MOR 3’UTR. Importantly, this line shows specificity and fidelity of MOR expression throughout the brain and with respect to function, there were no differences in behavioral responses to morphine when compared to wild type mice, nor are there any alterations in Oprm 1 gene expression or receptor density. To assess Cre recombinase activity, MOR-Cre mice were crossed with the floxed GFP-reporters, Rosa LSLSun1-sfGFP or Rosa LSL-GFP-L10a . The latter allowed for cell type specific RNA sequencing via TRAP (Translating Ribosome Affinity Purification) of striatal MOR+ neurons following opioid withdrawal. The breadth of utility of this new tool will greatly facilitate the study of opioid biology under varying conditions.
Journal Article
Material Removal on Hydrogen-Terminated Diamond Surface via AFM Tip-Based Local Anodic Oxidation
Diamond is a promising next-generation semiconductor material, offering a wider band gap, higher electron mobility, and superior thermal conductivity compared with silicon. However, its exceptional hardness makes it challenging to fabricate. In this study, we demonstrate a novel approach to realize material removal on hydrogen-terminated diamond surfaces by atomic force microscope (AFM) tip-based local anodic oxidation. By adjusting both the applied voltage and hydrogen plasma etching parameters, the material is removed over an area larger than the AFM tip size. Notably, the hardness of the material surrounding the removal zone is significantly reduced, enabling it to be scratched with a silicon tip. These findings open a promising pathway for improving the machinability of diamonds in future device applications.
Journal Article
A Liquid-Surface-Based Three-Axis Inclination Sensor for Measurement of Stage Tilt Motions
In this paper a new concept of a liquid-surface-based three-axis inclination sensor for evaluation of angular error motion of a precision linear slide, which is often used in the field of precision engineering such as ultra-precision machine tools, coordinate measuring machines (CMMs) and so on, is proposed. In the liquid-surface-based three-axis inclination sensor, a reference float mounting a line scale grating having periodic line grating structures is made to float over a liquid surface, while its three-axis angular motion is measured by using an optical sensor head based on the three-axis laser autocollimation capable of measuring three-axis angular motion of the scale grating. As the first step of research, in this paper, theoretical analysis on the angular motion of the reference float about each axis has been carried out based on simplified kinematic models to evaluate the possibility of realizing the proposed concept of a three-axis inclination sensor. In addition, based on the theoretical analyses results, a prototype three-axis inclination sensor has been designed and developed. Through some basic experiments with the prototype, the possibility of simultaneous three-axis inclination measurement by the proposed concept has been verified.
Journal Article
Multi-view neural 3D reconstruction of micro- and nanostructures with atomic force microscopy
Atomic Force Microscopy (AFM) is a widely employed tool for micro- and nanoscale topographic imaging. However, conventional AFM scanning struggles to reconstruct complex 3D micro- and nanostructures precisely due to limitations such as incomplete sample topography capturing and tip-sample convolution artifacts. Here, we propose a multi-view neural-network-based framework with AFM, named MVN-AFM, which accurately reconstructs surface models of intricate micro- and nanostructures. Unlike previous 3D-AFM approaches, MVN-AFM does not depend on any specially shaped probes or costly modifications to the AFM system. To achieve this, MVN-AFM employs an iterative method to align multi-view data and eliminate AFM artifacts simultaneously. Furthermore, we apply the neural implicit surface reconstruction technique in nanotechnology and achieve improved results. Additional extensive experiments show that MVN-AFM effectively eliminates artifacts present in raw AFM images and reconstructs various micro- and nanostructures, including complex geometrical microstructures printed via two-photon lithography and nanoparticles such as poly(methyl methacrylate) (PMMA) nanospheres and zeolitic imidazolate framework-67 (ZIF-67) nanocrystals. This work presents a cost-effective tool for micro- and nanoscale 3D analysis.
Shuo Chen and colleagues present a cost-effective neural network-based method to deal with tip-sample convolution artifacts in atomic force microscopy. Their method merges multiview atomic force microscopy images into precise 3D models of complex micro- and nanostructures.
Journal Article
In vivo brain GPCR signaling elucidated by phosphoproteomics
Advanced mass spectrometry methods enable monitoring of tens of thousands of phosphorylation sites in proteins. This technology can potentially distinguish cellular signaling pathways that produce beneficial effects from those that produce unwanted side effects. Liu et al. treated mice with various agonists of the kappa opioid receptor (a G protein–coupled receptor) and monitored changes in phosphorylation over time in different brain regions. The phosphorylation patterns revealed distinct patterns of signaling in various brain tissues, some of which were associated with unwanted side effects. Science , this issue p. eaao4927 High-throughput monitoring of phosphorylation helps define drug actions in the brain. A systems view of G protein–coupled receptor (GPCR) signaling in its native environment is central to the development of GPCR therapeutics with fewer side effects. Using the kappa opioid receptor (KOR) as a model, we employed high-throughput phosphoproteomics to investigate signaling induced by structurally diverse agonists in five mouse brain regions. Quantification of 50,000 different phosphosites provided a systems view of KOR in vivo signaling, revealing novel mechanisms of drug action. Thus, we discovered enrichment of the mechanistic target of rapamycin (mTOR) pathway by U-50,488H, an agonist causing aversion, which is a typical KOR-mediated side effect. Consequently, mTOR inhibition during KOR activation abolished aversion while preserving beneficial antinociceptive and anticonvulsant effects. Our results establish high-throughput phosphoproteomics as a general strategy to investigate GPCR in vivo signaling, enabling prediction and modulation of behavioral outcomes.
Journal Article
UY-NET: A Two-Stage Network to Improve the Result of Detection in Colonoscopy Images
The human digestive system is susceptible to various viruses and bacteria, which can lead to the development of lesions, disorders, and even cancer. According to statistics, colorectal cancer has been a leading cause of death in Taiwan for years. To reduce its mortality rate, clinicians must detect and remove polyps during gastrointestinal (GI) tract examinations. Recently, colonoscopies have been conducted to examine patients’ colons. Even so, polyps sometimes remain undetected. To help medical professionals better identify abnormalities, advanced deep learning algorithms that can accurately detect colorectal polyps from images should be developed. Prompted by this proposition, the present study combined U-Net and YOLOv4 to create a two-stage network algorithm called UY-Net. This new algorithm was tested using colonoscopy images from the Kvasir-SEG dataset. Results showed that UY-Net was significantly accurate in detecting polyps. It also outperformed YOLOv4, YOLOv3-spp, Faster R-CNN, and RetinaNet by achieving higher spatial accuracy and overall accuracy of object detection. As the empirical evidence suggests, two-stage network algorithms like UY-Net will be a reliable and promising aid to image detection in healthcare.
Journal Article