Synthesis of polyamide-hydroxyapatite nanocomposites

Simultaneous one-pot syntheses of PA66 and HAp were carried out by extracting H₂O and CO₂ from PA66 monomers and HAp raw materials, respectively, resulting in the formation of a polyamide (PA) 66-hydroxyapatite (HAp) nanocomposite. During the process, a spherical nano-sized HAp particle was precipitated following dissolution of micro-sized CaHPO₄・2H₂O. The PA66 monomers were subsequently adsorbed onto the generated HAp product. Some of the adsorbed PA66 monomers formed a bound polymer on HAp, and an increase in the adhesiveness of the PA66-HAp interface was observed as the polymerization progressed. During this process, the synthesis of a nanocomposite from a micro-sized raw material and creation of an autonomous strong interface between the matrix and filler was achieved. In addition, the shape of the resultant HAp was controllable and could be modified to needle shape by the addition of F⁻ and Mg²⁺ ions to the raw material. HAp could also be changed to plate shape via octa-calcium phosphate (OCP). Notably, during the synthesis, the filler shape of the nanocomposite could be controlled to 0D (particle), 1D (needle), and 2D (plate).     

In-process measurement of topography change of grinding wheel by using hydrodynamic pressure

This paper deals with an in-process measurement method for topography change of a grinding wheel, which can apply to wet grinding. A pressure sensor is set beside a grinding wheel with a small gap. When grinding fluid is dragged into the gap, hydrodynamic pressure, which corresponds to the gap length and the topography, can be measured. This method is applied to a cylindrical grinding machine. No electromagnetic properties of a workpiece and a grinding wheel affect measured results. The pressure is decreased with the increase of the gap length when the grinding fluid is supplied in the tangential direction of the grinding wheel. Spectra of the pressure are measured with an FFT analyzer. Higher frequency components are increased with the progress of grinding because of turbulent flow. Loading and dulling of a grinding wheel can be detected by the proposed method as well as its wear.     

Synthesis and Preliminary Evaluation of 11C-Labeled VU0467485/AZ13713945 and Its Analogues for Imaging Muscarinic Acetylcholine Receptor Subtype 4

Muscarinic acetylcholine receptors (mAChRs) have five distinct subunits (M₁–M₅) and are involved in the action of the neurotransmitter acetylcholine in the central and peripheral nervous system. Attributed to the promising clinical efficacy of xanomeline, an M₁/M₄-preferring agonist, in patients of schizophrenia and Alzheimer's disease, M₁- or M₄-selective mAChR modulators have been developed that target the topographically distinct allosteric sites. Herein we report the synthesis and preliminary evaluation of ¹¹C-labeled positron emission tomography (PET) ligands based on a validated M₄R positive allosteric modulator VU0467485 (AZ13713945) to facilitate drug discovery. [¹¹C]VU0467485 and two other ligands were prepared in high radiochemical yields (>30 %, decay-corrected) with high radiochemical purity (>99 %) and high molar activity (>74 GBq μmol⁻¹). In vitro autoradiography studies indicated that these three ligands possess moderate-to-high in vitro specific binding to M₄R. Nevertheless, further physiochemical property optimization is necessary to overcome the challenges associated with limited brain permeability.     

Automated Synthesis of (rac)‐, (R)‐, and (S)‐[18F]Epifluorohydrin and Their Application for Developing PET Radiotracers Containing a 3‐[18F]Fluoro‐2‐hydroxypropyl Moiety

To introduce the 3‐[¹⁸F]fluoro‐2‐hydroxypropyl moiety into positron emission tomography (PET) radiotracers, we performed automated synthesis of (rac)‐, (R)‐, and (S)‐[¹⁸F]epifluorohydrin ([¹⁸F] 1 ) by nucleophilic displacement of (rac)‐, (R)‐, or (S)‐glycidyl tosylate with ¹⁸F^? and purification by distillation. The ring‐opening reaction of (R)‐ or (S)‐[¹⁸F] 1 with phenol precursors gave enantioenriched [¹⁸F]fluoroalkylated products without racemisation. We then synthesised (rac)‐, (R)‐, and (S)‐ 2‐{5‐[4‐(3‐[¹⁸F]fluoro‐2‐hydroxypropoxy)phenyl]‐2‐oxobenzo[d]oxazol‐3(2H)‐yl}‐N‐methyl‐N‐phenylacetamide ([¹⁸F] 6 ) as novel radiotracers for the PET imaging of translocator protein (18 kDa) and showed that (R)‐ and (S)‐[¹⁸F] 6 had different radioactivity uptake in mouse bone and liver. Thus, (rac)‐, (R)‐, and (S)‐[¹⁸F] 1 are effective radiolabelling reagents and can be used to develop PET radiotracers by examining the effects of chirality on their in vitro binding affinities and in vivo behaviour.     

Light source position and reflectance estimation from a single view without the distant illumination assumption

Several techniques have been developed for recovering reflectance properties of real surfaces under unknown illumination. However, in most cases, those techniques assume that the light sources are located at infinity, which cannot be applied safely to, for example, reflectance modeling of indoor environments. In this paper, we propose two types of methods to estimate the surface reflectance property of an object, as well as the position of a light source from a single view without the distant illumination assumption, thus relaxing the conditions in the previous methods. Given a real image and a 3D geometric model of an object with specular reflection as inputs, the first method estimates the light source position by fitting to the Lambertian diffuse component, while separating the specular and diffuse components by using an iterative relaxation scheme. Our second method extends that first method by using as input a specular component image, which is acquired by analyzing multiple polarization images taken from a single view, thus removing its constraints on the diffuse reflectance property. This method simultaneously recovers the reflectance properties and the light source positions by optimizing the linearity of a log-transformed Torrance-Sparrow model. By estimating the object's reflectance property and the light source position, we can freely generate synthetic images of the target object under arbitrary lighting conditions with not only source direction modification but also source-surface distance modification. Experimental results show the accuracy of our estimation framework.     

Camera Spectral Sensitivity and White Balance Estimation from Sky Images

Photometric camera calibration is often required in physics-based computer vision. There have been a number of studies to estimate camera response functions (gamma function), and vignetting effect from images. However less attention has been paid to camera spectral sensitivities and white balance settings. This is unfortunate, since those two properties significantly affect image colors. Motivated by this, a method to estimate camera spectral sensitivities and white balance setting jointly from images with sky regions is introduced. The basic idea is to use the sky regions to infer the sky spectra. Given sky images as the input and assuming the sun direction with respect to the camera viewing direction can be extracted, the proposed method estimates the turbidity of the sky by fitting the image intensities to a sky model. Subsequently, it calculates the sky spectra from the estimated turbidity. Having the sky \(RGB\) values and their corresponding spectra, the method estimates the camera spectral sensitivities together with the white balance setting. Precomputed basis functions of camera spectral sensitivities are used in the method for robust estimation. The whole method is novel and practical since, unlike existing methods, it uses sky images without additional hardware, assuming the geolocation of the captured sky is known. Experimental results using various real images show the effectiveness of the method.     

Describing Upper-Body Motions Based on Labanotation for Learning-from-Observation Robots

We have been developing a paradigm that we call learning-from-observation for a robot to automatically acquire a robot program to conduct a series of operations, or for a robot to understand what to do, through observing humans performing the same operations. Since a simple mimicking method to repeat exact joint angles or exact end-effector trajectories does not work well because of the kinematic and dynamic differences between a human and a robot, the proposed method employs intermediate symbolic representations, tasks, for conceptually representing what-to-do through observation. These tasks are subsequently mapped to appropriate robot operations depending on the robot hardware. In the present work, task models for upper-body operations of humanoid robots are presented, which are designed on the basis of Labanotation. Given a series of human operations, we first analyze the upper-body motions and extract certain fixed poses from key frames. These key poses are translated into tasks represented by Labanotation symbols. Then, a robot performs the operations corresponding to those task models. Because tasks based on Labanotation are independent of robot hardware, different robots can share the same observation module, and only different task-mapping modules specific to robot hardware are required. The system was implemented and demonstrated that three different robots can automatically mimic human upper-body operations with a satisfactory level of resemblance.     

Illumination normalization with time-dependent intrinsic images for video surveillance

Variation in illumination conditions caused by weather, time of day, etc., makes the task difficult when building video surveillance systems of real world scenes. Especially, cast shadows produce troublesome effects, typically for object tracking from a fixed viewpoint, since it yields appearance variations of objects depending on whether they are inside or outside the shadow. In this paper, we handle such appearance variations by removing shadows in the image sequence. This can be considered as a preprocessing stage which leads to robust video surveillance. To achieve this, we propose a framework based on the idea of intrinsic images. Unlike previous methods of deriving intrinsic images, we derive time-varying reflectance images and corresponding illumination images from a sequence of images instead of assuming a single reflectance image. Using obtained illumination images, we normalize the input image sequence in terms of incident lighting distribution to eliminate shadowing effects. We also propose an illumination normalization scheme which can potentially run in real time, utilizing the illumination eigenspace, which captures the illumination variation due to weather, time of day, etc., and a shadow interpolation method based on shadow hulls. This paper describes the theory of the framework with simulation results and shows its effectiveness with object tracking results on real scene data sets.     

The separation of reflected and transparent layers from real-world image sequence

This paper presents an optimal method for the separation of reflected and transparent layers from real-world scene images. Whereas past research has been applied to indoor environments and static cameras, our technique can be used for outdoor scenes and motion cameras. The method is based on spatio-temporal analysis, especially using epipolar plane images (EPI). The edge and color information of EPI has been used to segment the areas on EPIs efficiently and separate the reflected and transparent layers. This method can be used for refining building textures by removing reflections from captured images for the purpose of city modeling.