STM Studies of TbTe3: Evidence for a fully Incommensurate Charge Density Wave

We observe unidirectional charge density wave ordering on the cleaved surface of TbTe3 with a Scanning Tunneling Microscope at ~6 K. The modulation wave-vector q_{CDW} as determined by Fourier analysis is 0.71 +/- 0.02 * 2 pi/c. (Where c is one edge of the in-plane 3D unit cell.) Images at different tip-sample voltages show the unit cell doubling effects of dimerization and the layer below. Our results agree with bulk X-ray measurements, with the addition of ~(1/3) * 2 pi/a ordering perpendicular to the CDW. Our analysis indicates that the CDW is incommensurate.Comment: 4 pages, 4 figure

Segmentation of Fuzzy and Touching Cells Based on Modified Minimum Spanning Tree and Concave Point Detection

In order to segment fuzzy and touching cell images accurately, an improved algorithm is proposed based on minimum spanning tree (MST) and concave point detection. First, the cell images are smoothed and enhanced by a Gaussian filter. Then, the improved minimum spanning tree algorithm is used to segment the cell images. The MST algorithm is modified from three aspects, namely, weight function of edges, difference function of internal and inter region, and threshold function and parameter k. Furthermore, the problem of cell touching is solved by means of concave point detection. According to the rugged topography of touching cells, the concave points are found from the concave regions in the touching cell images, which are used to find the separation points quickly and accurately. Experimental results indicate that the new algorithm is ideal and effective

Residence times of receptors in dendritic spines analyzed by simulations in empirical domains

Analysis of high-density superresolution imaging of receptors reveal the organization of dendrites at the nano-scale resolution. We present here simulations in empirical live cell images, which allows converting local information extracted from short range trajectories into simulations of long range trajectories. Based on these empirical simulations, we compute the residence time of an AMPA receptor (AMPAR) in dendritic spines that accounts for receptors local interactions and geometrical organization. We report here that depending on the type of the spine, the residence time varies from one to five minutes. Moreover, we show that there exists transient organized structures, previously described as potential wells that can regulate the trafficking of AMPARs to dendritic spines.Comment: 19 page

ENHANCEMENT ANALYSIS OF IMMUNE FLUORESCENT CELL IMAGES

There are different patterns of immune fluorescence cells, which serve in determining different autoimmune disease. Hence, clearly identifying the features of the figures in the image will assist in automating the classification of these patterns. This project aims to enhance the quality of the Hep2-cell images obtained from Indirect Immune Fluorescence (IIF) Test. The enhancement of the quality in this project will be focused on enhancing the contrast, reducing the noise, and sharpening the edges of images. This enhancement will have a real serious impact on the stages coming after, which are patterns recognition and automatic classification. Creating an automatic battern classification system will improve the diagnostic process of the autoimmune disease instead of handling it manually. Consequently, many disadvantages of the manual interpretation can be overcome, such as level of expertise, time consuming and prone to mistakes. This research analyzed the performance of three enhancement approaches namely wavelet transform filter, diffusion filter, and wavelet transform filter combined with diffusion filter. The combination of wavelet transform filter with diffusion filter produced better result. However, the diffusion filter produced best result among all the three enhancement approach of the indirect immune fluorescence images. The recommendation for the future work is to explore an automatic determination of noise variance in the image when wavelet transform filter is being applied

Performance analysis of machine learning and deep learning architectures for malaria detection on cell images

Plasmodium malaria is a parasitic protozoan that causes malaria in humans. Computer aided detection of Plasmodium is a research area attracting great interest. In this paper, we study the performance of various machine learning and deep learning approaches for the detection of Plasmodium on cell images from digital microscopy. We make use of a publicly available dataset composed of 27,558 cell images with equal instances of parasitized (contains Plasmodium) and uninfected (no Plasmodium) cells. We randomly split the dataset into groups of 80% and 20% for training and testing purposes, respectively. We apply color constancy and spatially resample all images to a particular size depending on the classification architecture implemented. We propose a fast Convolutional Neural Network (CNN) architecture for the classification of cell images. We also study and compare the performance of transfer learning algorithms developed based on well-established network architectures such as AlexNet, ResNet, VGG-16 and DenseNet. In addition, we study the performance of the bag-of-features model with Support Vector Machine for classification. The overall probability of a cell image comprising Plasmodium is determined based on the average of probabilities provided by all the CNN architectures implemented in this paper. Our proposed algorithm provided an overall accuracy of 96.7% on the testing dataset and area under the Receiver Operating Characteristic (ROC) curve value of 0.994 for 2756 parasitized cell images. This type of automated classification of cell images would enhance the workflow of microscopists and provide a valuable second opinion