New marked point process models for microscopy images

In developing new materials, the characterization of microstructures is one of the key steps. To characterize the microstructure, many microscope modalities have been devised and improved over decades. With the increase in image resolution in the spatial and time domains, the amount of image data keeps increasing in the fields such as materials science and biomedical engineering. As a result, image processing plays a critical role in this era of science and technology. In materials image analysis, image segmentation and feature detection are considered very important. The first part of this research aims to resolve the segmentation problem caused by blurring artifacts in scanning electron microscopy (SEM) images. This blurring issue can lead to a bridged channel problem, which becomes an obstacle in analyzing the microstructures. To tackle the problem, we propose a joint deconvolution and segmentation (JDS) method. As a segmentation method, we use the expectation-maximization/maximization of the posterior marginals (EM/MPM) method, using the Markov random field (MRF) prior model. Experiments show the proposed method improves the segmentation result at object boundaries. The next phase of the image segmentation is detecting image features. In the second part of this research, we detect channel configurations in materials images. We propose a new approach of channel identification, based on the marked point process (MPP) framework, to effectively detect channels in materials images. To describe a higher level of structures in an image, the MPP framework is more effective than the MRF prior model. The reversible-jump Markov chain Monte Carlo (RJMCMC) algorithm embedded with simulated annealing is used as an optimization method, and a new switching kernel in an RJMCMC is used to reduce computational time. The channel configuration is useful in characterizing materials images. In addition, this information can be used to reduce the bridged channel problem more effectively. In materials image processing, one of the most important goals of feature detection is identifying the 3D structure of objects from 3D microscope datasets. The final part of this research is to perform fast and accurate estimation of 3D object configurations from a 3D dataset. We propose a fast 3D fitting method to improve the computational complexity over a full-search 3D MPP method. Experiments show that the fast 3D fitting method significantly decreases execution time compared to the full 3D MPP method

The Mx/G/1 queue with queue length dependent service times

We deal with the MX/G/1 queue where service times depend on the queue length at the service initiation. By using Markov renewal theory, we derive the queue length distribution at departure epochs. We also obtain the transient queue length distribution at time t and its limiting distribution and the virtual waiting time distribution. The numerical results for transient mean queue length and queue length distributions are given.Bong Dae Choi, Yeong Cheol Kim, Yang Woo Shin, and Charles E. M. Pearc

CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals

Brain-computer interface (BCI) is a communication system between humans and computers reflecting human intention without using a physical control device. Since deep learning is robust in extracting features from data, research on decoding electroencephalograms by applying deep learning has progressed in the BCI domain. However, the application of deep learning in the BCI domain has issues with a lack of data and overconfidence. To solve these issues, we proposed a novel data augmentation method, CropCat. CropCat consists of two versions, CropCat-spatial and CropCat-temporal. We designed our method by concatenating the cropped data after cropping the data, which have different labels in spatial and temporal axes. In addition, we adjusted the label based on the ratio of cropped length. As a result, the generated data from our proposed method assisted in revising the ambiguous decision boundary into apparent caused by a lack of data. Due to the effectiveness of the proposed method, the performance of the four EEG signal decoding models is improved in two motor imagery public datasets compared to when the proposed method is not applied. Hence, we demonstrate that generated data by CropCat smooths the feature distribution of EEG signals when training the model.Comment: 4 pages, 1 tabl

Cellular energy stress induces AMPK-mediated regulation of YAP and the Hippo pathway.

YAP (Yes-associated protein) is a transcription co-activator in the Hippo tumour suppressor pathway and controls cell growth, tissue homeostasis and organ size. YAP is inhibited by the kinase Lats, which phosphorylates YAP to induce its cytoplasmic localization and proteasomal degradation. YAP induces gene expression by binding to the TEAD family transcription factors. Dysregulation of the Hippo-YAP pathway is frequently observed in human cancers. Here we show that cellular energy stress induces YAP phosphorylation, in part due to AMPK-dependent Lats activation, thereby inhibiting YAP activity. Moreover, AMPK directly phosphorylates YAP Ser 94, a residue essential for the interaction with TEAD, thus disrupting the YAP-TEAD interaction. AMPK-induced YAP inhibition can suppress oncogenic transformation of Lats-null cells with high YAP activity. Our study establishes a molecular mechanism and functional significance of AMPK in linking cellular energy status to the Hippo-YAP pathway

The C-terminal region of Bfl-1 sensitizes non-small cell lung cancer to gemcitabine-induced apoptosis by suppressing NF-κB activity and down-regulating Bfl-1

Gemcitabine is used to treat several cancers including lung cancer. However, tumor cells often escape gemcitabine-induced cell death via various mechanisms, which include modulating bcl-2 family members and NF-κB activation. We previously reported that the C-terminal region of Bfl-1 fused with GFP (BC) is sufficient to induce apoptosis in 293T cells. In the present study, we investigated the anti-tumor effect of combined BC gene therapy and gemcitabine chemotherapy in vitro and in vivo using non-small cell lung cancer cell lines and a xenograft model. Cell lines were resistant to low dose gemcitabine (4-40 ng/ml), which induced NF-κB activation and concomitant up-regulation of Bfl-1 (an NF-κB-regulated anti-apoptotic protein). BC induced the apoptosis of A549 and H157 cells with caspase-3 activation. Furthermore, co-treatment with BC and low dose gemcitabine synergistically and efficiently induced mitochondria-mediated apoptosis in these cells. When administered alone or with low dose gemcitabine, BC suppressed NF-κB activity, inhibited the nuclear translocation of p65/relA, and down-regulated Bfl-1 expression. Furthermore, direct suppression of Bfl-1 by RNA interference sensitized cells to gemcitabine-induced cell death, suggesting that Bfl-1 importantly regulates lung cancer cell sensitivity to gemcitabine. BC and gemcitabine co-treatment also showed a strong anti-tumor effect in a nude mouse/A549 xenograft model. These results suggest that lung cancer cells become resistant to gemcitabine via NF-κB activation and the subsequent overexpression of Bfl-1, and that BC, which has both pro-apoptotic and NF-κB inhibitory effects, could be harnessed as a gene therapy to complement gemcitabine chemotherapy in non-small cell lung cancer

Degenerate r-truncated Stirling numbers

For any positive integer $r$, the $r$-truncated (or $r$-associated) Stirling number of the second kind $S_{2}^{(r)}(n, k)$ enumerates the number of partitions of the set $\{1, 2, 3, \dots, n\}$ into $k$ non-empty disjoint subsets, such that each subset contains at least $r$ elements. We introduce the degenerate $r$-truncated Stirling numbers of the second kind and of the first kind. They are degenerate versions of the $r$-truncated Stirling numbers of the second kind and of the first kind, and reduce to the degenerate Stirling numbers of the second kind and of the first kind for $r = 1$. Our aim is to derive recurrence relations for both of those numbers

Origin, criterion, and mechanism of vortex-core reversals in soft magnetic nanodisks under perpendicular bias fields

We studied dynamics of vortex-core reversals driven by circular rotating fields along with static perpendicular magnetic fields of different direction and strength. We found that the application of perpendicular fields H p modifies the starting ground state of vortex magnetizations, thereby instigating the development of a magnetization dip mz,dip in the vicinity of the original core up to its threshold value, m z,dip cri ???-p, which is necessary for vortex-core reversals, where p is the initial core polarization. We found the relationship of the dynamic evolutions of the mz,dip and the out-of-plane gyrofields hz, which was induced, in this case, by vortex-core motion of velocity ??, thereby their critical value relation ??crihz cri. The simulation results indicated that the variation of the critical core velocity ??cri with Hp can be expressed explicitly as ??cri / ?? cri 0 = (??/ ??0) | -p- m z,dip g |, with the core size ?? and the starting ground-state magnetization dip m z,dip g variable with H p, and for the values of ?? cri 0 and ??0 at H p =0. This work offers deeper and/or new insights into the origin, criterion and mechanism of vortex-core reversals under application of static perpendicular bias fields.open7