Most multicellular organisms require apoptosis, or programmed cell death, to function properly and survive. On the other hand, morphological and biochemical characteristics of apoptosis have remained remarkably consistent throughout evolution. Apoptosis is thought to have at least three functionally distinct phases: induction, effector, and execution. Recent studies have revealed that reactive oxygen species (ROS) and the oxidative stress could play an essential role in apoptosis. Advanced microscopic imaging techniques allow biologists to acquire an extensive amount of cell images within a matter of minutes which rule out the manual analysis of image data acquisition. The segmentation of cell images is often considered the cornerstone and central problem for image analysis. Currently, the issue of segmentation of mitochondrial cell images via deep learning receives increasing attention. The manual labeling of cell images is time-consuming and challenging to train a pro. As a courtesy method, mitochondrial cell imaging (MCI) is proposed to identify the normal, drug-treated, and diseased cells. Furthermore, cell movement (fission and fusion) is measured to evaluate disease risk. The newly proposed drug-treated, normal, and diseased image segmentation (DNDIS) algorithm can quickly segment mitochondrial cell images without supervision and further segment the highly drug-treated cells in the picture, i.e., normal, diseased, and drug-treated cells. The proposed method is based on the ResNet-50 deep learning algorithm. The dataset consists of 414 images mainly categorised into different sets (drug, diseased, and normal) used microscopically. The proposed automated segmentation method has outperformed and secured high precision (90%, 92%, and 94%); moreover, it also achieves proper training. This study will benefit medicines and diseased cell measurements in medical tests and clinical practices.

This work presents a novel planar branch-line coupler topology developed for dual-band operation using an E-shaped impedance transformer network to supplant the conventional microstrip line. Explicit closed-form design equations for dual-band operation are derived using the ABCD matrices. The studied coupler features a large dual-band frequency ratio with a compact size. A prototype coupler centered at 1 and 8 GHz is first presented and experimentally examined to demonstrate the large frequency ratio. Further, another prototype coupler centered at 2.4 and 5.2 GHz for potential WLAN applications is also developed and experimentally characterized. Measurements and simulations for both prototype couplers show good performance within the studied dual-band frequencies.

This paper proposes a new dual horizontal squash capsule network (DHS-CapsNet) to classify the lung and colon cancers on histopathological images. DHS-CapsNet is made up of encoder feature fusion (EFF) and a novel horizontal squash (HSquash) function. The EFF aggregates the extracted feature from the 2-lane convolutional layers, which provides rich information for better accuracy. HSquash is proposed as a squash function to ensure that vectors are effectively squashed and produces sparsity for a high discriminative capsule to extract important information from images with varied backgrounds. To present the effectiveness of DHS-CapsNet empirically, we applied this method on histopathological images (LC25000 dataset). We achieved better results of 99.23% compared to traditional CapsNet (85.55%). The DHS-CapsNet provides the top-1 classification error of 0.77% compared to 14.45% of the traditional CapsNet. Our results illustrate that our method improves CapsNet and can be adopted as a computer-aided diagnostic method to support doctors in lung and colon cancer diagnostics.

Bowtie antenna-based time reversal mirror (TRM), incorporating with randomly distributed and arbitrarily shaped wire metamaterials medium, is proposed to realize super-resolution target detection. The achieved performance for standard and scatterer bowtie antenna TRM is compared and discussed. The dual-band bowtie antennas resonate at 2.45 GHz and 5.2 GHz and a super-resolution of 0.0817 of the free-space wavelength at 2.45 GHz has been achieved. For the first time, studies show that the TRM with microstructure perturbations (namely scatterers) can enhance the resolution in some cases. Proposing a method of super-resolving transmission of electromagnetic waves is very important to realize multi-independent channels in a compact space for the related applications.

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